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AbruptSLR

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MAXIMUM CREDIBLE DOMINO SCENARIO (MCDS) – BAYESIAN NETWORK (BN)

[click on images to enlarge them]

This MCDS-BN thread assumes that, while imperfect, Hansen et al. (2016), DeConto & Pollard (2016) and the CMIP6 'Wolf Pack' models can service as reasonable starting points for developing a Maximum Credible Domino Scenario (which can be thought of as a 'Perfect Storm' scenario) – Domino Effect Analysis using Bayesian Networks (MCDS-BN) for characterizing risks associated with coming climate change; while the MCDS – FT thread uses an approximate (non-rigorous) Maximum Credible Domino Scenario - Domino Fault Tree Analysis (MCDS-FT) to estimate probabilities of the events and feedbacks cited in this MCDS-BN thread (and that the MCDS-REF thread provides selected MCDS references and selected related conversion factors).  In this regard, Hansen et al. (2016), DeConto & Pollard (2016) and the CMIP6 'Wolf Pack' published model projections were all peer reviewed, which provides some measure of credibility as starting point references.

Climate risk associated with the single MCDS-BN case presented in this thread is discussed in the first post of the MCDS-FT thread for a 1% probability of occurrence by 2100; while to fully characterize the long-tail climate risk would require many more (an entire suite) of MCDS-BN cases, with both lower and higher probabilities of occurrence, for a given date.  Also, for a fuller understanding of what I am trying to convey, I recommend opening both the MCDS-BN thread and the MCDS-FT thread simultaneously in different windows as you progress through the threads, as I have tried to keep this MCDS-BN thread as simple as practicable for those who just want to know what my (AbruptSLR's) opinion is about long-tail climate risk.

It should be noted that the risk characterization of the single MCDS-BN projection (presented in this thread) is different than that of the three starting point references.  In many ways the MCDS Bayesian Network presented in this thread is the result of an event-based storyline (see Sillmann et al. 2020) to estimate climate risk for one portion of the probability density function (see Post 1 of the MCDS-FT thread), where deep uncertainty is addressed using AbruptSLR's associated guided Bayesian prior (opinion).  The first attached image (from Sillmann et al. 2020) shows how decision makers can be presented climate risk information in different formats depending on the level of (deep) uncertainty associated with the climate change issue under consideration.  In general consensus climate science (CCS) documents (like AR5 or CMIP5) tend to use the Level 2 presentation using families of radiative forcing scenarios (dominated by anthropogenic GHG forcing, like the SSP and RCP forcing scenario families) by using caveats to indicate that deep uncertainties (like ice-climate, or cloud, feedbacks) are so small, or so slow, that they can safely be ignored in the CCS projections.  However, the MCDS-BN presented here is but one of multiple plausible/credible futures illustrated in the first image by the Level 4 diagram of ways to address deep uncertainty using an event-based story line suitable for domino effect analysis (see Khakzad et al. 2012). The second image from Wunderling et al. 2020 conceptually illustrates how five key tipping point elements [Greenland Ice Sheet, West Antarctic Ice Sheet, Atlantic Meridional Overturning Circulation (AMOC), El-Niño Southern Oscillation (ENSO) and the Amazon rainforest] can interact synergistically to create a domino effect within a 1.5C to 2.0C range for GMSTA, using CCS projections for ECS, if the ice sheets contribute a significant level of freshwater flux.

Sillmann J. et al. (14 December 2020), "Event‐based storylines to address climate risk", Earth's Future, https://doi.org/10.1029/2020EF001783
 
&

Khakzad, N. et al. (09 June 2012), "Domino Effect Analysis Using Bayesian Networks", Risk Analysis, https://doi.org/10.1111/j.1539-6924.2012.01854.x

https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1539-6924.2012.01854.x

&

Wunderling, N. et al. (3 April 2020), "Interacting tipping elements increase risk of climate domino effects under global warming", Earth System Dynamics, https://doi.org/10.5194/esd-2020-18.

https://esd.copernicus.org/preprints/esd-2020-18/esd-2020-18.pdf


Caption for the second image: "Figure 1. Interactions between climate tipping elements and their roles in tipping cascades. The Greenland Ice Sheet, West Antarctic Ice Sheet, Atlantic Meridional Overturning Circulation (AMOC), El-Niño Southern Oscillation (ENSO) and the Amazon rainforest are depicted together with their main interaction pathways (Kriegler, 2009). The interaction links between the tipping elements are colour-marked, where red arrows depict enhancing effects and blue arrows depict dampening effects. Where the direction is unclear, the link is marked in grey. A more thorough description of each of the tipping elements and the links can be found in Tables 1 and 2. Where tipping cascades arise, the relative size of the dominoes illustrates in how many model representations the respective climate components initiates (red domino) or is part of (blue domino) cascading transitions. Standard deviations for these values are given in Figs. S1(a) and (b). Generally, the polar ice sheets are found to more frequently take on the role as initiators than the equatorial tipping elements."

The third image presents an analogy of a domino wave similar to that proposed in the MCDS-BN presented in the fourth image.  The MCDS-BN shown in the fourth image assumes that anthropogenic radiative forcing will follow SSP5-8.5 until 2060 (except that from 2050 to 2060 the anthropogenic radiative forcing is dominated by a major war) and that from 2061 to 2150, anthropogenic radiative forcing will follow SSP5-2.6 (due to the impact of the assumed major war associated with major climate change driven impacts).  The legend in the fourth image describes the assumed events, associated actions and annotated mean parameters: ECSeff, ERF, P, Date, FF, EEI, deltaSLR and GMSTA, and I note that the Freshwater Flux (FF) includes only meltwater from calved icebergs (whether from ice shelves or grounded glacial ice; which in major calving events are assumed to form armadas of icebergs that may take decades to completely melt); while deltaSLR includes the influence of the volume of grounded glacial ice above floatation as soon as the calving event occurs and I note that EEI is closely associated with FF (and I note that many/most consensus climate scientists only focus on the SLR risks associated with FF and ignore it's close association with EEI).  I also note that the probability (P) indicated for each Date represents the cumulative probability for the chains of events during the nominally ten-year time frame associated with the annotated parameters.  Finally, I note that the mean value of ECSeff shown is based on my interpretation both the CMIP6 Wolf Pack projections and on ice-climate feedback mechanism (& the pattern effect).

While some readers may think that my estimate of a 65% chance of having moved well past a triggering point for a domino chain of events leading to an 'Ice Apocalypse' circa 2040 is doomsday thinking that will harm the effort to fight against anthropogenic climate change; my thinking on this issue is that by using the 'Precautionary Principle' now we can still shift some of the domino events in the chain/wave sufficiently to say prevent: 1) a major world war circa 2050 to 2060; 2) a sharp increase in oceanic hypoxia circa 2100, or 3) a flip into: a Pliocene-like atmospheric pattern circa 2110, or into a Miocene-like atmospheric pattern circa 2130, or into an equable climate pattern circa 2150; all of which should be highly motivating goals for any decision maker who cares about his/her children/grandchildren.

Furthermore, my frequent criticism that consensus climate science, CCS, frequently errs on the side of least drama, ESLD, essentially implies that I believe that much of CCS greenwashes climate change risk by largely leaving climate change risk assessments (see the first post in the MCDS-FT thread) to economists (& government officials, insurance actuaries, banks & Wall Street) whose models include stress tests for Black Swan events.  However, the following quotes by the economist Henry Hazlitt make it clear modern capitalism (as addressed in consensus economic models) does not properly account for the impacts of credible climate domino scenarios on the sustainability of our modern global socio-economic systems, say until 2150 as considered by MCDS:

“Economics is haunted by more fallacies than any other study known to man. This is no accident. The inherent difficulties of the subject would be great enough in any case, but they are multiplied a thousandfold by a factor that is insignificant in, say, physics, mathematics or medicine - the special pleading of selfish interests. While every group has certain economic interests identical with those of all groups, every group has also, as we shall see, interests antagonistic to those of all other groups. While certain public policies would in the long run benefit everybody, other policies would benefit one group only at the expense of all other groups. The group that would benefit by such policies, having such a direct interest in them, will argue for them plausibly and persistently. It will hire the best buyable minds to devote their whole time to presenting its case. And it will finally either convince the general public that its case is sound, or so befuddle it that clear thinking on the subject becomes next to impossible.

In addition to these endless pleadings of self-interest, there is a second main factor that spawns new economic fallacies every day. This is the persistent tendency of man to see only the immediate effects of a given policy, or its effects only on a special group, and to neglect to inquire what the long-run effects of that policy will be not only on that special group but on all groups. It is the fallacy of overlooking secondary consequences.”
― Henry Hazlitt

&

“The bad economist sees only what immediately strikes the eye; the good economist also looks beyond. The bad economist sees only the direct consequences of a proposed course; the good economist looks also at the longer and indirect consequences. The bad economist sees only what the effect of a given policy has been or will be on one particular group; the good economist inquires also what the effect of the policy will be on all groups.”
― Henry Hazlitt

&

“Today is already the tomorrow which the bad economist yesterday urged us to ignore.”
― Henry Hazlitt

Anthropogenic climate change occurs because of the why that our modern global socio-economic systems function; thus, in my opinion, it is naive for CCS to make projections without properly considering the selfish interests that economic models are forced to deal with, as cited by Hazlitt.  Furthermore, for capitalism to appropriately deal with the risks associated with cascades of climate tipping points, then consensus climate scientists must estimate the high-end climate risks such as illustrated here for the MCDS, so that market prices can be required to internalize these high-end risk disbenefits, so that fossil fuel companies are properly incentivized to leave fossil fuels in the ground (i.e. incentivized not to extract fossil fuels in the first place).
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #1 on: July 02, 2021, 09:00:37 PM »
The following TOC presents the MCDS-BN topics covered by the indicated replies (see also the MCDS-FT thread for associated topics and MCDS-REF for associated key references).

Table of Contents

Domino Effect Analysis Using Bayesian Network            Post 1

Table of Contents                        Reply 1

Introduction                           Replies 2 to 10

Discussion of the Differences Between Consensus Climate Science (CCS) Projections and Domino Effect Analysis Using Bayesian Network Risk Assessments    Replies 11 to 12

Discussion of Forcing Scenarios and Relationships to Both Consensus Climate Science (CCS) and MCDS Risk Assessments                     Replies 13 to 18

EEI (Earth Energy Imbalance, in W/m2) Discussion             Replies 19 to 20

Macro-MCDS-BN (from 2020 to 2150)                Replies 21 to 31

BN 2020-2030                           Reply 32
X1 = Abrupt Jakobshavn, & Helheim, Grounding Line Retreat;       Replies 32 to 34

BN 2030-2035/36                         Reply 35
X3 = Triggering of MICI Behavior for Thwaites;             Replies 35 to 44
X2 = Beaufort Gyre Reversal/Discharge;                Reply 45
X4 = Triggering of BSB Ice MICI Discharge;             Reply 45
X5 = Triggering of PIG MICI Response from the BSB
 and/or Ice Shelf Loss;                      Replies 46 to 47
X6 = Triggering of other ASE & Bellingshausen Marine Glaciers
 from the BSB and/or loss of its Ice Shelf.               Reply 48
X7 = Abrupt seasonal loss of Arctic Sea Ice (ASI), implies both
 freshwater hosing & albedo loss.                   Reply 48

BN 2040 – 2050                        Reply 49
X8 = Abrupt seasonal loss of Antarctic Sea Ice (AASI), from rainfall.   Reply 49
X9 = The FRIS collapses due to hydrofracturing from rainfall      Reply 49
X10 = Triggering of Totten MISI retreat.               Reply 50
X11 = The RIS collapses due to hydrofracturing from rainfall      Reply 51
X12 = Rainfall events for the GrIS                  Reply 52

BN 2050 – 2060                        Reply 53
X13 = Collapse of Siple Coast Glaciers, West Antarctica         Reply 53
X14 = Collapse of Byrd Glacier, East Antarctica            Reply 53
X15 = Triggering of Wilkes Basin MISI retreat            Reply 54
X16 = Triggering Aurora Subglacial Basins retreat            Reply 54
X17 = Weddell Sea Glaciers                     Reply 55
X18 = 79 North & Zachariae Glacier MISI retreats            Reply 55
X19 = Recovery Glacier                     Reply 56

BN 2060-2070                           Reply 57
Y20 = Arctic Sea Ice Albedo Flip Feedback               Reply 57
Y21 = Antarctic Sea Ice Albedo Flip                  Reply 57
Y22 = MOC Slowdown/ENSO Positive Feedback            Reply 57
Y23 = Moderate Methane pulse (Thermokarst, Arctic
& Antarctic hydrates, tropical lakes)                  Reply 58
Y24 = Amazon dieback                     Reply 58

BN 2070-2080                           Reply 59
Y25 = West Antarctic Seaways & Circulation            Reply 59
Y26 = Major Permafrost thaw                  Reply 60
Y27 = Boreal forest wildfires                     Reply 60

BN 2080-2090                           Reply 60
Y28 = Sharp increase in Atmospheric River Rain events         Reply 60
Y29 = Sharp increase in high latitude rainfall            Reply 61
Y30 = Sharp increase in other carbon cycle feedbacks         Reply 61

BN 2090 -2100                        Reply 61
Y31 = GrIS Albedo Loss Feedback                  Reply 61
Y32 = Regional Oceanic Hypoxia States               Reply 61

BN 2100-2110                           Reply 62
Y33 = Atmospheric flip into a Pliocene-like Pattern            Reply 62
Y34 = Major West Antarctic Seismic / Volcanic event         Reply 62

BN 2110-2120                           Reply 63
Y35 = Major Pulse of Methane from the East Siberian
 Arctic Shelf, & GrIS, hydrates                  Replies 63 to 64
Y36 = Major Change in Cloud feedback               Reply 65

BN 2120-2130                           Reply 65
Y37 = Atmospheric Flip into a Miocene-type Pattern in the NH      Reply 65

BN 2130-2140                           Reply 66
Y38 = Loss of Arctic Winter Sea Ice                  Reply 66

BN 2140-2150                           Reply 66
Y39 = Flip of the atmosphere into an Eocene-like Equable
 Atmospheric Pattern in the NH                  Replies 66 to 67

Conclusion                            Reply 68

“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #2 on: July 02, 2021, 09:05:46 PM »
Introduction

As stated in the opening post, this new thread is the first (and primary) of three threads (MCDS-BN, MCDS-FT and MCDS-REF) providing an updatable overview of my opinion (guided by over four times the number references listed in MCDS-REF) of how an 'Ice Apocalypse' could unfold in something like just over hundred years, with three threads (in order of presentation) being:

   i) A Maximum Credible Domino Scenario - Domino Effect Analysis using Bayesian Networks  (MCDS-BN) thread underpinned by Hansen et al. (2016) and their 5-year doubling freshwater hosing scenario (see the first two images); and by the early MICI (Marine Ice Cliff Instability) work by DeConto, Pollard and Alley (2015-2018) and by output from the CMIP6 'Wolfpack' model projections (see the third image).  These MCDS scenarios are based on roughly 10-year intervals (from 2020 to 2150) of Bayesian Networks (see the fourth image for the period from 2020 to 2040, where the Xi-Xj black arrows are hosing-induced feedbacks on subsequent hosing events; Xj-Yi purple arrows are hosing-induced feedbacks on climate state Yi; and Yi-Yj orange arrows are non-hosing feedbacks that change climate sensitivity) using Domino Effect Analysis (see Khakzad, N. et al. 2012).  Also, I note that Bayesians and Frequentist (while both are scientific) treat the expression/determination of probability differently, particularly in the face of Deep Uncertainty (see discussion in the MCDS-FT thread under the section heading 'Dynamical Statistics and Domino Fault Tree Modeling'.

   ii) A Maximum Credible Domino Scenario – Domino Fault Tree Analysis (MCDS-FT) thread to try to substantiate the 'credible' probability of such Domino Bayesian Networks of freshwater hosing events and their associated rough-order probabilities.

   iii) A (MCDS-REF) list of references used to support a 5-year doubling MCDS scenario, and/or other members of a family of such scenarios (in the tradition of the IPCC's radiative forcing scenario families such as: RCP or SSP).

I provide this three-thread summary of my input into the Ice Apocalypse thread as some readers have indicated that they have trouble understanding the approach that I have tried to use in the 'Ice Apocalypse' thread where my various posts in that thread (& elsewhere in this forum), where I have posted findings of new research as I find them in order to gradually bring the fat/long-tail (or deep uncertainty) risks of a 'Climate Apocalypse' sufficiently into focus so that consensus climate scientists, and decision makers, stop eliminating (or caveating-out) these 'Climate Apocalypse' risks, this century, from their consensus climate summaries like the IPCC's upcoming AR6 (or CMIP7).

Furthermore, the purpose of these three MCDS threads is to summarize how I see the associated climate risk not fully addressed by Consensus Climate Science (CCS), including my understanding of probabilities, and it is not to provide a peer reviewable document nor to answer contrarian red herrings.  That said, I also plan to cite shortcomings in climate model projections by Hansen, DeConto, Pollard, Alley, etc.  For instance, I believe that a MICI mechanism can form in the Thwaites Gateway between 2030 & 2040 without any hydrofracturing and the MCDS-FT thread discusses my reasoning on this matter.  Also, net precipitation (Precipitation less Evaporation) from clouds into the Southern Ocean is well understood until at least 2030 and after that the freshwater input from calved ice shelves and from calved key marine glaciers will dominate the freshwater signature of the Southern Ocean no later than 2040 (if my opinions have merit).

Also, I believe that it would be best for readers to limit their general comments/posts to the 'Ice Apocalypse' thread in order not to further confuse readers of this summary thread which I hope to update annually.  That said, if readers feel that they can provide insights on how long-tail 'Climate Apocalypse' uncertainties/risk can be reduced/clarified then go ahead and post; however, I will not respond to trolls in this summary thread, and I may not respond to serious posts either.

Caption for the first image: "Figure 5. (a) Total freshwater flux added in the North Atlantic and Southern oceans and (b) resulting sea level rise. Solid lines for 1m sea level rise, dotted for 5m.  One Sverdrup (Sv) is 106 m3 s-1, which is ~ 3 x 104 Gt year-1."
 
Caption for the second image: "Figure 7. (a) Surface air temperature (oC) relative to 1880-1920-for several scenarios. (b) Global energy imbalance (Wm-2) for the same scenarios."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #3 on: July 02, 2021, 09:06:43 PM »
While I do believe that Hansen et al. (2016)'s 5-year doubling time for freshwater flux is very likely the associated maximum credible freshwater flux input; however, I do not believe that other general assumptions made by Hansen et al (2016), DeConto & Pollard (2016) and the CMIP6 Wolf Pack do not represent maximum credible input to a MCDS-BN case, including:

1) For Hansen et al. (2016): a) assumes a nominal ECS of about 3C; b) assumes that tipping points into higher climate states do not occur over the coming nominal one hundred years; c) uses a low resolution mid-level model that tends to project middle of the road values and which underestimates upwelling of warm CDW; d)

2) For DeConto & Pollard (2016): a) assumes an effective ECS significantly less than those by the CMIP6 Wolf Pack; b) uses a low resolution mid-level model that tends to project middle of the road values and which underestimates upwelling of warm CDW; c) limits the rate of MICI-driven groundline retreat to one half of that observed for the Jakobshavn Glacier; d) utilize initial ice shelf condition that are less fragile than is the observed case today; e) assuming that MICI events cannot occur until GMSTA increases sufficiently for ice melting to occur on the surface of key ice shelves in sufficient quantities to induce hydrofracturing of those key ice shelves.

3) For the CMIP6 Wolf Pack: a) utilize minimal ice-climate feedbacks and freshwater flux feedbacks (and I note that the faster that these positive feedback mechanisms are activated, the greater the contribution from the 'pattern effect' will be to increasing ECS in coming decades); b) utilize, at best, MISI ice sheet models and do not use MICI ice sheet models; c) assume initial climate conditions that reflect less climate risk than I believe to be the current case.

“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #4 on: July 02, 2021, 09:10:22 PM »
True climate risk is clouded in deep uncertainty (see the first image [from Bakker et al. 2017] regarding an 'expert opinion' of deep uncertainty associated with abrupt ice sheet contribution to SLR); which also implies a risk of abrupt contribution to planetary imbalance ala Hansen et. al., 2016.  However, in my opinion, the upper bound of the Bakker et al. (2017) figure is not a true upper bound as it does not appear to fully consider domino interactions between freshwater hosing events.

Bakker, A.M.R., Wong, T.E., Ruckert, K.L. et al. Sea-level projections representing the deeply uncertain contribution of the West Antarctic ice sheet. Sci Rep 7, 3880 (2017). https://doi.org/10.1038/s41598-017-04134-5

https://www.nature.com/articles/s41598-017-04134-5

Caption for image 1 Representation of Deep Uncertainty Estimated by Expert Opinion Associated with Potential WAIS Contribution to SLR Through 2100, from Bakker et al. (2017).

Compounded on top of scientific deep uncertainty, the way that consensus climate science (CCS) has chosen to frame climate risk to global decision makers yet additional layers of 'masking' that further obscure our true climate risk.  For instance, the second image (after Sterman 2009; which means that all specific CO2 values in the image are out of date) presents the anthropogenic forcing problem as a highly simplified bathtub analogy limited to CO2 accumulation in the atmosphere as an imbalance between anthropogenic CO2 emissions into the bathtub (atmosphere) less the sinks draining CO2 out of the bathtub (atmosphere); while acknowledging that no one is sure how much CO2 concentration in that atmosphere is 'safe'.

While the simplified bathtub analogy does help the public to somewhat appreciate the dynamic nature of the CO2 imbalance associated with modern anthropogenic CO2 emissions; unfortunately, this analogy also distracts public attention (thus obscuring) the great number of other Earth System imbalances, including (but not limited to): Earth Energy Imbalance (EEI); imbalances of other GHGs (such as methane and ozone); and imbalances of Earth Systems that redistribute energy from high surface energy areas (such as the equatorial regions to the high latitude regions) that are currently causing an acceleration of the pattern effect (primarily associated with SST patterns) that in turn is currently causing climate sensitivity to increase.  While this, and other, dynamic imbalance(s) is/are discussed elsewhere in either this MCDS-BN thread, or in the MCDS-FT thread; here I briefly discuss the pattern effect as an example of how CCS simplified messaging obscures the true climate risk associated with the dynamic nature of imbalances of key Earth Systems.

Langenbrunner (2020) presents the third image to illustrate how CMIP5's, and even more so CMIP6's, projections illustrate how the pattern effect contributes to an increasing climate sensitivity (ECS) with time (and note that the higher the rate of radiative forcing the greater the greater the impact that the pattern effect has on climate sensitivity).

Furthermore, Langenbrunner (2020) states:

"As CO2 increases, the climate system responds with a range of fast and slow feedbacks. Fast feedbacks act over decades, like warming-induced water vapour increases, but slow feedbacks are more lethargic, such as ocean temperature and circulation changes that manifest over centennial timescales. Earth counteracts its own warming via radiative cooling, but its effectiveness at this decreases over time due to these slowly evolving feedbacks — primarily because of changes in the global sea surface temperature (SST) pattern.

This phenomenon is called the ‘pattern effect’, and it influences how much the planet will warm from increased greenhouse gases."

Langenbrunner, B. The pattern effect and climate sensitivity. Nat. Clim. Chang. 10, 977 (2020). https://doi.org/10.1038/s41558-020-00946-y

https://www.nature.com/articles/s41558-020-00946-y

Unfortunately, Langenbrunner (2020) associates the pattern effect only with changes in GHG (primarily CO2) concentrations; whereas E3SMv1 projections prove that the pattern effect is also directly associated with freshwater flux (FF) into the ocean; which both reduces radiative cooling at high latitudes (associated with FF cooling of SSTs in the North Atlantic and in the Southern Ocean) and increases SST in the equatorial ocean regions (which for example results in an increase in negative cloud feedback).  However, CCS obscures the risk of an abrupt acceleration of the pattern effect associated with the abrupt acceleration of freshwater flux (FF) events postulated in this MCDS.

