Guest Post by Willis Eschenbach
I chanced to plot up the lower tropospheric temperatures by broad latitude zones today. This is based on the data from the satellite microwave sounding unit (MSU), as analyzed by the good folks at the University of Alabama at Huntsville. Here are the results, divided into tropical, extratropical, and polar. I’ve divided them at the Arctic and Antarctic Circles at 67° North and South, and at the Tropics of Capricorn and Cancer at 23° N & S.
Figure 1. Satellite-based microwave sounding unit temperatures (red line) from the University of Alabama Huntsville. Blue line shows a loess smooth, span=0.4. Data from KNMI (NCDF file, 17 Mb)
So … is this something to worry about?
Well, let’s take a look. To start with, the tropics have no trend, that’s 40% of the planet. So all you folks who have been forecasting doom and gloom for the billions of poor people in the tropics? Sorry … no apparent threat there in the slightest. Well, actually there is a threat, which is the threat of increased energy prices from the futile war on carbon—rising energy prices hit the poor the hardest. But I digress …
What else. Southern Extratropics? No trend. South of the Antarctic Circle? No trend, it cooled slightly then warmed slightly back to where it started.
So that’s 70% of the planet with no appreciable temperature trend over the last third of a century …
What else. Northern Extratropics? A barely visible trend, and no trend since 2000.
And that means that 96% of the planet is basically going nowhere …
Now, that leaves the 4% of the planet north of the Arctic Circle. It cooled slightly over the first decade and a half. Then it warmed for a decade, and it has stayed even for a decade …
My conclusion? I don’t see anything at all that is worrisome there. To me the surprising thing once again is the amazing stability of the planet’s temperature. A third of a century, and the temperature of the tropics hasn’t budged even the width of a hairline. That is an extremely stable system.
I explain that as being the result of the thermoregulatory effect of emergent climate phenomena … you have a better explanation?
My best regards to everyone,
w.
PLEASE! If you disagree with what I or anyone says, QUOTE THE WORDS that you disagree with, and say why you disagree with them. That way we can understand each other. Vague statements and handwaving opinions are not appreciated.
DATA: All data and R code as used are here in a zip file.
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Jos:
At January 29, 2014 at 7:53 am you say
True, and it is equally true that it is
Cl;early, the “more dramatic” fact is also the most honest and most informative in each of these two statements.
Richard
PS Are you Tim Yeo because yesterday that sleazy dimwit demonstrated to the world that he wants to promote the same stupid point as you have?
Trend lines plotted with statistical functions always have a slope. In this case the slopes are zero. Saying there is “no trend” is different than saying the trends are zero.
Inspection of the North Pole plot may (or may not) show some kind of non-zero trend, but in the context of multidecadal trends, I’d say the North Pole plot also shows no significant warming.
To test that statement, you could show the North Pole plot to some local mathematicians without context or labels. Then they might say those data show a slight sinusoidal pattern.
The most recent data also show values not significantly different from the long term average.
BTW, I’ve recently found that the lunar effect on Arctic ice cover is found at two periods : 27.6d , lunar distance and 29.94d. The latter is a significant find since it is the synodic (visible) lunar cycle _ in the winter months_, not the annual average. This shows that the strongest influence of the new/full moon cycle is during the winter.
http://climategrog.wordpress.com/?attachment_id=756
It has already been noted by some that Dec-Jan-Feb AO index has most effect on the annual minimum in September without an physical link being proposed or why we should be looking at DJF values.
My frequency analysis shows why.
RichardLH:
I appreciate that you have replied to me with your post at January 29, 2014 at 8:12 am.
Unfortunately your reply does not remove my failure to understand what you are trying to do and why.
I broke things down to a set of questions which explained what I don’t understand and your answer does not address any of them.
Anyway, you did provide a reply. So, thankyou.
Richard
euanmearns,
http://images.remss.com/msu/msu_data_monthly.html
You can watch it happening month by month there (the “Anomaly” option).
One can also easily see exactly how “global” all this so-called “Global Warming” since 1979 has been by clicking “Trend” – and I must say, not exactly the map they promise we’ll see
bw says:
Trend lines plotted with statistical functions always have a slope. In this case the slopes are zero. Saying there is “no trend” is different than saying the trends are zero.
Good point. A far better approach is to say there is “no trend” in any climate data and then start doing some proper analysis. Virtually nowhere in climate is a trivial linear function an reasonable model to fit to the data. So there is “no trend” is the correct statement.
bw:
At January 29, 2014 at 8:17 am you say
If you choose to be pedantic then try to get it right.
Trend lines plotted with statistical functions always have a slope. In this case the slopes are not discernibly different from zero at 95% confidence. Saying “the slopes are zero” is different from saying the trends are are not discernibly different from zero at stated confidence.
