Guest Post by Willis Eschenbach
There is a new paper out by Xu and Powell, “Uncertainty of the stratospheric/tropospheric temperature trends in 1979–2008: multiple satellite MSU, radiosonde, and reanalysis datasets” (PDF, hereinafter XP2011). It shows the large differences between the satellite, balloon (radiosonde), and reanalysis temperatures for the troposphere and the stratosphere. The paper is well worth a read, and is not paywalled. Figure 1, from their paper, shows their tropospheric temperature trends by latitudinal band from each of the sources.
Figure 1. From XP2011 Fig. 3a. Original caption says: Inter-comparison of tropospheric temperature (TCH2) trends (K decade−1) for the MSU (RSS, UAH, STAR), Radiosonde (RATPAC, HADAT2, UK, RAOBCORE, RICH) and Reanalysis (JRA25, MERRA, NCEP- CFSR, NCEP-NCAR, NCEP-DOE) products for the period of 1979–2008. (a) Trend changes with latitude for each individual dataset;
In Figure 1, the three groups are divided by color. The satellite observations are in blue. The balloon-borne observations are in green. And the climate reanalysis model results are in orange. Now, bear in mind that these various results are all purporting to be measuring the same thing—which way and how much the temperature of the lower troposphere is trending. The paper closes with the following statement (emphasis mine):
In general, greater consistency is needed between the various data sets before a climate trend can be established in any region that would provide the reliability expected of a trusted authoritative source.
I can only heartily agree with that. However, there are a few conclusions that we can draw in the interim.
First, despite the fact that these are all plotted together as though they were equals, in fact only two of the groups represent observational data. The results shown in orange are all computer model outputs. Unfortunately, these model outputs are usually referred to as “reanalysis data”. They are not data. They are the output of a special kind of computer climate model. This kind of climate model attempts to match its output to the known datapoints at a given instant (temperatures, pressures, winds, etc.). It is fed a stream of historical data, including satellite MSU and other data as well as station reports from around the world. It then gives its best estimate of what is happening where we have no data, in between the stations and the observation times.
Given that the five different reanalysis products were all fed on a very similar diet of temperatures and pressures and the like, I had expected them to be much, much closer together. Instead, they are all over the map. So my first conclusion is that not only are the outputs of reanalysis models not data. As a group they are also not accurate. They don’t even agree with each other. To see what the rest of the data shows, I have removed the reanalysis model outputs in Figure 2.
Figure 2. Same as in Figure 1, but with the computer reanalysis model results removed, leaving satellite (blue) and balloon-borne (green) observations.
The agreement between the balloon datasets is not as good as that of the satellite data, as might be expected from the difference in coverage between the satellite data (basically global) and balloon data (in certain scattered locations).
Once the computer model results are removed, we find much better agreement between the actual observations. Figure 3 shows the correlation between the various datasets:
Figure 3. Correlations between the various observations (Satellite and Balloon) and computer model (Reanalysis) data. Red indicates the lowest correlation, blue shows the highest correlation. Bottom row shows the correlation of each dataset with the average of all datasets. HadAT is somewhat affected due to incomplete coverage (only to -50°S see Fig. 2), as is RAOBCORE to a lesser degree (coverage to -70°S).
Numerically, this supports the overall conclusion of Figure 1, which is that as a group the reanalysis model results do not agree well with each other. This certainly does not give confidence in the idea of blindly treating such model output as “data”.
Finally, Figure 4 shows the three satellite records, along with the MERRA reanalysis model output.
Figure 4. Same as in Figure 1, but with the balloon and computer reanalysis model results removed, leaving satellite (blue) and one reanalysis model (violet).
In general the three satellite records are in good agreement. The STAR and RSS datasets are extremely similar, somewhat disturbingly so, in fact. Their correlation is 1.00. It make me wonder if they are not sharing large portions of their underlying analysis mathematics. If so, one might hope that they would resolve whatever small differences remain between them.
I have read, but cannot now lay my hands upon, a document which said that the RSS team use climate model output as input to a part of their calculation of the temperature. In contrast, the UAH team do not use climate model for that aspect of their analysis, but do a more direct calculation. (I’m sure someone will be able to verify or falsify that.) [UPDATE: Stephen Singer points to the document here, which supports my memory. The RSS team uses the output of the CCSM3 climate model as input to their analysis.] If so, that could explain the similarity between MERRA and the RSS/STAR pair. On the other hand, the causation may be going the other way—the reanalysis model may be overweighting the RSS/STAR input … because remember, some dataset from among the satellite data, perhaps the RSS data, is used as input for the reanalysis models.