Also, as Arctic/Polar Amplification increases with global warming (note that a couple of decades ago the rate Arctic Amplification was about twice that of the global average while for the past several years the rate of Arctic Amplification has increased to about four times the global average), the relatively rapid redistribution of atmospheric heat energy from the tropics to high latitudes via atmospheric Rossby Waves (see the fourth image) is slowed (due to the associated decrease in the atmospheric gradient from the tropics to high latitudes. thus increasing the energy imbalance from the tropics to high latitudes); which, results in an acceleration of the rate of Tropical Ocean SSTA increase with time; which, is perhaps the most significant parameter associated with the risk of the Northern Hemisphere (NH) atmosphere flipping into to higher climate states (such as Pliocene-like, Miocene-like or Eocene-like) by say 2150.

As a footnote, I have read many papers where consensus climate science researchers focus on the slow response modes of changes in the MOC (Meridional Overturning Current); and either ignore, or discount, the fast response modes (such as atmospheric teleconnections from the tropical oceans to the polar regions) associated with changes in SSTs that are associated with likely future slowing of the MOC due to such factors as freshwater fluxes into the ocean and projected rapid decreases in anthropogenic aerosols into the North Atlantic region (see Hassan et al. 2021).  Thus, readers should be careful not to accept consensus climate science conclusions that do not consider all inputs and domino effects for climate model projection.


“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #5 on: July 02, 2021, 09:15:19 PM »
At a 2016 EGU press conference DeConto said (see the linked video) his 2016 work implies that tipping points for major sea level rise occur between 2 and 2.7C (see the first image) above pre-industrial (DeConto starts about minute 22:10, also I need that after 2016 CCS peer pressure induced DeConto to examine and publish (see the second image) projections from a model with a lower ECS value than his 2016 model; which, given the results of the CMIP6 Wolf Pack is one reason that I focus on the DeConto & Pollard 2016 results).

http://client.cntv.at/egu2016/press-conference-8

Furthermore, per the third attached image, per the 'Wolf Pack's" UKESM1-0-LL for SSP8.5, we are likely to reach a GMSTA of 2C by about 2030 and of 2.7C by about 2040; which infers that per DeConto at the 2016 EGU we should experience a major sea level rise tipping point in that timeframe (if the Wolf Pack is correct), which roughly corresponds to Hansen et al. (2016)'s 5-year doubling scenario cited in my first post in this thread.  In this regard, the third attached image (from Hansen et al. 2016) indicates that for a 10-year doubling (note that this reference does not provide projections for a 5-year doubling scenario so I will prorate from the 10-year doubling case) time freshwater hosing event an abrupt slowdown of the AMOC (Atlantic Meridional Overturning Circulation) will be trigger no later than 2040 (which would likely occur a few years earlier [say 2035 to 2036, which is when this MCDS postulates that Thwaites Glacier will initiate a MICI-type of collapse in the SH; which is postulated to trigger a freshwater hosing event from a temporary reversal of the Beaufort Gyre in the NH and which follows Hansen et al. 2016's 5-year doubling case in two hemispheres] under the 5-double scenario considered in this MCDS-BN).  Furthermore, the fourth attached image shows a projection of Surface Air Temperature (SAT), with a 1880-1920 baseline and SRES A1B forcing, by 2065 for a 10-year double case with ice melting in both hemispheres (as assumed by MCDS-BN) and assuming that ECS ~ 3C; which shows a marked increase in SAT of between 2 and 3C over the Equatorial Pacific Ocean (which will increase major El Nino frequency and poleward advection of atmospheric Rossby Waves) and a GMSTA of 1.78C, and as I am assuming that ECS is about 5.36C (instead of 3C) and a 5-year doubling case, I estimate that the SATs shown in this image may likely occur between 2035 to 2040 for the MCDS-BN being presented here.

Finally, I note that the fourth image (from Previdi et al 2013), indicates that ECS during the Anthropocene will converge toward ESS values over 7C due changes in: ice sheets, vegetation, albedo and GHG; which the MCDS hypothesizes will occur circa 2150.

 
Image 4 caption: "Figure 2. Schematic showing the climate sensitivity (◦C) to an instantaneous doubling of the atmospheric CO2 concentration versus the time required to achieve this equilibrium surface temperature response (in years since CO2 doubling). Different coloured circles represent the three main types of climate sensitivity discussed in the text, specifically the fast feedback sensitivity, the Earth system sensitivity (ESS) including ice sheet/vegetation albedo feedbacks, and the ESS additionally including climate-greenhouse gas (GHG) feedbacks. Dashed lines indicate the approximate time-scales on which climate–GHG feedbacks and ice-sheet albedo feedbacks are expected to become significant (decades or longer and centuries or longer, respectively). We suggest that the ESS including both ice sheet/vegetation albedo and climate–GHG feedbacks is the most relevant form of climate sensitivity in the Anthropocene."

Previdi, M., B.G. Liepert, D. Peteet, J. Hansen, D.J. Beerling, A.J. Broccoli, S. Frolking, J.N. Galloway, M. Heimann, C. Le Quéré, S. Levitus, and V. Ramaswamy, 2013: Climate sensitivity in the Anthropocene. Q. J. Roy. Meteorol. Soc., 139, 1121-1131, doi:10.1002/qj.2165.
Climate sensitivity in the Anthropocene - Previdi - 2013 - Quarterly Journal of the Royal Meteorological Society - Wiley Online Library

https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.2165

Abstract: "Climate sensitivity in its most basic form is defined as the equilibrium change in global surface temperature that occurs in response to a climate forcing, or externally imposed perturbation of the planetary energy balance. Within this general definition, several specific forms of climate sensitivity exist that differ in terms of the types of climate feedbacks they include. Based on evidence from Earth's history, we suggest here that the relevant form of climate sensitivity in the Anthropocene (e.g. from which to base future greenhouse gas (GHG) stabilization targets) is the Earth system sensitivity including fast feedbacks from changes in water vapour, natural aerosols, clouds and sea ice, slower surface albedo feedbacks from changes in continental ice sheets and vegetation, and climate-GHG feedbacks from changes in natural (land and ocean) carbon sinks. Traditionally, only fast feedbacks have been considered (with the other feedbacks either ignored or treated as forcing), which has led to estimates of the climate sensitivity for doubled CO2 concentrations of about 3°C. The 2×CO2 Earth system sensitivity is higher than this, being ∼4-6°C if the ice sheet/vegetation albedo feedback is included in addition to the fast feedbacks, and higher still if climate-GHG feedbacks are also included. The inclusion of climate-GHG feedbacks due to changes in the natural carbon sinks has the advantage of more directly linking anthropogenic GHG emissions with the ensuing global temperature increase, thus providing a truer indication of the climate sensitivity to human perturbations. The Earth system climate sensitivity is difficult to quantify due to the lack of palaeo-analogues for the present-day anthropogenic forcing, and the fact that ice sheet and climate-GHG feedbacks have yet to become globally significant in the Anthropocene. Furthermore, current models are unable to adequately simulate the physics of ice sheet decay and certain aspects of the natural carbon and nitrogen cycles. Obtaining quantitative estimates of the Earth system sensitivity is therefore a high priority for future work."

“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #6 on: July 02, 2021, 09:21:30 PM »
As noted previously, this MCDS-BN estimates that key marine glacial regions of the AIS will begin to collapse sooner and more rapidly than estimate by DeConto & Pollard (2016), see the first image, for reasons including:

1. The CMIP6 Wolf Pack projection of ECS this century is higher than that assumed by DeConto & Pollard (2016); and because the MCDS estimates the ECSeff will be higher than that estimated by the CMIP6 Wolf Pack beginning around 2070 due to the acceleration of the pattern effect impact on ECS associated with the MCDS estimates freshwater flux (see the MCDS-FT thread).

2. The Thwaites Glacier will initiate a MICI-type of collapse without the need for the hydrofracturing assumed by DeConto & Pollard (2016); which, the MCDS estimates will trigger a chain reaction of AIS marine glacier collapses, and other FF events, as indicated in the MCDS-BN (see the first post in this thread).

Indeed, the MCDS estimates that the 5m of SLR projected by DeConto & Pollard (2016) to occur circa 2200 (see the RCP 8.5 with ice-climate feedback case in the first and second images); will have already occurred circa 2060; which roughly corresponds the Hansen et al. (2016)'s 5-year double case for SLR (see Reply 2 in this thread).

Furthermore, the third and fourth images (adapted from Hansen et al. 2016), indicates (respectively) that for a 10-year doubling case that circa 2035 to 2040 that the AMOC will have begun to abruptly slow down and that the impact of the FF induced pattern effect will also be meaningful (and higher than estimated by DeConto & Pollard 2016) circa 2035 to 2040.

 
Caption for the third attached image: "Figure 33. (a) AMOC (Sv) at 28o N in simulations with the forcing of Sect. 4.2 (i.e., including freshwater injection of 720 Gt year-1 in 2011 around Antarctica, increasing with a 10-year doubling time, and half that amount around Greenland). (b) SST (o C) in the North Atlantic region (44-60o N, 10-50o W).

 
Caption for the fourth image: MCDS-BN Estimated Global SAT (Surface Air Temperature) Distribution between 2035 and 2040 (per MCDS as adapted from Hansen et al. 2016)

“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #7 on: July 02, 2021, 09:26:37 PM »
The linked perspective piece, and the first image, by Siegert et al. (2020), helps to put some of DeConto & Pollard (2016)'s assumptions into perspective.

Martin Siegert, Richard B. Alley, Eric Rignot, John Englander and Robert Corel (2020), "Twenty-first century sea-level rise could exceed IPCC projections for strong-warming futures", One Earth, https://doi.org/10.1016/j.oneear.2020.11.002

https://www.cell.com/action/showPdf?pii=S2590-3322%2820%2930592-3

Abstract: "While twentieth century sea-level rise was dominated by thermal expansion of ocean water, mass loss from glaciers and ice sheets is now a larger annual contributor. There is uncertainty on how ice sheets will respond to further warming, however, reducing confidence in twenty-first century sea-level projections. In 2019, to address the uncertainty, the Intergovernmental Panel on Climate Change (IPCC) reported that sea-level rise from the 1950s levels would likely be within 0.61–1.10 m if warming exceeds 4o C by 2100. The IPCC acknowledged greater sea-level increases were possible through mechanisms not fully incorporated in models used in the assessment. In this perspective, we discuss challenges faced in projecting sea-level change and discuss why the IPCC’s sea-level range for 2100 under strong warming is focused at the low end of possible outcomes. We argue outcomes above this range are far more probable than below it and discuss how decision makers may benefit from reframing IPCC’s terminology to avoid unintentionally masking worst-case scenarios."

Extract: "Ice-shelf thinning and flow acceleration in the Amundsen Sea Embayment of West Antarctica, especially Thwaites and Pine Island Glaciers, could lead to retreat into deep interior basins with as much as 3 m of sea-level rise before stabilization on the next bottleneck. Some large East Antarctic glaciers have begun changing, and those draining the Wilkes, Aurora and other basins have even greater potential to raise sea level. Similarly, in Greenland, marinebased outlets underlain by deep channels extending inland (e.g., Petermann, Humboldt, Zachariae, Nioghalvfjerdsfjorden, Jakobshavn) house enough ice to raise sea level by 2.3 m.
One model that simulated ice-shelf loss and retreat from bottlenecks, including calving from grounded ice cliffs, found that, for cases in which strong anthropogenic warming triggered major West Antarctic retreat, the marine basins largely deglaciated over the following century or so. This model restricted ice-cliff calving to rates that have been exceeded elsewhere; much of the contribution to rapid sea-level rise came from the great thickness of the West Antarctic ice and the huge width of the calving front that developed during retreat. A West Antarctic calving-cliff retreat would produce much higher and wider cliffs with much larger stresses than any that have been observed, so calving might be faster, or indeed much faster, than previously measured. Ice-shelf loss and calving-cliff retreat are well recognized at the fjord scale but often are not included well in ice-sheet models. This is partly because of inherent difficulties in simulating fracture processes. (Small differences in conditions may cause a ceramic coffee cup dropped on a hard floor to bounce unharmed or break into fragments.) Furthermore, with no recently observed catastrophic retreats on the scale of Thwaites Glacier or other major Antarctic basins, models cannot be well calibrated against observational or historical data. The transition from non-floating to floating ice, commonly called the grounding line, is really a complex grounding zone with important but poorly known processes. In addition to calving of grounded ice blocks, retreat and faster flow are promoted by preferential melting undercutting the base of marine-ending ice cliffs, a process not yet included in many ice-sheet models. More broadly, the issue of melting at grounding zones of ice shelves and grounded cliffs remains an area of active research. Ice sheet models are sensitive to basal melt rates imposed at grounding zones. Large observed fluctuations in grounding-zone position during tidal cycles promote ocean-water flushing and melting over many kilometers, and inclusion of grounding-zone melting in models is required to match some recent observations. Inclusion of such processes in models, and their interactions with cliff undercutting and calving, could amplify projected ice-sheet response to warming. The grounding-zone transition remains the least well explored and most challenging part of the ice-ocean system, and yet is central to the future evolution of these glaciers.

In our considerable but anecdotal experience, many coastal planners, policymakers, and members of the general public fail to fully appreciate the meaning of the likely range in the assessed sea-level-rise projections. Instead, some treat the high end of the likely range as a worst-case scenario or else the upper end for practical designs. Such a situation is far from ideal because the upper limit of the IPCC likely range was never intended as a worst-case scenario.

Sea-level rise will be one of the most challenging issues faced by society in the coming decades unless we decarbonize fully by mid-century. An objective appreciation and more-effective dissemination of what sea-level rise is possible under strong warming, as opposed to what is deemed likely or is currently accounted for by numerical models, would better inform decision makers, who must increase decarbonization ambition to avoid the most severe of outcomes."

Caption for the first image: "Figure 1. Analysis of ice-sheet mass balance and IPCC sea-level projections (A) Measured ice loss from Greenland and Antarctica plotted against IPCC Fifth Assessment Report predictions. AR5 upper range relates to the business-as-usual RCP8.5 scenario, whereas the AR5 lower range corresponds to the RCP2.6 scenario of strong action on carbon dioxide emissions.32 (B) Components of observed and predicted, as in (A), annual sea-level contributions from Greenland and Antarctica between 2007 and 2017, broken into components of ice dynamics and surface mass balance."

As that Siegert et al. (2020) has made it clear that: "… the upper limit of the IPCC likely range was never intended as a worst-case scenario", while the MCDS is intended to serve as one example of a maximum credible domino scenario; I now consider conceptually how a domino chain of abrupt freshwater flux (FF) events in coming decades can disrupt the temporary quasi-static state (see the second image of a Chaos Theory strange attractor) of numerous Earth Systems that could readily be tipped into higher states (see the third image) by a perturbation such as a FF event like a MICI-type of collapse of the Thwaites Glacier.  Finally, the fourth image conceptually illustrates how an accumulation of tipped Earth Systems (ala Chaos Theory) can push the atmosphere into higher climate states such as a Pliocene, Miocene or Eocene like climate state.

Image 2 caption: Conceptual Representation of a Chaos Theory Strange Attractor; Which is Conceptually Relevant to: a) Climate Feedback Mechanisms/Phenomena Like ENSO Events and to b) Climate States.
 
Image 3 caption: Conceptual Representation of How: a) Climate Feedback Mechanisms and b) Climate States; can Transition More Rapidly from One State into a Higher State (using the illustrated quasi-static ratching action) More Rapidly Than by the Conventionally Assumed Action.

Image 4 caption: Schematic Transition of Climate States Represented as Chaos Theory Strange Attractor

“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #8 on: July 02, 2021, 09:30:20 PM »
As a follow-on to my last post, the first image (from Previdi et al 2013), conceptually illustrates how an accumulation of triggered Earth Systems can push our climate into a higher climate state characterized by a higher ECS value.
 
Image 1 caption: "Figure 1. A climate forcing F triggers a series of feedbacks (represented by the feedback parameter λ) which determine the resulting equilibrium global mean surface temperature change, or climate sensitivity, T. Delay in this equilibrium temperature response due to ocean and cryosphere inertia leads to a net planetary heat uptake Q. Different types of climate sensitivity are distinguished by the climate feedbacks that they include. (a) Fast Feedback Sensitivity: Climate sensitivity to an imposed external forcing depends solely on fast climate feedbacks due to changes in water vapour, clouds, and sea ice. Processes regarded as forcings are (from top to bottom) anthropogenic perturbations of atmospheric composition (including greenhouse gases and aerosols) due to fossil fuel burning, volcanic eruptions, variations in solar luminosity, changes in anthropogenic land use and land/ocean ecosystem management, climate-related changes in terrestrial carbon sequestration, climate-related changes in ocean carbon sequestration, surface albedo changes from land ice and vegetation, and variations in insolation (incoming solar radiation) due to changes in Earth’s orbit. The fast feedback sensitivity to a doubling of atmospheric CO2 has been estimated to be about 3◦C. (b) Earth System Sensitivity including Ice Sheet/Vegetation Albedo Feedbacks: If surface albedo changes from land ice and vegetation are regarded as a feedback, the climate sensitivity to a doubling of CO2 increases to about 4–6◦C. (c) Earth System Sensitivity additionally including Climate–GHG Feedbacks: If changes in atmospheric greenhouse gas (GHG) concentrations resulting from climate-related changes in terrestrial and ocean carbon sequestration are also regarded as a feedback, the 2×CO2 climate sensitivity is higher still (>4–6◦C). (d) Earth System Sensitivity additionally including Human Behaviour Feedbacks: In the most comprehensive type of climate sensitivity, changes in human activity (e.g. changes in fossil fuel burning, land use and land/ocean ecosystem management) in response to ongoing climate change are regarded as a feedback. (Note that human behaviour changes can be either a forcing or a feedback, since they can initiate Earth system change and also be a response to that change.)"

As an example of how our current high rates of anthropogenic forcing could push the atmosphere into higher climate states, the second and third images from Burke et al, (2018) illustrate how ESMs can project atmospheric conditions similar to Pliocene as early as 2070, and atmospheric conditions similar to Eocene could begin as early as 2150, using RCP 8.5 GHG forcing without any abrupt FF forcing.  Finally, for this post, I provide the fourth conceptual image that shows how (depending on the pathway followed that determines various Earth System parameter such as the pattern effect) the Earth's climate could abruptly change from one climate state into a higher quasi-stable climate state that may take millennia to reverse.

Burke, K. D. et al. (December 26, 2018), Pliocene and Eocene provide best analogs for near-future climates", PNAS, 115 (52) 13288-13293; https://doi.org/10.1073/pnas.1809600115
https://www.pnas.org/content/115/52/13288

Significance
The expected departure of future climates from those experienced in human history challenges efforts to adapt. Possible analogs to climates from deep in Earth’s geological past have been suggested but not formally assessed. We compare climates of the coming decades with climates drawn from six geological and historical periods spanning the past 50 My. Our study suggests that climates like those of the Pliocene will prevail as soon as 2030 CE and persist under climate stabilization scenarios. Unmitigated scenarios of greenhouse gas emissions produce climates like those of the Eocene, which suggests that we are effectively rewinding the climate clock by approximately 50 My, reversing a multimillion year cooling trend in less than two centuries.

Abstract
As the world warms due to rising greenhouse gas concentrations, the Earth system moves toward climate states without societal precedent, challenging adaptation. Past Earth system states offer possible model systems for the warming world of the coming decades. These include the climate states of the Early Eocene (ca. 50 Ma), the Mid-Pliocene (3.3–3.0 Ma), the Last Interglacial (129–116 ka), the Mid-Holocene (6 ka), preindustrial (ca. 1850 CE), and the 20th century. Here, we quantitatively assess the similarity of future projected climate states to these six geohistorical benchmarks using simulations from the Hadley Centre Coupled Model Version 3 (HadCM3), the Goddard Institute for Space Studies Model E2-R (GISS), and the Community Climate System Model, Versions 3 and 4 (CCSM) Earth system models. Under the Representative Concentration Pathway 8.5 (RCP8.5) emission scenario, by 2030 CE, future climates most closely resemble Mid-Pliocene climates, and by 2150 CE, they most closely resemble Eocene climates. Under RCP4.5, climate stabilizes at Pliocene-like conditions by 2040 CE. Pliocene-like and Eocene-like climates emerge first in continental interiors and then expand outward. Geologically novel climates are uncommon in RCP4.5 (<1%) but reach 8.7% of the globe under RCP8.5, characterized by high temperatures and precipitation. Hence, RCP4.5 is roughly equivalent to stabilizing at Pliocene-like climates, while unmitigated emission trajectories, such as RCP8.5, are similar to reversing millions of years of long-term cooling on the scale of a few human generations. Both the emergence of geologically novel climates and the rapid reversion to Eocene-like climates may be outside the range of evolutionary adaptive capacity.

Caption for the third attached image: "Mid-Pliocene reconstructed annual sea surface temperature anomaly"
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #9 on: July 02, 2021, 09:34:31 PM »
As a follow-on to my late post, Feng et al. (2020) provides an example of how ESM simulations of the Mid-Pliocene climate state demonstrate how changes in SST (dominated by changes in the tropical ocean SST) control the atmospheric climate state (see the first image) and in this regards I remind readers that the pattern effect under SSP5-8.5 GHG forcing and ice-climate forcing can rapidly increase the tropical ocean SSTs within decades.

Feng, R. et al (07 July 2020), "Increased Climate Response and Earth System Sensitivity From CCSM4 to CESM2 in Mid‐Pliocene Simulations", JAMES, https://doi.org/10.1029/2019MS002033

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019MS002033

Three new equilibrium mid‐Pliocene (MP) simulations are implemented with the Community Climate System Model version 4 (CCSM4) and Community Earth System Model versions 1.2 (CESM1.2) and 2 (CESM2). All simulations are carried out with the same boundary and forcing conditions following the protocol of Pliocene Model Intercomparison Project Phase 2 (PlioMIP2). These simulations reveal amplified MP climate change relative to the preindustrial going from CCSM4 to CESM2, seen in global and polar averages of surface warming, sea ice reduction in both the Arctic and the Antarctic, and weakened Hadley circulation. The enhanced global mean warming arises from enhanced Earth system sensitivity (ESS) to not only CO2 change but also changes in boundary conditions primarily from vegetation and ice sheets. ESS is amplified by up to 70% in CCSM4 and up to 100% in CESM1.2 and CESM2 relative to the equilibrium climate sensitivity of respective models. Simulations disagree on several climate metrics. Different from CCSM4, both CESM1.2 and CESM2 show reduction of cloud cover, and weakened Walker circulation accompanied by an El Niño‐like mean state of the tropical Pacific in MP simulations relative to the preindustrial. This El Niño‐like mean state is consistent with paleo‐observational sea surface temperatures, suggesting an improvement upon CCSM4. The performances of MP simulations are assessed with a new compilation of observational MP sea surface temperature. The model‐data comparison suggests that CCSM4 is not sensitivity enough to the MP forcings, but CESM2 is likely too sensitive, especially in the tropics.

Plain Language Summary
Our knowledge of past climate evolves with both new paleo‐observations and advancements in modeling past climates. Using the mid‐Pliocene (MP, 3.205 million years ago) as an example, we demonstrate how to implement geological reconstructions of past topography, bathymetry, and vegetation distribution in Earth system models (ESMs); how to initialize these experiments; and, finally, the new knowledge learnt from simulations with three consecutive versions of the ESMs from the same model lineage. In our simulations, the MP climate warms substantially more than the estimated amount of warming that only consider changes in CO2 radiative forcing. The simulated MP climate features strongly amplified polar warmth, massive loss of Arctic and Antarctic summer sea ice, and weakened Northern Hemispheric cell of the Hadley circulation. Interestingly, two newer versions of ESMs are more sensitive to not only CO2 changes but also changes in biome range and ice sheets than the earlier version. Paleo‐observations suggest that MP global warming is underestimated by the previous versions of models but may be overestimated by the latest version.

