While working on the half dozen other datasets, I’ve also been continuing my now famous work on sea ice. After some small battles and study, I am now able to process the gridded data from NSIDC. This allows me to calculate a reasonable ice area variation. Below are two plots of the gridded data and masks as presented for the bootstrap algorithm. All data were provided by the NSIDC – Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I Passive Microwave Data.
There will be no dissent. There will be no discussion of potential dissent and no independent consideration which may result in the potential for dissent.
These guys are going to turn me into a denier, it’s like they want me to be a denier, maybe I’m too dangerous to their plans. It will be their fault, they like Tamino are the ones who can’t have a reasonable discussion, or maybe my questions are too hard.
Check out this burner that I sent to them regarding the warming antarctic paper they are circularly celebrating.
I wonder if you know when the data and code for this will be released. If it has, where can I find it?
It doesn’t matter to me if the antarctic is warming or not, but I would like to know the details of this study. I’ve read the paper and SI and it isn’t exactly chock full of detail.
Eric Steig was on line answering other peoples questions at the time, he had to have seen my email but instead of answering, it was CUT, SNIPPED, CHOPPED, CENSORED AND BLOCKED.
Not my fault.
This is a continuation of my previous post on temperature amplification.
Where I used a frequency sorting method to plot the amplification factor against time. The following graph was a result of that calculation as shown in the previous post. I put it here for comparison.
The next plot is exactly the same as above, using the GISS data.
Barak Obama is keeping his promises, well some of them. Not the ones about not hiring lobbiests to the top of government or coming clean on where the original stimulus money went but the other ones, the green policy and increased load on American business.
It should say – Obama launches task force AT the middle class but whatever.
Reporting from Washington — On a day when the nation’s gross domestic product suffered its worst slide in three decades, President Obama ordered the creation of a task force on the middle class and signed executive orders aimed at strengthening labor unions.
After some thought, I’ve decided to break this post into several parts. It’s just too big to do all at once and in blog world, the worst thing for your blog is to wait a week between posts. First I will do the surface vs sat data, then I will do the climate model data. It should provide some interesting comparisons between actual measurements and climate models.
I’ve been working on Lower troposphere temperature trend amplification as compared to ground temperatures. Models predict an even amplification factor at various timescales of about 1.3 times in the tropics and 1.2 times globally. Here’s a quote from Dr John Christy which makes this the third time I’ve used it but I think it sets the meaning behind some of the graphs.
The global-mean short term tropospheric amplification factor of 1.2 (it’s 1.3 in the tropics) indicates (a) that the ocean’s thermal inertia (sfc datasets use SSTs) works against large shorter-term changes while the atmosphere is much less massive and can respond to a greater extent and (b) there is a lapse-rate feedback process where the lapse rate tends to move toward the moist adiabat when thermally forced from below. Why we don’t see this amplification factor in the trend metric (which models show also occurs for the trend) likely deals with the feedbacks of the climate system – there appear to be negative feedbacks on longer time scales that models don’t capture. This is a hypothesis we want to test.
Continue reading “Tropospheric Temperature Trend Amplification”
Steig wrote:
Continue reading “Mann’s CoAuthor Doesn’t Like being Compared to Mann”
In a dramatic and unprecedented switch of focus, from winter to summer, scientists have now used computer models to predict the demise of the antarctic Emperor penguin. Well —- in a hundred years.
Continue reading “First it was Polar Bears Now It’s Penguins”
I spent my blog time today discussing temp variance with Willis Eschenbrach on Climate Audit. Link HERE.
In the meantime for the six people who come here that don’t regularly read watts up with that. Check out this amazing article Anthony watts posted.
The non-braindead could tell he was never muzzled as he claimed because he never even slowed down saying ridiculous non-stop rubbish.
The shrill cries of the polyscienticians are ramping up. A host of new “revalations” have come up in the last few weeks as the world cools in the face of the unique opportunity our new socialist government was given. Beauties such as the warming antarctic and now the fact that global warming can’t be stopped. The timing is perfect to drum up support for the increased legislation promised by our US socialist leaders.
Irreversible, yup they said it, as in never to be reversed.
Changes in rain, temperature and sea level are largely irreversible for more than 1,000 years after carbon dioxide emissions are completely stopped, a study released today by federal scientists concludes.