This leads to the interesting situation where the output of the CCSM3 is used as input to the RSS temperature estimate. Then the RSS temperature estimate is used as input to a reanalysis climate model … recursion, anyone?
Finally, this points to the difficulty in resolving the question of tropical tropospheric amplification. I have written about this question here. The various datasets give various answers regarding how much amplification exists in the tropics.
CONCLUSIONS? No strong ones. Reanalysis models are not ready for prime time. There is still a lot of variation in the different measurements of the global tropospheric temperature. This is sadly typical of the problems with the a number of the other observational datasets. In this case, this affects the measurement of tropical tropospheric amplification. Further funding is required …
Regards to all,
w.
DATA:
The data from Figure 1 is given below, in comma-separated format
Latitude, STAR , UAH , RSS , RATPAC , HADAT , IUK , RAOBCORE , RICH , JRA25 , MERRA , NCEP-CFSR , NCEP-DOE , NCEP-NCAR -80, -0.104, -0.244, -0.134, 0.085, , 0.023, , 0.023, -0.243, -0.154, 0.028, 0.294, 0.304 -70, -0.074, -0.086, -0.094, 0.09, , -0.035, 0.071, -0.034, -0.218, -0.115, -0.045, 0.147, 0.148 -60, -0.055, -0.142, -0.074, 0.09, , -0.088, 0.1, -0.148, -0.285, -0.051, -0.094, 0.059, 0.104 -50, 0.005, -0.069, -0.043, -0.006, 0.138, 0.022, 0.081, 0.01, -0.232, 0.032, 0.029, 0.03, 0.114 -40, 0.07, -0.076, 0.026, -0.01, 0.118, -0.107, 0.08, 0.074, -0.081, 0.115, 0.116, -0.005, 0.077 -30, 0.143, 0.082, 0.087, 0.114, 0.123, 0.122, 0.127, 0.126, 0.047, 0.178, 0.22, 0.047, 0.108 -20, 0.182, 0.08, 0.13, 0.12, 0.085, 0.087, 0.143, 0.125, 0.116, 0.213, 0.289, 0.071, 0.097 -10, 0.199, 0.056, 0.153, 0.114, -0.02, 0.082, 0.116, 0.098, 0.069, 0.226, 0.313, -0.003, 0.053 0, 0.195, 0.038, 0.154, 0.089, 0.038, 0.028, 0.136, 0.089, 0.063, 0.284, 0.324, -0.007, 0.061 10, 0.179, 0.034, 0.144, 0.09, 0.064, 0.192, 0.162, 0.137, 0.087, 0.273, 0.328, 0.027, 0.065 20, 0.21, 0.093, 0.166, 0.09, 0.18, 0.16, 0.194, 0.207, 0.115, 0.245, 0.307, 0.115, 0.114 30, 0.23, 0.133, 0.162, 0.247, 0.239, 0.137, 0.238, 0.291, 0.152, 0.257, 0.307, 0.154, 0.153 40, 0.238, 0.164, 0.161, 0.237, 0.213, 0.189, 0.246, 0.3, 0.153, 0.244, 0.268, 0.161, 0.194 50, 0.241, 0.125, 0.161, 0.24, 0.314, 0.213, 0.247, 0.283, 0.166, 0.236, 0.238, 0.161, 0.201 60, 0.299, 0.167, 0.222, 0.283, 0.289, 0.207, 0.335, 0.324, 0.224, 0.288, 0.266, 0.202, 0.239 70, 0.317, 0.177, 0.245, 0.288, 0.289, 0.237, 0.427, 0.393, 0.254, 0.304, 0.269, 0.232, 0.254 80, 0.357, 0.276, 0.301, 0.278, 0.438, 0.384, 0.501, 0.323, 0.226, 0.328, 0.326, 0.235, 0.26
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I had to lol at the ‘fundamental orifice’ comment. Too funny. Ok, continue.
I think correlation isn’t a good measure to compare these trends. RSS and Star are almost perfectly correlated though they differ by an offset (or factor, they could even differ by any positve factor and would still be perfectly correlated).
Perhaps, a rms difference is a better measure.
