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Gas Glo
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I think we have had a mega melt week as in 1,000,000 km^2 CT area reduction. 2011.4822 -1.2551445 7.7860675 9.0412121 .... 2011.5013 -1.5253255 6.7199564 8.2452822 This is the second non overlaping time this year. 2010 had 5 all overlapping and 2007 had 2 overlapping weeks. There has only been those 9 nine since 2003.
NSIDC monthly average numbers - second lowest for extent and area behind 2010, but only just second for both: 2006 6 Goddard N 11.06 8.34 2007 6 Goddard N 11.49 8.15 2008 6 PRELIM N 11.46 8.47 2009 6 NRTSI-G N 11.49 8.86 2010 6 NRTSI-G N 10.87 7.98 2011 6 NRTSI-G N 11.01 8.14
This would provide an excellent complement to both the volume and "% of more than 1 year of ice" figures already collected. Minimum thicknesses of recent years 2004 1.546714961 317 2005 1.427439172 315 2006 1.459023708 308 2007 1.232745313 310 2008 1.303704143 315 2009 1.330650417 315 2010 1.104540845 310 Day 265 thicknesses 2004 2.24608419 2005 2.131858687 2006 2.185062579 2007 2.104383479 2008 2.212321682 2009 1.809586749 2010 1.414008132 Or if you wish look (& copy to your favourite spreadsheet) at data at https://spreadsheets.google.com/spreadsheet/ccc?key=0AjpGniYbi4andFpmUzNMa1c0R1hLd1lIUno1OXJWcXc&hl=en_US#gid=0
Artful Dodger "Derek: How has this energy left the system?" Derek Moran "Blowing in the wind, as humidity in the air, that once was ice." What happens to the water vapour? It rains out at some point. Perhaps it could have travelled out of the Arctic circle area before that happens but such air is likely replaced with other moist air. When it rains out, the energy is given up to the atmosphere and it is easier for the energy to be lost to space from there than from the ocean. That loss of energy is subequent to condensation. The energy isn't lost through the process of evaporation or sublimation. The energy is still there just not as a temperature effect. If the energy had remainer there as a temperature effect energy is slowly lost through warmer body radiating more energy. But which loses more energy? A slow loss over ~11 days that water remains in the air (or is average residence time different in Actic?) or avoiding that slow loss of energy but then getting a big hit though the temperature effect reappearing some time later possibly high in the atmosphere which is a good location for losing heat rapidly? ?o)
Toggle Commented Jul 1, 2011 on Temps June 2011 at Arctic Sea Ice
>"Another very low extent decrease reported." But another large area decrease 7.102 from 7.280 a 178k drop. I would suggest that it is area not extent that matters because it is area that drives the albedo feedback effect. Area is well above 2010 but is below 2007.
Yes, I am now using anomaly not raw. Raw area was eliminated as a predictor in June 29th 16:59 post while the 30 June 15:55 post explains the anomalies I am now using are from Gompertz fits.
Toggle Commented Jun 30, 2011 on 2011 Century Breaks at Arctic Sea Ice
Hi Larry, does that mean you won't be able to submit in time for June SEARCH? Presumably it is possible to do the analysis of past years and submit a report where the projection is a function of this years June PIOMAS data. I have nearly finished doing this. Can I send this to you?
Toggle Commented Jun 30, 2011 on 2011 Century Breaks at Arctic Sea Ice
Maybe I will get the hang of the analysis I am trying to do eventually. In the last instalment June area and Century breaks didn't help much. So I realised to predict the anomaly what we want to use is not the area but the anomaly of the area from a gompertz fit of area at end of June. This worked better so I also looked at volume anomaly from gompertz fit of volume data at end of June. So The RMSE of estimates that I now get are: Predicting NSIDC monthly minimum using linear regression on year gives RMSE of 0.508 Using Gompertz non linear fit reduces this RMSE to 0.438 Adding end of June area anomaly from gompertz fit of end June area reduces RMSE to 0.3715 Instead adding end of June volume anomaly from gompertz fit of end June volume reduces RMSE to 0.396 so area appears better than volume. Using both area and volume RMSE is reduced to 0.358 If I were to consider adding Arctic oscilation or NAO to try to add a predictor for ice export, what lag would be appropriate? I haven't heard many suggestions for other predictors that I could consider using.
Toggle Commented Jun 30, 2011 on 2011 Century Breaks at Arctic Sea Ice
IJIS extent falls by a mere 39k but CT area falls by 231k. How inconsistant is that? It takes the area is back below 2007.
