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Author Topic: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME  (Read 12313 times)

AbruptSLR

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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #50 on: November 01, 2016, 11:32:45 PM »
While the linked open access reference gives too much creditability to story lines for low ESS values for my tastes; nevertheless it does provide a methodology for using Bayesian inference to present credibile story lines for high ESS values (such as those described by Steven Sherwood).  Thus in balance I believe that the recommended approach is a positive development, which (if adopted) could help better manage risk:

Bjorn Stevens, Steven C. Sherwood, Sandrine Bony & Mark J. Webb (31 October 2016), "Prospects for Narrowing Bounds on Earth's Equilibrium Climate Sensitivity", Earth's Future, DOI: 10.1002/2016EF000376

http://onlinelibrary.wiley.com/doi/10.1002/2016EF000376/abstract

Abstract: "The concept of Earth's Equilibrium Climate Sensitivity (ECS) is reviewed. A particular problem in quantifying plausible bounds for ECS has been how to account for all of the diverse lines of relevant scientific evidence. It is argued that developing and refuting physical storylines (hypotheses) for values outside any proposed range has the potential to better constrain these bounds and to help articulate the science needed to narrow the range further. A careful reassessment of all important lines of evidence supporting these storylines, their limitations, and the assumptions required to combine them is therefore urgently required."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
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AbruptSLR

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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #51 on: November 04, 2016, 09:49:39 PM »
The linked research on improved reconstruction of the last glacial cycle based on 263 marine sediment cores will help to calibrate climate models thus improving future case projections:

Lorraine E. Lisiecki & Joseph V. Stern (13 October 2016), "Regional and global benthic δ18O stacks for the last glacial cycle", Paleoceanography, DOI: 10.1002/2016PA003002


http://onlinelibrary.wiley.com/doi/10.1002/2016PA003002/full

Abstract: "Although detailed age models exist for some marine sediment records of the last glacial cycle (0–150 ka), age models for many cores rely on the stratigraphic correlation of benthic δ18O, which measures ice volume and deep ocean temperature change. The large amount of data available for the last glacial cycle offers the opportunity to improve upon previous benthic δ18O compilations, such as the “LR04” global stack. Not only are the age constraints for the LR04 stack now outdated but a single global alignment target neglects regional differences of several thousand years in the timing of benthic δ18O change during glacial terminations. Here we present regional stacks that characterize mean benthic δ18O change for 8 ocean regions and a volume-weighted global stack of data from 263 cores. Age models for these stacks are based on radiocarbon data from 0 to 40 ka, correlation to a layer-counted Greenland ice core from 40 to 56 ka, and correlation to radiometrically dated speleothems from 56 to 150 ka. The regional δ18O stacks offer better stratigraphic alignment targets than the LR04 global stack and, furthermore, suggest that the LR04 stack is biased 1–2 kyr too young throughout the Pleistocene. Finally, we compare global and regional benthic δ18O responses with sea level estimates for the last glacial cycle."

See also:
Underwood, E. (2016), Ancient ocean floor seashells improve model of past glaciers, Eos, 97, doi:10.1029/2016EO062231. Published on 01 November 2016

https://eos.org/research-spotlights/ancient-ocean-floor-seashells-improve-model-of-past-glaciers?utm_source=eos&utm_medium=email&utm_campaign=EosBuzz110416

Extract: "More accurate reconstruction of ice sheets over the past 150,000 years could help scientists predict future climate change."
“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: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #52 on: November 05, 2016, 10:22:51 PM »
The linked article discusses efforts to improve carbon cycle projections within climate models:


Xiao, J., Y. Luo, and G. Shrestha (2016) , “Improving Carbon Cycle Projections for Better Carbon Managemet”, Eos, 97, doi:10.1029/2016EO062341


https://eos.org/meeting-reports/improving-carbon-cycle-projections-for-better-carbon-management


Extract: “One important step toward carbon management is developing the science that predicts carbon cycles. Over the past 10 years, the North American Carbon Program (NACP) has helped to significantly advance observation and monitoring systems in carbon cycle research. Observation and monitoring programs such as AmeriFlux and the National Ecological Observatory Network (NEON) are essential to improving understanding of the carbon cycle through diagnosing the magnitude, spatial patterns, and temporal variability of carbon fluxes and stocks.

However, the value of these programs would be greatly increased if their results could be used to constrain future projections of carbon cycle dynamics in response to climate change and human activities, including carbon management efforts.”
“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: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #53 on: November 07, 2016, 06:13:20 PM »
The linked reference discusses the use of boreal fire paleo-records in NH ice cores, to assist in calibrating climate change projections:

Legrand, M., McConnell, J., Fischer, H., Wolff, E. W., Preunkert, S., Arienzo, M., Chellman, N., Leuenberger, D., Maselli, O., Place, P., Sigl, M., Schüpbach, S., and Flannigan, M.: Boreal fire records in Northern Hemisphere ice cores: a review, Clim. Past, 12, 2033-2059, doi:10.5194/cp-12-2033-2016, 2016.

http://www.clim-past.net/12/2033/2016/

Abstract. Here, we review different attempts made since the early 1990s to reconstruct past forest fire activity using chemical signals recorded in ice cores extracted from the Greenland ice sheet and a few mid-northern latitude, high-elevation glaciers. We first examined the quality of various inorganic (ammonium, nitrate, potassium) and organic (black carbon, various organic carbon compounds including levoglucosan and numerous carboxylic acids) species proposed as fire proxies in ice, particularly in Greenland. We discuss limitations in their use during recent vs. pre-industrial times, atmospheric lifetimes, and the relative importance of other non-biomass-burning sources. Different high-resolution records from several Greenland drill sites and covering various timescales, including the last century and Holocene, are discussed. We explore the extent to which atmospheric transport can modulate the record of boreal fires from Canada as recorded in Greenland ice. Ammonium, organic fractions (black and organic carbon), and specific organic compounds such as formate and vanillic acid are found to be good proxies for tracing past boreal fires in Greenland ice. We show that use of other species – potassium, nitrate, and carboxylates (except formate) – is complicated by either post-depositional effects or existence of large non-biomass-burning sources. The quality of levoglucosan with respect to other proxies is not addressed here because of a lack of high-resolution profiles for this species, preventing a fair comparison. Several Greenland ice records of ammonium consistently indicate changing fire activity in Canada in response to past climatic conditions that occurred during the last millennium and since the last large climatic transition. Based on this review, we make recommendations for further study to increase reliability of the reconstructed history of forest fires occurring in a given region.
“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: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #54 on: November 07, 2016, 11:53:07 PM »
The linked reference indicates that current climate models over-estimate the influence of DMS on climate response and recommend appropriate re-calibration of the DMS mechanisms in these models:

Erik Hans Hoffmann, Andreas Tilgner, Roland Schrödner, Peter Bräuer, Ralf Wolke and Hartmut Herrmann (November 2016), "An advanced modeling study on the impacts and atmospheric implications of multiphase dimethyl sulfide chemistry", PNAS, vol. 113 no. 42,  11776–11781, doi: 10.1073/pnas.1606320113

http://www.pnas.org/content/113/42/11776

Abstract: "Oceans dominate emissions of dimethyl sulfide (DMS), the major natural sulfur source. DMS is important for the formation of non-sea salt sulfate (nss-SO42−) aerosols and secondary particulate matter over oceans and thus, significantly influence global climate. The mechanism of DMS oxidation has accordingly been investigated in several different model studies in the past. However, these studies had restricted oxidation mechanisms that mostly underrepresented important aqueous-phase chemical processes. These neglected but highly effective processes strongly impact direct product yields of DMS oxidation, thereby affecting the climatic influence of aerosols. To address these shortfalls, an extensive multiphase DMS chemistry mechanism, the Chemical Aqueous Phase Radical Mechanism DMS Module 1.0, was developed and used in detailed model investigations of multiphase DMS chemistry in the marine boundary layer. The performed model studies confirmed the importance of aqueous-phase chemistry for the fate of DMS and its oxidation products. Aqueous-phase processes significantly reduce the yield of sulfur dioxide and increase that of methyl sulfonic acid (MSA), which is needed to close the gap between modeled and measured MSA concentrations. Finally, the simulations imply that multiphase DMS oxidation produces equal amounts of MSA and sulfate, a result that has significant implications for nss-SO42− aerosol formation, cloud condensation nuclei concentration, and cloud albedo over oceans. Our findings show the deficiencies of parameterizations currently used in higher-scale models, which only treat gas-phase chemistry. Overall, this study shows that treatment of DMS chemistry in both gas and aqueous phases is essential to improve the accuracy of model predictions."


