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Chris, Antarctica is quite a bit larger than the Laurentide ever was. And in the paleo-climate record, Antarctica's ice sheet seems to be mostly regulated by CO2 levels instead of insolation. So Hansen has a point that Antarctica's ice sheet may be subject to exponential melt, not inhibited by the millennial delay that used to regulate ice sheet decay, now that CO2 levels are way above where they ever were during the Eemian. Either way, the nice thing is that this is a "discussion" paper, and several comments have already been posted :
Toggle Commented Jul 28, 2015 on Junction June 2015 at Arctic Sea Ice
Also, I appreciate your suggestion to exclude each year from the training set. Let me see how that affects the results, by apart from the obvious expectation that it will increase the standard deviation somewhat, it seems to me that it has no physical meaning to do so, and instead would be only be a statistical exercise.
An epic paper if you ask me.
Toggle Commented Jul 26, 2015 on Junction June 2015 at Arctic Sea Ice
I agree, Blain. What a wonderful read. For the record, here is the paper in full : What I find especially important about this paper is that it addresses several issues that have been points of contingencies in discussions with 'sceptics'. Such as : "There is evidence of ice melt, sea level rise to +5–9 m, and extreme storms in the prior interglacial period that was less than 1 ◦C warmer than today. " Which points out clearly that if we worm our planet a bit more than we already have, that there will be serious concequences. and "Our climate model exposes amplifying feedbacks in the Southern Ocean that slow Antarctic bottom water formation and increase ocean temperature near ice shelf grounding lines, while cooling the surface ocean and increasing sea ice cover and water column stability. " which clearly addresses the issue of why Antarctic ice extent appears to be increasing while Antarctica (and the planet) is warming. and " We focus attention on the Southern Ocean’s role in affecting atmospheric CO2 amount, which in turn is a tight control knob on global climate.The millennial (500–2000 year) time scale of deep ocean ventilation affects the time scale for natural CO2 change, thus the time scale for paleo global climate, ice sheet and sea level changes. " Which explains why CO2 is lagging behind temperature in the paleoclimate record (an issue which was brought up time and again in discussions about Al Gore's movie), while emphasizing that CO2 is the control knob on global climate. and "This millennial carbon cycle time scale should not be misinterpreted as the ice sheet time scale for response to a rapid human-made climate forcing. Recent ice sheet melt rates have a doubling time near the lower end of the 10–40 year range." Which re-states that we should not expect the same "millennial" timeframe delay in climate change now that we are increasing CO2 levels directly, and instead we should expect an exponential (doubling time of 10-40 years) increase in ice sheet melt, going forward. and finally the statement that Earth's energy imbalance is crucial in what we can expect to happen in the near future : "We conclude that 2 ◦C global warming above the preindustrial level, which would spur more ice shelf melt, is highly dangerous. Earth’s energy imbalance, which must be eliminated to stabilize climate, provides a crucial metric." And that was just the abstract. Massive amount of evidence in the paper itself.
Toggle Commented Jul 26, 2015 on Junction June 2015 at Arctic Sea Ice
Thanks, Bfraser ! I was surprised myself that the 2012 extent was so nicely in line with my hindcast (using snow cover and open water next to ice from the June records only). However, it may be that the 2012 hindcast was merely a coincidence of choosing the training period (1992-2012 in my case). A different training period (especially one including the years before 1992 and after 2012) would obtain a higher trend line, and thus a prediction further away from the actual 2012 minimum. Problem is that we really don't know exactly where the "trend" line is. What is more interesting in my opinion is that my method (of including snow cover in the prediction) obtains a better standard deviation than a plain linear trend (or even a Gomperz curve prediction). That tells me that the albedo effect during summer melting season is quantifiable in snow cover and extent-area in June and this significantly affects the September minimum. What I'm still looking for is if the summer melting season is significantly affected by other variables, or if going to a smaller timescale (like weeks instead of months as I did so far) would improve our estimate of albedo feedback during summer.