Extract: "In our simulations, the global mean warming solely from CO2 radiative forcing can be estimated with the known model ECSs. Using the MP and PI CO2 (ln(400/284.7)) and ECSs of individual models, we estimate CO2‐induced global warming of 1.6°C, 2.0°C, and 2.6°C in CCSM4‐MP, CESM1.2‐MP, and CESM2‐MP, respectively. Increasing ECS in CESM2 (Gettelman et al., 2019) and in many CMIP6 models has been attributed to the incorporation of aerosol‐cloud interactions into the model and a stronger positive extratropical cloud feedback (Zelinka et al., 2020). Nonetheless, a greater ECS is insufficient to explain the increase in surface warming from CCSM4‐MP to CESM2‐MP (Table 2). Changes in boundary conditions amplify the simulated global mean warming by 70% of the ECS‐estimated warming in CCSM4 but by 100% in both CESM1.2 and CESM2 (Table 2).

Due to the sparse sampling, it is unclear whether the observational SSTs contain enough information to estimate the global mean surface warming of the MP. We address this question by examining the relationship between global mean surface warming (Δ{Ts}global‐model) and simulated mean SST warming averaged across paleo‐observation sites (Δ{SST}sites‐model) (Figure 11). Δ{Ts}global‐model and Δ{SST}sites‐model are calculated for each 30‐year period of the last 900 model years of MP simulations. Surface temperature (ΔTs) and SST anomalies (ΔSST) are calculated relative to the last 100‐year averages of the PI simulations. Treating each pair of Δ{Ts}global‐model and Δ{SST}sites‐model as one realization of climate mean state, we notice an excellent linear fit between Δ{Ts}global‐model and Δ{SST}sites‐model across the three simulations, suggesting strong predictive power of Δ{SST}sites‐model for Δ{Ts}global‐model (Figure 11).

Finally, we find that MP SST observations are sufficient to rank simulation skill between models. Based on those data, MP global mean surface warming is largely underestimated by CCSM4 but overestimated by CESM2. Both CESM1.2 and CESM2 are better at capturing the amplified northern high latitude warming than CCSM4 is. Yet CESM2 overestimates warming in the tropics. Overall, paleoclimate constraints from the MP suggest that CESM2 may be overly sensitive to forcings from CO2, vegetation, and ice sheet changes."

First image caption: "Figure 11 Regression of simulated global mean surface temperature anomaly (Δ{Ts}global‐model) against simulated SST anomaly averaged over the paleo‐observational sites (Δ{SST}sites‐model). Shadings show 95% prediction interval. The mean and one standard deviation of observed SST anomaly are also shown (Δ{SST}sites‐obs). Anomalies are differences between MP and PI. Filled markers: 30‐year model averages."

Thus, if one considers not only CO2-equivalent forcing but also ice-climate (including freshwater hosing) feedback, then it is plausible that by the end of this century that we could (collectively) be in equable climatic conditions comparable to the Early Eocene as discussed in Zhu et al. (2019):

Jiang Zhu et al. (18 Sep 2019), "Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks", Science Advances, Vol. 5, no. 9, eaax1874, DOI: 10.1126/sciadv.aax1874

https://advances.sciencemag.org/content/5/9/eaax1874

Abstract: "The Early Eocene, a period of elevated atmospheric CO2 (>1000 ppmv), is considered an analog for future climate. Previous modeling attempts have been unable to reproduce major features of Eocene climate indicated by proxy data without substantial modification to the model physics. Here, we present simulations using a state-of-the-art climate model forced by proxy-estimated CO2 levels that capture the extreme surface warmth and reduced latitudinal temperature gradient of the Early Eocene and the warming of the Paleocene-Eocene Thermal Maximum. Our simulations exhibit increasing equilibrium climate sensitivity with warming and suggest an Eocene sensitivity of more than 6.6°C, much greater than the present-day value (4.2°C). This higher climate sensitivity is mainly attributable to the shortwave cloud feedback, which is linked primarily to cloud microphysical processes. Our findings highlight the role of small-scale cloud processes in determining large-scale climate changes and suggest a potential increase in climate sensitivity with future warming."

Furthermore, the MCDS presents just one of many long/fat right tail scenarios that could lead to 'Hothouse Earth' conditions circa 2150, primarily by slowing the MOC sufficiently to lead to a mean 5C increase in SSTA in the Tropical Oceans by early next century; which might be sufficient to flip the NH into an equable atmospheric pattern (see the second, third and fourth images from Schneider et al. 2019); due to changes in cloud feedback, which due to hysteresis would take many millennia to reverse.

Tapio Schneider , Colleen M. Kaul and Kyle G. Pressel (2019), "Possible climate transitions from breakup of stratocumulus decks under greenhouse warming", Nature Geoscience, https://doi.org/10.1038/s41561-019-0310-1

https://www.nature.com/articles/s41561-019-0310-1

Abstract: "Stratocumulus clouds cover 20% of the low-latitude oceans and are especially prevalent in the subtropics. They cool the Earth by shading large portions of its surface from sunlight. However, as their dynamical scales are too small to be resolvable in global climate models, predictions of their response to greenhouse warming have remained uncertain. Here we report how stratocumulus decks respond to greenhouse warming in large-eddy simulations that explicitly resolve cloud dynamics in a representative subtropical region. In the simulations, stratocumulus decks become unstable and break up into scattered clouds when CO2 levels rise above 1,200 ppm. In addition to the warming from rising CO2 levels, this instability triggers a surface warming of about 8 K globally and 10 K in the subtropics. Once the stratocumulus decks have broken up, they only re-form once CO2 concentrations drop substantially below the level at which the instability first occurred. Climate transitions that arise from this instability may have contributed importantly to hothouse climates and abrupt climate changes in the geological past. Such transitions to a much warmer climate may also occur in the future if CO2 levels continue to rise."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #10 on: July 02, 2021, 09:38:24 PM »
As a follow-up on my last post, Romps (2020) points out that once the CO2 levels match equable climate conditions (such as due to carbon cycle feedbacks such as occurred during the Eocene/PETM), where global mean surface temperatures are near 300K, that there is a risk that a perturbation (such as a rapid release of methane from high-latitude methane hydrate decomposition due to the equable climate conditions) could rapidly increase ECS from the 6.6C cited by Zhu et al. (2019) up to the much higher values shown in the first attached image.

David M. Romps (2020), "Climate Sensitivity and the Direct Effect of Carbon Dioxide in a Limited-Area Cloud-Resolving Model", J. Climate, 33 (9): 3413–3429, https://doi.org/10.1175/JCLI-D-19-0682.1

https://journals.ametsoc.org/jcli/article/33/9/3413/344997/Climate-Sensitivity-and-the-Direct-Effect-of

Abstract
Even in a small domain, it can be prohibitively expensive to run cloud-resolving greenhouse gas warming experiments due to the long equilibration time. Here, a technique is introduced that reduces the computational cost of these experiments by an order of magnitude: instead of fixing the carbon dioxide concentration and equilibrating the sea surface temperature (SST), this technique fixes the SST and equilibrates the carbon dioxide concentration. Using this approach in a cloud-resolving model of radiative–convective equilibrium (RCE), the equilibrated SST is obtained as a continuous function of carbon dioxide concentrations spanning 1 ppmv to nearly 10 000 ppmv, revealing a dramatic increase in equilibrium climate sensitivity (ECS) at higher temperatures. This increase in ECS is due to both an increase in forcing and a decrease in the feedback parameter. In addition, the technique is used to obtain the direct effects of carbon dioxide (i.e., the rapid adjustments) over a wide range of SSTs. Overall, the direct effect of carbon dioxide offsets a quarter of the increase in precipitation from warming, reduces the shallow cloud fraction by a small amount, and has no impact on convective available potential energy (CAPE).

To remind readers that the tropical ocean SST can be rapidly increased due to Arctic Amplification which is per Riboldi et al (2020) currently leading to a deceleration of eastward propagating Rossby waves (which convey heat from the tropics poleward see the second attached image).  This partially explains why I expect the advection of heat from the tropical oceans to the poles to progressively slowdown by 2100, sufficiently to change cloud feed enough to cause a flip to an equable atmospheric climate pattern by about 2150.

Jacopo Riboldi et al. (28 September 2020), "On the linkage between Rossby wave phase speed, atmospheric blocking and Arctic Amplification", Geophysical Research Letters, https://doi.org/10.1029/2020GL087796

https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL087796?af=R

Abstract
It has been hypothesized that enhanced Arctic warming with respect to midlatitudes, known as Arctic Amplification, had led to a deceleration of eastward propagating Rossby waves, more frequent atmospheric blocking and extreme weather in recent decades. We employ a novel, daily climatology of Rossby wave phase speed between March 1979 and November 2018, based on upper‐level wind data, to test this hypothesis and describe phase speed variability. The diagnostic distinguishes between periods of enhanced or reduced eastward wave propagation and is related to the occurrence of blocking and extreme temperatures over midlatitudes. While remaining tied to the upper‐level geopotential gradient, decadal trends in phase speed did not accompany the observed reduction in the low‐level temperature gradient. These results confirm the link between low phase speeds and extreme temperature events, but indicate that Arctic Amplification did not play a decisive role in modulating phase speed variability in recent decades.

Plain Language Summary
The Arctic is warming more rapidly than midlatitudes and the temperature difference between those regions is being reduced. As a result, it has been hypothesized that the jet stream will decrease in intensity and its meanders will move more slowly eastward, leading to more persistent or even extreme weather conditions. As the persistence of weather can substantially vary within and between seasons, assessing long‐term changes is not trivial. To tackle this problem, we develop a weather speedometer and quantify the west‐east displacements of jet meanders over Northern Hemisphere midlatitudes. This metric diagnoses whether jet meanders are on average propagating eastward (positive values), stagnating or even retrogressing westward (negative values) on each day between March 1979 and November 2018. Using this metric, we confirm that low speed periods are related to temperature extremes over northern midlatitudes. We also assess that there has not been an overall decrease in the propagation of jet meanders despite the significant reduction of the meridional temperature difference observed in recent decades. Results suggest the need of an improved understanding of the factors determining the persistence of weather conditions and remind caution is needed when attributing recent extreme weather to an increased stagnation of jet stream meanders.

The third image shows how the current atmospheric pattern with Hadley, Ferrel and Polar, cells could change into one large Hadley Cell under Eocene-like equable climate state conditions.

Finally, for this post, I provide the fourth image that shows different paleoclimate states (including Pliocene, Miocene and Eocene), and how GHG forcing following RCP 8.5 (without and ice-climate feedbacks) is rapidly approaching forcing that tipped Earth's climate state into those paleoclimate conditions.
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #11 on: July 02, 2021, 09:45:08 PM »
Discussion of the Differences Between Consensus Climate Science (CCS) Projections and Domino Effect Analysis Using Bayesian Network Risk Assessments (readers may want to follow the comparable discussions for replies in the MCDS-FT thread grouped under the 'Dynamical Statistics and Domino Fault Tree Modeling section heading)

I begin this section of replies discussing the differences between CCS projections and my risk assessments by means of domino effect analysis using a Bayesian network, by first presenting two images from Khakzad et al. (2012); where the first image summarizes the domino effect analysis procedure for determining the propagation pattern for the MCDS-BN discussed in this thread, where the propagation pattern was found to end circa 2150 due primarily to my inability to identify any credible remaining tipping points (that could have led to still higher values of effective climate sensitivity), but also due to my assumed reduction in anthropogenic radiative forcing due to my assumed degradation of modern civilization.  In this regard, it is important to realize that after circa 2060 the MCDS-BN estimates a marked reduction in effective radiative forcing, ERF, due both to an estimated marked reduction in freshwater flux, FF, and anthropogenic radiative forcing (from SSP5-8.5 down to SSP5-2.7); however, the MCDS-BN estimates that the ECSeff continues to increase from circa 2060 to circa 2150 due to the estimate high rate of climate change progressively triggering positive feedback mechanism (Y20 to Y39) which is estimated to progressively push Earth Systems into higher climate states (eventually including Pliocene-like, Miocene-like and Equable climate states).

The second image shows how auxiliary nodes can be used to simplify the presentation of a domino effect Bayesian Network; which is what I have done for my presentation of the MCDS-BN; which, is actually a modified Bayesian Network, where the auxiliary nodes are presented as by the dates (at the end of a nominal ten-year timeframe) for which annotated parameters are summarized; which represent the cumulative results of the chain of events in the preceding nominally ten-year period.

Caption: "Fig. 4 Modified BN to incorporate the union of tertiary and quaternary events using auxiliary nodes L1 and L2, respectively."

In Sweet et al. (2017), NOAA [as a leading publisher of consensus climate science (CCS) SLR guidance documents] at least acknowledges that their high-end scenarios do not include either ice-cliff or ice-shelf feedback processes, as indicated by the following extract.

Extract: "Systematic GMSL and RSL assessments will continue to refine the scenarios per contemporary scientific understanding of the processes contributing to extreme and rapid sea level change. Important to estimates of the probability of the higher-end scenarios (though not factored into the probability estimates included in this report or yet available in the peer reviewed literature) are contributions from ice-cliff and ice-shelf feedback processes that may significantly increase ice-sheet contributions to GMSL rise, particularly under high emissions scenarios (DeConto and Pollard, 2016)."

Such, CCS documents (see the third and fourth images) highlight the need for the development of a family MCDS-BN cases in order to better characterize high-end risks associated with ice-climate feedback mechanisms that could result in over 5m of eustatic SLR by 2100 as indicated in both this, and the MCDS-FT, threads.

Sweet, W.V., R.E. Kopp, C.P. Weaver, J. Obeysekera, R.M. Horton, E.R. Thieler, and C. Zervas, 2017: Global and Regional Sea Level Rise Scenarios for the United States. National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD. 75 pp.

https://tidesandcurrents.noaa.gov/publications/techrpt83_Global_and_Regional_SLR_Scenarios_for_the_US_final.pdf

Extract: In Figure 8, temporal evolution of the scenarios is illustrated through year 2100 relative to GMSL reconstructions from 1800 to 1900 based upon the meta-analysis of geological and tide gauge data of Kopp et al. (2016a), from 1900 to 2010 from the tide gauge analysis of Hay et al. (2015), and from 1992–2015 based upon satellite altimetry analysis updated from Nerem et al. (2010). The six GMSL rise scenarios are also shown (Table 4) relative to the probability of exceedance in 2100 as assessed by the RCP-based probabilistic projections of Kopp et al. (2014). Note that the GMSL rise scenarios assume that the rate of ice-sheet mass loss increases with a constant acceleration; however, this might not be the case (DeConto and Pollard, 2016), so it is, for example, possible to be on the Intermediate scenario early in the century but the High or Extreme scenario late in the century. Under the methodological assumptions of Kopp et al. (2014), in 2100 the Low scenario has a 94% to 100% chance of being exceeded under RCP2.6 and RCP8.5, respectively, whereas the Extreme scenario has a 0.05% to a 0.1% chance of being exceeded. However, as discussed in section 3, new evidence regarding the Antarctic ice sheet, if sustained, may significantly increase the probability of the Intermediate-High, High, and Extreme scenarios, particularly for RCP8.5 projections based upon Kopp et al. (2014). These ice-sheet modeling results have not yet been incorporated into a (conditional) probabilistic analysis of GMSL.

Note that the probabilities given by Kopp 2014 were developed before DeConto & Pollard 2016 and thus are not current.

Caption for fourth image: "Figure 12.2: (a) The relationship between peak global mean temperature, atmospheric CO2, maximum global mean sea level (GMSL), and source(s) of meltwater for two periods in the past with global mean temperature comparable to or warmer than present. Light blue shading indicates uncertainty of GMSL maximum.  Red pie charts over Greenland and Antarctica denote fraction, not location, of ice retreat.  Atmospheric CO2 levels in 2100 are shown under RCP8.5. …."






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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #12 on: July 02, 2021, 09:55:21 PM »
Some readers may feel that references such as Gilford et al. (2020), indicate that researchers such as DeConto and Pollard have rejected their 2016 eustatic SLR projections in favor of somewhat less dramatic eustatic SLR projections as shown in the first attached image.  However, to me any such interpretation is a misunderstanding of the scientific method as a continuing process (see the second image), where the findings of all research is only as relevant as that research's underlining assumptions, and is thus subject to iterative revision to slowly converge to a closer approximation of reality.  In this regard, DeConto & Pollard (2016) assumed one set of initial model conditions/sensitivity/ice mass loss processes; while Gilford et al. (2020) assumed a different set of initial model conditions/sensitivity/ice mass loss processes, and indeed Edwards et al. (2019) assumed yet another set of initial model conditions/sensitivity/ice mass loss processes.

Gilford, D.M., Ashe, E.L., DeConto, R.M., Kopp, R.E., Pollard, D. & Rovere, A. (2020), "Could the Last Interglacial constrain projections of future Antarctic ice mass loss and sea-level rise?", Journal of Geophysical Research: Earth Surface, https://doi.org/10.1029/2019JF005418.

Indeed, some consensus climate scientists point at Edwards et al. (2019) as justification for ignoring MICI types of ice mass loss in their model projections; thus, I point out that Edwards et al. (2019) used a statistical emulator of ice sheet mass loss over the past 1 million years to indicate that most likely MISI behavior could possibly account for the paleo record; however, this finding is a far different matter than concluding that MICI types of behavior can be discounted in model projections for the rest of this century for reasons including:

First, following SSP5-8.5 thru at least 2040, we may well trigger feedback mechanisms that could result in Mid-Pliocene (3.3 Ma–3 Ma) conditions by (or before) the end of the century, thus the Edwards et al. (2019) emulation of the past 1Ma are likely not relevant; while Pollard, DeConto & Alley (2018)'s evaluation of MICI under Mid-Pliocene conditions are more relevant.

Second, we are approaching Mid-Pliocene conditions thousands of times faster than occurred during the Mid-Pliocene; which Pollard, DeConto & Alley (2018) took into account by abruptly introducing Mid-Pliocene conditions onto modern AIS conditions in their model.

Third, both Edwards et al (2019) and Pollard, DeConto & Alley (2018) use AIS boundary & starting conditions for the modern AIS that are less aggressive than what is currently observed in 2019 w.r.t. such factors as: a) the large subglacial cavity at the base of the Thwaites Ice Tongue; b) the loss of ice shelf buttressing of the Southwest Tributary Glacier in Pine Island Bay; and c) the amount of ice-climate feedback mechanisms that are already being activated (including MOC slowing, shifting of the ENSO towards more frequent El Nino events & increased advection of warm CDW towards the grounding lines of key AIS marine glaciers).

Finally, the emulator used by Edwards et al. (2019) did not include the impact of the Antarctic ozone holes (as this did not occur in paleo-times); which this ozone hole has accelerated ice mass loss from Antarctica and projections of the impact of continuing GHG emissions on the westerly winds over the Southern Ocean, indicate that as the ozone hole heals the regional impact of increasing GHG atmospheric concentrations will keep these regional westerly winds in an optimal zone for promoting ice mass loss for at least the next few decades.

Next, Lee et al. (2019) provides information that appears to refute the most significant claims of Edwards et al. (2019); and confirms that MICI-type of ice sheet collapse is a valid risk this century (see the third attached image):

Lee, B.S, Murali Haran, Robert Fuller, David Pollard, Klaus Keller (24 March 2019), "A Fast Particle-Based Approach for Calibrating a 3-D Model of the Antarctic Ice Sheet", arXiv:1903.10032v1

https://arxiv.org/abs/1903.10032
https://arxiv.org/pdf/1903.10032.pdf

Abstract: "We consider the scientifically challenging and policy-relevant task of understanding the past and projecting the future dynamics of the Antarctic ice sheet. The Antarctic ice sheet has shown a highly nonlinear threshold response to past climate forcings. Triggering such a threshold response through anthropogenic greenhouse gas emissions would drive drastic and potentially fast sea level rise with important implications for coastal flood risks. Previous studies have combined information from ice sheet models and observations to calibrate model parameters. These studies have broken important new ground but have either adopted simple ice sheet models or have limited the number of parameters to allow for the use of more complex models. These limitations are largely due to the computational challenges posed by calibration as models become more computationally intensive or when the number of parameters increases. Here we propose a method to alleviate this problem: a fast sequential Monte Carlo method that takes advantage of the massive parallelization afforded by modern high performance computing systems. We use simulated examples to demonstrate how our sample-based approach provides accurate approximations to the posterior distributions of the calibrated parameters. The drastic reduction in computational times enables us to provide new insights into important scientific questions, for example, the impact of Pliocene era data and prior parameter information on sea level projections. These studies would be computationally prohibitive with other computational approaches for calibration such as Markov chain Monte Carlo or emulation-based methods. We also find considerable differences in the distributions of sea level projections when we account for a larger number of uncertain parameters."

Extract: "We chose these three parameters because they are considered to be important in modeling the long-term evolution of the Antarctic ice sheet (Edwards et al., 2019; DeConto and Pollard, 2016).

Calibration can be improved by considering an important source of uncertainty, the state of the Antarctic ice sheet during the Pliocene era …

This method includes the recent study of Edwards et al. (2019), who found that the important mechanism of marine ice cliff instability (MICI) is not necessary to capture past variations.  In this case, future sea level projections are considerably lower.  In contract, our new approach that accounts for more parametric uncertainties suggests that MICI may still be important and future sea level projections may be much higher, especially considering potential Pliocene windows."

Next, I provide the fourth attached image indicating the climate feedback timescales assumed by most CCS climate model projections; while I note that both MICI-type feedbacks and domino scenarios associated with the current fast rate of anthropogenic radiative forcing could well collapse the CCS assumed timescale for ESS from multi-millennia down to roughly one century; which implies that ECSeff could be as high as 6C circa 2120 as indicated by the MCDS-BN discussed in this thread.

Finally, I note that the word Episteme is a Greek philosophical term, meaning "to know". Thus epistemic security involves measures ensuring that we do in fact know what we know, that we can identify claims that are unsupported or not true, and that our information systems are robust to "epistemic threats", whether from climate change contrarians or from consensus climate science's tendency to err on the side of least drama.
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #13 on: July 02, 2021, 09:59:32 PM »
Discussion of Forcing Scenarios and Relationships to Both Consensus Climate Science (CCS) and MCDS Probability of Occurrence.


While the MCDS-FT thread contains more detailed discussion about effective radiative forcing (ERF); in the coming series of posts I address more general discussion related to CCS and MCDS forcing scenarios, with only relatively light discussions related to effective radiative forcing; such as noting that Zhou et al. (2021) indicates that the current high rate of radiative forcing is contributing to a high effective climate sensitivity, particularly with regard to spatial inhomogeneities in both sea surface temperature (SST) and sea ice change (see the four attached images).  Here, I note that the authors do not consider changes in effective radiative forcing associated with abrupt freshwater hosing events.

Zhou, C., Zelinka, M.D., Dessler, A.E. et al. Greater committed warming after accounting for the pattern effect. Nat. Clim. Chang. (2021). https://doi.org/10.1038/s41558-020-00955-x

The linked reference indicates that projected GMSTA by CMIP5 models are 9 to 30% too low, due to model bias associated with trends in the Equatorial Pacific sea surface gradient; which confirms that CMIP5 estimates for climate sensitivity are too low.

Watanabe, M., Dufresne, J., Kosaka, Y. et al. Enhanced warming constrained by past trends in equatorial Pacific sea surface temperature gradient. Nat. Clim. Chang. (2020). https://doi.org/10.1038/s41558-020-00933-3

The linked reference (& associated linked article) explains how ECS could currently be above 5C, as indicated by several CMIP6 projections:

Bjordal, J., Storelvmo, T., Alterskjær, K. et al. Equilibrium climate sensitivity above 5 °C plausible due to state-dependent cloud feedback. Nat. Geosci. 13, 718–721 (2020). https://doi.org/10.1038/s41561-020-00649-1

https://www.nature.com/articles/s41561-020-00649-1

Abstract: "The equilibrium climate sensitivity of Earth is defined as the global mean surface air temperature increase that follows a doubling of atmospheric carbon dioxide. For decades, global climate models have predicted it as between approximately 2 and 4.5 °C. However, a large subset of models participating in the 6th Coupled Model Intercomparison Project predict values exceeding 5 °C. The difference has been attributed to the radiative effects of clouds, which are better captured in these models, but the underlying physical mechanism and thus how realistic such high climate sensitivities are remain unclear. Here we analyse Community Earth System Model simulations and find that, as the climate warms, the progressive reduction of ice content in clouds relative to liquid leads to increased reflectivity and a negative feedback that restrains climate warming, in particular over the Southern Ocean. However, once the clouds are predominantly liquid, this negative feedback vanishes. Thereafter, other positive cloud feedback mechanisms dominate, leading to a transition to a high-sensitivity climate state. Although the exact timing and magnitude of the transition may be model dependent, our findings suggest that the state dependence of the cloud-phase feedbacks is a crucial factor in the evolution of Earth’s climate sensitivity with warming."