See also the linked article entitled: "Impact of sea smell overestimated by present climate models".

http://phys.org/news/2016-11-impact-sea-overestimated-climate.html
Extract: "The results show that the role of DMS in Earth's climate is still not sufficiently understood - despite many global model studies. "Our simulations indicate that the increased DMS emissions lead to higher aerosol particle mass loads but not necessarily to a higher number of particles or cloud droplets. The modeling results are important to understand the climate processes between ocean and atmosphere. In addition, geoengineering ideas are constantly being discussed, which are hoping for more cooling clouds by fertilizing the ocean", explains Prof. Hartmut Herrmann from TROPOS. However, this study suggests that the production of sulfur dioxide is less pronounced and the effects on the back-reflection effect of the clouds are lower than expected. Therefore, the corresponding geoengineering approaches could be less effective than assumed."


“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: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #55 on: November 11, 2016, 05:15:01 PM »
The linked reference discusses research at the Center for Space Research (CSR) where they used mass concentration (mascon) block methodology to improve the resolution of GRACE gravity readings (see attached image with the application of mascon methodology to 2012 GRACE data) around the world.  Clearly such work will help calibrate sea level rise projections:

Himanshu Save, Srinivas Bettadpur & Byron D. Tapley (10 October 2016), "High-resolution CSR GRACE RL05 mascons", JGR Solid Earth, DOI: 10.1002/2016JB013007


http://onlinelibrary.wiley.com/doi/10.1002/2016JB013007/abstract


Abstract: "The determination of the gravity model for the Gravity Recovery and Climate Experiment (GRACE) is susceptible to modeling errors, measurement noise, and observability issues. The ill-posed GRACE estimation problem causes the unconstrained GRACE RL05 solutions to have north-south stripes. We discuss the development of global equal area mascon solutions to improve the GRACE gravity information for the study of Earth surface processes. These regularized mascon solutions are developed with a 1° resolution using Tikhonov regularization in a geodesic grid domain. These solutions are derived from GRACE information only, and no external model or data is used to inform the constraints. The regularization matrix is time variable and will not bias or attenuate future regional signals to some past statistics from GRACE or other models. The resulting Center for Space Research (CSR) mascon solutions have no stripe errors and capture all the signals observed by GRACE within the measurement noise level. The solutions are not tailored for specific applications and are global in nature. This study discusses the solution approach and compares the resulting solutions with postprocessed results from the RL05 spherical harmonic solutions and other global mascon solutions for studies of Arctic ice sheet processes, ocean bottom pressure variation, and land surface total water storage change. This suite of comparisons leads to the conclusion that the mascon solutions presented here are an enhanced representation of the RL05 GRACE solutions and provide accurate surface-based gridded information that can be used without further processing."

See also:
https://eos.org/research-spotlights/a-new-model-to-improve-gravity-models?utm_source=eos&utm_medium=email&utm_campaign=EosBuzz111116

Extract: "At the Center for Space Research at the University of Texas at Austin, Save et al. created a more accurate gravity model without the need for postprocessing procedures to remove noise. They used a different set of functions called mass concentration (mascon) blocks, which estimate anomalies within targeted grid locations. They divided up Earth into a geodesic grid, which consisted of 40,950 hexagonal tiles and 12 pentagonal tiles, and estimated the mass anomaly—how much the mass of one block deviated from a long-term average mass of that block—for each. This provided them with a 14-year (and counting) time series of mass change for all the tiles over the entire Earth.

Mascon mass anomalies have been used for a few years now and can be applied at global scales, which means multiple fields of research—the study of water on land, in the world’s oceans, and locked up in ice—can benefit from these models. However, the solutions presented here have an additional advantage: They are derived from GRACE data alone and don’t require input from other geophysical models or data sources.

To test the new model’s accuracy, the team compared it with independent ocean bottom pressure recorder data and estimates of ice mass loss from multiple studies. They found excellent agreement. Furthermore, because of the resolution of the traditional GRACE gravity models, the land hydrology and ice loss signals along the coast used to “leak” into the ocean. The researchers’ new model shows little or no leakage of land signals into the ocean and may help scientists from many Earth science fields to improve their regional and global studies."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

themgt

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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #56 on: November 12, 2016, 12:13:03 AM »
http://advances.sciencemag.org/content/2/11/e1501923

Nonlinear climate sensitivity and its implications for future greenhouse warming


Abstract:
Global mean surface temperatures are rising in response to anthropogenic greenhouse gas emissions. The magnitude of this warming at equilibrium for a given radiative forcing—referred to as specific equilibrium climate sensitivity (S)—is still subject to uncertainties. We estimate global mean temperature variations and S using a 784,000-year-long field reconstruction of sea surface temperatures and a transient paleoclimate model simulation. Our results reveal that S is strongly dependent on the climate background state, with significantly larger values attained during warm phases. Using the Representative Concentration Pathway 8.5 for future greenhouse radiative forcing, we find that the range of paleo-based estimates of Earth’s future warming by 2100 CE overlaps with the upper range of climate simulations conducted as part of the Coupled Model Intercomparison Project Phase 5 (CMIP5). Furthermore, we find that within the 21st century, global mean temperatures will very likely exceed maximum levels reconstructed for the last 784,000 years. On the basis of temperature data from eight glacial cycles, our results provide an independent validation of the magnitude of current CMIP5 warming projections.

AbruptSLR

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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #57 on: November 14, 2016, 10:35:42 PM »
The linked article is entitled: "Heading into SC16 CENATE Flexes its Growing Muscle".  Reading between the lines, the article indicates that machine learning will likely soon help with calibrating (see the attached image) the ACME climate model:

https://www.hpcwire.com/2016/11/08/heading-sc16-cenate-flexes-growing-muscle/

Extract: "In September, the Center for Advanced Technology Evaluation (CENATE) at Pacific Northwest National Laboratory (PNNL) took possession of NVIDIA’s DGX-1 GPU-based (Pascal 100) supercomputer.
...
“We have established the lofty goal for us to even design some neuromorphic technologies that are doing machine learning natively and not as you can do machine learning for example on a GPU in which you sort of come in from behind and map machine workload to the architecture of the GPU,” says Adolfy Hoisie, PNNL’s chief scientist for computing and CENATE’s principal investigator and director. All things (time and money) being available, “We would like those neuromorphic systems chips, whatever, to actually cast them in silicon.”

Yet a third project with the DGX-1 is work by distinguished PNNL researcher Ruby Leung and her team with portions of their climate modeling code. In particular, says Hoisie, they are look at portion of code already running on GPUs and are benchmarking the code’s performance on DGX-1: “We are going to be able to say the DGX-1 is this much faster or it is not or whatever the case may be and look at what needs to be done to improve the performance as the specs of the machine allows.” Leung is the Chief Scientist of Department of Energy Accelerated Climate Modeling for Energy (ACME)."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
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AbruptSLR

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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #58 on: November 14, 2016, 11:09:53 PM »
Per the linked UC San Diego website, UCSD will help to calibrated ACME for several different considerations including the potential near-term collapse of key portions of the Antarctic Ice Sheet:

https://scripps.ucsd.edu/research/proposals/accelerated-climate-modeling-energy-acme-ocean-and-sea-ice-processes

Extract: "“A goal of ACME is to simulate the changes in the hydrological cycle, with a specific focus on precipitation and surface water in orographically complex regions such as the western United States and the headwaters of the Amazon,” the report states.
 
To address biogeochemistry, ACME researchers will examine how more complete treatments of nutrient cycles affect carbon–climate system feedbacks, with a focus on tropical systems; and investigate the influence of alternative model structures for below-ground reaction networks on global-scale biogeochemistry–climate feedbacks.
 
For cyrosphere, the team will examine the near-term risks of initiating the dynamic instability and onset of the collapse of the Antarctic Ice Sheet due to rapid melting by warming waters adjacent to the ice sheet grounding lines.
 
The experiment would be the first fully coupled global simulation to include dynamic ice shelf–ocean interactions for addressing the potential instability associated with grounding line dynamics in marine ice sheets around Antarctica."