I don't think 2015 will break the 2012 record. The melting season simple started too late and progressed too slow during June to break that record. But there is so much fragmented ice at the ice margin and lower elevations, that it seems pretty clear that area and extent will be going down pretty steeply for quite some time. This (low concentration in the ice margin) is also apparent on the low resolution observations as we can see from Wipneus' concentration graph : So I don't think there will be any slow-down over the next couple of weeks, as we saw with 2013 and 2014.
Toggle Commented Jul 21, 2015 on Junction June 2015 at Arctic Sea Ice
That last sentence should read as follows : But until then, my opinion is that you are probably going to loose both bets, but your bet with Chris Reynolds is still in your favor. That is because I think that the accellerated shorter term (since 1992) SIA trend is a fairer representation of reality than the long term trend (since 1979).
Hi Jim, I owe you an opinion about your bet with 'bitchilly' and with Chris Reynolds. If I understand correctly, the bet with 'bitchilly' is that you win if Sea Ice Area as recorded by Cryosphere Today dips below 1 million km^2 by 2022. Otherwise you lose. And if I understand the bet with Chris Reynolds correctly, it uses the same conditions, but the wager is 1000 to 1 in your favor. Now, I'm assuming CT SIA is from this record : First, I found the minimum SIA for each year for the entire record (1979-2014), and regressed it against time. I obtained a beta (SIA minimum decline per year) of 0.069590. So each year the CT SIA minimum reduces 69 k km^2. Also, I obtained an 'alpha' (reference at year 2000) of 4.114881 million km^2, a correlation factor R=0.88 and a standard deviation over the decline line of 388 k km^2. What that means in plain English is that the predictions for various years, including 2022 for CT SIA minimum, using a long term decline line, comes out like this : 2012: predict 3.28, final 2.23, delta -1.05 2013: predict 3.21, final 3.55, delta 0.34 2014: predict 3.14, final 3.48, delta 0.34 2015: predict 3.07, final ???? 2022: predict 2.58, final ???? with the long term standard deviation of 338 k km^2 over that prediction, the 1.58 million km^2 difference by 2022 represents some 4.67 sigma. Now 3.2 sigma is already 0.1 % probability, so if we go by the long term trend, and we assume Gaussian distribution, then you made 2 bad bets, which you are likely to loose (even considering Chris' 1000/1 odds. Second test I did was regress over a shorter period, which reflects how SIA decline accellerated, and using the period since 1992-2012 which excludes the 'rebound' 2013 and 2014 years. That experiment is kind of a 'best-case' scenario for your bet, and here are the results : 21 years analysed : beta : -0.113878 (114 k km^2/year decline) alpha (referenced to year 2000) : 4.235367 Correlation (R): -0.888165 Standard deviation over the predicted decline line is 357 k km^2, and here are some key years : 2012: predict 2.87, final 2.23, delta -0.63 2013: predict 2.75, final 3.55, delta 0.80 2014: predict 2.64, final 3.48, delta 0.84 2015: predict 2.53, final ???? 2022: predict 1.73, final ???? The 0.73 million km^2 difference between 1.73 and 1.0 to win your bet is now only 2 sigma, which means that with this modern trend you have something like 2 % probability of winning these bets. That means that 'bitchilly' still made a good bet (by a factor of 50 in 'bitchilly's favor), and Chris Reynolds made a bad bet (by a factor of 20 in your favor). Now, all that said, let me add a note. These regressions are based on the assumption that sea ice decline will be somewhat linear over the 7 years to come, and that the SIA uncertainty distribution is roughly Gaussian. And that is not guaranteed. It may be that there are more non-linear effects kicking in once the Arctic SIA cover get closer and closer to 1 million km^2. Which, after all, represents a wide open Arctic ocean, one that the planet has not experienced for a very long time, so we don't have any president for that situation. It seems to me that you have placed your bets on that situation realizing before 2022. That is possible, but we don't have any evidence of that being a realistic scenario. I would like to spend some more time to see if there is any indication of such accellerated melt for particular years that may suggest that accellerated (non-Gaussian) melt occurs when SIA dips exceptionally low ((2012 comes to mind). But until then, my opinion is that you probably made two bad bets.