Personally, I think “no trend” conveys the meaning to the chosen audience succinctly.
Richard
I think that ferdberple in his January 29, 2014 at 6:08 am post, ” The graphs suggest that the tropics are very efficient at maintaining temperatures withing a narrow range, and the paleo records support this. The tropical ocean heats up, convection at the ICZ increases, the Trades strengthen, cooling the tropics.” is on the right track. As I commented on your post about the CERRES data, it looks more and more like the Earth’s climate system is a closed loop system. Looking for the control input, I can’t find anything that is more stable than the Ocean and land temperatures within the horse latitudes. The return flow for the Hadley cells passes over this area interacts with the convection at the ICZ.
Stephen Wilde says:
“If the system tries to accumulate more energy then warm water pumps further into the Arctic Ocean, […] If the system starts to lose energy the flow of warm water reduces”
No transport of warmer water into the Arctic increases when the system is loosing energy, specifically when the AO/NAO are more negative, which typically accompany El Nino episodes.
Gail Combs says:
January 29, 2014 at 6:01 am
You forgot the best one, they warned that “this drought could last until December!” I believe it was made towards the end of February, 2012! A few weeks before the Heavens opened almost permanently! 😉
50 years ago the kelvin actually did have a °, but it lost it with the new definition in 1967: “The triple point of water is 273.16 K”.
But celsius kept its °, 0°C is defined as 273.15K.
Can’t say I’ve seen the data split into the tropics, extratropics and poles before. Thanks for digging it up and posting it.
Amazing how the warming has been just a northern hemisphere phenomenon.
It will be interesting when all the polar bears and other animals in the northern hemisphere are all dead from catastrophic climate change, while the southern hemisphere remains just dandy.
richardscourtney says:
January 29, 2014 at 8:20 am
I though I summarised your point of view quite well.
You do not believe it is possible to derive a ‘Mean’ at all.
Others do.
I use their definition and observe that many disparate points of view can be best assimilated by using proxy methodology.
Greg says:
January 29, 2014 at 7:52 am
“PMHinSC says:
I was taught that 273K was 273,000 and left to wonder what the units are.
===
I doubt you were taught that, you were probably looking out of the window and listening with one ear. ;)”
Actually I was probably dreaming about girls. I think I will, however, continue to believe that a number without units (ratios excepted) are like steak without wine; something is missing. No criticism intended.
DS – thanks for link.
http://www.euanmearns.com/wp-content/uploads/2014/01/temp_anom_N_Pole.png
Hope this link to screen capture works, did a polar view, Dec 2010, some amazing bright spots at high latitude. And a wacky radial distribution of positive and negative anomalies that I’ve not seen before. Taking into account what Greg says, how do you account for the extreme high temp anomalies over N Canada and Greenland in Dec?
Greg Goodman says:
January 29, 2014 at 8:23 am
“Virtually nowhere in climate is a trivial linear function an reasonable model to fit to the data.”
It is valid WITHIN the range of the data, Outside of that it has no real meaning. Could be used for infilling missing values. Lousy at predictions at either end.
‘Linear trend’ = ‘Tangent to the curve’ = ‘Flat Earth’
Willis, I don’t think it would make any difference, but you shouldn’t use KNMI when you can go straight to the UAH website for the data, since the version available at KNMI is not the most up to date-version 5.5 versus 5.6.
@ur momisugly Greg- 1988, 1997, 2010, are all strong ENSO events. Tropical temperatures closely correlate with ENSO.
Ulric Lyons said:
” transport of warmer water into the Arctic increases when the system is losing energy, specifically when the AO/NAO are more negative, which typically accompany El Nino episodes”
If the system is already losing energy then pumping more water into the Arctic for faster loss to space would be a positive feedback but the system feedbacks appear to be negative.
So, I think that more warm water into the Arctic must be a response to warming as a negative system response.
Of course, on certain time scales there could well be periods when the air and ocean circulation systems are out of phase but over the millennial solar cycle I think I have it right.
The AO seems to respond to solar variability as witness the recent record negative near the lowest part of the current solar cycle.
Why are your graphs so different from the browser tool?
MSU & AMSU Time Series Trend Browse Tool
http://images.remss.com/msu/msu_time_series.html
Channel = Total Lower Troposphere
Tropics (-25’S to 25’N) = +.107K per decade
North Mid Latitudes (25’N to 60’N) = +.185K per decade
North Polar (60’N to 82.5’N) = +.325K per decade
Note: if 82.5’N to 90’N were included in this analysis the increase would be even greater!
Global (-70’S to 82.5’N) =+.125K per decade
Retired Engineer John said:
“it looks more and more like the Earth’s climate system is a closed loop system. Looking for the control input, I can’t find anything that is more stable than the Ocean and land temperatures within the horse latitudes. The return flow for the Hadley cells passes over this area interacts with the convection at the ITCZ.”