The linked reference confirms that the CMIP6 models do a good job of reasonably matching recently observed GMSTA values:

Fan, X. et al. (1 October 2020), "Global surface air temperatures in CMIP6: historical performance and future changes", Environmental Research Letters, Volume 15, Number 10; https://doi.org/10.1088/1748-9326/abb051

https://iopscience.iop.org/article/10.1088/1748-9326/abb051

Abstract
Surface air temperature outputs from 16 global climate models participating in the sixth phase of the coupled model intercomparison project (CMIP6) were used to evaluate agreement with observations over the global land surface for the period 1901–2014. Projections of multi-model mean under four different shared socioeconomic pathways were also examined. The results reveal that the majority of models reasonably capture the dominant features of the spatial variations in observed temperature with a pattern correlation typically greater than 0.98, but with large variability across models and regions. In addition, the CMIP6 mean can capture the trends of global surface temperatures shown by the observational data during 1901–1940 (warming), 1941–1970 (cooling) and 1971–2014 (rapid warming). By the end of the 21st century, the global temperature under different scenarios is projected to increase by 1.18 °C/100 yr (SSP1-2.6), 3.22 °C/100 yr (SSP2-4.5), 5.50 °C/100 yr (SSP3-7.0) and 7.20 °C/100 yr (SSP5-8.5), with greater warming projected over the high latitudes of the northern hemisphere and weaker warming over the tropics and the southern hemisphere. Results of probability density distributions further indicate that large increases in the frequency and magnitude of warm extremes over the global land may occur in the future.

Furthermore, Mackie et al (2020) discusses how HadGEM3-GC3.1 projections related to climate responses from the modeled increasing Antarctic iceberg and ice shelf melt.  Key identified issues include that:

- Increased meltwater is increasing (temporarily) sea ice extent while reducing AABW production; both of which contribute to a slowing of the MOC.

- Increase snowfall at lower latitudes of Antarctica is applying more driving force on key marine glaciers; which will cause both ice velocities and iceberg calving to increase; and increased snow that falls directly into the ocean also contributes to both a slowdown of the MOC and increased advection of warm CDW towards key marine glacier grounding lines.

Mackie, S., Inga J. Smith; Jeff K. Ridley; David P. Stevens and Patricia J. Langhorne (2020), "Climate response to increasing Antarctic iceberg and ice shelf melt, J. Climate 1–70; https://doi.org/10.1175/JCLI-D-19-0881.1

Finally (for this post), Smith et al. (2020) presents findings on effective radiative forcing from 17 CMIP6 models and it finds that the CMIP6 models did not use particularly negative radiative forcing associated with aerosols and that the climate sensitivity values reported by the CMIP6 models (including the Wolf Pack values) were determined independently of the assumed aerosol forcings as indicated by the following extract:

"Therefore, there is no evidence to suggest that the increasing spread in climate sensitivity in CMIP6 models, particularly related to high-sensitivity models, is a consequence of a stronger negative present-day aerosol forcing and little evidence that modelling groups are systematically tuning climate sensitivity or aerosol forcing to recreate observed historical warming."

Smith, C. J., Kramer, R. J., Myhre, G., Alterskjær, K., Collins, W., Sima, A., Boucher, O., Dufresne, J.-L., Nabat, P., Michou, M., Yukimoto, S., Cole, J., Paynter, D., Shiogama, H., O'Connor, F. M., Robertson, E., Wiltshire, A., Andrews, T., Hannay, C., Miller, R., Nazarenko, L., Kirkevåg, A., Olivié, D., Fiedler, S., Lewinschal, A., Mackallah, C., Dix, M., Pincus, R., and Forster, P. M.: Effective radiative forcing and adjustments in CMIP6 models, Atmos. Chem. Phys., 20, 9591–9618, https://doi.org/10.5194/acp-20-9591-2020, 2020.
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #14 on: July 02, 2021, 10:03:25 PM »
To continue my light discussion related to effective radiative forcing (ERF) and associated climate feedback mechanisms, the Murry et al (2021) opinion piece discusses (see the first three images) cloud-phase climate feedback and the importance of ice-nucleating particles (INP), and it states that:

"We propose that a concerted research effort is required to reduce substantial uncertainties related to the poorly understood sources, concentration, seasonal cycles and nature of these ice-nucleating particles (INPs) and their rudimentary treatment in climate models. The topic is important because many climate models may have overestimated the magnitude of the cloud-phase feedback, and those with better representation of shallow oceanic clouds predict a substantially larger climate warming."

As consensus climate scientists (e.g. CMIP5 and see the first image) tend to discount uncertainties in cloud-phase climate feedback in their projected ranges for ECS; it is highly risky to rely on consensus climate change guidance (e.g. AR5).

Murray, B. J., Carslaw, K. S., and Field, P. R.: Opinion: Cloud-phase climate feedback and the importance of ice-nucleating particles, Atmos. Chem. Phys., 21, 665–679, https://doi.org/10.5194/acp-21-665-2021, 2021.

Extract: "Liquid-only clouds in the marine boundary layer at low latitudes are generally expected to decrease in amount in a warmer world, exerting a positive feedback (Ceppi et al., 2017). However, for clouds at higher latitudes or higher altitudes where the temperature is below the freezing point of water, the response to warming can be entirely different (see Fig. 2). The key difference in “mixed-phase” clouds is that the formation and precipitation of ice crystals can strongly reduce the amount of supercooled liquid water, which accounts for most of the cloud reflectivity. If aerosol particles capable of nucleating ice, ice-nucleating particles (INPs), are present and are active at the local cloud temperature, then the supercooled liquid water content and albedo of these clouds can be dramatically reduced through ice-related microphysical processes (Vergara-Temprado et al., 2018; Komurcu et al., 2014; Storelvmo, 2017).

The strength of this feedback depends on the balance between ice and supercooled water in the present and future climate (Fig. 3); however, the cloud-phase feedback is treated in climate models with varying levels of detail.

It has become clear over the last few years that many models may overestimate the magnitude of the cloud-phase feedback, especially in the Southern Ocean. There are well-known model biases in the Southern Ocean with too much SW radiation making it to the surface due to shallow clouds not being sufficiently reflective (Bodas-Salcedo et al., 2012; Trenberth and Fasullo, 2010). In many models, these shallow clouds contain too little supercooled water, exposing the dark ocean underneath and resulting in sea surface temperatures around 2 ∘C too warm (Wang et al., 2014). This bias has profound implications for the strength of the cloud-phase feedback. Tan et al. (2016) demonstrated that the strength of the cloud-phase feedback was strongly dependent on the amount of supercooled liquid water in present-day clouds (Fig. S1 in the Supplement). The ECS in their control case, where the model was run in its default configuration, was 4.0 ∘C. But, when the amount of supercooled water in the present-day climate was increased to be more consistent with satellite data, the ECS increased to 5.3 ∘C. Similarly, Frey and Kay (2018) showed that ECS increased from 4.1 to 5.6 ∘C when they increased the amount of supercooled water to better match observations of absorbed shortwave radiation over the Southern Ocean. The fact that ECS is sensitive to the balance between supercooled water and ice in clouds means that we have to improve our understanding of ice-related microphysical processes. In particular, we need a concerted effort to understand the atmospheric abundance of INPs, the aerosol type that catalyses ice formation in mixed phase clouds and plays a major role in defining the cloud-phase feedback.

A significant input of INPs to clouds in the Southern Ocean in the present climate would imply a strong negative cloud-phase feedback and that these clouds have a strong buffering effect on warming by anthropogenic CO2. Conversely, if the INP source is weak, as contemporary measurements suggest (McCluskey et al., 2018a; Schmale et al., 2019; Welti et al., 2020), then the cloud-phase feedback would be far less negative than over the Northern Hemisphere. In addition, there is the potential that sources of INPs in the Southern Hemisphere become more prominent in the future as a response to warming, which would lead to a positive feedback.

There is substantial evidence that the cloud-phase feedback has been too negative in climate models, and the correction of this will lead to larger ECS values."

Caption for the first image: "Figure 1 The equilibrium climate sensitivity plotted against cloud feedback parameter for CMIP5 and CMIP6 models. The left plot is for total cloud feedback parameter, while the right one is for shallow clouds (< 680 hPa) that are poleward of 45∘. The data are from Zelinka et al. (2020). The correlation between low cloud feedback and ECS that has emerged in CMIP6 models indicates that the treatment of mixed-phase low clouds is critical for driving inter-model ECS variability."
 
Caption for the second image: "Figure 2 The cloud-phase feedback and its relationship with ice-nucleating particles (based on Storelvmo et al., 2015). For shallow marine clouds, the replacement of ice by liquid water leads to more reflective clouds and less shortwave radiation reaching the low albedo ocean surface, resulting in a negative climate feedback."
 
Caption for the third image: "Figure 3 Cartoons illustrating how the response of mixed-phase clouds to a changing climate is controlled by the ice-nucleating particle concentration. (a) With a relatively high INP concentration ([INP]), there is a large potential for liquid to replace ice as climate warms and isotherms shift upwards, resulting in a strong negative shortwave feedback. (b) With a relatively low INP concentration, clouds contain relatively little ice in the present climate, so there is less ice to replace with liquid water and a relatively small negative feedback. (c) Setting the temperature changes aside, there may be either increases or decreases in INP concentration in the future that clouds will respond to. We have shown the effect of an increase in INP concentration where we would expect a decrease in liquid water path and a positive feedback."

Finally (for this post), I note that the IPCC's various suites of radiative forcing scenarios (e.g. SRES, RCP or SSP) carry no guarantee that they simulate any future occurrence, but only that they represent consistent sets of assumptions so that the output/projections from different climate models (preferably Earth System Models, ESM).  In this regard, the fourth attached image indicates that CMIP6 projections of GMSTA by 2100 range up to 8C for a radiative forcing of 8.5 MW/sq-m; while the similar CMIP5 projections only range up to 6C by 2100 for the same radiative forcing (but following a different pathway).
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #15 on: July 02, 2021, 10:11:59 PM »
In this post, I shift my focus from general feedback mechanisms (and their associated ERFs) to ice-climate feedback mechanisms (see the MCDS-FT thread for more in-depth discussions) associated with changes in the Meridional Overturning Circulation (MOC), and I note that Weijer et al (2020) indicates that when evaluating meaningful hosing events, that CMIP6 projects a strong decline in the AMOC by about 34% to 45% by 2100.

Weijer W. et al. (24 May 2020), "CMIP6 Models Predict Significant 21st Century Decline of the Atlantic Meridional Overturning Circulation", Geophysical Research Letters, https://doi.org/10.1029/2019GL086075

https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019GL086075?af=R

Next, I note that Pattyn et al. (2020) indicates that the ASI is a potential source of significant future freshwater hosing.

Pattyn, F. and Mathieu Morlighem (20 Mar 2020), "The uncertain future of the Antarctic Ice Sheet", Science, Vol. 367, Issue 6484, pp. 1331-1335, DOI: 10.1126/science.aaz5487

https://science.sciencemag.org/content/367/6484/1331?rss%253D1=

Furthermore, the IPCC’s SROCC says that “Thwaites Glacier is particularly important because it extends into the interior of the WAIS, where the bed is >2000m below sea level in places”.  Although, the SROCC also notes that while MISI requires a retrograde bed slope to occur, MICI could even happen on a flat or seaward-inclined bed.

Also, all high-end CMIP6 models only consider MISI (see the first attached image of a E3SMv1 projections for the AIS by 2100), but the high-end models indicate irreversible ice loss in both the Greenland ice sheet and the WAIS will occur somewhere between 1.5C and 2C global average warming.  While models with MISI behavior this ice mass loss would occur over multiple centuries or less for unmitigated forcing scenarios.  However, hydro-fracturing can drive calving in a MICI mechanism with ice cliff faces much lower than 100m above sea level (see the image), and that surface ice melting at the base of the Thwaites Ice Tongue already periodically occurs in January months and that a possible future Super El Nino event (say in the 2030 to 2035 timeframe) could provide sufficient surface ice melting at the base of the Thwaites Ice Tongue to drive hydro-fracture-driving calving if the melange permits such calving in that timeframe.

1. E3SMv1 projections indicate that glacial (including ice shelf, see images 2 and 3) ice melting from both the GIS and the AIS (see the fourth image) is currently contributing to a slow-down of the MOC and that MISI-types of calving will accelerate this slow-down in coming decade, resulting in a relatively high value for TCR of about 2.93C for the rest of this century.  However, this projection ignores both MICI-types of mechanisms and a cascade of freshwater hosing events modulated by the bipolar seesaw mechanism via both atmospheric and oceanic telecommunication mechanism.

2. In my opinion there is a reasonable chance that the current slow-down of the AMOC (which is increasing the ocean heat content particularly in both the Southern Ocean and the North Atlantic Ocean, see the first attached image) will serve to increase the frequency and magnitude of periodic warm ocean water fluxes into the fjords of key marine terminating glaciers in Greenland (and also in Antarctica) so (in my opinion) that in the 2025 to 2030 timeframe that there will be a 5-year long surge of (ice cliff related) accelerated calving events from such key marine terminating glaciers as: Jakobshavn, Petermann, Zachariae, etc. that would both accelerate the slow-down of the MOC (thus warming the SST in the Tropical Pacific which would then atmospherically telecommunicate additional heat to the WAIS) and would contribute to a small increase in sea level in coastal West Antarctica.

3.  The result of the freshwater hosing event cited in item 2 would serve to help destabilize the marine glaciers in the Amundsen Sea Embayment, ASE; which together with a likely Super El Nino event and a likely subglacial lake drainage event beneath Thwaites, in the 2030-2035 timeframe; might likely both cause the PIIS to collapse and cause the Thwaites Glacier to undergo an MICI-type of collapse of the entire Byrd Subglacial Basin (BSB) by 2035.  Such a freshwater hosing event would not only further slow the MOC [by slowing AABW formation, around Antarctica, see Nakayama et al (2020)]; but would also slight rise sea level around Greenland and would push relatively warm Pacific water through the Bering Strait into the Beaufort Sea (see the second attached image from NOAA 2017).

4. The warm Pacific water intruding into the Beaufort Sea by 2035 cited in item 3 would likely trigger an abrupt release of freshwater from the Beaufort Gyre into first the Arctic Ocean (where it would destabilize the halocline resulting in abrupt melting of the Arctic Sea Ice) and then into the North Atlantic, where it would likely contribute to an abrupt slow-down of the AMOC, possibly in the 2035 to 2040 timeframe; which by the bipolar seesaw mechanism could trigger the abrupt collapse of both the FRIS and the RIS in the 2040 to 2045 timeframe that could contribute to the collapse of the vast majority of the remaining WAIS within the 2045 to 2090 time frame.

Obviously, this cascade of freshwater-hosing-event-related tipping points modulated by bipolar seesaw mechanisms would likely continue beyond 2090 (and would likely trigger other positive feedback mechanisms prior to 2090); such as:

a) an increase in the frequency and intensity of both El Nino events (promoting more surface melting events in West Antarctica) and of Atmospheric River events impacting Greenland (promoting more surface melting events) both of which should increase hydrofracking of ice cliffs and of ice shelves.

b) Activation of MICI-type of behavior in key EAIS marine glaciers like Totten, and Byrd, possibly as early as 2050.


Finally (for this post), I remind readers that on November 17, 2017, Eric Holthaus published an article entitled "Ice Apocalypse".

https://grist.org/article/antarctica-doomsday-glaciers-could-flood-coastal-cities/

This article focused on the long-tail issues associated with the Marine Ice Cliff Instability (MICI) mechanism, as presented in 2016 by DeConto and Pollard;

&

DeConto and Pollard (2016) resulted from earlier work that tried to match model hindcasts with paleorecords such as Pollard, DeConto and Alley (2015).

https://www.sciencedirect.com/science/article/pii/S0012821X14007961

In response to Holthaus' article Tamsin Edwards wrote an article entitled "How soon will the 'ice apocalypse' come?" on November 23, 2017.

https://www.theguardian.com/science/head-quarters/2017/nov/23/climate-change-how-soon-will-the-ice-apocalypse-come-antarctica

Edwards' article included a number of consensus climate science critiques of Hothaus' article (which she thinks projections of the coming of an Ice Apocalypse as being 'Too soon, and too certain') including:

- We are not yet certain that anthropogenic radiative forcing is responsible for the triggering of an MISI mechanism for the Thwaites Glacier as documented by Eric Rignot et al.

- DeConto & Pollard's MICI model does not adequately simulate the influence of key model parameters controlling the rate and nature of such a collapse mechanism and that they did not adequately characterize the initial conditions required to trigger a MICI-type of collapse.
Subsequently, Edwards et al (2019) which presents a lower bound MISI effort; which the authors claim matches the paleorecord without the need to invoke MICI behavior; which the authors imply is sufficient to discredit the MICI mechanism.

https://www.nature.com/articles/s41586-019-0901-4

Also, T. Edwards' has commented that no other model other than Pollard and DeConto (2016) indicates such rapid ice mass loss from the WAIS ignores the significance of modeling efforts such as the following:

David Pollard, Won Chang, Murali Haran, Patrick Applegate, and Robert DeConto (2016), "Large ensemble modeling of the last deglacial retreat of the West Antarctic Ice Sheet: comparison of simple and advanced statistical techniques", Geosci. Model Dev., 9, 1697–1723, doi:10.5194/gmd-9-1697-2016

http://www.geosci-model-dev.net/9/1697/2016/gmd-9-1697-2016.pdf
www.geosci-model-dev.net/9/1697/2016/

&

Won Chang, Murali Haran, Patrick Applegate, David Pollard (October 7, 2015), "Improving Ice Sheet Model Calibration Using Paleoclimate and Modern Data"

http://arxiv.org/pdf/1510.01676.pdf

&

Schroeder, D.M., Donald D. Blankenship, Duncan A. Young, and Enrica Quartini, (2014), "Evidence for elevated and spatially variable geothermal flux beneath the West Antarctic Ice Sheet", PNAS, doi: 10.1073/pnas.1405184111

http://www.pnas.org/content/early/2014/06/04/1405184111.abstract

http://www.pnas.org/content/suppl/2014/06/04/1405184111.DCSupplemental
&

Coletti, A. J., DeConto, R. M., Brigham-Grette, J., and Melles, M.: A GCM comparison of Pleistocene super-interglacial periods in relation to Lake El'gygytgyn, NE Arctic Russia, Clim. Past, 11, 979-989, doi:10.5194/cp-11-979-2015, 2015.

http://www.clim-past.net/11/979/2015/cp-11-979-2015.pdf
http://www.clim-past.net/11/979/2015/cp-11-979-2015.html

&

Applegate, P.J., Parizek, B.R., Nicholas, R.E. et al. (2015), "Increasing temperature forcing reduces the Greenland Ice Sheet’s response time scale", Clim Dyn 45: 2001. https://doi.org/10.1007/s00382-014-2451-7

https://link.springer.com/article/10.1007/s00382-014-2451-7#citeas
&
https://static-content.springer.com/esm/art%3A10.1007%2Fs00382-014-2451-7/MediaObjects/382_2014_2451_MOESM1_ESM.pdf


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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #16 on: July 02, 2021, 10:20:32 PM »
Sherwood et al. (2020), using multiple lines of evidence, indicates that it is very likely that S (effective ECS) is between 2.0 and 5.7 C. 

S. Sherwood et al. (22 July 2020), "An assessment of Earth's climate sensitivity using multiple lines of evidence", Reviews of Geophysics, https://doi.org/10.1029/2019RG000678

https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019RG000678

As to why such relatively high values of climate sensitivity was not verified by early CCS projections (like CMIP5), Lou et al (2020) presents findings of the E3SM calibration efforts for secondary organic aerosols (SOA).  Their findings indicate that the direct radiative forcing from SOAs are currently significant and that future levels of SOA radiative forcing is subject to potentially significant changes due to factors that influence the SOA lifetimes for different assumed future pathways.

Lou, S. et al. (17 November 2020), "New SOA treatments within the Energy Exascale Earth System Model (E3SM): Strong production and sinks govern atmospheric SOA distributions and radiative forcing", JAMES, https://doi.org/10.1029/2020MS002266

https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020MS002266

Abstract
Secondary organic aerosols (SOA) are large contributors to fine particle mass loading and number concentration and interact with clouds and radiation. Several processes affect the formation, chemical transformation and removal of SOA in the atmosphere. For computational efficiency, global models use simplified SOA treatments, which often do not capture the dynamics of SOA formation. Here we test more complex SOA treatments within the global Energy Exascale Earth System Model (E3SM) to investigate how simulated SOA spatial distributions respond to some of the important but uncertain processes affecting SOA formation, removal and lifetime. We evaluate model predictions with a suite of surface‐, aircraft‐ and satellite observations that span the globe and the full troposphere. Simulations indicate that both, a strong production (achieved here by multigenerational aging of SOA precursors that includes moderate functionalization) and a strong sink of SOA (especially in the mid‐upper troposphere, achieved here by adding particle‐phase photolysis) are needed to reproduce the vertical distribution of organic aerosol (OA) measured during several aircraft field campaigns; without this sink, the simulated mid‐upper tropospheric OA is too large. Our results show that variations in SOA chemistry formulations change SOA wet removal lifetime by a factor of 3 due to changes in horizontal and vertical distributions of SOA. In all the SOA chemistry formulations tested here, an efficient chemical sink i.e. particle‐phase photolysis, was needed to reproduce the aircraft measurements of OA at high altitudes. Globally, SOA removal rates by photolysis are equal to the wet removal sink, and photolysis decreases SOA lifetimes from 10 days to ~3 days. A recent review of multiple field studies found no increase in net OA formation over and downwind biomass burning regions, so we also tested an alternative, empirical SOA treatment that increases primary organic aerosol (POA) emissions near source region and converts POA to SOA with an aging timescale of 1 day. Although this empirical treatment performs surprisingly well in simulating OA loadings near the surface, it overestimates OA loadings in middle and upper troposphere compared to aircraft measurements, likely due to strong convective transport to high altitudes where wet removal is weak. The default improved model formulation (multigenerational aging with moderate fragmentation and photolysis) performs much better than the empirical treatment in these regions. Differences in SOA treatments greatly affect the SOA direct radiative effect, which ranges from ‐0.65 W m‐2 (moderate fragmentation and photolysis) to ‐2 W m‐2 (moderate fragmentation without photolysis). Notably, most SOA formulations predict similar global indirect forcing of SOA calculated as the difference in cloud forcing between present‐day and pre‐industrial simulations.

Plain language Summary
Secondary organic aerosols (SOA) are formed in the atmosphere by oxidation of organic gases emitted from natural biogenic, anthropogenic, and biomass burning sources. In many regions of the atmosphere, SOA greatly contributes to fine particle mass loadings and number concentrations and affects clouds and radiation. Integrating insights from global atmospheric modeling and measurements, we show that strong chemical production achieved here by multigenerational chemistry including moderate fragmentation of SOA precursors, and strong chemical sinks represented by particle‐phase photolysis are needed to explain the aircraft‐observed vertical profiles of SOA over multiple regions including North America, Equatorial Oceans and Southern Oceans. Photolysis reduces SOA simulated global SOA lifetimes from 10 days to 3 days. Within the same model physics and cloud treatments, we show that changes in SOA chemistry formulations change SOA wet removal lifetimes by a factor of 3. Simulations show that SOA exerts a strong direct radiative forcing in the present day ranging from ‐0.65 W m‐2 to ‐2 W m‐2. Future measurements and modeling are needed to better constrain the photolytic and heterogeneous chemical removal of SOA at high‐altitude atmospheric conditions.