Manfred says:
November 7, 2011 at 4:36 pm
There’s always more to learn from a dataset. That’s why I included their data at the bottom of the post, so you and others could do those alternative kinds of analyses that might reveal more.
w.
Where does the difference between RSS and UAH of perhaps 0.05 deg/decade come from ?
Is this a mistake ? RSS and UAH should be much closer:
http://www.woodfortrees.org/plot/uah/plot/rss
http://www.woodfortrees.org/plot/uah/trend/offset:0.23/plot/rss/trend/offset:0.14
barry says:
November 7, 2011 at 4:19 pm
First, barry, thank you for a very interesting post.
Second, clearly my words have not been clear.
When I said that they were “all purporting to be measuring the same thing,” I meant they were purporting to measure the mid-tropospheric temperature trend (Channel 2, actually a weighted average). It might have been clearer to say that they were all “purporting to be estimating the same thing”.
Usually, however, when we say we are “measuring the temperature” with a thermometer … would you protest and say that actually we were measuring the volumetric expansion of mercury and using that to estimate the temperature?
So you are assuredly correct sensu stricto … and I think most people understood my meaning, that in a perfect world those measurements should all agree, because they are all measuring (estimating) the same variable. So any departures from that ideal would point to flaws/weaknesses in either the observations or the method or both.
w.
Manfred says:
November 7, 2011 at 5:52 pm
It’s been the subject of much discussion. IIRC it’s to do with using different methods in the processing of the raw data to produce the temperature estimates.. I alluded to that in the head post.
w.
Stephen Singer says:
November 7, 2011 at 2:13 pm
Many thanks, stephen, that’s the document I asked for in the head post. I’ve added it up top, along with the following comment:
Gotta love the power of crowdsourcing, guys like you make it work.
w.
Septic Matthew says:
Has anyone produced an explanation of why the far SH is cooling, the periequtorial region near unchanged, and the far NH is warming?
—
Matt, go see icecap.us. There is a link about high warm bias in measure arctic and antarctic temperatures. More anecdotal evidence that there was a real decline to hide.
Manfred, likewise a simple offset of +0.085 to the UAHs’ raw data seems to show that UAH is clearly higher in recent months (or RSS low): Most data points closely match in the body viewed that way.
http://www.woodfortrees.org/plot/uah/offset:+0.085/plot/rss
Willis,
I completely agree. That there are flaws and weaknesses (and strengths) in the various data sets and methods is well-known, and discussed at length in the literature, here, the IPCC and all over the semi-popular climate blogosphere.. The point of your article seems to be that reanalyses should not be treated ‘blindly’ as data, because they diverge most from the ensemble and each other. But I’m not sure that the point doesn’t rest on a straw man. Who is ‘blindly’ treating reanalyses as empirical data?
The same authors find good agreement amongst data sets for the global mid-troposhpere. The discrepancies are mainly to do with trends at latitude, but the global results are not far off each other. The ensemble mean (for want of a better term) seems to be pretty robust – with the caveat that there is some cross-fertilisation of data! Less confidence is attached to stratospheric trends – the most one could say about them is that the global trend is all the same sign (negative), but the rate globally and zonally tend to diverge. Least well-constrained of all is stratospheric trends over the Antarctic.
Of course, putting the skeptic cap on firmly, one must consider that the paper itself may be flawed. I’ve not the skill to assess that. But I certainly appreciate learning about the trend differences between data sets depending on latitude – something I didn’t know about before today.
W says ‘… model outputs are usually referred to as “reanalysis data”. They are not data.’
Words that should be hung in any science class. Thany you Willis!
Interesting that STAR and UAH have the tropics trending in opposite directions. That looks like a fundamental disagreement about the planet’s heat engine, to me.
Bomber_the_Cat says:
November 7, 2011 at 1:54 pm
“In general the three satellite records are in good agreement. The STAR and RSS datasets are extremely similar, somewhat disturbingly so, in fact”.
Willis, all the satellite datasets use the same satellite data.
=========
I’ve heard that claim also, but I think it is not quite true and that at times UAH and RSS use different instruments. Here’s an excerpt from a Roy Spencer article http://www.drroyspencer.com/2011/07/on-the-divergence-between-the-uah-and-rss-global-temperature-records/ :
“Anyway, my UAH cohort and boss John Christy, who does the detailed matching between satellites, is pretty convinced that the RSS data is undergoing spurious cooling because RSS is still using the old NOAA-15 satellite which has a decaying orbit, to which they are then applying a diurnal cycle drift correction based upon a climate model, which does not quite match reality. …”
I read that as RSS is using NOAA-15 and (by implication) UAH is using a different satellite as well as different processing.