Toggle Commented Jun 30, 2011 on SIE 2011 update 9: back and forth at Arctic Sea Ice
Is it a rather small catamaran for a large expance of open water? Or perhaps winds are more likely to be with the beauford gyre giving better wind assistance in that direction?
Toggle Commented Jun 30, 2011 on Across the North Pole at Arctic Sea Ice
Hmm. On changing from predicting NSIDC daily area area minimum to predicting the NSIDC monthly extent anomaly from Gompertz fit I am now trying to predict a noisier data set so the RMSEs have gone up. So I need to get used to these higher RMSEs before I can make much sense of them. Predicting NSIDC monthly minimum using linear regression on year gives RMSE of 0.508 Using Gompertz non linear fit reduces this RMSE to 0.438 Using linear regession of NSIDC area at end of June to predict Gompertz anomaly give RMSE of 0.423 Using linear regession of total reductions over 100k area during April-June to predict Gompertz anomaly give RMSE of 0.422 Using muliple linear regession of total reductions over 100k area during April-June and area at end of June to predict Gompertz anomaly give RMSE of 0.42 So neither the June area or century break data are reducing the RMSE much. 3 out of 10 sets of random numbers reduced RMSE by more so neither of these appear very useful.
Toggle Commented Jun 29, 2011 on 2011 Century Breaks at Arctic Sea Ice
4) I was wondering whether you might transfer my posts to the century break thread. I am not so sure about about "thorough". I don't really like the linear of multiple linear regression when we all (even inc W Connolley) agree there is downward acceleration. To effectively get than sort of multiple non-linear regression where the non linear is only for one variable, time, then 'all' I need to do is change the predictand from the minimum to the anomaly of the minimum from the smooth non linear function. I am thinking of Larry Hamiltons' Gompertz fit as the smooth non linear function. I wonder if Larry is planning to submit an update to his prediction to the June SEARCH report. Whether he is or not, what factors would you want to throw at this muliple (sort of non) linear regression? Obvious ones occuring to me include: Area at end of June for albedo effect, Volume near end of June for less ice disappears faster, Arctic oscilation for some of ice export effect, Area reductions over 100k km^2 per day in April to June, What else, suggestions welcome? I am suggesting changing from average km^2 reduction on century break days to the reductions over 100k km^2 per day because small difference between above and below the 100k threshold can affect the average noticably whereas the effect on total reductions in excess of 100k is going to be small. Seems like there is lots more to do rather than having been thorough.....
Toggle Commented Jun 29, 2011 on 2011 Century Breaks at Arctic Sea Ice
I was wondering whether you might transfer my posts to the century break thread. I am not so sure about about "thorough". I don't really like the linear of multiple linear regression when we all (even inc W Connolley) agree there is downward acceleration. To effectively get than sort of multiple non-linear regression where the non linear is only for one variable, time, then 'all' I need to do is change the predictand from the minimum to the anomaly of the minimum from the smooth non linear function. I am thinking of Larry Hamiltons' Gompertz fit as the smooth non linear function. I wonder if Larry is planning to submit an update to his prediction to the June SEARCH report. Whether he is or not, what factors would you want to throw at this muliple (sort of non) linear regression? Obvious ones occuring to me include: Area at end of June for albedo effect, Volume near end of June for less ice disappears faster, Arctic oscilation for some of ice export effect, Area reductions over 100k km^2 per day in April to June, What else, suggestions welcome? I am suggesting changing from average km^2 reduction on century break days to the reductions over 100k km^2 per day because small difference between above and below the 100k threshold can affect the average noticably whereas the effect on total reductions in excess of 100k is going to be small. Seems like there is lots more to do rather than having been thorough.....
Toggle Commented Jun 29, 2011 on SIE 2011 update 9: back and forth at Arctic Sea Ice
>" but the rest would claim lunacy" Well the idea of doing that through August 2011, I think, should be dismissed as lunacy even though I do think August could show a noticably larger drop than previous years' Augusts due to thinner ice. Lower reductions in August could be accomodated with larger reductions in July. However, with area 260k higher (320k yesterday) than in 2007 that has got to give 2007 an albedo advantage that could wipe out any thinner ice advantage we have this year even if the weather is as melt causing as 2007. I could be talking rubbish here if the thinner ice is greyer but I suspect thin grey ice is still fairly reflective?