Furthermore, aerosols are an important potential mechanism for masking long-tailed risk within the guidance provided by such consensus science documents.  To get a better grip on what this might mean, given that research such as McCoy et al (2017) and Rosenfeld et al (2018), indicate that Fareo (Current aerosol radiative forcing) is likely much more negative than -1.0 Wm-2, I provide Figures 1 & 3 from Mauritsen & Pincus (2017).  Where Figure 1 shows observed values of TCR and ECS used in AR5 (also I provide the third image that shows how observed values of ECS relates to true values of ECS); and Figure 3 shows that for Fareo more negative than -1.0 Wm-2, that observed TCR is higher than assumed by AR5 (see note below), indicating that the Centennial committed warming will be higher than previously estimated:

Thorsten Mauritsen, Robert Pincus. Committed warming inferred from observations. Nature Climate Change, 2017; DOI: 10.1038/NCLIMATE3357

https://www.nature.com/articles/nclimate3357

Also, I note that for:
i) Equilibrium Climate Sensitivity (ECS)
The AR5 assesses ECS as likely to be 1.5°C to 4.5°C. ECS is extremely unlikely to be less than 1°C and very unlikely to be greater than 6°C.

This compares with the Fourth Assessment Report, which assessed ECS as likely to be 2 to 4.5°C.

ii) Transient Climate Response (TCR)
The AR5 assesses TCR as likely to be 1°C to 2.5°C and extremely unlikely to be greater than 3°C. In the Fourth Assessment Report, the assessed range of TCR was very unlikely to be less than 1.0°C and very unlikely to be greater than 3.0°C. The assessed ranges are therefore quoted differently, making direct comparison difficult, but compared with the previous report there has been a decrease in the assessed likelihood that the TCR is over 3.0°C from <10% to <5%.

The linked reference confirms that using consensus science climate assumptions, anthropogenic aerosol forcing has masked and delayed the formation of the North-Atlantic warming hole (NAWH), and now that anthropogenic aerosol emissions are already decreasing we can expect that the formation and growth of the NAWH will accelerate (beginning now); which will accelerate the slowdown of the MOC.

Guy Dagan et al. (04 November 2020), "Aerosol forcing masks and delays the formation of the North‐Atlantic warming hole by three decades", Geophysical Research Letters, https://doi.org/10.1029/2020GL090778

https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL090778?af=R

Abstract
The North‐Atlantic warming hole (NAWH) is referred to as a reduced warming, or even cooling, of the North‐Atlantic during an anthropogenic‐driven global warming. A NAWH is predicted by climate models during the 21st century and its pattern is already emerging in observations. Despite the known key role of the North‐Atlantic surface temperatures in setting the Northern‐Hemisphere climate, the mechanisms behind the NAWH are still not fully understood. Using state‐of‐the‐art climate models, we show that anthropogenic aerosol forcing opposes the formation of the NAWH (by leading to a local warming) and delays its emergence by about 30 years. In agreement with previous studies, we also demonstrate that the relative warming of the North‐Atlantic under aerosol forcing is due to changes in ocean heat fluxes, rather than air‐sea fluxes. These results suggest that the predicted reduction in aerosol forcing during the 21st century may accelerate the formation of the NAWH.

Plain Language Summary
Anthropogenic aerosols are particles suspended in the atmosphere, which were released due to anthropogenic activity. These particles have a general cooling effect on the Earth due to their interactions with radiation and with clouds. Here we show that the surface temperature in the North‐Atlantic Ocean is predicted to increase due to aerosol forcing (despite the global cooling). This trend is the opposite of the surface temperature trend predicted due to increase in green‐house gases (global warming with a warming “hole” in the North‐Atlantic, trend known as the North‐Atlantic warming hole ‐ NAWH). Using state‐of‐the‐art climate models, we show that aerosol forcing delays the formation of the NAWH by about 30 years. This trend could have important climatic impacts due to the key role of the North‐Atlantic surface temperatures in setting the Northern‐hemisphere’s climate, and due to the predicted reduction in aerosol forcing in the next few decades.


W.R.T. ECS (& GMSTA), other general masking mechanisms include:

(a) Temporary (observed at least from roughly 1998 to 2013) atmospheric conditions in the Tropical Pacific that not only temporarily increased the frequency of lower level cloud cover with negative feedback, but also above average La Nina-like conditions and generally negative PDO values; which, accelerated the sequestration of heat in the ocean, which was partially release during the 2015-16 El Nino and largely advected to the Southern Ocean.

(b) The temporary acceleration of anthropogenic aerosol emissions (largely associated with coal-fired power plants in both in China and elsewhere) that temporarily induced both negative forcing & negative feedback; which are now being rapidly reduced.

(c) A temporary acceleration of the absorption of carbon dioxide by land-based plants associated both with higher atmospheric carbon dioxide concentrations, and with global warming; which will soon be reverses as vegetative stress related to rapid climate change release temporarily sequestered carbon from vegetation into the atmosphere

(d) Decadal scale thermal inertia fluctuations associated the ocean, atmosphere and cryosphere.
 
(f) A probable underestimation of both natural and anthropogenic negative aerosol forcing and feedback.

(g) aerosol emissions, with high sulfur content, from shipping, and Canadian forests, have been masking the full impact of global warming (i.e. without these two negative feedback GMSTA would already be over 1.5C).

Pefanis et al. (2020) discusses how increasing phytoplankton growth in the Arctic Ocean can contribute to increasing Arctic Amplification, in coming decades.

Pefanis, V. et al. (26 October 2020), "Amplified Arctic Surface Warming and Sea Ice Loss Due to Phytoplankton and Colored Dissolved Material", Geophysical Research Letters, https://doi.org/10.1029/2020GL088795.

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL088795

Abstract: "Optically active water constituents attenuate solar radiation and hence affect the vertical distribution of energy in the upper ocean. To understand their implications, we operate an ocean biogeochemical model coupled to a general circulation model with sea ice. Incorporating the effect of phytoplankton and colored dissolved organic matter (CDOM) on light attenuation in the model increases the sea surface temperature in summer and decreases sea ice concentration in the Arctic Ocean. Locally, the sea ice season is reduced by up to one month. CDOM drives a significant part of these changes, suggesting that an increase of this material will amplify the observed Arctic surface warming through its direct thermal effect. Indirectly, changing advective processes in the Nordic Seas may further intensify this effect. Our results emphasize the phytoplankton and CDOM feedbacks on the Arctic ocean and sea ice system and underline the need to consider these effects in future modeling studies to enhance their plausibility."

Also, Zhu and Poulsen (2020) demonstrate that CCS likely underestimates climate sensitivity in the next one hundred years, not only because masking due to aerosols, but also due to the fact that climate sensitivity increases with increasing GMST.  In this regards, Zhu and Poulsen (2020) indicates that as the ice cloud fraction decreases the negative cloud feedback weakens and a less negative cloud feedback means a higher climate sensitivity (see the second image) well before all of the ice in the clouds is completely gone (which corresponds with observed cloud data such as that shown in the third image).

Zhu J. and Christopher J. Poulsen (02 September 2020), "On the increase of climate sensitivity and cloud feedback with warming in the Community Atmosphere Models", Geophysical Research Letters, https://doi.org/10.1029/2020GL089143

Abstract
Modeling and paleoclimate proxy‐based studies suggest that equilibrium climate sensitivity (ECS) depends on the background climate state, though the reason is not thoroughly understood. Here we study the state dependence of ECS over a large range of global mean surface temperature (GMST) in the Community Atmosphere Model (CAM) versions 4, 5, and 6 by varying atmospheric CO2 concentrations. We find a robust increase of ECS with GMST in all three models, albeit at different rates, which is primarily attributed to strengthening of the shortwave cloud feedback (λcld) at both high and low latitudes. Over high latitudes, increasing GMST leads to a reduction in the cloud ice fraction, weakening the (negative) cloud‐phase feedback due to the phase transition of cloud ice to liquid and thereby strengthening λcld. Over low‐latitude regions, increasing GMST strengthens λcld likely through the nonlinear increase in water vapor, which causes low‐cloud thinning through thermodynamic and radiative processes.

Plain Language Summary
Equilibrium climate sensitivity (ECS) is defined as the equilibrium increase in global mean temperature as a result of a doubling of atmospheric CO2 concentration. The latest assessment by the Intergovernmental Panel on Climate Change reported a likely ECS range of 1.5–4.5°C. Narrowing the ECS range is of paramount importance for prediction of future warming. Earth’s surface has experienced prolonged periods of large magnitude warming in the geological past, which provide important empirical information on ECS. To quantitatively use the paleoclimate information, we need a complete understanding of how ECS may depend on the background climate. In this study, we investigate the physical mechanisms responsible for the state dependence of ECS using three climate models that have distinct model physics. In all three models, we find that ECS grows as the background climate warms, i.e., a warmer climate is more sensitive to external forcing. We attribute the increase of ECS to both high‐ and low‐latitude cloud processes. Over high latitudes, cloud ice fraction decreases with global warming, weakening the potential for mixed‐phase clouds to reflect solar radiation and amplifying surface warming. Over low latitudes, global warming enhances the efficiency of processes that make clouds less opaque, again, amplifying surface warming.

Key Points
•   ECS increases with CO2‐induced global warming in CAM 6, 5, and 4, and is primarily attributed to the strengthening of cloud feedback
•   High‐latitude λcld strengthens with warming due to a decrease of cloud ice fraction and a weakening of the negative cloud‐phase feedback
•   Low‐latitude λcld strengthening is linked to cloud thinning over subsidence regions likely caused by cloud interactions with water vapor

Finally (for this post), the fourth image indicates that CMIP6 models with high values of ECS also have high values of TCR; so if the CMIP6 Wolf Pack models are generally correct, we will soon experience relatively high values of TCR even without strong domino activation of positive feedback mechanisms this century.

 

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #17 on: July 02, 2021, 10:25:08 PM »
Next, the first image illustrates that CCS (like that by the IPCC) has done a poor job of establishing a clear pre-industrial GMST baseline; which, increases that risk that climate sensitivity may well be higher than indicated by CCS documents like AR5.
 
Furthermore, if AR5 projections on SLR were 'fair and balanced', one would not expect that SLR is currently tracking AR5's worst-case SLR scenario.  However, the linked article, and associated reference, makes it clear that SLR is currently tracking AR5's worst-case SLR scenario; and these documents do not even consider the deep uncertainty associated with possible future MICI-type of behavior:

Title: "Sea level rise from ice sheets track worst-case climate change scenario"

https://phys.org/news/2020-08-sea-ice-sheets-track-worst-case.html

Extract: "Ice sheets in Greenland and Antarctica whose melting rates are rapidly increasing have raised the global sea level by 1.8cm since the 1990s, and are matching the Intergovernmental Panel on Climate Change's worst-case climate warming scenarios."

&

Slater, T., Hogg, A.E. & Mottram, R. Ice-sheet losses track high-end sea-level rise projections. Nat. Clim. Chang. (2020). https://doi.org/10.1038/s41558-020-0893-y

https://www.nature.com/articles/s41558-020-0893-y

Abstract: "Observed ice-sheet losses track the upper range of the IPCC Fifth Assessment Report sea-level predictions, recently driven by ice dynamics in Antarctica and surface melting in Greenland. Ice-sheet models must account for short-term variability in the atmosphere, oceans and climate to accurately predict sea-level rise."

The linked reference (& non-associated image 2) provides some idea of consensus climate science's deep uncertainty regarding projected SLR this century.  To me this suggests that consensus climate science has declined to face the full uncertainties associated with: a) climate sensitivity, b) freshwater hosing events and c) MICI mechanism; which indicates that we are likely going to see more surprises in projected SLR in coming decades (say from E3SMv4), as ice-ocean interactions are better understood/modeled:

Andra J. Garner et al. (29 October 2018), "Evolution of 21st Century Sea Level Rise Projections", Earth's Future, https://doi.org/10.1029/2018EF000991

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018EF000991

Abstract
The modern era of scientific global‐mean sea level rise (SLR) projections began in the early 1980s. In subsequent decades, understanding of driving processes has improved, and new methodologies have been developed. Nonetheless, despite more than 70 studies, future SLR remains deeply uncertain. To facilitate understanding of the historical development of SLR projections and contextualize current projections, we have compiled a comprehensive database of 21st century global SLR projections. Although central estimates of 21st century global‐mean SLR have been relatively consistent, the range of projected SLR has varied greatly over time. Among studies providing multiple estimates, the range of upper projections shrank from 1.3–1.8 m during the 1980s to 0.6–0.9 m in 2007, before expanding again to 0.5–2.5 m since 2013. Upper projections of SLR from individual studies are generally higher than upper projections from the Intergovernmental Panel on Climate Change, potentially due to differing percentile bounds or a predisposition of consensus‐based approaches toward relatively conservative outcomes.

Plain Language Summary
In spite of more than 35 years of research, and over 70 individual studies, the upper bound of future global‐mean sea level rise (SLR) remains deeply uncertain. In an effort to improve understanding of the history of the science behind projected SLR, we present and analyze the first comprehensive database of 21st century global‐mean SLR projections. Results show a reduction in the range of SLR projections from the first studies through the mid‐2000s that has since reversed. In addition, results from this work indicate a tendency for Intergovernmental Panel on Climate Change reports to err on the side of least drama—a conservative bias that could potentially impede risk management.

Also I note that consensus climate science (CCS), like the IPCC's Fifth Assessment Report (AR5), focuses on either a mode or a median projected values (like Global Mean Surface Temperature Anomaly, GMSTA) with an associated range of likelihood and an associated confidence level, based on CCS interpretations of available paleo and observed data and on the relevant model projections (with the understanding that all models are wrong but some models are useful) using IPCC generated families of radiative forcing scenarios (like the suit of SSP families 1 thru 5 [see the first and second images] and in particular the SSP5 -8.5 family [see the third image, and which assumes a peak global population of about 8 billion while we are currently exceeding 7.8 billion]) developed for comparison purposes between model projections.  However, as stated in Siegert et al. (2020): "… the upper limit of the IPCC likely range was never intended as a worst-case scenario" (see the third and fourth images).
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #18 on: July 02, 2021, 10:31:58 PM »
As a continuation of my last post, I note that the first image indicates that SSP5 – 8.5 assumes that global population will peak at just over 8 billion just after 2060; while the second image of a 2019 UN global population projection indicates that most likely (without global war and/or a global socio-economic collapse) world population will exceed the SSP – 8.5 assumption soon after 2025, and that world population will most likely peak around 11billion circa 2100 (indicating the potential for radiative forcing to equal or exceed that assumed by SSP5 – 8.5, particularly if third world countries try to improve their socio-economic circumstances using fossil fuels).  However, the MCDS case presented here assumes that anthropogenic radiative forcing will follow SSP5 – 8.5 only until roughly 2060 (with the relatively high radiative forcing from 2050 to 2060 being largely associated with assumed global wars [Where it is assumed that this global war and associated socio-economic degradation makes it impractical to implement any effective solar geoengineering measures.  MCDS also assumes: no nuclear winter, no AI apocalypse, no geoengineering apocalypse and no catastrophic global biological warfare/mishap; however, abrupt climate change, and limited resources, from 2030 to 2060 is viewed as a stress multiplier that increases the risks of conventional war, nuclear warfare {limited or otherwise}, biological war, AI warfare, and geoengineering] in this period, see the third image), and that after roughly 2060 that anthropogenic radiative forcing will follow SSP5 – 2.6.

Finally, I end this sub-section of posts on ' Forcing Scenarios and Relationships to Both Consensus Climate Science (CCS) and MCDS Risk Assessments' by providing the fourth image from Hansen & Sato (2012) that indicates that for GHG radiative forcing equal to current conditions that a domino-triggered, abrupt-acceleration of ice-climate feedbacks (as considered by the MCDS case discussed here) would result in an effective ECS of over 6C in coming decades.
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #19 on: July 02, 2021, 10:34:54 PM »
EEI (Earth Energy Imbalance, in W/m2) Discussion

I provide the following discussion about Earth Energy Imbalance (EEI) to help readers to better understand both that:
a) That EEI is a better measure of possible changes in climate state (see the first image), in coming decades due to a potential cascade of tipping points including ice-climate tipping points, than is GMSTA, and
b) That EEI can occur not only due to anthropogenic GHG emissions, but also from freshwater hosing events (as indicated by Hansen et al. 2016) and rapid decreases in anthropogenic aerosol emissions (see Hassan et al. 2021).

Regarding potential changes in climate state, I note that:
a) Increases in climate state are typically associated with rapid increases in climate sensitivity.

b) Consensus climate scientists who point at paleo examples of rapid changes in climate state (that occur over hundreds or thousands of years) generally do not make it clear to decision makers (for example paleo estimates of ECS are averaged together with values of ECS estimates from models and modern observation without any adjustments to account for our current situation, in CCS documents like AR5) that such examples all had much slower (twenty to thousands of time slower) rates of forcing than current rates of forcing and that the forcing in these paleo examples were all focused on one hemisphere (or the other), while current forcing is roughly acting simultaneously in both hemispheres).

c) Due to lag time in climate feedback mechanisms, society may well pass a tipping point, triggering a domino chain reaction of feedback mechanism; before, society realizes that it has triggered an irreversible tipping point.

von Schuckmann et al. (2020) quantifies the Earth energy imbalance (EEI), between 1960 and 2018, and indicates that EEI is the most critical number for representing prospects for continued global warming and climate change, even more so than GMSTA (see also the last three associated images).  This work helps to bring the implications of Hansen et al. (2016) into perspective.

von Schuckmann, K., Cheng, L., Palmer, M. D., Hansen, J., Tassone, C., Aich, V., Adusumilli, S., Beltrami, H., Boyer, T., Cuesta-Valero, F. J., Desbruyères, D., Domingues, C., García-García, A., Gentine, P., Gilson, J., Gorfer, M., Haimberger, L., Ishii, M., Johnson, G. C., Killick, R., King, B. A., Kirchengast, G., Kolodziejczyk, N., Lyman, J., Marzeion, B., Mayer, M., Monier, M., Monselesan, D. P., Purkey, S., Roemmich, D., Schweiger, A., Seneviratne, S. I., Shepherd, A., Slater, D. A., Steiner, A. K., Straneo, F., Timmermans, M.-L., and Wijffels, S. E.: Heat stored in the Earth system: where does the energy go?, Earth Syst. Sci. Data, 12, 2013–2041, https://doi.org/10.5194/essd-12-2013-2020, 2020.

https://essd.copernicus.org/articles/12/2013/2020/

Abstract
Human-induced atmospheric composition changes cause a radiative imbalance at the top of the atmosphere which is driving global warming. This Earth energy imbalance (EEI) is the most critical number defining the prospects for continued global warming and climate change. Understanding the heat gain of the Earth system – and particularly how much and where the heat is distributed – is fundamental to understanding how this affects warming ocean, atmosphere and land; rising surface temperature; sea level; and loss of grounded and floating ice, which are fundamental concerns for society. This study is a Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory and presents an updated assessment of ocean warming estimates as well as new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960–2018. The study obtains a consistent long-term Earth system heat gain over the period 1971–2018, with a total heat gain of 358±37 ZJ, which is equivalent to a global heating rate of 0.47±0.1 W m−2. Over the period 1971–2018 (2010–2018), the majority of heat gain is reported for the global ocean with 89 % (90 %), with 52 % for both periods in the upper 700 m depth, 28 % (30 %) for the 700–2000 m depth layer and 9 % (8 %) below 2000 m depth. Heat gain over land amounts to 6 % (5 %) over these periods, 4 % (3 %) is available for the melting of grounded and floating ice, and 1 % (2 %) is available for atmospheric warming. Our results also show that EEI is not only continuing, but also increasing: the EEI amounts to 0.87±0.12 W m−2 during 2010–2018. Stabilization of climate, the goal of the universally agreed United Nations Framework Convention on Climate Change (UNFCCC) in 1992 and the Paris Agreement in 2015, requires that EEI be reduced to approximately zero to achieve Earth's system quasi-equilibrium. The amount of CO2 in the atmosphere would need to be reduced from 410 to 353 ppm to increase heat radiation to space by 0.87 W m−2, bringing Earth back towards energy balance. This simple number, EEI, is the most fundamental metric that the scientific community and public must be aware of as the measure of how well the world is doing in the task of bringing climate change under control, and we call for an implementation of the EEI into the global stocktake based on best available science. Continued quantification and reduced uncertainties in the Earth heat inventory can be best achieved through the maintenance of the current global climate observing system, its extension into areas of gaps in the sampling, and the establishment of an international framework for concerted multidisciplinary research of the Earth heat inventory as presented in this study. This Earth heat inventory is published at the German Climate Computing Centre (DKRZ, https://www.dkrz.de/, last access: 7 August 2020) under the DOI https://doi.org/10.26050/WDCC/GCOS_EHI_EXP_v2 (von Schuckmann et al., 2020).
 
Caption for the second image: "Figure 6. Earth heat inventory (energy accumulation) in ZJ (1 ZJ = 1021 J) for the components of the Earth’s climate system relative to 1960 and from 1960 to 2018 (assuming constant cryosphere increase for the year 2018). See Sects. 1–4 for data sources. The upper ocean (0–300 m, light blue line, and 0–700 m, light blue shading) accounts for the largest amount of heat gain, together with the intermediate ocean (700–2000 m, blue shading) and the deep ocean below 2000 m depth (dark blue shading). Although much lower, the second largest contributor is the storage of heat on land (orange shading), followed by the gain of heat to melt grounded and floating ice in the cryosphere (gray shading). Due to its low heat capacity, the atmosphere (magenta shading) makes a smaller contribution. Uncertainty in the ocean estimate also dominates the total uncertainty (dot-dashed lines derived from the standard deviations (2σ) for the ocean, cryosphere and land; atmospheric uncertainty is comparably small). Deep ocean (> 2000 m) is assumed to be zero before 1990 (see Sect. 1 for more details). The dataset for the Earth heat inventory is published at the German Climate Computing Centre (DKRZ, https://www.dkrz.de/) under the DOI https://doi.org/10.26050/WDCC/GCOS_EHI_EXP_v2. The net flux at TOA from the NASA CERES program is shown in red (https://ceres.larc.nasa.gov/data/, last access: 7 August 2020; see also for example Loeb et al., 2012) for the period 2005–2018 to account for the golden period of best available estimates. We obtain a total heat gain of 358 ± 37 ZJ over the period 1971–2018, which is equivalent to a heating rate (i.e., the EEI) of 0.47±0.1 W m−2 applied continuously over the surface area of the Earth (5.10×1014 m2 ). The corresponding EEI over the period 2010–2018 amounts to 0.87±0.12 W m−2 . A weighted least square fit has been used taking into account the uncertainty range (see also von Schuckmann and Le Traon, 2011)."
 
Caption for the third image: "Figure 7. Overview on EEI estimates as obtained from previous publications; references are listed in the figure legend. For IPCC AR5, Rhein et al. (2013) is used. The color bars take into account the uncertainty ranges provided in each publication, respectively. For comparison, the estimates of our Earth heat inventory based on the results of Fig. 6 have been added (yellow lines) for the periods 1971–2018, 1993–2018 and 2010–2018, and the trends have been evaluated using a weighted least square fit (see von Schuckmann and Le Traon, 2011, for details on the method)."
 
Caption for the fourth image: "Figure 8. Schematic presentation on the Earth heat inventory for the current anthropogenically driven positive Earth energy imbalance at the top of the atmosphere (TOA). The relative partition (in %) of the Earth heat inventory presented in Fig. 6 for the different components is given for the ocean (upper: 0–700 m, intermediate: 700–2000 m, deep: > 2000 m), land, cryosphere (grounded and floating ice) and atmosphere, for the periods 1971–2018 and 2010–2018 (for the latter period values are provided in parentheses), as well as for the EEI. The total heat gain (in red) over the period 1971–2018 is obtained from the Earth heat inventory as presented in Fig. 6. To reduce the 2010–2018 EEI of 0.87 ± 0.12 W m−2 towards zero, current atmospheric CO2 would need to be reduced by −57 ± 8 ppm (see text for more details)."