One more note about my projection adjustment. About latent heat and time delay from heat sources. During the final stages of the melting season (late August and early September) the Arctic does not receive enough sunlight any more to melt by insolation directly. The heat that melts the sea ice at during these final melting stages is thus caused by latent heat. The heat from the land, the air, and the water around the Arctic which has build up during the earlier months. So late stage melting (August and September) should be affected by how much heat was build up at lower latitudes. And thus, it is my expectation that my main variable (snow cover in high summer) will affect late stage melting (in August and maybe even September). With this year's June snow cover being VERY low (second after 2012) this is a good one to test my theory that snow cover in summer affects mostly late stage Arctic melting. If this year's minimum will end up above 5 M km^2, then my theory (of snow cover's delayed effect) is probably incorrect. Maybe latent heat build-up by snow cover decline in summer does not affect late stage melting much. But if this year's melting season will show persistent declines in August and maybe even September (and thus end up at 4.6 or below) then we should consider that snow cover early on DOES have a strong effect on latent heat buidup and thus late season melting... Food for thought.
Interesting bet, Jim ! My gut feeling is that you have a good chance of loosing that one. And I also think that Chris Reynolds is overly confident with his 1000-to-1 bet. But I did not run the numbers yet. So let me do that first, and I'll get back to you with my opinion on your bet.
Thanks Neven, but we'll see in September. Also, I must say that I am relieved that the Arctic did not immediately continue its nose-dive after 2012, but instead continued a more linear downtrend. Which of course is good news (or at least not terribly bad news) for the Arctic, although it puts my bet with William Connelley at risk : But I must say again, as I did back then in 2011, when I stated that the Arctic sea ice decline was running way below the linear decline line, and showed signs of quadratic collapse (in volume), that : I sure hope that I am wrong. Because if I am right, Arctic sea ice is in much worse shape than the IPCC expects it to be, with potentially disastrous consequences for Arctic wildlife and climate patterns across the entire Northern Hemisphere. And thus, I'd be happy to pay up next year if I was proven wrong.
Just submitted my revised SIPN prediction for the July report : 4.6 M km^2, with a standard deviation of 370 k km^2. Most of you will know that my method is based on a metric for the "whiteness" of the Arctic in June, as a predictor for the amount of ice that will melt out between June and September. I use Rutgers' snow cover as a variable, as well as NSIDC's (extent - area) as metric for "dark water next to ice" as well as of course, "area" as a metric for whiteness of the Arctic in June. Reasoning is explained here : and below in the "short description" of the method as I submitted to SIPN. Now, Rutgers snow cover in June 2015 just came in and it is VERY low (second lowest only after 2012) : Also, while in May, sea ice was pretty compact (VERY low extent while area was just below average) June showed a major decrease in area, while "extent" reduced much slowed, and thus ice "concentration" collapsed. You can see that very well in Wipneus' graph : Result is that at the end of June, snow cover being VERY low, a LOT of heat is being absorbed by land, and major areas of the Arctic sea ice pack show melting ponds. This means that the Arctic became a LOT darker in June than it was in May, and thus my method, which predicted 4.9 M km^2 in May, now predicts significantly less ice in September : 4.6 M km^2. Standard deviation on this prediction is about 370 k km^2. Here is the method explained in more detail, as submitted under the "short description" to SIPN ARCUS for the July report : ------ *** September 2015 monthly average projection : Pan Arctic Ice EXTENT : 4.6 million km^2 (with 370 k km^2 standard deviation on the prediction) *** Short explanation of outlook method : The basic concept behind my method pertains to estimating albedo-based Arctic amplification during the melting season. I use the “whiteness” of the Arctic in June as a predictor for how much ice will melt out between June and September. Specifically, I set up a formula which reflects how “dark” areas near the Arctic in June would create heat that will melt out ice over the months until the September