Edit: See also (which does not include modeling of DeConto & Pollard's hydrofracturing or cliff failure mechanisms):

https://www.alcf.anl.gov/projects/accelerated-climate-modeling-energy-0

Extract: "For the cryosphere, the objective is to examine whether a near-term risk exists of initiating the dynamic instability and onset of the collapse of the Antarctic Ice Sheet due to rapid melting by warming waters adjacent to the ice sheet grounding lines. ACME capstone simulations will include a 100-year pre-industrial control followed by an ensemble of five to six 80-year (1970–2050) simulations."

Edit2: See also:

https://github.com/ACME-Climate

« Last Edit: November 14, 2016, 11:18:05 PM by AbruptSLR »
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AbruptSLR

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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #59 on: November 15, 2016, 12:18:10 AM »
The linked reference discusses efforts to improve sea ice forecasts with continued warming; which is important for projecting Polar Amplification:

Ivanova, D.P., P.J. Gleckler, K.E. Taylor, P.J. Durack, and K.D. Marvel (Sept 21, 2016), "Moving beyond the total sea ice extent in gauging model biases", J. Clim., doi:10.1175/JCLI-D-16-0026.1.

http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-16-0026.1

Abstract: "Reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. In this study we describe several approaches to improve how model biases in total sea ice distribution are quantified, and apply them to historically forced simulations contributed to the Coupled Model Intercomparison Project phase 5 (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent is often used to evaluate model performance. We introduce a new approach which investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differences between simulated and observed sea ice. Using several observational data sets, we apply several new methods to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. We show that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. Our results suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the fine scale structure of sea ice characteristics, however, our "sector scale" metric aids to reduce the impact of compensating errors in hemispheric integrals."
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AbruptSLR

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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #60 on: November 21, 2016, 07:38:37 PM »
The linked reference compares model projections against the 20th century observations and finds that no model matched all observations and that model projections for ENSO showed the most variability.  Thus if ENSO is a driver for a Lorenz attractor induce amplification of climate sensitivity, then we would not expect the CMIP5 projections to have adequately identified this risk:

Järvinen, H., Seitola, T., Silén, J., and Räisänen, J.: Multi-annual modes in the 20th century temperature variability in reanalyses and CMIP5 models, Geosci. Model Dev., 9, 4097-4109, doi:10.5194/gmd-9-4097-2016, 2016.

http://www.geosci-model-dev.net/9/4097/2016/

Abstract. A performance expectation is that Earth system models simulate well the climate mean state and the climate variability. To test this expectation, we decompose two 20th century reanalysis data sets and 12 CMIP5 model simulations for the years 1901–2005 of the monthly mean near-surface air temperature using randomised multi-channel singular spectrum analysis (RMSSA). Due to the relatively short time span, we concentrate on the representation of multi-annual variability which the RMSSA method effectively captures as separate and mutually orthogonal spatio-temporal components. This decomposition is a unique way to separate statistically significant quasi-periodic oscillations from one another in high-dimensional data sets.

The main results are as follows. First, the total spectra for the two reanalysis data sets are remarkably similar in all timescales, except that the spectral power in ERA-20C is systematically slightly higher than in 20CR. Apart from the slow components related to multi-decadal periodicities, ENSO oscillations with approximately 3.5- and 5-year periods are the most prominent forms of variability in both reanalyses. In 20CR, these are relatively slightly more pronounced than in ERA-20C. Since about the 1970s, the amplitudes of the 3.5- and 5-year oscillations have increased, presumably due to some combination of forced climate change, intrinsic low-frequency climate variability, or change in global observing network. Second, none of the 12 coupled climate models closely reproduce all aspects of the reanalysis spectra, although some models represent many aspects well. For instance, the GFDL-ESM2M model has two nicely separated ENSO periods although they are relatively too prominent as compared with the reanalyses. There is an extensive Supplement and YouTube videos to illustrate the multi-annual variability of the data sets.
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #61 on: November 29, 2016, 05:35:04 PM »
Knowledge of ice thickness is important for calibrating our climate change projections:

Farinotti, D., Brinkerhoff, D., Clarke, G. K. C., Fürst, J. J., Frey, H., Gantayat, P., Gillet-Chaulet, F., Girard, C., Huss, M., Leclercq, P. W., Linsbauer, A., Machguth, H., Martin, C., Maussion, F., Morlighem, M., Mosbeux, C., Pandit, A., Portmann, A., Rabatel, A., Ramsankaran, R., Reerink, T. J., Sanchez, O., Stentoft, P. A., Singh Kumari, S., van Pelt, W. J. J., Anderson, B., Benham, T., Binder, D., Dowdeswell, J. A., Fischer, A., Helfricht, K., Kutuzov, S., Lavrentiev, I., McNabb, R., Gudmundsson, G. H., Li, H., and Andreassen, L. M.: How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment, The Cryosphere Discuss., doi:10.5194/tc-2016-250, in review, 2016.

http://www.the-cryosphere-discuss.net/tc-2016-250/


Abstract. Knowledge of the ice thickness distribution of glaciers and ice caps is an important prerequisite for many glaciological and hydrological investigations. A wealth of approaches has recently been presented for inferring ice thickness from characteristics of the surface. With the Ice Thickness Models Intercomparison eXperiment (ITMIX) we performed the first coordinated assessment quantifying individual model performance. A set of 17 different models showed that individual ice thickness estimates can differ considerably – locally by a spread comparable to the observed thickness. Averaging the results of multiple models, however, significantly improved the results: On average over the 21 considered test cases, comparison against direct ice thickness measurements revealed deviations in the order of 10 ± 24% of the mean ice thickness (1-sigma estimate). For models relying on multiple data sets -- such as surface ice velocity fields, surface mass balance, or rates of ice thickness change – the results highlighted the sensitivity to input data consistency. Together with the requirement of being able to handle large regions in an automated fashion, the capacity of better accounting for uncertainties in the input data will be a key for an improved next generation of ice thickness estimation approaches.
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #62 on: November 29, 2016, 09:05:59 PM »
The linked reference indicates that plant turnover due to climate change was amplified when climate models were calibrated using Late Quaternary records

D. Nogués-Bravo, S. Veloz, B. G. Holt, J. Singarayer, P. Valdes, B. Davis, S. C. Brewer, J. W. Williams & C. Rahbek (2016), "Amplified plant turnover in response to climate change forecast by Late Quaternary records", Nature Climate Change, Volume: 6, Pages: 1115–1119, doi:10.1038/nclimate3146


http://www.nature.com/nclimate/journal/v6/n12/full/nclimate3146.html


Abstract: "Conservation decisions are informed by twenty-first-century climate impact projections that typically predict high extinction risk. Conversely, the palaeorecord shows strong sensitivity of species abundances and distributions to past climate changes, but few clear instances of extinctions attributable to rising temperatures. However, few studies have incorporated palaeoecological data into projections of future distributions. Here we project changes in abundance and conservation status under a climate warming scenario for 187 European and North American plant taxa using niche-based models calibrated against taxa–climate relationships for the past 21,000 years. We find that incorporating long-term data into niche-based models increases the magnitude of projected future changes for plant abundances and community turnover. The larger projected changes in abundances and community turnover translate into different, and often more threatened, projected IUCN conservation status for declining tree taxa, compared with traditional approaches. An average of 18.4% (North America) and 15.5% (Europe) of taxa switch IUCN categories when compared with single-time model results. When taxa categorized as ‘Least Concern’ are excluded, the palaeo-calibrated models increase, on average, the conservation threat status of 33.2% and 56.8% of taxa. Notably, however, few models predict total disappearance of taxa, suggesting resilience for these taxa, if climate were the only extinction driver. Long-term studies linking palaeorecords and forecasting techniques have the potential to improve conservation assessments."
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #63 on: December 01, 2016, 11:04:51 PM »
The linked reference provides a refined model for simulating Arctic methane emissions and places limits on the amounts of methane that may have recently been emitted from the ESAS:

Warwick, N. J., Cain, M. L., Fisher, R., France, J. L., Lowry, D., Michel, S. E., Nisbet, E. G., Vaughn, B. H., White, J. W. C., and Pyle, J. A.: Using δ13C-CH4 and δD-CH4 to constrain Arctic methane emissions, Atmos. Chem. Phys., 16, 14891-14908, doi:10.5194/acp-16-14891-2016, 2016.

http://www.atmos-chem-phys.net/16/14891/2016/


Abstract. We present a global methane modelling study assessing the sensitivity of Arctic atmospheric CH4 mole fractions, δ13C-CH4 and δD-CH4 to uncertainties in Arctic methane sources. Model simulations include methane tracers tagged by source and isotopic composition and are compared with atmospheric data at four northern high-latitude measurement sites. We find the model's ability to capture the magnitude and phase of observed seasonal cycles of CH4 mixing ratios, δ13C-CH4 and δD-CH4 at northern high latitudes is much improved using a later spring kick-off and autumn decline in northern high-latitude wetland emissions than predicted by most process models. Results from our model simulations indicate that recent predictions of large methane emissions from thawing submarine permafrost in the East Siberian Arctic Shelf region could only be reconciled with global-scale atmospheric observations by making large adjustments to high-latitude anthropogenic or wetland emission inventories.