So energy imbalance is presently +0.87 W/m2/K. How much more does the Earth have to warm to wipe out that imbalance? That connection between energy imbalance and temperature is known as the climate sensitivity.

If climate sensitivity is ~0.75 K/(W/m2) (corresponding to ~3°C for doubled CO2), then that tells us that we need to warm the climate about 1°C *more* to reach energy balance, at which point the Earth will be in energy balance.

Except it won't be. We're still emitting carbon dioxide, so by the time the planet has warmed enough to wipe out the +0.87 W/m2 of energy imbalance that we measure today, we'll have emitted a whole lotta CO2 that will have increased the energy imbalance.

See also:

Loeb, N.G. et al. (15 June 2021), "Satellite and Ocean Data Reveal Marked Increase in Earth's Heating Rate", Geophysical Research Letters, https://doi.org/10.1029/2021GL093047

&

Title: "Joint NASA, NOAA Study Finds Earth's Energy Imbalance Has Doubled"

https://www.nasa.gov/feature/langley/joint-nasa-noaa-study-finds-earths-energy-imbalance-has-doubled

Extract: "Researchers have found that Earth’s energy imbalance approximately doubled during the 14-year period from 2005 to 2019.

"It's likely a mix of anthropogenic forcing and internal variability," said Loeb. "And over this period they're both causing warming, which leads to a fairly large change in Earth's energy imbalance. The magnitude of the increase is unprecedented." …

The study does conclude, however, that unless the rate of heat uptake subsides, greater changes in climate than are already occurring should be expected."

See also:

Title: "Don’t Worry about CO2, Worry about the Earth’s ‘Energy Balance’ -
The “most fundamental” climate metric takes a troubling turn"

By Chelsea Harvey, E&E News on June 16, 2021

https://www.scientificamerican.com/article/dont-worry-about-co2-worry-about-the-earths-energy-balance/

Extract: "The researchers also conducted an extra analysis to figure out why the imbalance is quickly worsening. Greenhouse gases in the atmosphere are clearly a major driver. But as the planet has warmed, it’s triggered other feedback cycles that have further increased the imbalance.

Melting ice is one of these feedbacks.



It’s a reminder that not all the consequences of climate change are linear. The climate system is full of feedback loops, which can quicken the speed at which the planet warms and changes.

If the Earth’s energy imbalance continues to worsen, the researchers warned, then they would expect “even greater changes in climate in the coming decades.”"

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #20 on: July 02, 2021, 10:36:53 PM »
The first image (from a Skeptical Science article), provides a longer-term context for EEI.

Also, Huang et al (2021) concludes that consensus climate feedback assessments have generally neglected the nonlinear, and coupling, effects of radiative feedbacks.

Huang, H. and Yi Huang (26 March 2021), "Nonlinear coupling between longwave radiative climate feedbacks", Journal of Geophysical Research: Atmospheres, https://doi.org/10.1029/2020JD033995

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020JD033995?af=R

Abstract
The radiative feedbacks of such climate variables as air temperature, water vapor and clouds influence the energy budget of the climate system. Measuring the strength of each feedback is of critical importance for understanding the climate sensitivity and its spread in climate models. Most feedback analyses to‐date, such as those using the kernel method, have been based on a linear decomposition of the radiation budget and neglect the nonlinear effects between the feedbacks. In this work, we quantify the coupling effects between different longwave radiative feedbacks based on partial radiative perturbations using a radiative transfer model. Two climate change scenarios, the El niño – Southern Oscillation (ENSO) and the quadrupling CO2 (4xCO2), are examined. We find that the coupling effect between water vapor and cloud is the strongest among all the coupling effects and can amount to 50% or greater of the univariate cloud feedback. This significant coupling effect results from the masking effect of the two feedbacks on each other and can be well explained by a simple analytic model.

Plain Language Abstract
Climate feedback assessments have generally neglected the nonlinear effects of the radiative feedbacks. We for the first time quantify the coupling effects between the longwave radiative feedbacks using accurate radiative transfer calculations. The cloud‐water vapor coupling is identified to be the most important coupling effect. An analytical model is developed to explain and estimate the coupling effect.

Finally, for this post, Kramer et al. (2021) present direct evidence of increasing global radiative forcing:

Kramer, R.J. et al. (25 March 2021", "Observational evidence of increasing global radiative forcing", Geophysical Research Letters, https://doi.org/10.1029/2020GL091585

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL091585?af=R

Abstract
Changes in atmospheric composition, such as increasing greenhouse gases, cause an initial radiative imbalance to the climate system, quantified as the instantaneous radiative forcing. This fundamental metric has not been directly observed globally and previous estimates have come from models. In part, this is because current space‐based instruments cannot distinguish the instantaneous radiative forcing from the climate’s radiative response. We apply radiative kernels to satellite observations to disentangle these components and find all‐sky instantaneous radiative forcing has increased 0.53±0.11 W/m2 from 2003 through 2018, accounting for positive trends in the total planetary radiative imbalance. This increase has been due to a combination of rising concentrations of well‐mixed greenhouse gases and recent reductions in aerosol emissions. These results highlight distinct fingerprints of anthropogenic activity in Earth’s changing energy budget, which we find observations can detect within 4 years.

Plain Language Summary
Climate change is a response to energy imbalances in the climate system. For example, rising greenhouse gases directly cause an initial imbalance, the radiative forcing, in the planetary radiation budget, and surface temperatures increase in response as the climate attempts to restore balance. The radiative forcing and subsequent radiative feedbacks dictate the amount of warming. While there are well‐established observational records of greenhouse gas concentrations and surface temperatures, there is not yet a global measure of the radiative forcing, in part because current satellite observations of Earth’s radiation only measure the sum total of radiation changes that occur. We use the radiative kernel technique to isolate radiative forcing from total radiative changes and find it has increased from 2003 through 2018, accounting for nearly all of the long‐term growth in the total top‐of‐atmosphere radiation imbalance during this period. We confirm that rising greenhouse gas concentrations account for most of the increases in the radiative forcing, along with reductions in reflective aerosols. This serves as direct evidence that anthropogenic activity has affected Earth’s energy budget in the recent past.


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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #21 on: July 02, 2021, 10:41:00 PM »
Macro-MCDS-BN (from 2020 to 2150)

This series of posts is intended to provide a simplified summary of my proposed MCDS-BN scenario presented in this thread and supported by even more posts in the MCDS-FT thread.  I begin by providing a table of key MCDS-BN parameters (from 2020 to 2100) in the first image.  Furthermore, as freshwater flux events are fundamental to the MCDS-BN the second and third images present assumed sources and associated quantities (in both Sverdups and changes in eustatic sea level rise in meters) of freshwater fluxes from 2020 to 2060.  Finally, the fourth image from Pan et al. (2021)

Finally (for this Reply), I note that Replies 22 thru 2X discuss various mechanism that impact the overall MCDS-BN sequence of events (in addition to Replies 1 thru 21); prior to discussions in subsequent Replies about mechanisms that impact individual events X1 thru X19 and Y20 thru Y39.

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #22 on: July 02, 2021, 10:44:00 PM »
Pan et al. (2021) includes the amplification of global sea level rise due to recent assessments of rapid postglacial rebound associated with a potential collapse of the WAIS.  The first image shows that the associated amplification of global sea level rise is not uniform with regard to location (see panel A) nor with regard to time (see panel B) after a potential collapse of the WAIS, and I note that panel A indicates that large volumes of relatively warm ocean water would be pushed/advected into the Arctic Ocean and the waters around Greenland; which will accelerate associated local climate change feedback mechanisms and Arctic Amplification.

Pan, L., et al. (30 Apr 2021), "Rapid postglacial rebound amplifies global sea level rise following West Antarctic Ice Sheet collapse", Science Advances, Vol. 7, no. 18, eabf7787, DOI: 10.1126/sciadv.abf7787

https://advances.sciencemag.org/content/7/18/eabf7787

Abstract: "Geodetic, seismic, and geological evidence indicates that West Antarctica is underlain by low-viscosity shallow mantle. Thus, as marine-based sectors of the West Antarctic Ice Sheet (WAIS) retreated during past interglacials, or will retreat in the future, exposed bedrock will rebound rapidly and flux meltwater out into the open ocean. Previous studies have suggested that this contribution to global mean sea level (GMSL) rise is small and occurs slowly. We challenge this notion using sea level predictions that incorporate both the outflux mechanism and complex three-dimensional viscoelastic mantle structure. In the case of the last interglacial, where the GMSL contribution from WAIS collapse is often cited as ~3 to 4 meters, the outflux mechanism contributes ~1 meter of additional GMSL change within ~1 thousand years of the collapse. Using a projection of future WAIS collapse, we also demonstrate that the outflux can substantially amplify GMSL rise estimates over the next century."
 
Caption for the first image: "Fig. 4 Global sea level changes after WAIS collapse.
Predicted sea level changes (A) 0 and (B) 2 ka after an instantaneous collapse of marine-based sectors of WAIS based on the PSU3D1 scenario (Fig. 2F) (17) and computed using the 3D viscoelastic Earth model V3DSD summarized in Fig. 2 (A and B)."

Pan et al. (2021) also provides projections for instantaneous ice mass loss from the WAIS on global mean sea level rise, as shown by the solid curve in the second image.  In my opinion, the lion share of ice mass loss in the WAIS could occur by about 2060 if one assumes a high-forcing (RCP 8.5) MICI type of scenario thru about 2035.

Next, I note that the third and fourth images (both from Vaughan et al. 2011) indicate note only will the local seafloor rebound by hundreds of meters due to a potential collapse of the WAIS but also new seaways will be formed that will redirect ocean circulation in manners that have not yet been included in any consensus climate model projects that I am aware; which, of course, represents a significant climate change risk.
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #23 on: July 02, 2021, 10:49:28 PM »
I provide the first attached image (from Marino & Zahn 2015) that illustrates one of many teleconnection between both hemispheres (related to the bipolar seesaw mechanism), that shows how an acceleration of the Westerly winds over the Southern Ocean (e.g. due to the Antarctic ozone hole and/or to increased atmospheric GHG concentrations) has increased Agulhas Leakage (around the Cape of Good Hope) that is also working to cool the surface of portions of the North Atlantic; which also results in a slowing of the AMOC (just like the North Atlantic Cold Blob [see the second image] associated with Greenland meltwater does); which, both increases climate sensitivity and accelerates the Atlantification of the Arctic Ocean (which both decreases sea ice extent and raises the risk of methane emissions from Arctic Ocean continental slopes).

Marino, G. and Zahn, R. (2015), "The Agulhas Leakage: the missing link in the interhemispheric climate seesaw?", PAGES MAGAZINE, VOLUME 23, NO 1

http://www.pages-igbp.org/download/docs/magazine/2015-1/PAGESmagazine_2015(1)_22-23_Marino.pdf

Furthermore, Simon et al. (2020) indicates that:

"The mass and salt transport through the Indian-Atlantic Ocean Gateway, via the Agulhas leakage, can be considered as a potential controlling factor in the Southern Hemisphere impacting on the North Atlantic salt budget. Today Agulhas leakage of ~5–15 Sverdrup (Sv) is one of the dominant sources of the upper branch of the Atlantic Meridional Overturning Circulation (AMOC), connecting the warm route around the southern tip of Africa with the North Atlantic. The advection of salt is communicated north within 2–4 decades suggesting a rather fast impact of Agulhas leakage on the AMOC."

Simon, M.H., Ziegler, M., Barker, S. et al. A late Pleistocene dataset of Agulhas Current variability. Sci Data 7, 385 (2020). https://doi.org/10.1038/s41597-020-00689-7


Also, Martinez-Moreno et al (2019) finds that:

"We find that the energy of the vortices has increased over the past two decades. Using our method, we are able to pinpoint that the energy increase occurs due to an increase in the mean amplitude of the vortices rather than in an increase in their number. Finally, the vortices show a clear response to the strengthening of winds in the Southern Ocean."

This increase of mesoscale eddy kinetic energy in the Southern Ocean over the past two decades in important for several reasons including that these mesoscale eddies advect warm surface waters (such as from Agulhas leakage of warm surface water from the Indian Ocean around the Cape of Good Hope, see the third image (showing surface currents/eddies) down into the CDW where subsequent upwelling can convey this heat to the basal side of ice shelves and to the grounding lines of key Antarctic marine glaciers.  Also, the fourth image shows that the maximum increase in Westerly wind speed over the Southern Ocean associated with the Antarctic Ozone Hole overlaps the region of maximum ocean eddy formation (shown in the second image).

Martínez‐Moreno, J. et al. (10 September 2019), "Kinetic Energy of Eddy‐Like Features From Sea Surface Altimetry", JAMES, https://doi.org/10.1029/2019MS001769

Caption for image 4: Observed change/increase in mean 850hPa Westerly winds during the satellite era
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #24 on: July 02, 2021, 10:55:06 PM »
Consensus climate science models do not include many ice-climate feedback mechanisms and thus cannot replicate virtually any of the paleo examples of abrupt climate (for example see: Erhart et al. 2019; Coletti et al. 2015, and see the first to attached images); however, both Corrick et al (2020) and Capron et al. (2021) provide evidence of the synchronous timing of abrupt climate changes around the globe during Dansgaard-Oeschger (DO) events during the last glacial period.  To me this clearly demonstrates that freshwater hosing events (see the third image) can trigger multiple abrupt changes; which were stabilized during the last glacial period, but which could abruptly trigger a transition into a new/higher climate state given our current unprecedented rate of climate change.  Such research confirms that ice-climate feedback mechanism can, and have, induced both local and global climate changes within periods of decades; which represents a climate change risk not properly addressed by consensus climate change projections (which focus excessively on potential anthropogenic GHG emission scenarios).

Capron, E., S. O. Rasmussen, T. J. Popp, T. Erhardt, H. Fischer, A. Landais, J. B. Pedro, G. Vettoretti, A. Grinsted, V. Gkinis, B. Vaughn, A. Svensson, B. M. Vinther and J. W. C. White (8 April 2021 ), “The anatomy of past abrupt warmings recorded in Greenland ice”, Nature Communications, DOI: 10.1038/s41467-021-22241-w

https://www.nature.com/articles/s41467-021-22241-w

Abstract: "Data availability and temporal resolution make it challenging to unravel the anatomy (duration and temporal phasing) of the Last Glacial abrupt climate changes. Here, we address these limitations by investigating the anatomy of abrupt changes using sub-decadal-scale records from Greenland ice cores. We highlight the absence of a systematic pattern in the anatomy of abrupt changes as recorded in different ice parameters. This diversity in the sequence of changes seen in ice-core data is also observed in climate parameters derived from numerical simulations which exhibit self-sustained abrupt variability arising from internal atmosphere-ice-ocean interactions. Our analysis of two ice cores shows that the diversity of abrupt warming transitions represents variability inherent to the climate system and not archive-specific noise. Our results hint that during these abrupt events, it may not be possible to infer statistically-robust leads and lags between the different components of the climate system because of their tight coupling."

See also:

Title: "Abrupt ice age climate changes behaved like cascading dominoes"

https://www.eurekalert.org/pub_releases/2021-04/uoc--aia040921.php

or

Title: "Dansgaard-Oeschger Events: Abrupt Ice Age Climate Changes Behaved Like Cascading Dominoes"
https://scitechdaily.com/dansgaard-oeschger-events-abrupt-ice-age-climate-changes-behaved-like-cascading-dominoes/
https://scitechdaily.com/dansgaard-oeschger-events-abrupt-ice-age-climate-changes-behaved-like-cascading-dominoes/


Extract: "Throughout the last ice age, the climate changed repeatedly and rapidly during so-called Dansgaard-Oeschger events, where Greenland temperatures rose between 5 and 16 degrees Celsius in decades. When certain parts of the climate system changed, other parts of the climate system followed like a series of dominos toppling in succession. This is the conclusion from an analysis of ice-core data by a group of researchers that included postdoc Emilie Capron and associate professor Sune Olander Rasmussen from the Section for the Physics of Ice, Climate and Earth at the Niels Bohr Institute, University of Copenhagen, in Denmark.

This new analysis reveals a surprisingly diverse set of dynamics within the Dansgaard-Oeschger events. The same physical processes changed together like a row of cascading dominoes, but surprisingly, neither the rate of change nor the order of the processes were the same from one event to the other.

The results led the international team of scientists to compare the ice-core data with new results from climate model simulations of the last ice age developed by co-author Guido Vettoretti, postdoc at the Niels Bohr Institute. This IPCC-class of climate model is the same type as those used to make projections of future climate change. The comparison revealed that the model showed the same type of entangled behaviour of sea ice, strength of ocean currents, and wind and precipitation patterns.
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #25 on: July 02, 2021, 10:58:31 PM »
As a follow-on to Reply 24 as to how ice-climate feedback mechanisms can trigger abrupt local and global climate change, the two linked sources by Francis 2018 and by Praetorius et al. (2018) illustrate how the telecommunication linkage of the subpolar North Pacific and the subpolar North Atlantic can work to abruptly increase climate sensitivity within decades.  The first image shows one recently identified circum-Arctic wave atmospheric circulation anomaly pattern associated with the recent ridiculously resilient ridge (a persistent atmospheric anticyclone that occurred over the far northeastern Pacific Ocean from 2011 to 2017 and which reoccurred in the 2020-2021 winter season, and which appears to be associated with the telecommunication of energy from the western tropical Pacific Ocean [see the second image] to the subpolar North Pacific, see Teng & Branstator 2017), and which results in the 'Cold Continent, Warm Arctic' paradigm and the synchronization of the subpolar North Pacific and the subpolar North Atlantic (see the third image).

See also:

Title: "Ridiculously Resilient Ridge"

Ridiculously Resilient Ridge - Wikipedia

Extract: "… recent research suggests that unusual oceanic warmth in the western tropical Pacific Ocean may have played a role in triggering and maintaining the Triple R over successive seasons."

Title: "Clarity and Clouds: Progress in Understanding Arctic Influences on Mid-latitude Weather", by J. A. Francis, 2018

https://arctic.noaa.gov/Report-Card/Report-Card-2018/ArtMID/7878/ArticleID/790/Clarity-and-Clouds-Progress-in-Understanding-Arctic-Influences-on-Mid-latitude-Weather

Extract: "A slower jet stream tends to favor a more meandering north-south path and to shift the mean jet latitude southward. A wavier pattern allows warm air to penetrate farther north and cold air to plunge farther south, compared to when the jet is strong and relatively straight (Fig. 1). Larger waves also tend to linger in one location, as do the surface weather systems they create, which results in persistent weather conditions that can turn into extreme events.

Since late 2013, the predominant weather regime over North America has featured a strong and persistent jet-stream ridge aligned north/south in the eastern Pacific, which diverts storms away from California and sends abnormally warm winds into Alaska (Fig. 1). This so-called "ridiculously resilient ridge" (Swain et al., 2017) is perpetuating drought, heatwaves, and extensive wildfires across much of western North America. A strong jet-stream ridge is often accompanied by a downstream (eastward) trough, which allows cold Arctic air to plunge southward, bringing persistent icy conditions to the southeastern U.S.A. (Cohen et al., 2018) and can spawn a parade of destructive nor'easters along the eastern seaboard, as in winters of 2013-14 and 2017-18."

Caption for the first image: "Fig. 1. Effects of Arctic temperature changes on the jet stream and mid-latitude weather. Adapted from the original sources: NOAA, Scientific American, and InsideClimate News."

Also, Praetorius et al. (2018) indicates that the warming of the North Pacific subpolar waters in likely the most important feedback for driving enhanced Arctic Amplification with continued global warming, and the second attached image demonstrates how the North Pacific subpolar water can be warmed directly by atmospheric telecommunication of energy from the Tropical Pacific.  If so this indicates that the CMIP5 projections likely underestimate ECS, and as ice-climate feedback would likely accelerate warming the Tropical Pacific, it is likely that CMIP6 projections will also underestimate ECS as these models do not consider ice-cliff failures or hydrofracturing; which can slow the MOC which warms the Tropical Pacific; also, by 2040 Arctic Amplification will likely be significantly higher that today due to increased water vapor migration from the North Pacific (associated with the increased telecommunication of Tropical Pacific energy due to increased El Nino frequencies), and decreased Arctic sea ice-albedo. 

Praetorius, S., Maria Rugenstein, Geeta Persad, Ken Caldeira. Global and Arctic climate sensitivity enhanced by changes in North Pacific heat flux. Nature Communications, 2018; 9 (1) DOI: 10.1038/s41467-018-05337-8

https://www.nature.com/articles/s41467-018-05337-8

Abstract: "Arctic amplification is a consequence of surface albedo, cloud, and temperature feedbacks, as well as poleward oceanic and atmospheric heat transport. However, the relative impact of changes in sea surface temperature (SST) patterns and ocean heat flux sourced from different regions on Arctic temperatures are not well constrained. We modify ocean-to-atmosphere heat fluxes in the North Pacific and North Atlantic in a climate model to determine the sensitivity of Arctic temperatures to zonal heterogeneities in northern hemisphere SST patterns. Both positive and negative ocean heat flux perturbations from the North Pacific result in greater global and Arctic surface air temperature anomalies than equivalent magnitude perturbations from the North Atlantic; a response we primarily attribute to greater moisture flux from the subpolar extratropics to Arctic. Enhanced poleward latent heat and moisture transport drive sea-ice retreat and low-cloud formation in the Arctic, amplifying Arctic surface warming through the ice-albedo feedback and infrared warming effect of low clouds. Our results imply that global climate sensitivity may be dependent on patterns of ocean heat flux in the northern hemisphere."

Extract: "Systematic cold biases in North Pacific and North Atlantic SSTs in CMIP5 models may thus partly lead to an underestimation of Arctic warming and sea-ice decline in climate projections, with important ramifications for climate and ecological tipping points in the Arctic."

See also:

Title: "Pacific Ocean's effect on Arctic warming"

https://www.sciencedaily.com/releases/2018/08/180807095149.htm

Extract: "Paleoclimate records show that climate change in the Arctic can be very large and happen very rapidly. During the last deglaciation, as the planet was starting to warm from rising greenhouse gases, there were two episodes of accelerated warming in the Arctic -- with temperatures increasing by 15°C (27°F) in Greenland over the course of decades. Both events were accompanied by rapid warming in the mid-latitude North Pacific and North Atlantic oceans."

Furthermore, the circum-Arctic wave (CAW) atmospheric circulation pattern discussed above can be related to the large-scale Arctic wildfires as discussed by Yasunari et al. (2021) and illustrated by the fourth image.

Yasunari, T.J. et al. (2021), "Relationship between circum-Arctic atmospheric wave patterns and large-scale wildfires in boreal summer", Environmental Research Letters, https://iopscience.iop.org/article/10.1088/1748-9326/abf7ef

Caption for the fourth image: "Figure 9. Schematic summary of the results obtained in this study. Anomalous anticyclones developed concomitantly in summer (July and August) over the circumpolar regions: Europe, Siberia, and subpolar North America (i.e. Alaska and Canada). Those anticyclones induce warm and dry forcings from the surface to the mid-troposphere. This study names this climate pattern the CAW pattern. Those anomalous anticyclones induce heatwaves over Europe and active wildfires over Siberia and subpolar North America. The wildfire smoke emitted OC and BC aerosols into the atmosphere, and those aerosols could reach the Arctic region to increase PM2.5 there. The interactions between the CAW pattern and the atmospheric aerosols must be investigated in future studies. Reproduced from Hayanon Science Manga Studio. CC BY 3.0."

While increases in GHG are likely associated with the CAW atmospheric pattern discussed above; I believe that this pattern is also linked to the ice-climate feedback associated with the slowing of the MOC (due to freshwater fluxes in both the Southern Ocean and the North Atlantic); which causes the Tropical Pacific sea surface temperatures to increase.

