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[ADS NIPR VISHOP (JAXA)] Arctic Sea Ice Extent.
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P.S. From June 4th to June 13th, the ASI on 2012 dropped 1,130K km2, an average of -126K km2 on 9 days.
Yesterdays article by Ramstorf.
http://www.realclimate.org/index.php/archives/2018/05/if-you-doubt-that-the-amoc-has-weakened-read-this/
Espen....
I've done this before but would like to again thank you for your clear, simple and timely tracking of this measure.
With over 1 million views on this thread, I believe I am speaking for others. They're just too shy to say anything.
In this sense, climate sensitivity can be seen as a distribution that is a local
property of the climate attractor.
a) i know that only land based ice would increas sea-level
No, but we have to stop being stupid. And that is really hard to do.
If the forecasts come true, I think everyone here will be grateful that this is late August and not solstice.... It would have been a complete massacre!
That's the same wording Shakova used about the Arctic findings.
The linked reference indicates that for high rates of GHG emissions (such as we have now) "… there is little chance of a hiatus decade occurring beyond 2030, even in the event of a large volcanic eruption. We further demonstrate that most non-volcanic hiatuses across CMIP5 models are associated with enhanced cooling in the equatorial Pacific linked to the transition to a negative IPO phase." As I believe that we are now entering a positive phase of the IPO, this research implies that we may never see another negative phase of the IPO (in the foreseeable future):
Nicola Maher, Alexander Sen Gupta and Matthew H. England, (2014), "Drivers of decadal hiatus periods in the 20th and 21st Centuries", Geophysical Research Letters, DOI: 10.1002/2014GL060527
http://onlinelibrary.wiley.com/doi/10.1002/2014GL060527/abstract
Abstract: "The latest generation of climate model simulations are used to investigate the occurrence of hiatus periods in global surface air temperature in the past and under two future warming scenarios. Hiatus periods are identified in three categories, (i) those due to volcanic eruptions, (ii) those associated with negative phases of the Interdecadal Pacific Oscillation (IPO) and (iii) those affected by anthropogenically released aerosols in the mid 20th Century. The likelihood of future hiatus periods is found to be sensitive to the rate of change of anthropogenic forcing. Under high rates of greenhouse gas emissions there is little chance of a hiatus decade occurring beyond 2030, even in the event of a large volcanic eruption. We further demonstrate that most non-volcanic hiatuses across CMIP5 models are associated with enhanced cooling in the equatorial Pacific linked to the transition to a negative IPO phase."
I'm not convinced that weather models suffer only from a lack of knowledge of initial conditions and certainly not because we are approaching physical limits of predictability (chaos).
Has weather forecasting not improved over recent years and is it likely to continue to improve? If so, is that just because there are better measurements, or are the models themselves also improving? If models can be improved, then they must not yet be perfect. So then they must suffer from flaws, such as oversimplifications, false assumptions, overfitting, and just a basic lack of knowledge of all the important factors (not to mention such things as the limits of computational capacity and programming errors...).
Furthermore, does the skill of weather forecasting not vary, and do we understand why? Although I am barely a novice let alone an expert, based on comments on this board, it seems that the answer is no. For instance, and most relevant here, it seems that forecasting is particularly difficult over the Arctic and is becoming even more difficult there, at least if this year is any indication. Why might that be?
Pat answers about our flawless understanding of the weather or climate miss the chief point. Obviously the particular difficulties in modelling short vs. long-term climate processes are not identical. Some factors critical to the former may be utterly irrelevant to the latter and vice versa. But undercutting both is a gaping void in our knowledge of much of the complex underlying physics, especially about how changes and feedbacks may affect the system.
I hardly would have thought that the limits of modelling poorly understood systems ought to be controversial on a forum dedicated to Arctic sea ice... Anyways, that's how it seems to me.
Weather models predict the evolution of the atmosphere, given information about its initial state. (In predictability theory this is called predictability of the first kind.) This is primarily an initial value problem, and is mainly dependent on detailed observations. Climate models predict the evolution of the statistical properties of the climate system in response to changes in external forcings over time. (This is called predictability of the second kind.) It is essentially a boundary value problem, and is more dependent on the modeling of the factors influencing the system.
Others have answered, but a simple way to think about it: put a pot of water on a stove. It would be very hard to predict the specifics of the convection currents within the water even if you knew everything about the initial conditions and the heat input. But you could pretty easily calculate about how long it would take to boil.
The low concentration gap from Greenland to the Pole in the central ice pack that I noticed yesterday is clearly visible.Interesting, it looks like there is going to be an atypical gap in the ice off the north coast of Greenland for a few days.
