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Rudmop
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The graphs above will be perfect because they are the actual data that is a result of the regression. I was not clear on that. So the way I got that data is from the raw data obtained off the PIOMAS/NSIDC Graphs. Here is that data: Same format as above, from April to September. 37 year average data during melting season. 15500000 28000 14200000 27000 12700000 23500 11000000 17100 8200000 12300 6500000 11300 September 2016 melt season; april-september 14500000 22500 13000000 21000 11000000 16600 9200000 10100 6750000 5900 4500000 4100 I made the graphs by plotting the NSIDC ice extent (area) on the x axis and the PIOMAS volume on the y axis. I set intercept to 0 and the slope was the depth of the ice on average. From the equation we can find the April and September ice Volume. Taking a difference in the volumes gives us the amount melted. There was less melted this year than the past 37 year average. I will need to add the freeze data from last sept 2015 to April 2016 if you want me to; however, you may want to collect that data.
Toggle Commented Oct 1, 2016 on PIOMAS September 2016 at Arctic Sea Ice
Plot these PIOMAS Volumes for the past 37 year average melting (~April-Sept), from PIOMAS against NSIDC Ice extent over the arctic melting period. The x axis is the left column, the y axis is the right column. Then I have the 2016 melt season below this. Put both the data sets in a spreadsheet and add the trendline, set intercept to 0, display function. Slope is the thickness. I'm sure you know how to do this, but I am only explaining to you what I did in my mind; simple enough I know. I like to be specific on my thought, so there is no guess work for someone else, in trying to replicate what I did. 15500000 28000 14200000 25560 12700000 22860 11000000 19800 8200000 14760 6500000 11700 9000000 16300 2016 Melt season 14500000 20300 13000000 18200 11000000 15400 9200000 12880 6750000 9450 4500000 6300
Toggle Commented Oct 1, 2016 on PIOMAS September 2016 at Arctic Sea Ice
But after correcting my spreadsheet error, I get an astonishing value of 1680 km^3 less ice volume added thoughout the arctic winter last winter, than the arctic ice volume added over the past 37 year winter average. If you take the difference in only PIOMAS data, not comparing to NSIDC, the volume differences are only 400 km^3. Using the linear regression for the PIOMAS/NSIDC graph, the average thickness of the 37 year average is .0017 km and it is .0014 for last year. During the winter of 2015, the arctic added 1.1 times less volume of ice c
Toggle Commented Sep 30, 2016 on PIOMAS September 2016 at Arctic Sea Ice
I'm guessing the 12 minute time difference between my whoops post and the British satire comment can be attributed to lag time in server speeds and time zones, or perhaps you read my whoops comment at posted the reply because you could not resist, or perhaps you used the 12 minutes to comment sometime between 22:14 and 22:59. I don't know which of those possibilities induce your affection for my style. If catching my errors and correcting them is the reason for the appeal, I'm human too. I'll quit using my phone and lunch to post so as to avoid those mistakes.
Toggle Commented Sep 30, 2016 on PIOMAS September 2016 at Arctic Sea Ice
....by the linear regression, as opposed to 16400km^3 by the PIOMAS data solely.
Toggle Commented Sep 30, 2016 on PIOMAS September 2016 at Arctic Sea Ice
Whoops, 2015-2016 winter ice build up was 14000 km^3.
Toggle Commented Sep 30, 2016 on PIOMAS September 2016 at Arctic Sea Ice
Plotting the PIOMAS volume on the Y axis and NSIDC area on the X axis gives a nice visual of how the surface volume changes relate through the freeze/thaw cycles. Performing a linear regression, smooths out the errors in the data. The slope also is the average thickness. Using the volumes obtained from the linear regression will allow for a determination of the ice volume gained in a winter or lost in a summer, by subtracting the final volume for the period in question from the starting volume of said period. This analysis removes errors in the data, as I stated. I just completed that analysis for the volume of ice gained last winter in the arctic. The past 30 year arctic winter volume increase of 15700 km^3 versus an winter increase for 2015-2016 of 26500 km^3. That is a whopper of a difference as compared to the differences taken solely from PIOMAS. The 37 year past volume differences, taken from the PIOMAS data only, yield 16800 km^3 of ice added over the arctic winter, versus 16400 km^3 of ice added over the 2015-2016 arctic winter.The numbers are what they are; I cannot change them. I am simply on a quest for the amount of energy used to melt the sea ice in the arctic. I am looking for differences in energy that are required to melt ice in the arctic. In this way I can relate that difference back to the coefficient of heating for carbon dioxide. So far the amountil of energy difference does not measure up against the difference that I have projected by the coefficient of heating for CO2. I have calculated this coefficient and will have my book out on it soon. I, like all climate scientists, am searching for a climate model that can make accurate predictions.
Toggle Commented Sep 30, 2016 on PIOMAS September 2016 at Arctic Sea Ice
As to the extent of cloudiness, I often check out weather stations such as the ones on Coffee Club Island, Longyear Beyen. Thule Airbase, etc. Albedo of course is another factor to consider. If we are taking all those years as an average, then the model is going to be based on average as a standard. If this year was grossly more different, concerning average albedo, cloudiness, advection, sunspot activity, etc. then perhaps it can't be compared to the past 37 year average melting. I think it would be interesting to perform the volume of ice produced from September 2015 to the peak ice volume in the following April, 2016, preform a linear regression, and compare that to the same analysis of the past 37 year average. Knowing the difference in ice volume produced could at least allow us to calculate a net energy loss difference. CO2 will randomly release photons of IR as it cools in the Arctic winter. This will help cool the warmer currents brought into the Arctic by the heat engine/thermohaline ocean conveyor belt. The Carnot efficiency is calculated from the difference in temperature of the hot and cold reserviors. Assuming this difference is due to differences in energy loss during the Arctic winter , past versus present, we can gain a better idea of how much heat is being lost. This year we know there was less mass of ice melted, so we know less energy was absorbed and trapped.
Toggle Commented Sep 30, 2016 on PIOMAS September 2016 at Arctic Sea Ice
The question I wished to answer from my investigation was : how much more energy today is melting the ice during the Arctic summer melt season. My hypothesis was at least as much of a difference as Carbon dioxide is trapping in. I was surprised to find less volume melted this past year than the last 37 average. Maybe low sunspot activity this year is also a factor.
Toggle Commented Sep 30, 2016 on PIOMAS September 2016 at Arctic Sea Ice
It is very interesting to plot the past 37 year average PIOMAS volumes in the Arctic, from April to September against the same time period surface area data from NSIDC. PIOMAS has an error of +/- 750 km^3 and NSIDC has an error of +/- 1,000,000 km^2. To fix the error, perform a linear regression, with the y intercept at 0. Now do the same for the 2016 data. The value of the slope is the average thickness of the ice. Take this thickness and multiply it by the surface area for each April and September. This of course will give you the volume. Take the difference in volumes from the April to September extremes for each graph. It shows that the past 37 year average had a melt volume of 16200 km^3 and this past 2016 had a melt volume of 14,000 km^3.
Toggle Commented Sep 30, 2016 on PIOMAS September 2016 at Arctic Sea Ice
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Sep 29, 2016