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Rob Dekker
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Looks like it going to be a test of NSIDC (extent graph) against PIOMAS (volume graph) this year.
Toggle Commented May 7, 2017 on PIOMAS May 2017 at Arctic Sea Ice
Yes Hans. As Bernie said about the billionaires that rule the nation : "Their Greed Has No End.". Right now, when we are about to find out if Arctic sea ice follows the PIOMAS "volume" graph of a fast decent to an ice free Arctic within this decade, or if it follows the "extent" graph that follows suggests an ice free Arctic around mid-century, right now we are faced with a US legislative forum that denies Global Warming, and will do nothing to curb the carbon emission trend.
Toggle Commented Apr 25, 2017 on PIOMAS April 2017 at Arctic Sea Ice
On this Earth Day, and the March of Science day, I place one OT, but still very much urgent comment : President Trump has, in three short months, approved the Keystone XL and Dakota Access pipelines, moved to slash the Environmental Protection Agency's budget to its lowest level in 40 years, and issued orders to roll back the Clean Water Rule, the Clean Power Plan and climate research and science. When we abandon science, we abandon informed democracy. Share this video if you can :
Toggle Commented Apr 23, 2017 on PIOMAS April 2017 at Arctic Sea Ice
DCS, I agree. The PIOMAS volume numbers are running so low this year, that it is almost scary. The extrapolation of 'volume' by PIOMAS hits ground zero much earlier than the extrapolation of 'extent' of sea ice. So I guess this year we will know if summer heat melts 'volume' or if it melts 'extent'.
Toggle Commented Apr 11, 2017 on PIOMAS April 2017 at Arctic Sea Ice
Bill, regarding this finding : 250/4640 = 0.053879 340/6310 = 0.053883 That IS "bloody spooky"....
Toggle Commented Apr 9, 2017 on PIOMAS April 2017 at Arctic Sea Ice
Bill, thank you for pointing out that a floe can more than quadruple its presence on the NSIDC monthly average simply by moving around. That is on top of the sensitivity to timing of refreezes and melts. All in all, this makes NSIDC's current method rather sensitive to events that have no relevance to sea ice extent or area. It looks like they are at the point of making a change considering the comment that Al posted above : The dataset is now clearly the most popular product we have due to our blog-style publication and thus changes will be made after considering any impact to the community With us here being part of the community, what do other people here think ? Should NSIDC change their method of calculating the NSIDC monthly average (to simply taking the average of daily numbers over the month) ?
Toggle Commented Apr 9, 2017 on PIOMAS April 2017 at Arctic Sea Ice
Thanks Bill, interesting example. I think that in as far as you floe does not reduce the ice concentration in the pixel it left behind below 15%, you are correct. Either way, the sensitivity to timing of refreeze and melt in any pixel in the NSIDC ice concentration maps, makes the NSIDC monthly average numbers less consistent and less predictable. For example, in my SIPN entries, I found that Sept SIA is more accurately predictable (SD of residuals about 250 k km^2) in June than is NSIDC's SIE number (SD of residuals about 340 k km^2).
Toggle Commented Apr 8, 2017 on PIOMAS April 2017 at Arctic Sea Ice
Neven wrote Now, the average of total melt for the past 10 years is 18269 km3, which means that at the end of this year's melting season the minimum could be only 2526 km3 (the lowest minimum on record reached in 2012 was 3673 km3). That is just scary. And I don't even want to think about the sentence you wrote after that.
Toggle Commented Apr 8, 2017 on PIOMAS April 2017 at Arctic Sea Ice
Al Rogers, thank you for explaining the NSIDC SIE numbers in detail. Last year September, there was a very significant 'boost' exactly because of the way NSIDC calculates their monthly average numbers As a result of the NSIDC monthly method you describe, the last week rapid refreeze in September caused NSIDC's Sept 2016 to end up at 5th place at 4.72 , while calculation methods using JAXA or NSIDC average over Sept daily numbers would have put Sept average at 3rd place (at about 4.4 or 4.5 M km^2). Because of the odd way in which NSIDC calculates the SIE monthly numbers, it is quite sensitive to sudden jumps in refreezing or melting, which makes the number less predictable. Which also makes it harder to 'predict' that number in the entries for SIPN.
Toggle Commented Apr 8, 2017 on PIOMAS April 2017 at Arctic Sea Ice
Needless to say that I think the conclusions from Ding et al 2017 are flawed. The valid conclusion that I think we can draw from the Ding et al 2017 paper findings is this : 60% of Arctic sea ice reduction is caused by summer-time climate change, while 40% is caused by climate change over the remaining 9 months. Which is a very interesting conclusion by itself.