The study, led by the National Oceanic and Atmospheric Administration senior scientist Susan Solomon, appears in this week’s Proceedings of the National Academy of Sciences.
Booming voice – Never to be reversed sed sed.
Lately I have heard another theory of global warming. A seemingly more insidious form created not by gasses collected in the atmosphere or hot air from the politicians (yet) but direct heating from well… energy. Well the skeptic in me immediately has to question this new form of heating simply because it sounds like a massive amount of energy. Can you imagine generating so much power you compete with the sun?
Well here’s an article which claims we will do exactly that.
Over the next 250 years, calculates Eric J. Chaisson in a recent paper, the earth’s population will start generating so much of its own heat – chiefly wasted from energy use – that it will warm the earth even without a rise in greenhouse gases. The only way to avoid it, he says, is to rethink how we generate energy.
I have been investigating the differences between satellite and GISS data. There is an unusual effect in the trend in which ground data’s long term trend is substantially less than the satellite lower troposphere data yet the short term trend of LT data is greater than ground. Models predict that the longer term 30 satellite trend should be 1.2 times the GISS data as well. Dr. Christy hypothesized that the difference between these measures is a missing feedback in the climate models.
By looking at the covariance between UAH and GISS two detrended year low pass filter data I found a UAH/GISS ratio of 1.15 times yet the 30 year trend is .127/.183 = 0.693. I emailed Dr. Christy for some clarification he gave permission for the following quote.
The global-mean short term tropospheric amplification factor of 1.2 (it’s 1.3 in the tropics) indicates (a) that the ocean’s thermal inertia (sfc datasets use SSTs) works against large shorter-term changes while the atmosphere is much less massive and can respond to a greater extent and (b) there is a lapse-rate feedback process where the lapse rate tends to move toward the moist adiabat when thermally forced from below. Why we don’t see this amplification factor in the trend metric (which models show also occurs for the trend) likely deals with the feedbacks of the climate system – there appear to be negative feedbacks on longer time scales that models don’t capture. This is a hypothesis we want to test.
If there are feedbacks on longer timescales affecting the 30 year trend but not the short term, we should be able to see that in the data. I have done the following analysis several ways now. Unfortunately I had trouble with R overwriting memory for some reason. I don’t see where it’s happening but it forced me to use a gaussian low pass filter to create my own bandpass as the Chebyshev and Butterworth filters caused R trouble when reapplied thousands of times.
This is why I don’t respond to references from real climate. They will say anything to support their theory of the moment is. Their latest is in direct contradiction to their latest from February, yet they already knew all of it and it is all perfectly consistent with ‘accepted’ global warming.
From my last post on temperature trend, I showed that there is a short term signal in the satellite data which has a higher rate of change than the ground data while the ground data 30 year trend is higher than the satellite data. Both are substantially different in trend yet models predict the UAH satellite trend should be 1.2 time GISS. Dr. Christy hypothesized that long term temperature trend has a negative feedback mechanism not accounted for in the models. If that were the case we should be able to look at various frequencies and their relative magnitude to see that UAH is higher than GISS at 2 years and as we approach the longer annual frequencies, GISS would trend higher than UAH.
My last post on this was
In the middle of the decade long cooling trend, you know the one which has buried the northern US in feet of snow, our illustrious scientists have determined that the recent ‘global warming’ is killing trees at twice the normal rate. This one got me off so a bit of Venting is in order for sure.
I have been studying the satellite vs ground temperature measurements lately trying to understand why they are so different. There is an unresolved dichotomy in the data. Thirty year longer term trend in the RSS and UAH data is less than GISS data while the short term 2 – 5 year temperature variation in RSS and UAH is between 10 and 25 percent greater than GISS. What makes this worse is that the models actually predict that RSS and UAH should have a 20 percent greater variation than ground measurement on both the short term and the longer term scales. This model predicted difference occurs becasue the measurements cover different sections of the troposphere. Satellite data measures a thick layer of lower atmosphere whereas ground measurments reflect temperatures immediately next to the surface.
The three datasets are plotted below. The GISS anomaly graph has been offset to lay on top of the others for easier comparison.
the Air Vent 