minimum. As an educated guess, such a formula could take the following form : Melt_formula = 0.25 * Snow - 1.0 * (Extent - Area) + 0.5 * Area With factors explained like this : For (Extent - Area): 1.0 (assuming that ALL solar radiation onto melting ice and into polynia will cause ice to melt later in the season. For (Area): 0.5 (assuming that half of the heat absorbed in the ocean OUTSIDE of the main pack will cause ice melt (while the other half would cause the ocean to warm up. For (snow cover): 0.25 (assuming that half the heat from lack of snow cover will be blown North, and half of that will go to ice melt. Then I set up a regression equation for how much ice will melt out between June and September : september_extent - june_area = alpha + beta * (Melt_Formula) ; When I tweek the factors, to obtain the best fit over the 1995-2012 range, the ‘Melt_Formula' that obtains the best correlation (R=0.93) is this one (centered to (extent - area): Melt_Formula = 0.434*snowcover - 1.0*(extent - area) + 0.65*area Which is remarkably close to the “educated guess” factors explained above. This suggests that this formula is realistic, and the effect is physically real. Using this formula, for the period 1992 - 2013, I obtain R=0.93, beta = 0.5588, and a prediction for Sept 2015 ice extent of 4.61 million km^2 with a standard deviation of 370 k km^2. The “beta” of 0.5588 means that for every km^2 of polynia/melting ponds in June, an extra 0.558 km^2 of sea ice will melt out between June and September. And the 0.434 factor on snow cover with that beta means that for every 1 km^2 snow cover loss in June, some 0.242 km^2 of sea ice will melt out by September. The interesting issue is that the standard deviation (at 370 k km^2) is significantly better than the 500 k km^2 or so that would be achieved for a simple linear trend. This means that the June “whiteness” signal is apparent in the September sea ice minimum, and serves well as a predictor. As for past performance, here are the results for what this method would have predicted for the past couple of years : ....... 2006: predict 5.34, final 5.91, delta 0.57 2007:predict 4.79, final 4.29, delta -0.50 2008: predict 5.01, final 4.72, delta -0.29 2009: predict 5.66, final 5.38, delta -0.28 2010: predict 4.39, final 4.92, delta 0.53 2011: predict 4.60, final 4.61, delta 0.01 2012: predict 3.71, final 3.62, delta -0.09 # -2013: predict 4.89, final 5.35, delta 0.46 # -2014: predict 4.93, final 5.28, delta 0.35 # -2015: predict 4.61, final ???? Note that the years tagged with a # are NOT part of the 1992-2012 regression learning period. This suggests that the “whiteness” of the Arctic in June, as expressed in the regression formula, using snow cover and (extent-area) as well as June “area” itself, explains a large part of the increase in September ice extent during the 2013 and 2014 season w.r.t. 2012 and other years.
According to Wipneus' concentration maps : the 'course' (25 km^2) NSIDC concentration dropped very quickly to normal levels, but the high resolution (3.125 km^2) concentration is still anomalously high. What I make of that (Wipneus please correct me if I'm wrong) is that there is a lot of thin ice at the edges turning to slush rather quickly, but the interior of the pack is still quite "white" and resilient. I think the jury is still out there on where 2015's minimum will end up.
Toggle Commented Jun 16, 2015 on Melt Pond May 2015 at Arctic Sea Ice
I posted this elsewhere but I think it is more applicable here : Larry, thank you for posting these public belief findings. I think your statements to be spot-on, at multiple levels : On this and other factual questions, it seems likely that many people chose answers derived from their more general beliefs On this and other factual questions, it seems likely that many people chose answers derived from their more general beliefs I find evidence in that also on other subjects, such as MH17, Keystone XL, evolution theory and AGW. On a positive note, mis-beliefs often just go away once evidence is overwhelming. In that regard, I find it encouraging that your survey shows, that the opinion of "climate is changing due to natural causes" is for the first time dipping below 50%.
Thank you Neven, for a well thought out, balanced overview of the situation in the Arctic at this point. I appreciate David Schröder's team melting pond assessment this year There is no doubt that melting ponds greatly affect the melting season locally, and thus his data is important and very well appreciated. Did you obtain any information on how David Schröder's team determines this info ?