That fits with my proposition that the adiabatic convective system is a closed loop due to KE converted to PE during uplift being reconverted back to KE on descent.
The two processes have to remain in balance for as long as the atmosphere is being held off the surface. Any period of imbalance results in a higher or lower atmosphere with a changed internal circulation.
That changed internal circulation is the negative system response that we perceive as climate change.
@richardscourtney and @richard LH
Actually, UAH and RSS are not measures of global *surface* temperature at all. They are measures of the temperature of a bulk atmospheric region, and effectively measure well above the surface-although the LT is closely connected to surface temperature.
However, it also is not a 1-1 connection. Rather, the global LT anomalies tend to be larger swings than the surface temperature-that is, when the surface warms, the LT warms more, and when the surface cools, the LT cools more. I took the average of GISS, HADCRUT4, and NCDC surface temperature annual anomalies (rebaselined to 1981-2010) detrended that average, and compared it to UAH LT annual anomalies similarly detrended. A simple regression suggested a best estimate for the factor by which LT anomalies vary more than the surface of about 1.44. So if either of you wants to compare UAH or RSS to the surface, you should probably divide them by such a factor first. I think that number is skewed high by GISS’s reduced interannual variance, it is probably closer to 1.2 or 1.3. Either way, the comparison done this way-comparing surface temperature trends in the official datasets, to “inferred” trends from satellites, is kind of enlightening: there seems to be a significant divergence of the trends: Satellite surface trends *inferred* from LT trends, are much lower than the official surface trends.
Stephen Wilde says:
“If the system is already losing energy then pumping more water into the Arctic for faster loss to space would be a positive feedback but the system feedbacks appear to be negative.”
Pumping more water would increase the energy loss from the system yes, but nontheless it warms the Arctic and raises the average global surface temperature, as does an El Nino.
“So, I think that more warm water into the Arctic must be a response to warming as a negative system response.”
No it causes a warming, as an El Nino also does, and they both reduce the energy of the system. They are a negative response, to a drop in solar forcing, see 1997/98 and 2009/10:
http://snag.gy/nf9SK.jpg
RichardLH:
Perhaps iot is not possible for us to stop talking past each other
January 29, 2014 at 8:55 am
You did not summarise my point, You ignored it.
Of course I “believe” it is possible to derive a mean!
I can obtain a mean of the weight of stones if I can measure their individual weights.
I can obtain a mean of the height of tides if I can measure their individual heights.
But I do not see how I can combine the mean weight of stones with the mean height of tides to determine a mean from it: of course, I could add the two values together and divide by 2 to obtain a number, but that would not be a mean. And that “others” choose to do that is no reason to accept their number.
What is “their definition”?
They do not have one: they each have a different definition so provide the equivalent of ‘mean stone weights’ and ‘mean tide heights’.
And what is your “proxy methodology”?
It seems to be normalising so the different versions of GASTA can be plotted over each other, and I fail to see what benefit that provides.
Richard
Willis, there’s an interesting paper by Kyle Swanson in GRL looking at the growing convergence of models with one another and their simultaneous divergence from reality; https://pantherfile.uwm.edu/kswanson/www/publications/GRL_selection.pdf. The Arctic really stands out as the place where dramatic warming takes place. What seems to have happened between CMIP3 and CMIP5 is that models now fit the Arctic better, but as a result fit everywhere else worse. So, as Swanson rightly points out, that suggests that the changes to the models probably didn’t even get the story behind the Arctic warming right, since if they did, they’d work just as well or better everywhere else too. Something to bear in mind when looking at these zonal trends — a strong trend in the NH high latitudes doesn’t count as some kind of “partial” vindication of the models if, in order to reproduce it, the models get worse in the tropics, extratropics and SH.
timetochooseagain:
I agree all you say in your post at January 29, 2014 at 9:32 am .
I especially agree your point
Yes!
Basically you are saying that UAH and RSS measure temperature in different sample points from GISS, HadCRUTn, etc..
But GISS, HadCRUTn, etc. also choose different sample points from each other.
So, your point applies to all the surface data sets, too.
And GISS, HadCRUTn, etc. uses a unique method for amalgamating, weighting, interpolating and extrapolating those points as a method to infer global temperature.
In other words, each of the data sets is a unique indication of a unique something and nobody knows what that something is for any of the data sets.
UAH and RSS are the most similar because they use the same sample data points with such high global coverage that they require least interpolation and extrapolation. But they differ in their methodology.
Direct comparison and/or combination of these data sets is like comparing the weights of stones to the heights of tides: there are physical reasons why there will be some relationship between the data on weights of stones and heights of tides obtained from the same beach, but nobody would claim they are the same thing.
Richard