Toggle Commented Mar 23, 2017 on Lowest maximum on record (again) at Arctic Sea Ice
Al Rogers I think it presents a useful result but that result is presented in such a way that it can be used to suggest that natural variability is responsible for perhaps half of the loss of Arctic SIE recorded over the last four decades - a suggestion which is patently false. You are right, but still it is a challenge to actually show where the paper goes wrong. I think I found the problem with Ding et al 2017 in this comment :
Toggle Commented Mar 23, 2017 on Lowest maximum on record (again) at Arctic Sea Ice
I dropped the same comment on William Connolley's site since Eric Steig (a coauthor whom I respect very much) commented there. Let's see what the response is.
Toggle Commented Mar 21, 2017 on Lowest maximum on record (again) at Arctic Sea Ice
For all the words written in this paper (Ding et al 2017) and all the correlations and experiments they performed it is surprising that they did not even investigate the most obvious test of AGW attribution of them all : A correlation between summer temperature and sea ice extent.
Toggle Commented Mar 21, 2017 on Lowest maximum on record (again) at Arctic Sea Ice
Jim, thank you so much for reporting on the Ding et al 2017 paper. I just read it in detail and would like to report my findings here. The essence of the conclusion (attribution to AGW) of the paper lies in this section : to estimate the anthropogenic contribution to the observed warming and sea-ice reduction in t he Arctic, two additional experiments are conducted. Exp-7 and 8 are equivalent to Exp-2 but we remove t he effects of global warming on the high-latitude winds, which are used to constrain the model in Exp-2 (Supplementary Fig . 8). These results show the same strong geopotential height increases as in Exp-2, with approximately 70% of Arctic low-level warming and sea-ice extent change (north of 70◦N) relative to Exp-2. Hence, these experiments suggest that ∼30% of the anomalous thermodynamic sea-ice extent reduction is attributable to anthropogenic influences on the Arctic circulation. Applying this estimate to the overall circulation-driven sea-ice trend established in Exp-6 (60%), we estimate that about ∼42% (70% × 60%) of the sea-ice decline observed since 1979 in September is due to internal variability. Now, both these fractions (70% and 60%) are questionable. First of all, the 60% number refers to the correlation between sea-ice trend and atmospheric circulation over the Arctic. However, that does NOT say how much atmospheric circulation over the Arctic is influenced by temperature. Since higher temperature means expanding air mass, geopotential height will always increase with increasing temperature, which their own findings in figure 1e of the paper clearly shows (best correlation of geopotential height at 200 mb is with temperature). So that 60% influence of atmospheric circulation can very well be simply caused by atmospheric temperature increase, which can easily be AGW in origin. After all, Z200 is high up in the atmosphere, which means that even during summer it is not much influenced by melting sea ice below. And the 70% (natural variability) refers only to the influence of “high-altitude winds”. Here, again, high altitude winds (such as the jet stream) are caused by geopotential height, which is again caused by temperature changes over the Arctic. If the Arctic warms more than the rest of the planet (due to albedo feedback or increase in moisture or any other reason), the geopotential height over the Arctic will increase more than the rest of the planet, and thus the high altitude winds will be less “cyclonic” than otherwise. That means this “70% (natural variability of the atmospheric circulation over the Arctic)” may very well be completely caused by temperature increase. So both numbers are highly dependent on temperature increase, and since the paper does NOT investigate the correlation of these variables to temperature increase, even though its own analysis establishes that correlation very clearly (fig. 1) its conclusions are not sustained by the evidence they collected. As William Connolly wrote (thank you Jim for reporting on that : they then convince themselves that most of the circulation changes that matter to the ice are not GW forced, and so must be natural variability; and hence the conclusion.
Toggle Commented Mar 21, 2017 on Lowest maximum on record (again) at Arctic Sea Ice
Rascal, thank you for your assessment of the PIOMAS volume data, as it reflects on the upcoming melting season. In short : Yours are intimidating numbers. A bit scary I may add.
Toggle Commented Mar 20, 2017 on Lowest maximum on record (again) at Arctic Sea Ice
Jim said : Rob - Don't forget the reanalyses from ECMWF: .... and NCEP Yeah. Thank Jim, you just tripled the amount of work to be done to find FDDs for past winters :o)
Toggle Commented Mar 16, 2017 on PIOMAS February 2017 at Arctic Sea Ice
Bdwo, from your first link : The ice that survives at least one summer melt season is typically thicker and more likely to survive future summers. My point exactly. That's the ice in the Central Arctic, which tends to survive multiple melting seasons...
Toggle Commented Mar 16, 2017 on PIOMAS February 2017 at Arctic Sea Ice
@P-maker What is missing, in my view, is an indicator of the “Freezing Power” in the Central Arctic Ocean, Actually what is missing is an indicator of the "Freezing Power" in the marginal ice zone : the area of the Arctic that tends to melt out. The Central Arctic is less relevant, since that's where the MYI hangs out ; the ice that does not affect the minimum "extent" in September that much. @Bill Thanks for the additional notes on area "weighted" temperatures, versus unweighted DMI numbers.