Finally, I believe that the observations reported by Oderiz et al. (2021) support my opinions stated above.

Odériz, I., R. Silva, T.R. Mortlock, N. Mori, T. Shimura, A. Webb, R. Padilla-Hernandez, S. Villers (20 May 2021), "Natural Variability and Warming Signals in Global Ocean Wave Climates", Geophysical Research Letters, https://doi.org/10.1029/2021GL093622
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #26 on: July 02, 2021, 11:01:33 PM »
As a follow-on to Reply 25, the three attached images (and the linked articles and the linked reference) show that meltwater from Greenland forms a cold spot in the North Atlantic that works to slow the MOC; which causes increases in SST in locations like the North American East Coast and the Tropical Pacific Ocean; and which also pushes warm Atlantic current waters deeper into the Arctic Ocean.

Title: "Gulf Stream current is now at its weakest in over 1,000 years"

https://www.theweathernetwork.com/ca/news/article/gulf-stream-atlantic-meridional-overturning-oscillation-weakest-in-millenium

Extract: "In the past decade, climate models have predicted that a slowdown in the AMOC was possible due to global warming. Climate studies have revealed evidence that the slowdown has been happening for several decades now and that it appears to be getting worse.

The entire ocean circulation in the North Atlantic runs on the differences in temperature and salt-content (salinity) of the water. Warm, salty surface water flows from the tropics to the Arctic. There, it will have cooled to the point where it becomes denser than the water around it and subsequently sinks. This draws in more water to 'fill the gap', thus driving the surface half of the conveyor. Meanwhile, the cold, dense water that sank then flows along the deep ocean back towards the south.

Increased rainfall amounts and the melting of the Greenland Ice Sheet, both caused by warming global temperatures, are adding more freshwater into the ocean's surface. This dilutes the salt content of the water, making it less dense. So, when it arrives in the north, the water takes longer to sink. As a result, the entire circulation slows down."


Title: "New studies confirm weakening of the Gulf Stream circulation (AMOC)"

https://www.realclimate.org/index.php/archives/2020/09/new-studies-confirm-weakening-of-the-gulf-stream-circulation-amoc/

Extract: "However, the latest generation (CMIP6) of climate models shows one thing: if we continue to heat up our planet, the AMOC will weaken further – by 34 to 45% by 2100. This could bring us dangerously close to the tipping point at which the flow becomes unstable."


The linked reference confirms that the AMOC has rapidly slowed in recent decades:

Caesar, L., McCarthy, G.D., Thornalley, D.J.R. et al. Current Atlantic Meridional Overturning Circulation weakest in last millennium. Nat. Geosci. 14, 118–120 (2021). https://doi.org/10.1038/s41561-021-00699-z

https://www.nature.com/articles/s41561-021-00699-z

Abstract: "The Atlantic Meridional Overturning Circulation (AMOC)—one of Earth’s major ocean circulation systems—redistributes heat on our planet and has a major impact on climate. Here, we compare a variety of published proxy records to reconstruct the evolution of the AMOC since about AD 400. A fairly consistent picture of the AMOC emerges: after a long and relatively stable period, there was an initial weakening starting in the nineteenth century, followed by a second, more rapid, decline in the mid-twentieth century, leading to the weakest state of the AMOC occurring in recent decades."

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #27 on: July 02, 2021, 11:05:49 PM »
As a follow-on to Reply 26; while the slowing of the AMOC (part of the MOC) is currently dominated by ice meltwater from the GrIS; meltwater from Antarctica is also currently contributing to the slowdown of the MOC, significantly because the ozone hole over Antarctica (associated with anthropogenic emissions) has increased positive Southern Annular Mode (SAM) index values (see the first and second images), which by definition is associated with an acceleration of Westerly winds over the Southern Ocean; which has lead to increased upwelling of warm Circumpolar Deep Water (CDW) over the Antarctic continental shelf in many locations leading to many key marine glaciers (& associated ice shelves as shown in the third image) including: Thwaites, PIG, Totten and others.  The accelerated meltwater from such key Antarctic marine glaciers & ice shelves has combined with increased meltwater from increased Antarctic sea ice (see the fourth image) and increased precipitation, in recent decades to freshen the surface waters over large portions of the Southern Ocean, and some of the ice-climate feedbacks and implications of this surface water freshening are discussed in Replies 28 thru 30.
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #28 on: July 02, 2021, 11:10:52 PM »
This is a follow-on to Reply 27, where the first image shows typical observed freshening of the near surface Southern Ocean waters circa 2017; due to a combination of glacial ice melt, sea ice melt and increased local precipitation; while the second image shows how the Antarctic Coastal Current circulates glacial meltwater and icebergs counterclockwise around Antarctica.  The third image shows a computer model projection of how this surface water freshening results in a positive feedback for increased upwelling of warm CDW and its advection towards the grounding lines of key Antarctic marine glaciers (note that the elevation of the CDW markedly increases circa 2035 to an elevation that would allow advection of the CDW to the grounding lines of many key Antarctic marine glaciers).  The fourth image shows various ice-climate feedback mechanisms associated with Antarctica and the Southern Ocean that both work to slow the MOC (by reducing AABW formation), to accelerate marine glacier ice mass loss from warm CDW; and reduced snow fall on coastal regions of Antarctica.
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #29 on: July 02, 2021, 11:16:30 PM »
This is a follow-on to Reply 28, where the first image shows the MOC from a Southern Ocean perspective indicating the old assumption that Antarctic Bottom Water (AABW) is only produced near: the Weddell Sea (FRIS), the Ross Sea (RIS) and near the Amery Ice Shelf; however, the second image shows the results of more recent observations indicating a fourth source of AABW near the Mertz Glacier Ice Tongue, and I note that AABW production is reducing at all four of these locations, all of which contributes to a slowing of the MOC.  The third image shows how a portion of the upwell warm CDW advects toward the Antarctic continental shelf, where some of it can reach key marine glaciers and ice shelves (& accelerate grounding line retreat and ice mass loss) as shown in the fourth image.
 
Caption for the first image: Meridional Overturning Circulation from a Southern Ocean Perspective.

Caption for the second image: Observed sources of AABW
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #30 on: July 02, 2021, 11:19:40 PM »
This is a follow-on to Reply 29, where the first image from the following linked reference shows the complex relationship between the marine glaciers in both the western and eastern South Greenland, iceberg calving, the local oceanic currents, the subpolar gyre and consequently the AMOC.  This relationship could both limit flushing of freshwater from the Beaufort Gyre, BG, into the North Atlantic (thus allowing the BG to accumulate still more freshwater) and could cause westerly winds around Antarctica to accelerate due to the bipolar seesaw; both of which would contribute to the ice-climate feedback associated with slowing of the MOC.  For clarity, the second and third images show alternate views of the first image.

Camilla S. Andresen et al. (2017), "Exceptional 20th century glaciological regime of a major SE Greenland outlet glacier", Scientific Reports 7, Article number: 13626, doi:10.1038/s41598-017-13246-x

http://www.nature.com/articles/s41598-017-13246-x?WT.feed_name=subjects_climate-sciences

Abstract: "The early 2000s accelerated ice-mass loss from large outlet glaciers in W and SE Greenland has been linked to warming of the subpolar North Atlantic. To investigate the uniqueness of this event, we extend the record of glacier and ocean changes back 1700 years by analyzing a sediment core from Sermilik Fjord near Helheim Glacier in SE Greenland. We show that multidecadal to centennial increases in alkenone-inferred Atlantic Water SSTs on the shelf occurred at times of reduced solar activity during the Little Ice Age, when the subpolar gyre weakened and shifted westward promoted by atmospheric blocking events. Helheim Glacier responded to many of these episodes with increased calving, but despite earlier multidecadal warming episodes matching the 20th century high SSTs in magnitude, the glacier behaved differently during the 20th century. We suggest the presence of a floating ice tongue since at least 300 AD lasting until 1900 AD followed by elevated 20th century glacier calving due to the loss of the tongue. We attribute this regime shift to 20th century unprecedented low sea-ice occurrence in the East Greenland Current and conclude that properties of this current are important for the stability of the present ice tongues in NE Greenland."

Extract: "Our results imply that model predictions of dynamic loss from ice streams in North Greenland need to account for threshold effects from sea-ice to better predict the future evolution of Greenland coastal glaciers in a warming North Atlantic Ocean."

 
Caption for the first attached image:"Map of the North Atlantic region showing the major surface ocean currents (colour of arrows indicate temperature; red = warm, blue = cold, yellow = mixture) and location of sites referred to in Fig. 3a–f. HG = Helheim Glacier; KG = Kangerdlugssuaq Glacier; JI = Jakobshavn Isbræ. Magenta circles show location of sediment cores discussed in the text and shown on Figs 2 and 3. The magenta box delineates the extent of the inset map. Bathymetric data are from IBCAO v3. Terrestrial topographic data are from the ETOPO1 Global Relief model and the GIMP surface digital elevation model. The inset map of Sermilik Fjord shows the location of sediment core ER07 and the local bathymetry."

See also:

Bellomo, K., Angeloni, M., Corti, S. et al. Future climate change shaped by inter-model differences in Atlantic meridional overturning circulation response. Nat Commun 12, 3659 (2021). https://doi.org/10.1038/s41467-021-24015-w



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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #31 on: July 02, 2021, 11:22:41 PM »
Schlemm & Levermann (2021), indicate that both DeConto & Pollard (2016) and Edwards et al. (2019) impose somewhat arbitrary upper bounds on the calving rates for ice cliff failures for marine, and marine terminating, glaciers.  Schlemm & Levermann (2021) then work to develop simplified procedures for estimating buttressing on ice cliffs faces, that they somewhat arbitrarily assume are at the floatation thickness (which gives minimum ice cliff calving rates), from frozen ice mélanges.  In this regard, the first image shows the minimum ice cliff calving rates for marine glacier in Antarctica, which the second image shows the assumed ice mélange model geometry, and the third image shows the minimum rates of glacier retreats associated with ice cliff failures that are in addition to (beyond) the rates of retreat associated with MISI types of failure.  To me this information represents a lower bound estimate for the rates of retreat of marine, and marine terminating, glacier in the coming decades.  Furthermore, I note that Schlemm & Levermann state that:

"In contrast, Thwaites Glacier is more than 70 km wide, and its ice shelf spreads into the open ocean. It has currently no embayment at all, and once it retreats, it lies in a wide basin that can provide little mélange buttressing. Hence, Thwaites Glacier has a much larger potential for large calving rates and runaway ice retreat (MICI) than Pine Island Glacier."

Schlemm, T. and Levermann, A.: A simple parametrization of mélange buttressing for calving glaciers, The Cryosphere, 15, 531–545, https://doi.org/10.5194/tc-15-531-2021, 2021.

https://tc.copernicus.org/articles/15/531/2021/

Abstract
Both ice sheets in Greenland and Antarctica are discharging ice into the ocean. In many regions along the coast of the ice sheets, the icebergs calve into a bay. If the addition of icebergs through calving is faster than their transport out of the embayment, the icebergs will be frozen into a mélange with surrounding sea ice in winter. In this case, the buttressing effect of the ice mélange can be considerably stronger than any buttressing by mere sea ice would be. This in turn stabilizes the glacier terminus and leads to a reduction in calving rates. Here we propose a simple parametrization of ice mélange buttressing which leads to an upper bound on calving rates and can be used in numerical and analytical modelling.

Extract: "Rather than imposing an upper bound on the calving rates as an ad hoc cut-off as done by DeConto and Pollard (2016) and Edwards et al. (2019), mélange buttressing gives a natural upper bound on the calving rate, which is reached smoothly. The value of the upper bound can be different for each glacier depending on the embayment geometry and may change seasonally in accord with mélange properties.

In the context of the Marine Ice Cliff Instability (MICI) hypothesis, one would expect a sudden and large increase in calving rates for ice cliffs higher than the stability limit. Despite a nonlinear increase in calving rates in the unbuttressed case, neither of the two stress-based calving parametrizations (Mercenier et al., 2018; Schlemm and Levermann, 2019) nor a combination of them shows discontinuous behaviour at the stability limit.

Embayment geometry plays an important role in determining how susceptible glaciers facing similar ocean conditions are to rapid ice retreat: Pine Island Glacier and Thwaites Glacier in West Antarctica face similar ocean conditions in the Amundsen Sea, where the warming ocean (Shepherd et al., 2004, 2018a) leads to the retreat and rifting of their buttressing ice shelves (Jeong et al., 2016; Milillo et al., 2019), and might be susceptible to both MISI and MICI. Pine Island terminates in an embayment about 45 km wide, currently filled by an ice shelf of roughly 60 km length. The upper part of the glacier lies in a straight narrow valley with a width of about 35 km (distances measured on topography and ice thickness maps provided by Fretwell et al., 2013). If Pine Island Glacier lost its current shelf, it would have a long and narrow embayment holding the ice mélange and would therefore experience strong mélange buttressing. In contrast, Thwaites Glacier is more than 70 km wide, and its ice shelf spreads into the open ocean. It has currently no embayment at all, and once it retreats, it lies in a wide basin that can provide little mélange buttressing. Hence, Thwaites Glacier has a much larger potential for large calving rates and runaway ice retreat (MICI) than Pine Island Glacier.

Future ocean warming and intrusion of warm ocean water under the ice mélange increase melting rates and the upper limit on calving rates. This could be another mechanism by which ocean warming increases calving rates.

The concept of cliff calving and a cliff calving instability is not without criticism. According to Clerc et al. (2019), the lower part of the glacier terminus, where shear failure is assumed to occur (Bassis and Walker, 2011; Schlemm and Levermann, 2019), is actually in a regime of thermal softening with a much higher critical stress and thus remains stable for large ice thicknesses. Tensile failure may occur in the shallow upper part of the cliff and initiate failure in the lower part of the cliff (Parizek et al., 2019). The critical subaerial cliff height at which failure occurs depends on the timescale of the ice shelf collapse: for collapse times longer than 1 d, the critical cliff height lies between 170–700 m (Clerc et al., 2019).

The mélange buttressing model proposed here does not depend on the specific calving mechanism, and it is not comprehensive, especially since it is not derived from first principles but from a macroscopic perspective. The advantage of the equation proposed here is the very limited number of parameters."
 
Caption for the first image: "Figure 1Potential shear-failure-based calving rates (Eq. 16) and tensile-failure-based calving rates (Eq. 15) in the grounded, marine regions of the Antarctic ice sheet. Floating ice is shown in white and grounded ice above sea level in grey. In the marine regions, ice is assumed to be at floatation thickness, which gives a minimal estimate of the potential calving rates. Estimates for shear calving rates go up to 65 km a−1, and estimates for tensile calving rates go up to 75 km a−1. If the grounding line retreat is faster than the speed with which the glacier terminus thins to floatation, calving rates could be even larger. Imposing an upper bound on the calving rates is necessary to prevent unrealistic, runaway ice loss."
 
Caption for the second image: "Figure 6Set-up of the idealized glacier experiments. Only half of the set-up is shown; the glacier is connected to an identical copy on the left to ensure periodic boundary conditions at the ice divide."
 
Caption for the third image: "Figure 8Time series of glacier retreat in addition to the MISI retreat, i.e. retreat caused by calving."

Lastly (for this MCDS-BN overview), I note that while the Larsen C Ice Shelf on the Antarctic Peninsula is expected to abruptly collapse circa 2030 (due to hydrofracturing) , this event is not evaluated in the MCDS-BN as a collapse of the Larsen C Ice Shelf is not expected to have a rapid impact on the MOC.

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #32 on: July 02, 2021, 11:27:57 PM »
BN 2020-2030
X1 = Abrupt Jakobshavn, & Helheim, Grounding Line Retreat.


The relatively simple first image taken from the MCDS-BN presented in the first post to this thread, largely consists of the rather complex FT1 arrow representing all initiation global conditions (see the MCDS-FT thread for more discussion of initial global conditions) starting in 2020 and all relevant global actions from 2020 to 2030 that contribute to the X1 event of 2030 +/- 5yrs of a temporary pulse of rapid grounding line retreat of both Jakobshavn (JAK) & Helheim (HEL) marine-terminating glaciers in Greenland, see the second  image from Felikson et al. (2020).  Furthermore, image 1 contains annotations of key parameters assumed at event X1 at 2030 +/- 5yrs of: a) a relatively high effective equilibrium climate sensitivity, ECSeff, associated with the CMIP6 UKESM1-0-LL projections for SSP8.5 radiative forcing; b) an effective radiative forcing, ERF, value extrapolated from measurements; c) an assumed probability of event X1 occurring; d) a freshwater flux, FF, in Sverdrups; e) an Earth Energy Imbalance, EEI, extrapolated from measurements; f) an increase in sea level (m) associated with event X1 and g) GMSTA associated with the CMIP6 UKESM1-0-LL projections for SSP8.5 radiative forcing.  The second image (from Felikson et al. 2020, Fig 3) shows both the locations of, and the recent ice fluxes from both Jakobshavn (JAK) Glacier and Helheim (HEL) Glacier.

Event X1 (and possibly event X2) is given an 85% of occurrence by 2030 +/- 5yrs as it is expected that a positive trend for the AMO from now to then will advect relatively warm ocean water into the fjords for both Jakobshavn and Helheim; which will weaken the mélange buttressing leading to grounding line retreat to regions of negative bed slope.

The third and fourth image are from Joughin et al. (2020), and Joughin et al. (2020) indicates that in recent decades the calving face of Jakobshavn has retreated, or advanced, in a correlation with the Atlantic Multidecadal Oscillation (AMO) index that influences the ocean water temperature in Disko Bay.  Furthermore, the reference states:

"Our point is not that cliff failure is irrelevant but rather that any glacier that reaches terminus heights where cliff failure may be a significant effect must already be in a state of rapid retreat. Far more important to long-term outlet glacier stability is how seasonal variability and oceanographic/atmospheric trends influence the calving rates of grounded glacier termini. In Antarctica and northern Greenland there are additional atmospheric temperature sensitivities (e.g. meltwater ponding and hydrofracture) that influence ice shelf stability (Scambos et al., 2000). In summary, while brittle failure cliff instability may be important in some circumstances, it is far more likely to play a role in a late-stage retreat than to serve as the process that would initiate such a retreat."

Thus, as the AMO index changes, in coming years, to introduce warm ocean water again into Disko Bay, we can expect the calving face for Jakobshavn to retreat rapidly and possibly by 2030 this calving facing may reach the region of retrograde bed slope, that would trigger a rapid release of calved ice into the North Atlantic Ocean, where it may well trigger a bipolar seesaw mechanism help to induce the initiation of a MICI-type of retreat of the ice in the Byrd Subglacial Basin in the 2030 to 2040 timeframe.

Joughin, I. et al. (2020): "A decade of variability on Jakobshavn Isbræ: ocean temperatures pace speed through influence on mélange rigidity", The Cryosphere, 14, 211–227, https://doi.org/10.5194/tc-14-211-2020

Image 2, from Felikson et al. (2020)

Caption for the second image: "Figure 3. Glacier thinning limits and potential for dynamic mass loss of 141 GrIS outlet glaciers. (a) Distances from ice sheet margin to thinning limits plotted against ice fluxes for glaciers in regions of gentle (circles) and mountainous (squares) bed topography. Purple markers indicate a group of glaciers with thinning limits >200 km from the ice margin; yellow markers indicate a group of glaciers with > 5 km3/year ice flux.  White x's inside purple markers indicate 9 glaciers in NW Greenland, discussed in the text. (b) Flowlines for each glacier drawn from the terminus to the predicted glacier thinning limit and colored according to groupings shown in a, shown on top of Greenland bed topography. Regions of mountainous bed topography (red coastlines) and gentle bed topography (blue coastlines) shown.  Upernavik Isstrom C (UPR-C), Cornell Glestcher (COR), Humboldt Gletcher (HUM), Kangerlussuaq Gletscher (KAN), Helheimgletscher (HEL) and Jakobshavn Isbrae (JAK) referenced in the text are labeled in both panels."
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #33 on: July 02, 2021, 11:30:26 PM »
X1 = Abrupt Jakobshavn, & Helheim, Grounding Line Retreat.

This is a follow-on to Reply 32, where the first image shows that in August 2013 to August 2015 timeframe the retreat of the Jakobshavn calving face came very close to the sill of a retrograde bed slope shown on this first image, that once crossed would likely accelerate image mass loss from Jakobshavn due to ice cliff failures.  The second and third images show recent AMO trends, and I note that so far 2021 has been a very active calving season for Jakobshavn, driven by the presence of relatively warm water in Disko Bay].  The fourth image shows the AMO SST variation from 1870 to 2013.

Given that the current warm phase of the AMO that started in about 2002 may, on average, last about 25 to 40 years this means that it is likely that the Jakobshavn calving face may likely cross the retrograde bed sill (see the first image) sometime between 2027 and 2042; and if so such an event would work to increase the probability of the an MICI-type of collapse of the ice in the BSB (via the bipolar seesaw mechanism) in the 2030 to 2040 timeframe.
 
Second image: AMO thru 2017

 
Third Image: AMO 2018 thru October 2020

 
Image 4: AMO SST Variation from 1870 to 2013

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #34 on: July 02, 2021, 11:32:47 PM »
X1 = Abrupt Jakobshavn, & Helheim, Grounding Line Retreat.

The first image shows a plan view of the bedrock contours (in 200m increments) for Helheim Glacier (& notice the zone with a negative bed slope).  The second image shows profile views of: the bed elevation, ice surface height change since 2001 (m); and ice speed change since 2001 in meter per day, for Helheim.  The third image shows a profile elevation view of the kinematics of a slump rotation of the ice calving face for Helheim.  The fourth image show a schematic interpretation by Parizek et al. (2019) of a slump type of an ice calving event for Helheim.
 
Image 4 from Parizek et al. 2019

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #35 on: July 02, 2021, 11:41:47 PM »
BN 2030 to 2035/2036
X3 = Triggering of MICI Behavior for Thwaites.
X2 = Beaufort Gyre Reversal/Discharge;
X4 = Triggering of BSB Ice MICI Discharge;



The first image taken from the MCDS-BN presented in the first post to this thread, largely consists of:
a) three complex FT arrows representing all initiation global conditions assuming abrupt grounding retreats for both Jakobshavn and Helheim, Glaciers circa 2030 +/- 5 yrs.
b) three freshwater flux events X3, X2 and X4 from 2035 to 2036 all +/- 5 yrs (where it is assumed that event X3 precedes and triggers events X2 and X4, but it noted that event X2 could precede event X3 without introducing a significant change in the values for ECSeff, ERF, P, or FF show on the MCDS-BN).
c) Collectively, the three events X3, X2 and X4 are assigned a 75% probability of occurrence, by 2036 +/- 5 yrs, for reasons including:

•   Event X1 (and possibly X2) will have slowed the MOC by 2035/36 thus telecommunicating energy from the Tropical Pacific directly to the Bellingshausen, Amundsen and Ross, Sea Sectors; which, will increase the probability of a Super El Nino, stronger local cyclones, and a strong ASL in this timeframe.
•   The subglacial lakes beneath the Thwaites Glacier will likely be fully recharged by this time; while will likely lead to a major discharge of basal meltwater at the base of the Thwaites Ice Tongue in this timeframe.
•   Richard Alley has noted that currently the Thwaites grounding line at the base of the Thwaites Ice Tongue is retreating towards the BSB at a rate of about 1km per year and thus this local grounding line will likely reach the negative bed slope leading to the BSB circa 2035/36.
•   Wild et al. (2021) has estimated that the Thwaites Eastern Ice Shelf will become unpinned circa 2035; which could lead to the complete collapse of the TEIS by 2036.
•   Computer models project a significant increase in the advection of warm CDW into the ASE circa 2035.
•   Acceleration of the ice velocities for the SW Tributary Glacier (near the Pine Island Bay) may likely reduce the restraint currently provided along the eastern shear margin of Thwaites Glacier circa 2035.


X3 = Triggering of MICI Behavior for Thwaites.