Extract: "Using current literature estimates for northern high-latitude methane emissions, our study suggests an ESAS methane source in the lower half of published estimated ranges (0.5 to 17 Tg yr-1/. This is in agreement with the study by Berchet et al. (2016), which used synoptic data from long-term methane measurement sites to constrain ESAS emissions from 0.5 to 4.3 Tg yr-1. We find that substantial adjustments in estimates of high-latitude methane source flux magnitudes or isotopic source signatures are required in order to reconcile East Siberian Arctic Shelf emissions as large as 17 Tg yr-1 with global-scale atmospheric observations of CH4 and δ13C-CH4. Depending on currently lacking information on the seasonality and isotopic signature of an ESAS source, these include reducing northern high-latitude wetland emissions by ~40% (to a value just below the minimum of a range of values predicted by process models), reducing northern high-latitude emissions from anthropogenic emission inventories by ~33% or a combination of the two.  Alternatively, a missing seasonal sink, such as the destruction of methane by boreal vegetation suggested by Sundqvist et al. (2012), could help reconcile large emissions from the ESAS with global-scale atmospheric observations. Further information on the isotopic signature and seasonality of an ESAS source would be of benefit in distinguishing between possible scenarios."
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #64 on: December 01, 2016, 11:23:26 PM »
The linked reference discusses possible impacts on phytoplankton from climate change induced changes in ocean chemistry and the ocean's iron cycle; and the need to improve models to better assess the risk that these changes might contribute to a positive feedback mechanism for climate change:

D. A. Hutchins & P. W. Boyd (2016), "Marine phytoplankton and the changing ocean iron cycle", Nature Climate Change, Volume: 6, Pages: 1072–1079, doi:10.1038/nclimate3147

http://www.nature.com/nclimate/journal/v6/n12/full/nclimate3147.html

Abstract: "The availability of the micronutrient iron governs phytoplankton growth across much of the ocean, but the global iron cycle is changing rapidly due to accelerating acidification, stratification, warming and deoxygenation. These mechanisms of global change will cumulatively affect the aqueous chemistry, sources and sinks, recycling, particle dynamics and bioavailability of iron. Biological iron demand will vary as acclimation to environmental change modifies cellular requirements for photosynthesis and nitrogen acquisition and as adaptive evolution or community shifts occur. Warming, acidification and nutrient co-limitation interactions with iron biogeochemistry will all strongly influence phytoplankton dynamics. Predicting the shape of the future iron cycle will require understanding the responses of each component of the unique biogeochemistry of this trace element to many concurrent and interacting environmental changes."
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #65 on: December 08, 2016, 08:47:26 PM »
The linked article entitled "Our most detailed view of Earth across space and time", provides links to Google new tool to watch climate change impacts on the Earth in timelapse mode. You can calibrate your own understanding of how much change is already occurring:

https://blog.google/products/earth/our-most-detailed-view-earth-across-space-and-time/


Extract: "To view the new Timelapse, head over to the Earth Engine website. You can also view the new annual mosaics in Google Earth's historical imagery feature on desktop, or spend a mesmerizing 40 minutes watching this YouTube playlist. Happy exploring!"
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #66 on: December 09, 2016, 11:07:09 PM »
The linked AGU abstract B42A-08 is entitled: "Development of a tropical ecological forecasting strategy for ENSO based on the ACME modeling framework".  For those attending the AGU the associated talk will be on Dec 15 2016 from 11:44 to 11:56am in Moscone West – 2020:

https://agu.confex.com/agu/fm16/meetingapp.cgi/Paper/198322

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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #67 on: December 14, 2016, 04:50:18 PM »
The linked article is entitled: "NASA releases new eye-popping view of carbon dioxide".  This research combines both satellite data and an advanced Earth system model to better understand the movement of carbon dioxide through the atmosphere; which, will help to better understand various sinks and sources for carbon dioxide.

http://phys.org/news/2016-12-nasa-eye-popping-view-carbon-dioxide.html

Extract: "A new NASA supercomputer project builds on the agency's satellite measurements of carbon dioxide and combines them with a sophisticated Earth system model to provide one of the most realistic views yet of how this critical greenhouse gas moves through the atmosphere.

Scientists know that nearly half of all human-caused emissions are absorbed by the land and ocean. The current understanding is that about 50 percent of emissions remain in the atmosphere, about 25 percent are absorbed by vegetation on the land, and about 25 percent are absorbed by the ocean. However, those seemingly simple numbers leave scientists with critical and complex questions: Which ecosystems, especially on land, are absorbing what amounts of carbon dioxide? Perhaps most significantly, as emissions keep rising, will the land and the ocean continue this rate of absorption, or reach a point of saturation?
The new dataset is a step toward answering those questions, explained Lesley Ott, a carbon cycle scientist at NASA Goddard and a member of the OCO-2 science team. Scientists need to understand the processes driving the "carbon flux"—the exchange of carbon dioxide between the atmosphere, land and ocean, Ott said."

See also:

https://www.carbonbrief.org/nasa-produces-first-3d-animation-global-carbon-emissions

&
http://oco.jpl.nasa.gov/
« Last Edit: December 14, 2016, 04:57:33 PM by AbruptSLR »
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #68 on: December 14, 2016, 05:14:12 PM »
Aerosols can have a large impact on the rate of Arctic Amplification via their impact on clouds, and the linked reference discusses the use of both satellite data and computer models to reduce the uncertainties associated with this important feedback mechanism:

Zamora, L. M., Kahn, R. A., Eckhardt, S., McComiskey, A., Sawamura, P., Moore, R., and Stohl, A.: Arctic aerosol net indirect effects on thin, mid-altitude, liquid-bearing clouds, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2016-1037, in review, 2016.

http://www.atmos-chem-phys-discuss.net/acp-2016-1037/

Abstract. Aerosol indirect effects have uncertain, but potentially large, impacts on the Arctic energy budget. Here, we have reduced uncertainty in current-day Arctic net aerosol indirect effects on the surface by better constraining various physical and microphysical characteristics of optically thin, liquid-containing clouds in clean, average and aerosol-impacted conditions using a combination of CALIPSO and CloudSat data and model output. This work provides a foundation for how future observational studies can evaluate previous model estimates of the aerosol indirect effect. Clouds over sea ice and open ocean show large differences in surface and meteorological forcing, including a near doubling of multi-layer cloud presence over the open ocean compared to sea ice. The optically thin cloud subset is susceptible to aerosols, and over sea ice we estimate a regional scale maximum net indirect effect on these clouds during polar night equivalent to ~ 0.6–0.8 W m−2 at the surface. Aerosol presence is related to reduced precipitation, cloud thickness, and radar reflectivity, and may be associated with an increased likelihood of cloud presence in the liquid phase. The observations are consistent with a thermodynamic indirect effect hypothesis and are inconsistent with a glaciation indirect effect.
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #69 on: December 14, 2016, 10:14:51 PM »
The linked article is entitled: "Predicting unpredictability: Information theory offers new way to read ice cores", and it addresses the reference entitled: "A First Step Toward Quantifying the Climate's Information Production Over the Last 68,000 Years," appeared in Advances in Intelligent Data Analysis XV, the proceedings of the 15th International Symposium on Intelligent Data Analysis, Stockholm, Sweden, October 13-15, 2016.