Toggle Commented Mar 13, 2017 on PIOMAS February 2017 at Arctic Sea Ice
There probably is a correlation between 2m temps and 925 hPa temps. Slater has both temps posted. The tricky part appears to be to retrieve these temps from CFS(v2). It looks like we need at least a GRIB2 parser and then need to figure out exactly which files (and at which index) contain the temp data, 2m or 925 hPa :
Toggle Commented Mar 12, 2017 on PIOMAS February 2017 at Arctic Sea Ice
Wait a minute. Slater also reports 2m temps : with the following note : The +80N T2M images on this site are similar to those shown by the Danish Meteorological Institute (DMI) except that I use the NOAA model (vs. ECMWF) and area weighting is applied. That is encouraging. Let me dig a bit further into that data. Also, Nico (Tealight) posts FDDs on Neven's graph page : which suggests he uses the DMI data.
Toggle Commented Mar 11, 2017 on PIOMAS February 2017 at Arctic Sea Ice
P-maker, indeed. Even if we work out these irregularities in the DMI data, it still does not serve as a good indicator of the ice thickness in the melting zone (70-80 deg), because the data is not area-weighted. For example, in the DMI temperature graph (on a 0.5 deg grid) attaches 40x the significance to the temperature within 0.5 deg of the NP as compared to the significance of a similar area at the 80 deg North lateral. And since area/distance around the NP is a quadratic function, the DMI graph is for 50% determined by the temperature between 87.5-90N, which is only 25% of the area North of 80deg. And Slater's data seems to be area-weighted, but as you correctly point out it is 925 hPa, which is too far from the surface to be indicative of ice growth during winter. I can (and will once I have the time) still run the correlation between these data sets and the September sea ice extent, but I think it is simply is too far from the melting zone to be significant. I have more hope for the PIOMAS ice thickness south of 80 deg, but for that I need access to gridded PIOMAS data, which requires some work (and more importantly : time, which I chronically lack).
Toggle Commented Mar 11, 2017 on PIOMAS February 2017 at Arctic Sea Ice
Thanks P-maker, For the moment, I'm still confused about that DMI temperature data (as a source to calculate FDDs from past years). I'm taking with Jim about this starting here : One thing that is revealing already is that that DMI 80N temp graph that we use here at Neven's so often : appears to be not "area weighted". Specifically the following declaration from DMI is important : However, since the model is gridded in a regular 0.5 degree grid, the mean temperature values are strongly biased towards the temperature in the most northern part of the Arctic! Therefore, do NOT use this measure as an actual physical mean temperature of the arctic. Humbling thoughts...
Toggle Commented Mar 10, 2017 on PIOMAS February 2017 at Arctic Sea Ice
For Frank : Two over-due comments : (1) I just realized that the correlation table I presented in this post : is the correlation of absolute sea ice 'area' in September against any of the (June) variables mentioned. The correlation between Ice MELT between June and September and the variables mentioned (which refers to the "feedback" of the variables) is smaller. When used as a 'predictor', the standard deviation of the residuals of course remains the same : About 340 k kM^2 for the June->September prediction. (2) Regarding Petty et al, thanks for the link ! It seems an extension to the work done by Schroder, who is also a contributor to the SIPN Arcus network. The issue I have with their method is that the standard deviation over their predictions is not much better than a simple linear extrapolation of sea ice melt. They produce 500 k km^2 standard deviation over the June->September prediction, while my (simple) statistical method sits at 340 k km^2. Yet their predictive methods for months before June (like March and April) exceed my method.
Toggle Commented Mar 9, 2017 on PIOMAS February 2017 at Arctic Sea Ice
Thanks guys, DMI North of 80 FDD numbers are worth checking out (for correlation with Sept extent). But I realized something : What we are really after is thickness of FYI. After all, the majority of ice that melts out each year is FYI. So rather than assuming a super-simple model (taking the SQRT of a measure (FDD) of freezing) from an area that contains mostly MYI, why not find a much better model that produces FYI thickness : PIOMAS ! What I'm after for that experiment is the sea ice thickness from PIOMAS for the areas that are known to be FYI : The areas outside of the prior year September sea ice minimum. So I need to go to a gridded source of PIOMAS.
Toggle Commented Mar 9, 2017 on PIOMAS February 2017 at Arctic Sea Ice
Thank you Neven. That is the best overview of the differences between PIOMAS and CryoSat-2 that I ever read. And let me add that I share your opinion that PIOMAS is probably closer to the truth. It is much harder to determine freeboard from 150 miles (?) high with cm accuracy than it is to model sea ice growth if you input atmospheric data (temperature, wind etc).
Toggle Commented Mar 9, 2017 on PIOMAS March 2017 at Arctic Sea Ice