Toggle Commented Jun 28, 2011 on SIE 2011 update 9: back and forth at Arctic Sea Ice
Doing a multiple linear regression using 3 predictors: year, end June Area and average km^2 reduction in century break days reduces the RMSE to 0.261. So adding end June to year reduced the RMSE from 0.3456 to 0.303 a reduction of 0.0426. Adding average reduction in century breaks as a third predictor seems to be capturing something else not in year or end Jun Area as the reduction from .303 to .261 a drop of 0.042 is very nearly as large as the drop from adding the end Jun area as my second best predictor. Of course, there is still the problem that we don't know the average area reduction in century breaks very well until near the end of the melt season. Could always try the average reduction in April-June century break days.....
Toggle Commented Jun 28, 2011 on SIE 2011 update 9: back and forth at Arctic Sea Ice
OK 'Average km^2 reduction in century break days' is probably better than total km^2 in century break days. Year Centuries Total km^2 Avg km^2 Min 1979 32 -4.6170642 -0.144283256 5.3067255 1980 18 -2.3684177 -0.131578761 5.5077119 1981 32 -4.4653372 -0.139541788 4.9564924 1982 32 -4.3404537 -0.135639178 5.13906 1983 44 -5.5245612 -0.125558209 5.386929 1984 26 -4.3045169 -0.165558342 4.6958923 1985 42 -5.7870403 -0.137786674 4.992847 1986 30 -4.0629231 -0.13543077 5.3818426 1987 33 -4.8633007 -0.147372748 5.2889948 1988 38 -5.6880888 -0.149686547 5.1448908 1989 46 -6.4194954 -0.139554248 4.8159156 1990 41 -5.8903435 -0.143666915 4.6289349 1991 42 -6.3517074 -0.151231129 4.4603844 1992 40 -5.8922116 -0.14730529 5.0267782 1993 43 -6.2021023 -0.144234937 4.4729533 1994 40 -5.8805938 -0.147014845 4.8160958 1995 44 -6.0084145 -0.136554875 4.4103012 1996 35 -4.7555421 -0.135872631 5.2381849 1997 45 -5.9793433 -0.132874296 4.8997059 1998 42 -6.0610489 -0.144310688 4.262403 1999 55 -8.1776087 -0.148683795 4.2044988 2000 39 -5.9563771 -0.152727618 4.1687655 2001 45 -6.6941426 -0.148758724 4.5336194 2002 38 -5.7568805 -0.151496855 4.0347104 2003 48 -7.0111419 -0.146065456 4.1416645 2004 33 -4.4579023 -0.135087948 4.2829733 2005 33 -4.7205853 -0.143048039 4.0917983 2006 33 -4.7038803 -0.142541827 4.0169191 2007 41 -6.135042 -0.149635171 2.9194391 2008 44 -6.9659804 -0.158317736 3.0035558 2009 42 -6.4141986 -0.152719014 3.4245975 2010 46 -6.477607 -0.140817543 3.0721295 There is no trend in the average numbers (-0.00027). Using this 'average km^2 reduction in century break days' only for linear regression results in RMSE of 0.61 only marginally better than number of century breaks 0.627 and a lot worse than just using year, 0.3456. However, despite not looking much better on above measures, when used in multiple linear regression with year, it fairs better. The RMSE is reduced from 0.3456 for using year only down to 0.312. This is better than any of my 10 sets of random numbers though one set got close, 0.314. For comparison using area at end of June and year in multiple linear regression reduces RMSE to 0.303. So as Patrice would have expected, area at end of June appears a better predictor than average km^2 decrease in century break days.
Toggle Commented Jun 28, 2011 on SIE 2011 update 9: back and forth at Arctic Sea Ice
Sorry, probably being thick but I am not sure what you are asking given that I have posted number of century breaks for each year above. Actually, it appears there could be a small trend of increasing by .35 century breaks per year. However, there have been fewer century breaks (320) in last 8 years than the previous 8 years (343). The trend is, by definition, as useful for predicting the minimum as the trend in years. I am saying that the wiggles don't appear useful to predicting the minimum. The average for 1979 to 2010 is 38.8 century breaks per year. If not that, are you asking about the decrease in km^2 of all the century break days in a year or something else? Decrease in km^2 of all the century break days sounds like desperation to find some useful numbers. Length of strings of consecutive century breaks or correlating number of century breaks to difference from Gompertz smoothed trend, or century breaks during certain periods sound more likely to find some usefulness to me.