This work demonstrates the value of applying information theory, and permutation entropy, to climate change data analysis.

https://www.sciencedaily.com/releases/2016/12/161206125325.htm

Extract: "At two miles long and five inches in diameter, the West Antarctic Ice Sheet Divide (WAIS) ice core is a tangible record of the last 68,000 years of our planet's climate.
Completed in 2011, the core is packed with information, but it's also packed with noise and error, making the climate story hard to read. Figuring out whether blips in the data are evidence of humans spewing carbon into the atmosphere, odd North Atlantic weather events, or equipment malfunctions often challenges the scientists trying to read the ice cylinder's story.

Drawing from information theory, a research team led by Santa Fe Institute Omidyar Fellow Joshua Garland has proposed new, more sophisticated techniques that promise to make ongoing interpretation of the WAIS core easier and extract new kinds of data that could change the way we think about Earth's climate.

"There is information in these records that we didn't know existed until now, and it has opened doors where we didn't even know there was a door before," says James W.C. White, director of the Institute of Arctic and Alpine Research and a collaborator on the project.

In information theory, entropy is a measure of the unpredictability of information content. Permutation entropy essentially is a way to quantify the predictability of a future event.

Imagine an isolated climate system, void of game changers like supervolcanos or humans. Everything you'd need to predict the future climate would be contained in Earth's climate history. When game changers arrive, they inject new information that couldn't have been predicted from the climate's past patterns -- and that should manifest as an increase in permutation entropy (i.e., more unpredictability).

In fact, there are early signs in the WAIS record of an entropy increase roughly 10,000 years ago, at the beginning of the Holocene, suggesting human impacts on our climate began well before the Industrial Revolution.

Confirmation of that finding is pending. Meanwhile, Garland and team have already made two other surprising discoveries using their technique. The first concerns Dansgaard-Oeschger events, during which Greenland rapidly warms during glacial periods, triggering ripple effects throughout the world.

Geoscientists hypothesize that these events begin with some kind of external shock. But when Garland and team looked at another core, the North Greenland Ice Core, there didn't appear to be an increase in permutation entropy -- in other words, no external shock -- suggesting the events are likely part of the climate's standard operating procedure."
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #70 on: December 14, 2016, 11:21:31 PM »
The linked reference uses information theory and Fisher information to demonstrate that beginning in 1978 mankind's actions began to significantly alter the behavior of Earth Systems as measured using changes in global-mean temperature from 1880 to 2015 (see the attached image).  To me this implies that ESMs calibrated to data gathered prior to 1978 likely ESLD w.r.t. probable climate change consequences.

Nasir Ahmad, Sybil Derrible, Tarsha Eason, Heriberto Cabezas (9 November 2016), "Using Fisher information to track stability in multivariate systems", Royal Society Open Science, DOI: 10.1098/rsos.160582

http://rsos.royalsocietypublishing.org/content/3/11/160582

Abstract: "With the current proliferation of data, the proficient use of statistical and mining techniques offer substantial benefits to capture useful information from any dataset. As numerous approaches make use of information theory concepts, here, we discuss how Fisher information (FI) can be applied to sustainability science problems and used in data mining applications by analysing patterns in data. FI was developed as a measure of information content in data, and it has been adapted to assess order in complex system behaviour. The main advantage of the approach is the ability to collapse multiple variables into an index that can be used to assess stability and track overall trends in a system, including its regimes and regime shifts. Here, we provide a brief overview of FI theory, followed by a simple step-by-step numerical example on how to compute FI. Furthermore, we introduce an open source Python library that can be freely downloaded from GitHub and we use it in a simple case study to evaluate the evolution of FI for the global-mean temperature from 1880 to 2015. Results indicate significant declines in FI starting in 1978, suggesting a possible regime shift."

Extract: "Naturally, these analyses are not sufficient to fully capture how the global climate is performing, however, the change in the FI trajectory during the late 1970s corresponds with the period in which our global societal demand (ecological footprint) also began to surpass the global biocapacity to supply that demand. Moreover, the latter part of the twentieth century is also noted for major anthropogenic global environmental impacts and studies identify this period as the base of a new Anthropocene epoch."
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #71 on: December 15, 2016, 03:47:22 AM »
The linked reference presents observational information of tunda carbon flux that can be used to better calibrate climate models:

Naama Raz Yaseef et al, Large CO2and CH4 Emissions from Polygonal Tundra During Spring Thaw in Northern Alaska, Geophysical Research Letters (2016). DOI: 10.1002/2016GL071220 


http://onlinelibrary.wiley.com/doi/10.1002/2016GL071220/abstract;jsessionid=CD1145754B761DD1CA20BF64A30DDF51.f04t03?systemMessage=Wiley+Online+Library+will+be+unavailable+on+Saturday+17th+December+2016+at+09%3A00+GMT%2F+04%3A00+EST%2F+17%3A00+SGT+for+4hrs+due+to+essential+maintenance.Apologies+for+the+inconvenience


Abstract: “The few pre-thaw observations of tundra carbon fluxes suggest that there may be large spring releases, but little is known about the scale and underlying mechanisms of this phenomenon. To address these questions, we combined ecosystem eddy flux measurements from two towers near Barrow, Alaska with mechanistic soil-core thawing experiment. During a 2-week period prior to snowmelt in 2014, large fluxes were measured, reducing net summer uptake of CO2 by 46% and adding 6% to cumulative CH4 emissions. Emission pulses were linked to unique rain-on-snow events enhancing soil cracking. Controlled laboratory experiment revealed that as surface ice thaws, an immediate, large pulse of trapped gases is emitted. These results suggests that the Arctic CO2 and CH4 spring pulse is a delayed release of biogenic gas production from the previous fall, and that the pulse can be large enough to offset a significant fraction of the moderate Arctic tundra carbon sink. “


See also the related linked article entitled: “Scientists measure pulse of CO2 emissions during spring thaw in the Arctic”.


http://phys.org/news/2016-12-scientists-pulse-co2-emissions-arctic.html


Extract: “When the frozen Arctic tundra starts to thaw around June of each year, the snow melting and the ground softening, the soil may release a large pulse of greenhouse gases, namely, carbon dioxide and methane. Little has been known about such releases.

Now scientists at the U.S. Department of Energy's (DOE) Lawrence Berkeley National Laboratory, in collaboration with a team of other scientists taking measurements both in the field and in the lab, have quantified the scale of such releases and explained the underlying mechanisms for the phenomenon. Their study was based on a spring pulse in northern Alaska that they documented in 2014 that included CO2 emissions equivalent to 46 percent of the net CO2 that is absorbed in the summer months and methane emissions that added 6 percent to summer fluxes. What's more, recent climate trends may make such emissions more frequent, the scientists conclude."
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #72 on: December 15, 2016, 06:19:12 PM »
The linked reference provides input on vertical land motion from land ice wastage; which is needed to help improve model projections:

Riva, R. E. M., Frederikse, T., King, M. A., Marzeion, B., and van den Broeke, M.: Brief Communication: The global signature of post-1900 land ice wastage on vertical land motion, The Cryosphere Discuss., doi:10.5194/tc-2016-274, in review, 2016.

http://www.the-cryosphere-discuss.net/tc-2016-274/

Abstract. Melting glaciers, ice caps and ice sheets have made an important contribution to sea-level rise through the last century. Self-attraction and loading effects driven by shrinking ice masses cause a spatially-varying redistribution of ocean waters that affects reconstructions of past sea level from sparse observations. We model the solid earth response to ice mass changes and find significant vertical deformation signals over large continental areas. We show how deformation rates have been strongly varying through the last century, which implies that they should be properly modelled before interpreting and extrapolating recent observations of vertical land motion and sea level change.
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #73 on: December 15, 2016, 08:34:20 PM »
The linked article is entitled: "Earth's magnetic fields could track ocean heat, study proposes".  This new satellite based technique provides another method to calibrate Earth System Models to better account for ocean heat uptake and content.

https://www.sciencedaily.com/releases/2016/12/161213093114.htm

Summary: "As Earth warms, much of the extra heat is stored in the planet's ocean. Monitoring the magnitude of that heat content is difficult, but a surprising feature of the tides could help. Scientists are developing a new way to use satellite observations of magnetic fields to measure heat stored in the ocean."