Toggle Commented Jun 15, 2015 on Melt Pond May 2015 at Arctic Sea Ice
Larry, thank you for posting these public belief findings. I find your statements to be spot-on, at multiple levels : On this and other factual questions, it seems likely that many people chose answers derived from their more general beliefs I find evidence in that also on other subjects, such as MH17, Keystone XL, evolution theory and AGW. On a positive note, mis-beliefs often just go away once evidence is overwhelming. In that regard, I find it encouraging that your survey shows, that the opinion of "climate is changing due to natural causes" is for the first time dipping below 50%.
navegante, yes, that is correct, especially since BOTH extent and area are running at record lows already.
Sorry to be late to the party (some trouble with NSIDC area numbers, and some uncertainty on snow numbers), but I just submitted my projection to ARCUS : 4.9 M km^2 with SD 470 k km^2. This estimate is based on linear regression of how 'dark' the Northern hemisphere was during April and May, as estimated by two variables : (1) Rutgers snow cover numbers from April and May and (2) NSIDC May numbers of (extent-minus-area) to estimate dark (leads and melting ponds. If I do the same linear regression excluding April snow numbers, I end up with a projection of 4.5, and a standard deviation of 520 k km^2, and honestly speaking, I think the April snow numbers just give a better SD because Northern Hemisphere snow numbers (including April) are following the normal linear down trend that can be expected from a globally warming planet. So I think my 4.9 is conservative, and I would not be surprised if I need to down-adjust that number steeply next month. Also note that even a simple linear trend obtains a SD of about 550 k km^2 or so, and thus I take my projection with a VERY LARGE grain of salt. June projection should be much better.
Bill Fothergill said It looks as though someone at NSIDC has done a bit of cut&paste without checking if the paragraph still makes sense. ... I would hazard a guess that, from Jan 2008 onwards, the offset value reduces to 0.029 - but it could certainly have been phrased better. Does that help? Yes, it does, and thank you for your reply. I also noticed the difference between the notes in the old and the new files, and that the new 'area' numbers are adjusted upward (by indeed about 0.3) for 2008 onward. So I think you are right that the new discontinuity is in 2008 only, and not in 2013. But I will contact the NSIDC helpdesk just to make sure. Wipneus said : Think of it: even if area equals extent every day of the month - 100% concentration within the ice extent, never any melt ponds or leads- grid cells that are only covered with ice during part of the month lead to an average concentration less than 100%. I had to read that sentence a couple of times, but I understand what you mean. And yes, this may be a problem for even my simple model, and it may explain differences between the NSIDC's area numbers and other data sets. Let me think about this a bit and then I will try to come up with a better method for determining "extent minus area" as a metric to determine leads and melting ponds. Thanks !
Also, I'd like some clear mind advice on something. I developed a super-simple model that uses Northern Hemisphere spring snow cover and a metric for melting ponds as a predictor for the September ice minimum : This model works very nicely, as it obtains better correlation numbers than plain extrapolation of the long term trend. For Northern hemisphere snow cover I use Rutgers Snow Lab monthly numbers, and for "melting ponds" (and other water close to ice) I use NSIDC numbers for (ice extent - minus - ice area) as a metric. This year, as Neven reports, ice "extent" in May is running very low, while ice "area" is sort of average. However, and here is the issue : NSIDC does not seem to share that observation. Here are the numbers : .. 2012 5 Goddard N 13.11 10.99 --> E-A = 2.12 Capie:A/E= 0.838 2013 5 Goddard N 13.08 11.20 --> E-A = 1.88 Capie A/E = 0.856 2014 5 Goddard N 12.77 10.99 --> E-A = 1.78 Capie A/E = 0.860 2015 5 NRTSI-G N 12.65 10.78 --> E-A = 1.87 Capie A/E = 0.852 Numbers obtained from here : So it seems that NSIDC does not really rate May 2015 as specifically high on Capie index, nor out of the ordinary for the "extent minus area" indicator that I use as a metric for "melting ponds" in May. So, for starters, I wonder why that difference in data sets (between NSIDC and IJIS extent and CT area) came about. Second, the problem may be related to the way that NSIDC calculates their sea ice "area" numbers. From the same NSIDC txt file I linked above : The "extent" column includes the area near the pole not imaged by the sensor. It is assumed to be entirely ice covered with at least 15% concentration. However, the "area" column excludes the area not imaged by the sensor. This area is 1.19 million square kilometers for SMMR (November 1978 through June 1987), 0.31 million square kilometers for SSM/I (July 1987 through December 2013), and 0.029 million square kilometers for SSMIS (January 2008 to present). Therefore, there is a discontinuity in the "area" data values in this file at the June/July 1987 boundary and at the December 2007/January 2008 boundary. which does not really make sense to me. Is the irregularity on "area" around the dec 2007/Jan 2008 boundary, or around December 2013 ? Or both ? I'm confused. Anyone know what's going on here ?