Toggle Commented Jun 28, 2011 on SIE 2011 update 9: back and forth at Arctic Sea Ice
I also know that Neven loves his century breaks so I hope he doesn't hate me too much for posting the following: I used Cryosphere today area numbers to get a longer data set in order to have some hope of determining whether the number of century breaks might have some useful meaning: Year Centuries Min 8 year sum 1979 32 5.3067255 1980 18 5.5077119 1981 32 4.9564924 1982 32 5.13906 1983 44 5.386929 1984 26 4.6958923 1985 42 4.992847 1986 30 5.3818426 256 1987 33 5.2889948 1988 38 5.1448908 1989 46 4.8159156 1990 41 4.6289349 1991 42 4.4603844 1992 40 5.0267782 1993 43 4.4729533 1994 40 4.8160958 323 1995 44 4.4103012 1996 35 5.2381849 1997 45 4.8997059 1998 42 4.262403 1999 55 4.2044988 2000 39 4.1687655 2001 45 4.5336194 2002 38 4.0347104 343 2003 48 4.1416645 2004 33 4.2829733 2005 33 4.0917983 2006 33 4.0169191 2007 41 2.9194391 2008 44 3.0035558 2009 42 3.4245975 2010 46 3.0721295 320 39 Last 365 days Correl year,centuries 0.455357601 Correl centuries,min -0.424719847 OK correlation coefficient seems on a similar scale to year so perhaps using number of centuries could be useful. So lets try it: Using linear regression on year produces a RMSE of 0.3456. Using linear regression on number of centuries is 0.627 (and we don't know the number of centuries until late in the season). Using multiple linear regression using both year and number of centuries reduces the RMSE to 0.3448. This is only marginally better than just using year, but it has to be better by the definition of the process. I tried using ten different sets of random numbers and in 7 out of those 10 attempts a set of random numbers outperformed the number of century breaks. The conclusion would seem to be that the number of century breaks do not appear to have much predictive power in the way used here as they appear worse than an average set of random numbers. There could of course be other ways of usefully using such information.
Toggle Commented Jun 28, 2011 on SIE 2011 update 9: back and forth at Arctic Sea Ice
Now got the update for 26th - a small 6k increase to 3672k, but not a figure for 27th yet.
Toggle Commented Jun 28, 2011 on SIE 2011 update 9: back and forth at Arctic Sea Ice
I am pretty sure Greenland has had ice to the North for the last 3000 years or so. The ice down the East coast is generally moving Southward. The transpolar drift makes the Fram Straight an exit route for some of the Arctic ice. The general pattern is shown by: http://www.whoi.edu/itp/images/itpall10.jpg With the Nares Straight open for more of 2010 than any other recent year, I doubt Greenland has has much less ice around it than shown in http://lance-modis.eosdis.nasa.gov/imagery/subsets/?mosaic=Arctic.2010260.terra.4km Greenland essentially ice free is, I hope, going to take at least several hundred years. Though I expect you meant the coast rather than a 6m sea level rise. Having had both passages sailed in a season, I would suggest next would be circumnavigation of Greenland without circumnavigating North pole. I would expect that to be possible well before a circumnavigation of Greenland without circumnavigating Ellesmere island could be done.
Toggle Commented Jun 25, 2011 on Hudson Bay at Arctic Sea Ice
Break up forecast is now 'on' 8 July rather than 'after' 16 days.
Toggle Commented Jun 24, 2011 on Barrow Break-up 2 at Arctic Sea Ice
The area shown on that map plus Canadian archipelago must have area of at least 10 million km^2 (we currently have some coverage of Hudson but that must be made up for by having less extent in Chukchi, Barents, Kara ...) The green and above colours seem to have larger area than purples so average thickness on that map must be at least 3m. So if the map is taken as accurate then the ice volume must be at least 30K Km^3. PIOMAS has volume at end of February as volume at 19.9K Km^3 (average of Jan/Feb is rather less). A difference of over 10,000 Km^3 seems quite significant.
Yes it is pretty having lots of red and pink but what temperatures are these compared to? What is the baseline period? Perhaps as well to also check out real temperatures rather than anomalies
Toggle Commented Jun 20, 2011 on 2011 webcam puddles at Arctic Sea Ice
May average is 20.173 k km^3 https://spreadsheets.google.com/spreadsheet/ccc?key=tF-Db0tyg3jtmSfeZ6PZqlA&authkey=CO2ht_8P#gid=0 not really useable like that (but if you want to copy to your own spreadsheet rather than downloading and unzipinng gz file). NSIDC average area is 10.34 M Km^2 Average thickness in May 2011 1.94m For 2010 21.203/10.48 = 2.02m
Toggle Commented Jun 17, 2011 on PIOMAS Version 2 at Arctic Sea Ice
Lodger, the last piomas volume number available is day 151 18.647. I make that 1 June so I was trying to use CT area for 1 June
Toggle Commented Jun 17, 2011 on PIOMAS Version 2 at Arctic Sea Ice