The second image confirms that primarily due to increase ocean forcing all of the marine glaciers in the ASE have experienced a significant acceleration of ice mass loss since the late 1970's when the ozone hole over Antarctica became prominent.  This trend has resulted in: a) a general deterioration of the ASE ice shelves (& ice tongue); b) a general retreat of grounding lines; including that the local grounding line at the base of the Thwaites Ice Tongue is now within about 10km of the negative bed slope leading into the BSB, c) the advection of warm CDW towards the grounding lines (including the Thwaites grounding line) have accelerated thru trenches in the continental shelf leading to all key marine glaciers in the ASE as indicated in the third image; and d) the fourth image shows that the advection of warm CDW to these grounding lines has also resulting in an acceleration of ice flow velocities in these key marine glaciers; which has resulting in a continuing thinning of the glacial ice near the grounding lines; which may lead to the formation of ice cliff in these grounding line areas if/when the ice shelves/tongue float away in coming decade(s); which could trigger local MICI-type mechanisms particularly near the base of the Thwaites Ice Tongue at the location of the negative bed slope circa 2035 (note that DeConto & Pollard 2016 assume that the Thwaites Ice Tongue will not collapse for several more decades when local surface temperatures maybe higher enough (following RCP 8.5) to induce failure via hydrofracturing).
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #36 on: July 02, 2021, 11:50:54 PM »
X3 = Triggering of MICI Behavior for Thwaites.

The first image shows local pathways for warm CDW to advect beneath both the TEIS and the Thwaites Ice Tongue; and I note that after this image was made, field observations have confirmed that modified CDW is advecting from the PIIS following a pathway south of the ridge shown in image 1 to beneath the TEIS as illustrated in the computer model results shown in the second image.  The third image shows the trend of how rapidly ASE ice shelves/tongue have been degrading in recent decades; while the fourth image focuses on the SW Tributary Glacier, which no longer buttressed by the PIIS and thus the ice flow velocity for this glacier is progressively accelerating; which may well lead to a reduction in restraint on the Thwaites Glacier flow from its eastern shear margin; which is linked to the SW Tributary Glacier.

See also:

Lars Boehme & Isabella Rosso (06 February 2021), "Classifying Oceanographic Structures in the Amundsen Sea, Antarctica", Geophysical Research Letters, https://doi.org/10.1029/2020GL089412

&

Alley, K.E. et al. (2021), "Two decades of dynamic change and progressive destabilization on the Thwaites Eastern Ice Shelf", The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-76


Image 3 from MacGregor et al 2012
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #37 on: July 02, 2021, 11:55:38 PM »
X3 = Triggering of MICI Behavior for Thwaites.

The first image shows the April 12, 2021 condition of the ice shelf in front of the SW Tributary Glacier and how it is progressively degrading as the Pine Island Southern Ice Shelf degrades.  As this degradation continues, the buttressing from the SW Tributary Ice Shelf will continue to degrade; which will result in the continuing acceleration of the ice velocities for the SW Tributary Glacier and the associated thinning of glacier near its retreating grounding line.  The second image shows an image of Thwaites Glacier ice velocities by Rignot et al. (2017) that I have annotated with the locations of what in the Ice Apocalypse thread I have called the Big and Little Ear subglacial cavities near the local Thwaites Glacier grounding line locations.  The third image shows part of an image of the changing Thwaites Glacier ground line locations by ilillo et al. (2019) on to which I have annotated to show the location of the MICI-type of ice cliff that I propose will be formed circa 2035/36 +/- 5 yrs due to many of the reasons cited in Reply 35. Finally, the fourth image shows the location of an abrupt collapse of a subglacial cavity, in the January 2012 to January 2013 timeframe near the current Little Ear cavity and I note that the collapse of this subglacial cavity coinsided with a surge of the Thwaites Ice Tongue and the associated drainage of four large upstream subglacial lakes beneath Thwaites Glacier; and I suspect that a similar chain of event could contribute to the formation of the MICI-type of ice cliffs located at the base of the Thwaites Ice Tongue as shown in image 3 (without invoking hydrofracturing).
 
Image 1 from paolo April 12 2021 Sentinel1 & Sentinel2 images of the Pine Island SWT & SIS
 
Image 2 (Annotated from Rignot et al. 2017)

 
Image 3 adapted from Milillo et al. 2019 Fig. 1 A, B & C
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #38 on: July 03, 2021, 12:00:58 AM »
X3 = Triggering of MICI Behavior for Thwaites.

The first image shows that there are no pinning points that could inhibit the rapid propagation of an MICI-type of ice cliff failure from the beginning of the negative bed slope at the base of the Thwaites Ice Tongue.  The second image shows the location of a past subglacial lake/cavity near the location of the current Little Ear subglacial cavity that likely collapsed in the January 2012 to January 2013 timeframe that lead to a 2013-2014 surge of the Thwaites Ice Tongue, in a chain of events that may lead to the formation of a MICI-type of ice cliff near the base of the Thwaite Ice Tongue circa 2035/36 (without invoking hydrofracturing).  Panel D (the top panel) of the third image shows that by 2017 only a few meters of ice mass surcharge was preventing icebergs floating over the Big Ear cavity from floating out to sea, and I prostulate that by 2035/36 this ice surchange will be eliminated, thus allowing the icebergs floating over an enlarged Big Ear subglacial cavity to float away by 2035/36, thus abruptly (from an ice creeping point of view) exposing the MICI-type of ice cliff at the base of the Thwaites Ice Tongue.  The fourth image is an annotated version of a figure from Jordan et al (2020), showing that the air draft (hf) of the postulated MICI-type ice cliff will be on the order of 140m.  Image 4 also shows that the slope of the negative bed slope (leading into the BSB) is sufficiently negative to trigger MICI-type of propagation per Bassis' 2021 calculations.

See also:

P. Milillo et al. (30 Jan 2019), "Heterogeneous retreat and ice melt of Thwaites Glacier, West Antarctica", Science Advances,Vol. 5, no. 1, eaau3433, DOI: 10.1126/sciadv.aau3433

&

Jordan, T. A., Porter, D., Tinto, K., Millan, R., Muto, A., Hogan, K., Larter, R. D., Graham, A. G. C., and Paden, J. D.: New gravity-derived bathymetry for the Thwaites, Crosson and Dotson ice shelves revealing two ice shelf populations, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-294, in review, 2020.

&

Bevan, S. L., Luckman, A. J., Benn, D. I., Adusumilli, S., and Crawford, A.: Brief Communication: Thwaites Glacier cavity evolution, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-66, in review, 2021.


Image 3 from Milillo et al. 2019 Fig. 1 D, E & F

 
Image 4 from Jordan et al (2020)
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #39 on: July 03, 2021, 12:06:06 AM »
X3 = Triggering of MICI Behavior for Thwaites.

As the postulated MICI-type of collapse of Thwaites Glacier beginning circa 2035/36 is central to the logic of the subsequent domino chain of events/impact of the subsequent MCDS Bayesian Network, I present the first image (of a profile view thru the base of the Thwaites Ice Tongue) to re-emphasize that currently the hydrostatic bottom elevation (see the red curve in image 1) is above the bed elevation also to the location of my postulated 140m high MICI-type of ice cliff (see Replies 37 & 38).  Thus, as the thickness of the glacial ice at the base of the Thwaites Ice Tongue thins (with increasing ice velocities and with ocean induced basal ice melting near the grounding line), I postulate that circa 2035/36 +/- 5yrs the glacial ice downstream of the postulated MICI-type of ice cliff will be free to float away.  The second and third images show Sentinel 2 images of the Thwaites Ice Tongue Gateway circa March 11, 2021 and I note that this image shows how pre-fractured the glacial ice is in this area; which suggests to me that once the pinning effect of the offshore pinning points is eliminated circa 2030 (primarily due to ocean melting of the local icebergs, see Wild et al. 2021 and image 4) that the icebergs all the way to the postulated MICI-type ice cliff will be free to abruptly float away, thus triggering the postulated MICI-type of collapse of Thwaites Glacier (without invoking hydrofracturing).
 
Image 2 Thwaites Ice Tongue Gateway March 11, 2021, Sentinel 2
 
Image 4(from Wild et al. 2021)
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #40 on: July 03, 2021, 12:10:11 AM »
X3 = Triggering of MICI Behavior for Thwaites.

The first image (from Bassis & Ma 2015) shows how for a 'cold' ice shelf (currently like the FRIS and RIS) basal fractures can partially heal themselves while 'warm' ice shelves (like the PIIS and TEIS) basal fractures are extended towards the surface by ice erosion within the basal fracture.  Such a mechanism, combined with the existing fractured state of the TEIS suggests to me that by 2035/36 that the TEIS will collapse (within 5 years of its postulated unpinning, see Wild et al. 2021).  The second image shows the depth to the MOHO; which illustrates how thin the lithosphere typically is in the BSB; which results in a relatively high local geothermal heat flux into the bases of the glacial ice in the Byrd Subglacial Basin (see the third and fourth images), and which will result in high post-glacial rebound if/when the glacial ice in the BSB calves into the ocean.

 
Image 1 from Bassis & Ma (2015)
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #41 on: July 03, 2021, 12:17:09 AM »
X3 = Triggering of MICI Behavior for Thwaites.

The first image shows the condition near the end of 2017 of the four key subglacial lakes beneath Thwaites that rapidly drained in the 2012 to 2014 timeframe (see Malczyk et al. 2020), and to me this condition suggests that there is a high probability that these four lakes will likely be fully recharged in the 2035/36 timeframe.  In panel 'a' of the second image, the blue curve shows the 12-month running average of the change in height at the tops of the ice shelves in the ASE and panel 'b' shows how that blue line closely correlated with the concurrent ONI index (ENSO).  And as the last super El Nino occurred in the 2015-16 season and such events typically happen roughly every 20-years one can estimate that the ASE ice shelves will again be uplifted by about 0.2m in the 2035-36 season; which would like break the associated grounding line ice seal containing the upstream basal hydrostatic pressure resulting in an jökulhlaup event associated with the concur drainage of the four key subglacial lakes upstream of the Thwaites Gateway; which could likely push any icebergs floating downstream of the postulated 140m high ice cliff in the Thwaites Gateway out to sea (thus abruptly eliminating any buttressing action previously provided by such floating icebergs).  Also, the third image shows that there is a correlation between ice flux from key ASE marine glaciers and a normalized ENSO index.  Furthermore, the fourth image illustrates how energy can be telecommunicated from the Tropical Pacific to coastal regions of West Antarctica during ENSO events.

Malczyk, G. et al. (09 November 2020), "Repeat Subglacial Lake Drainage and Filling Beneath Thwaites Glacier", Geophysical Research Letters, https://doi.org/10.1029/2020GL089658

Image 1 of Four Thwaites subglacial Lake Condition December 2017.
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #42 on: July 03, 2021, 12:22:35 AM »
X3 = Triggering of MICI Behavior for Thwaites.

As a follow-on to image 4 in Reply 41, the first image in this post also illustrates how energy from the Tropical Pacific can be telecommunicated thru the atmosphere during different phases of concurrent SAM and ENSO conditions.  The second image illustrates how during El Nino events the Amundsen Sea Low advects more warm air into the ASE; which both increases the flow of warm CDW into the ASE but also increases the sea surface elevation in the ASE.  The third image illustrates how increasing surface melting progressively depletes the air content of the firn in Antarctic ice shelves; while the fourth image shows that the firn in both PIIS and the TEIS are already almost fully saturated with water/ice (i.e. almost fully depleted of air content); which makes these key ice shelves susceptible to potential future hydrofracturing from future surface melt ponds on these key ice shelves.
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #43 on: July 03, 2021, 12:25:14 AM »
X3 = Triggering of MICI Behavior for Thwaites.

The first image shows trend lines of how cyclones in both the Ross and Bellingshausen Seas are becoming both more intense and more frequent due to climate change; which increases the risks of the key ASE ice shelves being subjected to both increased storm surge and to increased risk of intense rainfall events.  The second image shows the number of surface melt days during January of 2016 in West Antarctica, and I note that during January 2016 a Super El Nino event was underway, thus we may well experience atypically high surface melting in coastal West Antarctica during the next Super El Nino event (likely in the 2035/36 timeframe) due the advection of atmospheric energy from the Tropical Pacific to coastal West Antarctica; which would increase the risk of hydrofracturing of key ice shelves in the ASE in that timeframe.  The third image illustrates how surface meltwater can accelerate the collapse of ice shelves and the formation of ice cliffs that are susceptible to MICI-type of collapse.  The fourth image illustrates how relatively tall ice cliffs are susceptible to slump-types of failure that result in calved icebergs with relatively shallow drafts that would be unlikely to become pinned by existing subsea pinning points in the ASE; which would prevent any meaningful buttressing action from developing in any future mélange in the ASE associated with a MICI-type of collapse of glacial ice in the BSB.
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #44 on: July 03, 2021, 12:31:42 AM »
X3 = Triggering of MICI Behavior for Thwaites.

The first image shows the results of state-of-the-art (circa 2020) computer modeling of ice cliffs focused on conditions in the Thwaites Gateway.  The right-hand portion of this image illustrates ice dynamics that likely contributed to the formation of the Thwaites Ice Tongue, while the left-hand portion of this image illustrates how when the retrograde bed slope reaches about -0.01 with an associate ice thickness gradient of about -0.03 (as is the case for the location of my proposed MICI-type of ice cliff [shown in the third image of Reply 37]) that no matter what the ice flow velocity is, a MICI-type of marine glacier collapse mechanism will develop (which I estimate will happen circa 2035/36 +/- 5 yrs).  The second image illustrates how the drainage of freshwater basal ice meltwater from beneath a marine glacier with an ice cliff face will: a) result in turbulent mixing of the seawater at the ice cliff face which will prevent any significant buttressing from any nearby ice mélange and b) result in a syphon effect that will help to drawing in more warm CDW to the ice face; which will promote more ice cliff failures.  The third image shows the locations of two active volcanoes (Takahe and Hudson) that are positioned near the gateways of the Thwaites, and Pine Island, marine glaciers, respectively, and I note that the abrupt elastic rebound of the local glacial beds (seafloors) due to the potential abrupt MICI-type of collapse of glacial ice in the BSB and PIG drainage areas might abruptly trigger volcanic activity in these critical volcanoes.  The fourth image (from McConnell et al. 2017) shows evidence that about 17.7 ka Mt Takahe  erupted and sent halogen-rich emissions high into the atmosphere that caused ozone depletion of the stratosphere over Antarctica, and I note that such an ozone depletion could well keep the westerly winds over the Southern Ocean in a range that promotes upwelling of warm CDW that would promote grounding line retreat for key Antarctic marine glaciers (including Totten and Byrd Glaciers in the EAIS).

McConnell J.R., el al. (2017), "Synchronous volcanic eruptions and abrupt climate change ∼17.7 ka plausibly linked by stratospheric ozone depletion," PNAS, https://doi.org/10.1073/pnas.1705595114.

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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #45 on: July 03, 2021, 12:34:39 AM »
X2 = Beaufort Gyre Reversal/Discharge; (2020 to 2036)

Wang et al. (2021) report that:

"By using numerical experiments, we find that this capability varies with the Arctic sea ice decline nonmonotonically, with the largest capability at intermediate strength of sea ice decline.  Through enhancing the ocean surface stress, sea ice decline not only accumulates freshwater toward the Amerasian Basin but also tends to reduce the amount of freshwater in both the Eurasian and Amerasian basins by increasing the occupation of Atlantic-origin water in the upper ocean."

Thus, I assume that the Beaufort Gyre has recently been stabilized by the recent 'intermediate strength of sea ice decline' (see image 1); but that if/when Thwaites initiates a MICI-type of collapse circa 2035/36 that the associated influx of subarctic ocean water into the Arctic Ocean will temporarily accelerate the rate sea ice loss that will trigger a temporary reversal of the Beaufort Gyre (see image 2), that will result in an associate increase in freshwater flux into the North Atlantic.

Wang, S., Qiang Wang, Qi Shu, Zhenya Song, Gerrit Lohmann, Sergey Danilov, Fangli Qiao (25 May 2021), "Nonmonotonic Change of the Arctic Ocean Freshwater Storage Capability in a Warming Climate", Geophysical Research Letters, https://doi.org/10.1029/2020GL090951

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL090951?af=R

Abstract
Freshwater in the Arctic Ocean is one of the key climate components. It is not well understood how the capability of the Arctic Ocean to store freshwater will develop when freshwater supplies increase in a warming climate. By using numerical experiments, we find that this capability varies with the Arctic sea ice decline nonmonotonically, with the largest capability at intermediate strength of sea ice decline. Through enhancing the ocean surface stress, sea ice decline not only accumulates freshwater toward the Amerasian Basin but also tends to reduce the amount of freshwater in both the Eurasian and Amerasian basins by increasing the occupation of Atlantic-origin water in the upper ocean. An increase in river runoff modulates the counterbalance of the two competing effects, leading to the nonmonotonic changes of the Arctic freshwater storage capability in a warming climate.


X4 = Triggering of BSB Ice MICI Discharge; (2035 to 2036)
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #46 on: July 03, 2021, 12:39:12 AM »
BN 2036-2040
X5 = Triggering of PIG MICI Response from the BSB and/or Ice Shelf Loss;
X6 = Triggering of other ASE & Bellingshausen Marine Glaciers from the BSB and/or loss of its Ice Shelf.
X7 = Abrupt seasonal loss of Arctic Sea Ice (ASI), implies both freshwater hosing & albedo loss.



The first image taken from the MCDS-BN presented in the first post to this thread, largely consists of:
a) three complex FT arrows representing all initiation global conditions assuming freshwater fluxes from the abrupt grounding retreats for both Thwaites, the rest of the BSB glacial ice and a reversal of the Beaufort Gyre.
b) three freshwater flux events (and associated ice-climate feedbacks) X5, X6 and X7 circa 2040 +/- 5 yrs.
c) Cumulatively, the three events X5, X6 and X7 are assigned a 65% probability of occurrence, by 2040 +/- 5 yrs, for reasons including:
•   Once Thwaites sustains an MICI-type of collapse (event X3), here it is considered inevitable that events X5 and X6 will occur by 2040 +/- 5yrs due to geometry of the bedmap in the Amundsen and Bellingshausen Sea Sectors.
•   I believe that the Atlantification of the Arctic Ocean and the atmospheric telecommunication of energy from tropical latitude indirectly into Arctic latitudes (due to X1, X2, X3 and X4) will almost inevitably lead to seasonal Arctic Sea Ice loss by 2040 +/- 5 yrs.  Also, the assumed reversal of the Beaufort Gyre in 2036 would subject the Arctic Sea Ice to accelerated melting associated with changes to the Arctic Ocean halocline.


X5 = Triggering of PIG MICI Response from the BSB and/or Ice Shelf Loss

The second attached image shows the surface elevations in both the PIG and BSB catchment basins; which causes newly fallen snow to drain into one basin or the other; however, the third and fourth images show that the bed elevations indicate that MICI-type cliff failures initiated in the BSB would continue into the backend of the PIG catchment basin such that about half of the glacial ice in the PIG catchment basin would float-out thru the Thwaites Gateway under an MCDS-BN scenario.
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #47 on: July 03, 2021, 12:42:30 AM »
X5 = Triggering of PIG MICI Response from the BSB and/or Ice Shelf Loss


Furthermore, there is some small possibility that circa 2040 the PIIS may have retreated sufficiently (due to the advection of warm CDW beneath the PIIS as shown in images 1 and 2) to induce some ice cliff failures directly from PIG into the Pine Island Bay.  Additionally, the collapse of the PIIS circa 2040 (without hydrofracturing) my be facilitated by the inflow of ice streams 11 and/or 9 (see image 3); which could result in weaker shear margins along the sides of the PIIS as illustrated by the fourth attached image.
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #48 on: July 03, 2021, 12:45:46 AM »
X6 = Triggering of other ASE & Bellingshausen Marine Glaciers from the BSB and/or loss of its Ice Shelf.

Where the first two attached images make it clear, or not, I postulate that the initiation of a MICI-type of collapse of the Thwaites Glacier circa 2035/36 would lead to ice cliff failures on the backside of such ASE marine glaciers as: Haynes, Pope, Crosson/Smith and Dotson/Kohler and to small portions of glacial ice in the Bellingshausen Sea Sector, circa 2040.  Thus, I have included this freshwater flux event X6 in order to keep my flux calculations correct.


X7 = Abrupt seasonal loss of Arctic Sea Ice (ASI), implies both freshwater hosing & albedo loss.

Here a seasonal ice-free Arctic Ocean means that Arctic Sea Ice, ASI, area in September will be equal to, or less than, 1million square kilometers; which the first image indicates has about a 70% chance of occurring by circa 2040 when following RCP 8.5 with consensus climate science values for ECS (and without a Thwaites collapse and a Beaufort Gyre reversal).  The fourth image shows that CMIP6 projects rapid ASI area loss circa 2040 under SSP5-8.5 forcing.  While event X7 is not a freshwater flux event, it does represent a benchmark for accelerated Arctic ice/snow melting due to higher values of Arctic Amplification.
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Re: Maximum Credible Domino Scenario (MCDS) – Bayesian Network (BN)
« Reply #49 on: July 03, 2021, 12:50:35 AM »
BN 2040 – 2050
X8 = Abrupt seasonal loss of Antarctic Sea Ice (AASI), from rainfall.
X9 = The FRIS collapses due to hydrofracturing from rainfall
X10 = Triggering of Totten MISI retreat.
X11 = The RIS collapses due to hydrofracturing from rainfall
X12 = Rainfall events for the GrIS



The first image taken from the MCDS-BN presented in the first post to this thread, largely consists of:
a) five complex FT arrows representing all initiation global conditions assuming freshwater fluxes from the prior assumed X events.
b) five freshwater flux events (and associated ice-climate feedbacks) X8 thru X12 circa 2050 +/- 5 yrs.
c) Cumulatively, the five events X8 thru X12 are assigned a 35% probability of occurrence, by 2050 +/- 5 yrs, for reasons including:
•   Ice-climate feedbacks from events X1 thru X7 are assumed to have sufficiently accelerated the high-end CMIP6 projections to trigger events X8 thru X12 by 2050 +/- 5yrs.
•   Rainfall, and associated storm activities, at high latitudes (see Bintanja et al. 2017 & England et al. 2020) will be accelerated by ice-climate feedbacks from events X1 thru X7 more than other Earth System responses.  Such high latitude rainfall is assumed to result in the abrupt collapses of both the FRIS and the RIS circa 2050 due to hydrofracturing.  While the collapse of these ice shelves will not significantly raise sea level, the associated armadas of icebergs are assumed to trigger numerous/subsequent ice-climate feebacks.
•   Upwelling of warm CDW around Antarctica will be accelerated by events X1 thru X7.

Also note that in the MCDS-BN projections the GMSTA in 2050 (i.e. 2.0C) has been decreased from that shown for 2040 (i.e. 2.7C) as it is assumed that the SSTs for both the Southern Ocean and for the North Atlantic will substantially decrease due to prior freshwater fluxes.

Bintanja, R, et al. Towards a rain-dominated Arctic, Nature Climate Change (2017). DOI: 10.1038/nclimate3240
&
England, M.R., Polvani, L.M., Sun, L. et al. Tropical climate responses to projected Arctic and Antarctic sea-ice loss. Nat. Geosci. 13, 275–281 (2020). https://doi.org/10.1038/s41561-020-0546-9

X8 = Abrupt seasonal loss of Antarctic Sea Ice (AASI), from rainfall.

Image 2 shows a CMIP6 projection of Antarctic Sea Ice loss with time; and it is taken here that these CMIP6 projections for SSP5-8.5 will be accelerated by ice-climate feedbacks from events X1 thru X7 to match the associated MCDS-BN projections.

Image 2: CMIP6 Projection of Antarctic SIA


X9 = The FRIS collapses due to hydrofracturing from rainfall

The third and fourth images show that Hellmer et al. (2012) and E3SM (2020) project that ice mass loss from the FRIS will accelerate abruptly due to the intrusion of warm CDW beneath this ice shelf and it is taken here that these projections will be accelerated by ice-climate feedbacks associated with events X1 thru X7 to match the associated MCDS-BN projections.
 
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