Thank you Neven, for again a great overview of the early melting season. I share your conservative view that the low extent (especially from IJIS) is probably not a significant indicator. Low ice "extent" and average "area" suggests that the ice pack is compact and does not yet expose much water (melting ponds) in the ice pack, and thus amplification is limited. Thus,assuming "average" summer weather, I believe that the early melting start may taper off over the next month and get more in line with extent numbers of 2011 and alike. But the Arctic is notoriously unpredictable, and certainly has a few tricks up her sleeve. Also, a big THANK YOU to Chris Reynolds, for the May summary post : What a clear and wonderful read, explaining so comprehensively how the air pressure differences over the Arctic affect regional melting patterns. Hats off !
navegante said Rob, your comment seems to imply that a two-state, hysteretic, irreversible Arctic requires one of the two states to be year-round ice free. Cannot this state be just a seasonally ice free Arctic? Yes, that is true. A two-state, hysteretic Arctic does not need it's second state to be year-round ice free. I only gave that as an example of bifurcation. A more accurate description was made by Chris Reynolds, above : However with Bifurcations often comes rapid transitions. This paper suggests that this is not likely. although, while we witness the reduction of Arctic sea ice over the years, it may be difficult to judge afterwards if it was due to transition to a bifurcation state, or simply a rapid change due to underestimation of positive feedback factors. Either way, the real issue is in my opinion that even GCMs still appear to underestimate Arctic sea ice decline :
Either way, based on SIPN prediction June-Sept methods that have 500 k km^2 SD or less, the 2013 and 2014 Arctic summers were COLD compared to the long term "trend" line. Including my method that uses NH snow cover as a predictor.
Jim said I wondered if I might idly enquire what your next SIPN prediction might say? Short answer : By June 3 or 4 Rutgers will publish their NH snow numbers for May, and that's when I can publish a prediction. The long answer : For starters, I owe Slater et al an apology. Their 2014 prediction, based on melting pond data from in early May, came through while my, and many other, predictions that were smack in the middle of the pack ended up almost 2 SD's off. However, I noticed that the standard deviation of the September extent predictions by the SIPN contributors (including me) is stubbornly close to 500 k km^2 or more, no matter WHICH method was used. Which puts Hamilton's Gompertz fitting method at the same accuracy as Slater et al's early May melting pond method (both of which reported a 500 k km^2 standard deviation for the June->Sept prediction). My own method (which relies on snow cover in the NH) obtains a better SD, but turned out not serve as a good predictor for the 2013 and 2014 melting season. Which is somewhat concerning, since it suggests that (1) physical effects are less important than summer weather, but (2) the long term down trend is indisputable and does not depend much on the weather in summer. And I'm not even sure if that is a paradox simply an indication that Arctic summer weather create a 500 k km^2 SD on June to Sept sea ice extent.
Neven, I'm sorry that I just jump in here with a summary of a scientific paper after a year of absence. I've been busy fighting denial on (shorter term) issues like Keystone XL, Canadian tar sands, MH17 and Russian aggression in the Ukraine.