Willis Eschenbach says:
November 7, 2011 at 6:09 pm
Manfred says:
November 7, 2011 at 5:52 pm
Where does the difference between RSS and UAH of perhaps 0.05 deg/decade come from ?
Is this a mistake ? RSS and UAH should be much closer:
It’s been the subject of much discussion. IIRC it’s to do with using different methods in the processing of the raw data to produce the temperature estimates.. I alluded to that in the head post.
w.
————————————————
My point was that RSS and UAH TRENDS differ a lot in THIS study while they are now, after all these discussions, almost identical in reality (less 0.1 deg/decade difference). (http://www.woodfortrees.org/plot/uah/trend/offset:0.23/plot/rss/trend/offset:0.14).
What are you referring to, specifically? Cite?
barry says:
November 7, 2011 at 9:47 pm
Where does the difference between RSS and UAH of perhaps 0.05 deg/decade come from ?
What are you referring to, specifically? Cite?
====================
Eyeballing at figure 4 above, RSS and UAH trends differ approx. up to approx 0.12 K/decade, averaged over all latitudes perhaps 0.05 K/decade, probably even a bit more, because area at 0 deg latitude is largest.
Manfred says:
November 7, 2011 at 9:17 pm
My point was that RSS and UAH TRENDS differ a lot in THIS study while they are now, after all these discussions, almost identical in reality (less 0.1 deg/decade difference). (http://www.woodfortrees.org/plot/uah/trend/offset:0.23/plot/rss/trend/offset:0.14).
=========================
Sorry, should be “less 0.01 deg/decade difference”
Manfred, the woodfortrees graph is a plot of TLT trends for the UAH/RSS – lower troposphere. The graph from the study is of mid-troposphere temperatures. One of the results the authors arrive at is that there is less disagreement between trends the lower in the atmosphere you go. However, as they do not deal with TLT trends, I don’t know if that necessarily follows for lower tropospheric trends. Any case, the trends in your graph and the ones in the study are for different altitudes of the atmosphere.
@barry, that probably explains the differences. Cheers.
It seems to me that much of the excitement in climate science is generated by the scale of the X/Y axes. Looking at the graphs, we are talking about something around 0.2degC range between sets. Pamela Gray, November 7, 2011 at 6:43 am has said it best:
“…let’s round to the nearest whole degree and call anything less noise. That would solve a lot of problems with a noisy data set. Of course it would also cause a great deal of funding to simply dry up and blow away. Can anybody guess why?”
I didn’t see one other posting that took this on.
Septic Matthew says:
November 7, 2011 at 10:39 am
Has anyone produced an explanation of why the far SH is cooling, the periequtorial region near unchanged, and the far NH is warming?
Anything that is atmospheric related should have the same sign in both the NH and SH due to mixing. There might be a lag due to relative ocean areas, but there cannot be a difference in sign between hemispheres related to the atmosphere due to mixing. Atmospheric heat transport alone should prevent this.
What we are left with is the difference in ocean/land/ice surface areas between the hemispheres. This is the most logical cause and effect explanation for differential heating.
What has changed about the land? 100 years ago 4% of the land was used by humans. Today it is 40%, made possible largely through the introduction of widespread mechanization. Most of that change has been in the NH.
Atmospheric changes can’t be the cause because of the effects of mixing. A similar argument holds for the oceans, over a longer time scale. Only the land doesn’t mix, which does allow for differential heating.
In any case, measuring atmospheric temperatures without measuring humidity is nonsense science. Temperature on it own tells you almost nothing about the heat content of the atmosphere. It points to a huge failing in the IPCC and climate science to focus on temperature.
For example, in much of S and SE Asia, the summer monsoon is much hotter than the winter monsoon. Yet it is the summer monsoon that brings life to S and SE Asia.
It isn’t the temperature of the air that is important, it is its moisture content in relation to its temperature that determines feast or famine.
If scientists have a such hard time using all of today’s technology to measure “What is,” I wonder how good the “What was” data is that we are comparing against?
@ur momisugly commieBob
Oceans being a more efficient heat sink would only account for moderating the rate of warming in the Southern hemisphere, not for a cooling trend.