Extract: "Despite the significance of ocean heat to Earth's climate, it remains a variable that has substantial uncertainty when scientists measure it globally. Current measurements are made mainly by Argo floats, but these do not provide complete coverage in time or space. If it is successful, this new method could be the first to provide global ocean heat measurements, integrated over all depths, using satellite observations.

Tyler's method depends on several geophysical features of the ocean. Seawater is a good electrical conductor, so as saltwater sloshes around the ocean basins it causes slight fluctuations in Earth's magnetic field lines. The ocean flow attempts to drag the field lines around, Tyler said. The resulting magnetic fluctuations are relatively small, but have been detected from an increasing number of events including swell, eddies, tsunamis and tides.

"The recent launch of the European Space Agency's Swarm satellites, and their magnetic survey, is providing unprecedented observational data of the magnetic fluctuations," Tyler said. "With this comes new opportunities.""
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #74 on: December 16, 2016, 12:49:12 AM »
More on the Robert Tyler,  Boyler, Catherine Walker, Stephanie Schollaert Uz via FM16 press conference and youtube

https://www.youtube.com/watch?v=Tt8htQYJIlY

Understanding how much heat is stored in the ocean is a key part of deciphering and predicting climate change. Measuring that heat, however, is difficult and currently relies on a scattered network of buoys and sensors. A novel method presented at this briefing aims to quantify ocean heat content by satellite, using tricks of Earth’s magnetic field. The impacts of ocean heat, including new results presented at AGU, range from melting ice to affecting the base of the food web.

I am watching now. 

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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #75 on: December 16, 2016, 03:38:19 PM »
The linked reference provides guidance on how to better model ground heat flux (G) in climate models:

A. J. Purdy, J. B. Fisher, M. L. Goulden & J. S. Famiglietti (28 November 2016), "Ground heat flux: an analytical review of 6 models evaluated at 88 sites and globally", JGR Biogeosciences, DOI: 10.1002/2016JG003591


http://onlinelibrary.wiley.com/doi/10.1002/2016JG003591/full


Abstract: "Uncertainty in ground heat flux (G) means that evaluation of the other terms in the surface energy balance (e.g., latent and sensible heat flux, LE and H) remains problematic. Algorithms that calculate LE and H require available energy, the difference between net radiation, RNET, and G. There are a wide range of approaches to model G for large-scale applications, with a subsequent wide range of estimates and accuracies. We provide the largest review of these methods to date (N = 6), evaluating modeled G against measured G from 88 FLUXNET sites. The instantaneous mid-day variability in G is best captured by models forced with net radiation, while models forced by temperature show the least error at both instantaneous and daily timescales. We produce global decadal datasets of G to illustrate regional and seasonal sensitivities, as well as uncertainty. Global model mean mid-morning instantaneous G is highest during September, October, and November at 63.42 (+/- 16.84) Wm-2 while over December, January, and February G is lowest at 53.86 (+/- 18.09) Wm-2 but shows greater inter-model uncertainty. Results from this work have the potential to improve ET estimates and guide appropriate G model selection and development for various land uses."


See also the article entitled: "Earth’s ground heat flux should not be overlooked", Eos, 97, doi:10.1029/2016EO064241

https://eos.org/research-spotlights/earths-ground-heat-flux-should-not-be-overlooked?utm_source=eos&utm_medium=email&utm_campaign=EosBuzz121516

Extract: "The new study highlights the importance of G and points to areas ripe for model refinement. Results from the study have implications for other models that rely on G, such as those used to calculate evapotranspiration."
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #76 on: December 20, 2016, 10:56:53 PM »
The linked article is entitled: "These two sentences could hint at the next threat to climate science under Trump".  If this means that the Trump administration will only allow climate model projections that have been calibrated using data dominated by observations taken during the faux hiatus, then much of this thread and ACME projections could be rendered irrelevant.


https://www.washingtonpost.com/news/energy-environment/wp/2016/12/20/these-two-sentences-could-hint-at-the-next-threat-to-climate-science-under-trump/?utm_term=.3613aa0d809f

Extract: "But if a passage from the 2016 Republican Party platform is to be taken seriously, this could be just the beginning when it comes to challenging federal scientists in particular, and questioning how they do their work. The passage reads:

Information concerning a changing climate, especially projections into the long-range future, must be based on dispassionate analysis of hard data. We will enforce that standard throughout the executive branch, among civil servants and presidential appointees alike.


Again, it’s important to underscore that we don’t know what the incoming Trump administration will do. But there are also hints that it is worried about climate change models. In addition to asking who attended meetings relating to climate change in the now disavowed memo, the Energy Department transition team also posed a question about so-called “integrated assessment models” used to study the nation’s energy and climate future. It asked about some of the assumptions programmed into such models, including the agency’s view of the correct “equilibrium climate sensitivity.”"
« Last Edit: December 20, 2016, 11:24:23 PM by AbruptSLR »
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #77 on: December 26, 2016, 12:08:57 AM »
Findings from the linked reference can be used to help calibrate ESMs w.r.t. the marine carbon cycle:

Buchanan, P. J., Matear, R. J., Lenton, A., Phipps, S. J., Chase, Z., and Etheridge, D. M.: The simulated climate of the Last Glacial Maximum and insights into the global marine carbon cycle, Clim. Past, 12, 2271-2295, doi:10.5194/cp-12-2271-2016, 2016.

http://www.clim-past.net/12/2271/2016/

Abstract. The ocean's ability to store large quantities of carbon, combined with the millennial longevity over which this reservoir is overturned, has implicated the ocean as a key driver of glacial–interglacial climates. However, the combination of processes that cause an accumulation of carbon within the ocean during glacial periods is still under debate. Here we present simulations of the Last Glacial Maximum (LGM) using the CSIRO Mk3L-COAL (Carbon–Ocean–Atmosphere–Land) earth system model to test the contribution of physical and biogeochemical processes to ocean carbon storage. For the LGM simulation, we find a significant global cooling of the surface ocean (3.2 °C) and the expansion of both minimum and maximum sea ice cover broadly consistent with proxy reconstructions. The glacial ocean stores an additional 267 Pg C in the deep ocean relative to the pre-industrial (PI) simulation due to stronger Antarctic Bottom Water formation. However, 889 Pg C is lost from the upper ocean via equilibration with a lower atmospheric CO2 concentration and a global decrease in export production, causing a net loss of carbon relative to the PI ocean. The LGM deep ocean also experiences an oxygenation ( >  100 mmol O2 m−3) and deepening of the calcite saturation horizon (exceeds the ocean bottom) at odds with proxy reconstructions. With modifications to key biogeochemical processes, which include an increased export of organic matter due to a simulated release from iron limitation, a deepening of remineralisation and decreased inorganic carbon export driven by cooler temperatures, we find that the carbon content of the glacial ocean can be sufficiently increased (317 Pg C) to explain the reduction in atmospheric and terrestrial carbon at the LGM (194 ± 2 and 330 ± 400 Pg C, respectively). Assuming an LGM–PI difference of 95 ppm pCO2, we find that 55 ppm can be attributed to the biological pump, 28 ppm to circulation changes and the remaining 12 ppm to solubility. The biogeochemical modifications also improve model–proxy agreement in export production, carbonate chemistry and dissolved oxygen fields. Thus, we find strong evidence that variations in the oceanic biological pump exert a primary control on the climate.
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #78 on: December 26, 2016, 12:27:22 AM »
The linked open-access reference uses an Earth System Model, ESM, with intermediate complexity to examine the response of ice sheets during the Last Interglacial period.  The model estimates a 1.4m SLR contribution from Greenland and a 4.4 m SLR contribution from Antarctica; however, in my opinion the model is not sufficiently complex to address the risks of abrupt SLR.  Such research provides a lower bound with which to compare the findings of state of the art ESMs when they are released:


Goelzer, H., Huybrechts, P., Loutre, M.-F., and Fichefet, T.: Last Interglacial climate and sea-level evolution from a coupled ice sheet–climate model, Clim. Past, 12, 2195-2213, doi:10.5194/cp-12-2195-2016, 2016.


http://www.clim-past.net/12/2195/2016/
&
http://www.clim-past.net/12/2195/2016/cp-12-2195-2016.pdf


Abstract. As the most recent warm period in Earth's history with a sea-level stand higher than present, the Last Interglacial (LIG,  ∼  130 to 115 kyr BP) is often considered a prime example to study the impact of a warmer climate on the two polar ice sheets remaining today. Here we simulate the Last Interglacial climate, ice sheet, and sea-level evolution with the Earth system model of intermediate complexity LOVECLIM v.1.3, which includes dynamic and fully coupled components representing the atmosphere, the ocean and sea ice, the terrestrial biosphere, and the Greenland and Antarctic ice sheets. In this setup, sea-level evolution and climate–ice sheet interactions are modelled in a consistent framework.

Surface mass balance change governed by changes in surface meltwater runoff is the dominant forcing for the Greenland ice sheet, which shows a peak sea-level contribution of 1.4 m at 123 kyr BP in the reference experiment. Our results indicate that ice sheet–climate feedbacks play an important role to amplify climate and sea-level changes in the Northern Hemisphere. The sensitivity of the Greenland ice sheet to surface temperature changes considerably increases when interactive albedo changes are considered. Southern Hemisphere polar and sub-polar ocean warming is limited throughout the Last Interglacial, and surface and sub-shelf melting exerts only a minor control on the Antarctic sea-level contribution with a peak of 4.4 m at 125 kyr BP. Retreat of the Antarctic ice sheet at the onset of the LIG is mainly forced by rising sea level and to a lesser extent by reduced ice shelf viscosity as the surface temperature increases. Global sea level shows a peak of 5.3 m at 124.5 kyr BP, which includes a minor contribution of 0.35 m from oceanic thermal expansion. Neither the individual contributions nor the total modelled sea-level stand show fast multi-millennial timescale variations as indicated by some reconstructions.
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #79 on: January 18, 2017, 10:56:09 PM »
The linked article discusses the “Arctic Amplification: Climate Relevant Atmospheric and Surface Processes, and Feedback Mechanisms”, (AC)3, program to better understand Arctic Amplification:

Macke, A., et al. (17 January 2017), "Understanding causes and effects of rapid warming in the Arctic", Eos, 98, doi:10.1029/2017EO064803.

https://eos.org/project-updates/understanding-causes-and-effects-of-rapid-warming-in-the-arctic

Extract: "A new German research consortium is investigating why near-surface air temperatures in the Artic are rising more quickly than in the rest of the world.

Our current understanding of the rapid changes in the Arctic climate implies that atmospheric processes likely dominate the short-term warming mechanisms involved. Thus, research in (AC)³ has an atmospheric focus during Phase I, which was approved to obtain funding by DFG from January 2016 to December 2019. In Phases II and III (planned for January 2020 to December 2027) the researchers of TR 172 plan to investigate the interactions between oceanic and atmospheric components more thoroughly."
« Last Edit: January 18, 2017, 11:05:54 PM by AbruptSLR »
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #80 on: February 07, 2017, 11:41:35 PM »
The linked reference (with an open access pdf) can be used to help calibrate ESMs w.r.t. possible carbon input from the Arctic Tundra:

Tran, A. P., Dafflon, B., and Hubbard, S. S.: Coupled Land Surface-Subsurface Hydrogeophysical Inverse Modeling to Estimate Soil Organic Content and explore associated Hydrological and Thermal Dynamics in an Arctic Tundra, The Cryosphere Discuss., doi:10.5194/tc-2017-1, in review, 2017.

http://www.the-cryosphere-discuss.net/tc-2017-1/

Abstract. Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface–subsurface hydrological-thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon-climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological-thermal processes associated with annual freeze-thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets, including soil water liquid, temperature and electrical resistivity data (ERT), to estimate the vertical distribution of OC content. We subsequently explore the control of OC on hydrological-thermal behavior. We employ the Community Land Model to simulate nonisothermal surface-subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes and ice/liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate posterior distributions of desired model parameters. For hydrological-thermal to geophysical variable transformation, the simulated subsurface temperature, liquid and ice water content are explicitly linked to the soil apparent resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the estimation of OC and other parameters. We also quantified the propagation of uncertainty from the estimated parameters to prediction of hydrological-thermal responses. We find that compared to inversion of single dataset (either temperature or liquid or apparent resistivity), joint inversion of these datasets significantly reduces parameter uncertainty. We find that the joint inversion approach is able to estimate OC and sand content within the shallow active layer (0.3 m) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (0.6 m), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, the uncertainty of OC estimate at this depth remarkably decreases. Our study documents the value of the new surface-subsurface, deterministic-stochastic inversion approach, as well as the benefit of including multiple types of data to estimate OC and associated hydrological-thermal dynamics.
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #81 on: March 13, 2017, 02:29:57 AM »
I hope that the linked open access reference/research helps to calibrate CMIP6 in a timely fashion; as currently the results of CMIP5 are not adequately motivating policy makers to take effective action against climate change:

Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R., Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S. A., Marchand, R., Medeiros, B., Siebesma, A. P., Skinner, C. B., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M.: The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6, Geosci. Model Dev., 10, 359-384, doi:10.5194/gmd-10-359-2017, 2017.

http://www.geosci-model-dev.net/10/359/2017/

Abstract: “The primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud–climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions How does the Earth system respond to forcing? and What are the origins and consequences of systematic model biases? and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity.

A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO2 forcing and sea surface warming?

CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions.

1. How well do clouds and other relevant variables simulated by models agree with observations?
2. What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models?
3. Which models have the most credible representations of processes relevant to the simulation of clouds?
4. How do clouds and their changes interact with other elements of the climate system”
“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: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #82 on: March 19, 2017, 05:24:11 PM »
The linked reference indicates that CMIP5 does not adequately characterize low frequency internal climate variability (ICV); which plays an important role in modulating GMSTA.  This indicates that CMIP6 (and/or ACME) should make efforts to better model ICV.

Anson H. Cheung, Michael E. Mann, Byron A. Steinman, Leela M. Frankcombe, Matthew H. England & Sonya K. Miller (2017), "Comparison of Low Frequency Internal Climate Variability in CMIP5 Models and Observations", Journal of Climate, DOI: http://dx.doi.org/10.1175/JCLI-D-16-0712.1

http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-16-0712.1

Abstract: "Low frequency internal climate variability (ICV) plays an important role in modulating global surface temperature, regional climate, and climate extremes. However, it has not been completely characterized in the instrumental record and in the Coupled Model Intercomparison Project phase 5 (CMIP5) model ensemble. In this study, the surface temperature ICV of the North Pacific (NP), North Atlantic (NA), and Northern Hemisphere (NH) in the instrumental record and historical CMIP5 all-forcing simulations is isolated using a semi-empirical method wherein the CMIP5 ensemble mean is applied as the external forcing signal and removed from each time series. Comparison of ICV signals derived from this semi-empirical method as well as from analysis of ICV in CMIP5 pre-industrial control runs reveals disagreement in the spatial pattern and amplitude between models and instrumental data on multidecadal timescales (>20 years). Analysis of the amplitude of total variability and the ICV in the models and instrumental data indicates that the models underestimate ICV amplitude on low frequency timescales (>20 year in the NA, >40 year in the NP), while agreement is found in the NH variability. A multiple linear regression analysis of ICV in the instrumental record shows that variability in the NP drives decadal to interdecadal variability in the NH; whereas the NA drives multidecadal variability in the NH. Analysis of the CMIP5 historical simulations does not reveal such a relationship, indicating model limitations in simulating ICV. These findings demonstrate the need to better characterize low frequency ICV, which may help improve attribution and decadal prediction."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #83 on: April 11, 2017, 06:50:01 PM »
The linked open access reference provides paleo-guidance on how to better calibrate Earth System Models to account for possible future significant methane emissions from sources like thermokarst lakes due to permafrost degradation and also methane hydrate decomposition:

Gary Shaffer, e. al. (2017), "Implementation of methane cycling for deep time, global warming simulations with the DCESS Earth System Model (Version 1.2)", Geosci. Model Dev. Discuss., doi:10.5194/gmd-2017-23, 2017

http://www.geosci-model-dev-discuss.net/gmd-2017-23/gmd-2017-23.pdf

Abstract. Geological records reveal a number of ancient, large and rapid negative excursions of carbon-13 isotope. Such excursions can only be explained by massive injections of depleted carbon to the Earth System over a short duration. These injections may have forced strong global warming events, sometimes accompanied by mass extinctions, for example the Triassic-Jurassic and End-Permian extinctions, 201 and 252 million years ago. In many cases evidence points to methane as the dominant form of injected carbon, whether as thermogenic methane, formed by magma intrusions through overlying carbon-rich sediment, or from warming-induced dissociation of methane hydrate, a solid compound of methane and water found in ocean sediments. As a consequence of the ubiquity and importance of methane in major Earth events, Earth System models should include a comprehensive treatment of methane cycling but such a treatment has often been lacking. Here we implement methane cycling in the Danish Center for Earth System Science (DCESS) model, a simplified but well-tested
Earth System Model of Intermediate Complexity. We use a generic methane input function that allows variation of input type, size, time scale and ocean-atmosphere partition. To be able to treat such massive inputs more correctly, we extend the model to deal with ocean suboxic/anoxic conditions and with radiative forcing and methane lifetimes appropriate for high atmospheric methane concentrations. With this new model version, we carried out an extensive set of simulations for methane inputs of various sizes, time scales and ocean-atmosphere partitions to probe model behaviour. We find that larger methane inputs over shorter time scales with more methane dissolving in the ocean lead to ever-increasing ocean anoxia with consequences for ocean life and global carbon cycling. Greater methane input directly to the atmosphere leads to more warming and, for example, greater carbon dioxide release from land soils. Analysis of synthetic sediment cores from the simulations provides guidelines for the interpretation of real sediment cores spanning the warming events. With this improved DCESS model version and paleo-reconstructions, we are now better armed to gauge the amounts, types, time scales and locations of methane injections driving specific, observed deep time, global warming events.
“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: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #84 on: April 17, 2017, 07:20:42 PM »
The linked article entitled: "Humans on the verge of causing Earth’s fastest climate change in 50m years" (& the following linked associated paper) compare our current radiative forcings with paleo-conditions.  Such a comparison is useful for calibrating ESMs.


https://www.theguardian.com/environment/climate-consensus-97-per-cent/2017/apr/17/humans-on-the-verge-of-causing-earths-fastest-climate-change-in-50m-years

Extract: "A new study published in Nature Communications looks at changes in solar activity and carbon dioxide levels over the past 420 million years. The authors found that on our current path, by mid-century humans will be causing the fastest climate change in approximately 50 million years, and if we burn all available fossil fuels, we’ll cause the fastest change in the entire 420 million year record."

See also the referenced paper at:

https://www.nature.com/articles/ncomms14845
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #85 on: April 20, 2017, 12:36:01 PM »
The Last Glacial Termination, LGT, occurred from 18,000 to 11,650 kya, and the following reference, reconstructs the dynamic response of the Antarctic ice sheets to warming in this period in order to better evaluate Hansen's ice-climate feedback mechanisms.  The abstract concludes: "Given the anti-phase relationship between inter-hemispheric climate trends across the LGT our findings demonstrate that Southern Ocean-AIS feedbacks were controlled by global atmospheric teleconnections.  With increasing stratification of the Southern Ocean and intensification of mid-latitude westerly winds today, such teleconnections could amplify AIS mass loss and accelerate global sea-level rise."

Fogwill, et. al. (2017), "Antarctic ice sheet discharge driven by atmosphere-ocean feedbacks at the last Glacial Termination", Scientific Reports 7, Article number 39979, doi:10.1038/srep39979

https://www.nature.com/articles/srep39979


See also:
http://www.geosci-model-dev-discuss.net/gmd-2017-18/gmd-2017-18.pdf
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #86 on: May 02, 2017, 05:59:14 AM »
The linked reference discusses the path forward for accessing how clouds contribute to ECS:

Schneider et. al. (2017), “Climate goals and computing the future of clouds”, Nature

https://www.nature.com/articles/nclimate3190.epdf?author_access_token=8YAxZvRJ9VmeCNszahFtwNRgN0jAjWel9jnR3ZoTv0PNIymS0a6DKg1Lg6qILip-pzv_t_rOsiQWmNmfi3zRplp-SVlceJ8pnrqNVV9GFRVPHpBBntxq3Yi1qWjClH5e

« Last Edit: May 02, 2017, 11:50:41 PM by AbruptSLR »
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #87 on: May 14, 2017, 03:59:34 AM »
The linked reference can be used to help parse attribution of different types of forcing to climate sensitivity:

Keery, J. S., Holden, P. B., and Edwards, N. R.: Sensitivity of the Eocene Climate to CO2 and Orbital Variability, Clim. Past Discuss., doi:10.5194/cp-2017-60, in review, 2017.

http://www.clim-past-discuss.net/cp-2017-60/

Abstract. The early Eocene, from about 56 Ma, with high atmospheric CO2 levels, offers an analogue for the response of the Earth's climate system to anthropogenic fossil fuel burning. In this study we present an ensemble of 50 Earth system model runs with an early Eocene palaeogeography and variation in the forcing values of atmospheric CO2 and the Earth's orbital parameters. Two-dimensional model output fields are reduced to scalar values through simple summarising algorithms and by singular value decomposition. Relationships between these scalar results and the forcing parameters are identified by linear modelling, providing estimates of the relative magnitudes of the effects of atmospheric CO2 and each of the orbital parameters on important climatic features, including tropical-polar temperature difference, ocean-land temperature contrast, and Asian, African and S. American monsoon rains. Our results indicate that although CO2 exerts a dominant control on most of the climatic features examined in this study, the orbital parameters also strongly influence important components of the ocean-atmosphere system in a greenhouse Earth. In our ensemble, atmospheric CO2 spans the range 280–3000 ppm, and this variation accounts for over 95 % of the effects on mean air temperature, southern winter high-latitude ocean-land temperature contrast and northern winter tropical-polar temperature difference. However, the variation of precession accounts for over 75 % of the influence of the forcing parameters on the Asian and African monsoon rainfall, and obliquity variation accounts for over 65 % of the effects on winter ocean-land temperature contrast in high northern latitudes. Our method gives a quantitative ranking of the influence of each of the forcing parameters on key climatic model outputs, with additional spatial information from our singular value decomposition approach providing insights into likely physical mechanisms. The results demonstrate the importance of orbital variation as an agent of change in climates of the past.
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
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Re: Climate Model Test Beds: Calibrating Nonlinear ESMs focused on ACME
« Reply #88 on: May 21, 2017, 05:24:37 PM »
The linked reference updates/corrects a 2012 report that prematurely indicated that global warming from 2000 to 2010 was leading to a decrease in effective cloud height (which would indicate a negative feedback mechanism).  The 2017 report corrects an earlier err associated with glint that reduced the magnitude of the apparent negative feedback from 2000 to 2010, and then reported that the data from 2010 to 2015 indicated that the cloud height was increasing with global warming indicating a positive feedback mechanism.  The report ESLD to conclude that it is too early to say whether changes in cloud height results in a net positive or negative feedback.  However, the researchers fail to adjust for changes in IPO over the 2000 to 2015 period, & I believe that if they had they would likely find a net positive feedback with global warming.

Roger Davies et al. (2017), "Cloud heights measured by MISR from 2000 to 2015", Journal of Geophysical Research: Atmospheres, DOI: 10.1002/2017JD026456

http://onlinelibrary.wiley.com/doi/10.1002/2017JD026456/abstract

Abstract: "Davies and Molloy (2012) reported a decrease in the global effective cloud height over the first 10 years of Multiangle Imaging Spectroradiometer (MISR) measurements on the Terra satellite. We have reexamined their time series for possible artefacts that might especially affect the initial portion of the record when the heights appeared anomalously high. While variations in sampling were shown to be inconsequential, an artefact due to the change in equator crossing time that affected the first 2 years was discovered, and this has now been corrected. That correction, together with the extension of the time series by five more years, yields no significant overall trend in global heights during the first 15 years of Terra operation. The time series is dominated by large interannual fluctuations associated with La Niña events that mask any overall trend on a global scale. On a regional basis, the cloud heights showed significant interannual variations of much larger amplitude, sometimes with fairly direct cancellation between regions. There were unexplained differences between the two hemispheres in the timing of height anomalies. These differences persisted over a large range of extratropical latitudes, suggestive of teleconnections. Within the tropics, there were very strong changes associated with the Central Pacific and Indonesian Maritime Continent regions that oscillated out of phase with each other, with interannual amplitudes that exceeded 1 km."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson