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
Sparks (November 7, 2011 at 5:45 am )
You make a good attempt to prove something which is mathematically impossible.
Here’s your 8 day average temperature series as you present it –
1°C + 19°C + 3°C + 20°C + 2°C + 18°C + 0°C + 20°C :=83 /8 :=10.3 °C
Actually, the average is not 10.3, it is 10.375, and if you want to prove your point that you can get different answers by ‘mixing up’ the data, then you have to the sums accurately -then you’ll find you can’t.
For the next 8 day average temperature you say “1°C + 1°C + 1°C + 20°C + 1°C + 1°C + 0°C + 20°C :=44 / 8 :=5.5 °C”, but this is wrong. The temperatures total 45, not 44, so the correct average for the second set is 45/8 = 5.625
So, correcting for this and ignoring the obvious slip which DirkH pointed out…
First 8 days + second 8 days (10.375 + 5.625) / 2 := 16 / 2 = 8.0 °C
….and the complete 16 day average is (45+ 83)/2 = 128/16 := 8.0 °C…
…which is exactly the same – so dividing up the numbers differently makes no difference.
“Carrick says:
November 7, 2011 at 7:53 am
I generally don’t bother with Sks but followed it since you linked it. I agree with Tom Curtis’ critique of the SkS post you linked.
“Likely flawed” just shows your (IMO) confirmation bias.”
Likely flawed is maybe too strong, likely biased low is the appropriate description. It isn’t about confirmation bias it is about science and there is strong evidence that RSS and UAH have underestimated trends in the atmosphere. UAH in particular has a LONG history of making mistakes in their analysis (and allowing their analysis to subsequently be used to substantiate false claims vis-a-vis global temperature). I’m not familiar with Tom Curtis’ critique of the SKS post linked to, could you point me to the exact comment?
I think the point of this whole discussion is that the reanalysis products will always have inherent biases when the input datasets have them. I very much condemn the argument seen here and elsewhere quite frequently that the satellite records are the definitive “gatekeeper” of global temperature trends. As has been demonstrated throughout the history of their development, the satellite record has had substantial errors pointed out on numerous occasions (Zou et al 2010 have corrected some ones still embedded in the UAH and RSS analysis) and yet it is used as a gold standard against the surface temperature measurements. That’s the “confirmation bias”.
Reanalysis models are not ready for prime time but it has been on stage and in the limelight of the congress for enough time to be the dominate factor in their decision making. Why, because someone has presented models as undeniable proof of global warming to an all too receptive congress.
The problem is not the models but the unprofessionalism of those who present them for what they are not, those who willingly accept them without question and those who fund research with strings attached to the desired results.
Willis is right further funding is needed. It is the general purpose of the funding that has become the problem.
jens raunsø jensen says:
November 7, 2011 at 4:51 am
Linear trends are not my tool of choice by a long ways. I use them because their use is so widespread, but I don’t like them. Nature moves in steps and chunks, not in straight lines.
In addition any attempt to mechanically determine the “breakpoints” in climate data needs to be very carefully researched and tested. Far too often it seems like someone had a bright idea for an algorithm and applied it to 1,500 datasets without checking them all, individually, to see what the algorithm has done.
Thanks,
w.
barry says:
November 7, 2011 at 7:02 am
richard verney,
What is the point of ‘reanalysis data’ when you have empirical observational data?
All temperature data has problems. All. We just don’t have God-like observational tools. Which is why data has to be tested and adjusted where appropriate, and why reanalyses are done, combining many different data streams. The goal is always to improve the fidelity of the data.
Right, barry, we don’t have God-like observational tools, so we must “test” and “adjust” the data, which means we must act like God anyway, you know, to make the data fit the hypothesis we also simply confabulated right out of our “just so” imagination, and producing a perfect record of failure as to its predictions compared to empirical data, data which must therefore be wrong, now even including the satellite data to boot! We not being “God” and all.
Your God, Mammon, says, “Thou shalt always repeat memes, repeat, repeat, repeat.” Making them come true!
Well, barry, enc.: ~
But it’s just your imagination
Running away with you
It’s just your imagination
Running away again….. [Oh, the Temptations!]
“Imagine” that!
Reanalysis products are useful if you know what the limitations are – for example, anyone trying to do trend analysis with reanalysis should be extremely cautious. There are issues with data assimilation – eg, data sources change – and the resolution of the model can have a profound impact on many variables. Further, when people use tropopause products directly from these groups I cringe. It’s much better to calculate things like that yourself, so you know how the diagnostic was obtained, because a lot of these groups have no documentation.
The reason they’re useful is that they combine many different data sources through an assimilation process similar to the initialization of a weather model. The drawbacks are a lack of data in the SH, so you have varying degrees of data density, spurious trends, etc.
Again, these have their uses, you just need to be careful and understand the limitations. For us dynamics folk, they are often the best source to understanding mean states of the atmosphere – just not trends!
A pretty good description of reanalysis from RC:
http://www.realclimate.org/index.php/archives/2011/07/reanalyses-r-us/
Makes many of the same caveats as Willis does here, but provides more detail.
Robert says:
November 7, 2011 at 8:32 am
UAH was the first team to attempt calculating the temperature from the MSU sensors. Of course they made errors, that is inevitable. And unlike the folks at RC and at RSS, when the UAH errors were pointed out, they were acknowledged and fixed.
However, from there you part company with science, because this history of corrected errors should improve rather than decrease our confidence in their results. This kind of rooting out of error is what science does best, and what gives us confidence in the results.
For example, the measurements of the speed of light have become more and more accurate over the years … should this “LONG history” of errors in the calculation of the speed of light decrease or increase our confidence in the final result? Or take Milliken’s oil drop experiment, which famously has been shown to be flawed … should we believe less in the charge of the electron because of his error?
Finally, the UAH team did not “allow their analysis to subsequently be used to substantiate false claims”, any more than Robert Milliken did. There is no way to stop anyone’s analysis from being used by others in whatever way they might use it. All that sentence does is reveals you’ve left the scientific reservation entirely, and are now just feeding us your dislike of the UAH results …
w.
Robert says:
November 7, 2011 at 8:32 am
I very much condemn your argument seen here that I have said somewhere above that the satellite records are the definitive “gatekeeper” of global temperature trends, or of anything else. Recall that I said that I agreed strongly with the following from the paper:
and I closed by saying that:
So I said nothing about “gatekeepers” in any shape or form. In other words, Robert, you’re just pulling stuff out of your fundamental orifice in order to attack me. That loses you points and can get your vote cancelled, because very quickly people start to point and laugh and no one listens to you any more.
w.
ndcodeblue says:
November 7, 2011 at 9:56 am
Unfortunately, your logic is circular. You say that “Reanalysis products are useful if you know what the limitations are” … but if we know what the limitations are then we can fix them. The problem is, we don’t know what the limitations of any given model output might be, if we knew that we’d fix them and they wouldn’t be limitations.
So it’s not at all clear to me, ndcodeblue, just how you would verify whether some given result from some given reanalysis model was “limited” or not. I mean, the only way we could determine that the model gives bad answers would be to compare the model to the observations … but we only need the model because we don’t have the desired observations.
That’s what I mean by circular. If the reanalysis model tells us the temperature at point X was 17°C, we cannot check that temperature because we don’t have an observation at point X … if we did, we wouldn’t need the reanalysis model …
Finally, the existence of the five widely separated answers from the five different reanalysis models should make it clear that some of the models have very real problems … but that tells us nothing about the “limitations” of those models in any given instance.
w.
Bomber_the_Cat says:
November 7, 2011 at 8:29 am
Yes thanks Bomber_the_Cat, we established it was wrong, a good point that you raised tho is why I used 10.3 and not 10.375, these are just quick figures off the top of my head done on the fly to use as an example so I rounded it off, like I said, I was a little bit rushed. apologies.
“Dividing up the numbers differently makes no difference” correct, I wasn’t trying to say this.
it’s good thing you and dirk came along to put me right, it does show that readers here are sharp and don’t miss much.
“You make a good attempt to prove something which is mathematically impossible.”
I try, need more funding to be able to spend more time on it.
Either way despite the error the averages still do Not relate temperature to cause on a time line.
Robert: I very much condemn the argument seen here and elsewhere quite frequently that the satellite records are the definitive “gatekeeper” of global temperature trends. As has been demonstrated throughout the history of their development, the satellite record has had substantial errors pointed out on numerous occasions
FWIW, I agree with Willis’ response on this post of yours. That’s if you care how the audience responds.
Willis, this was a good post. Thanks.
Has anyone produced an explanation of why the far SH is cooling, the periequtorial region near unchanged, and the far NH is warming?
Sounds more accurate and precise… to me, climatically speaking! GK
Smokey has the good call.
If you are going to plot observational data, please provide reasonable (or ANY) error bars for that data.
We see less than 1 degree C total variance both within and between much of these various series so it would seem even minor sources of errors would make the observations (I assume they are simple arithmetic averages) meaningless.
Straining at gnats.
Aerosols certainly don’t explain that unless China and India have suddenly shifted to the southern hemisphere.
Aerosols do explain it.
This is satellite and radiosonde data measuring troposphere temps.
Aerosols block and scatter solar radiation directly warming the troposphere. And this is the mechanism by which they exert a cooling influence on the climate. This energy gets radiated out to space faster than solar radiation that reaches the land/ocean surface.
Which means troposphere temps don’t measure climate warming/cooling.
Long but interesting paper on aerosols.
http://medias.obs-mip.fr/igac/html/book/chap4/chap4.html#aerosols
Willis, I have one small input;
UAH data; I remember reading somewhere that Roy Spencer said that there is very good match between balloon data and satelite data. And that he therefore is confident the UAH data is correct.
Therefore I think you miss an important plot up there; UAH data versus balloon data.
Alone, together. Not tarnished by model outputs.
commieBob says November 7, 2011 at 8:22 am:
“——————Actually, it isn’t much of a mystery. The ratio of land to ocean in the NH is about 1:1.5 whereas in the SH it is 1:4. The ocean is much more efficient at storing heat than the land is.”
And therein, whether you realize it or not, you have explained “The Natural Greenhouse Effect” in a couple of sentences. – All you have got to do is to add the fact that the Earth spins on its axis fast enough for most of the surface (70.9%) to face the Sun every morning at a temperature that is by far much higher than -18 °C.
Next time, just before you start doing the ironing, suspend the iron by the handle (use a piece of string to tie the iron in place as the electrical cable is not meant for this particular purpose) a couple of feet or so above the floor upon which you must put a thermometer. (Make sure the thermometer is directly in line with the iron’s “hot-plate” and do use another “check thermometer” close by, but not too close, just in case the under-floor heating comes on) – Now turn the iron on and leave this set-up for an hour or two while doing regular temperature checks. – Note down the time-span between when the iron is turned on and back-radiation from the iron starts to warm the thermometer and by how much the rise of T is.
And by the way John Tyndall (1861) never used a thermometer in his experiment to prove that CO2 was a greenhouse gas.
All he proved was that CO2 and some other gases blocked the electro magnetic signal – or signals – from either the thermo-pile – or from the heat source – or more unlikely both, but as he never used a thermometer in the brass tube into which he introduced CO2, he never proved that “Black heat radiation from a “heat-source” can heat anything.
I’d go as far as saying that we are seeing 2 different signals in the SH and NH data.
In the SH, there are limited amounts of anthropogenic aerosols and the troposphere cooling is IMO largely natural. IE, the SH troposphere temps are showing a genuine climate cooling signal.
In the NH and tropics we seeing a anthropogenic aerosol signal and the tropospheric warming is in fact evidence the NH is cooling, for the reason I explained above.
Smokey says:
>>
Error bands are wider than the tenths of a degree puportedly being measured. When a normal y-axis is used, the obvious conclusion is that there’s nothing to panic about. The small changes over the past fifteen decades are down in the noise.
>>
The signal to noise ration does make finding out what is happening and proving or disproving why a whole lot harder.
However, it has no bearing on whether there is anything to worry about.
Remember the ostrich? Not being able to see the danger does not mean it does (or does not) exist.
Getting more serious about error estimation is part of what BEST claim to be about. Thoug I think they have some work to do before they publish.
Why does their algorithm take out the 1998 El Nino. ?
http://tinypic.com/r/11brrqe/5
I’m a little confused here. I agree the water is a superior heat retainer compared to land…..but if the NH land-to-ocean ratio is 1:1.5 and the SH is 1:4…..shouldn’t the exact opposite be happening.Or am I looking at the wrong temp sets(SST’S)?
“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. Why is it a surprise to you if they arrive at similar results? The real intriguing question is – why don’t they all come to exactly the same result?
Here’s a link to the RSS document that describes how they use radiative transfer models and GCM(CCSM3?) in their processing of the raw satellite AMSU data.
Perhaps you’ve seen this before, if not enjoy.
http://www.ssmi.com/data/msu/support/Mears_and_Wentz_TMT_TTS_TLS_submitted.pdf
fred berple,
You may have got the impression from the article that you are looking at lower tropospheric temperature trends, but channel 2 covers a broad band of the mid troposphere. 80% of the measurement is troposphere, and the rest is land surface and stratosphere. The comparison is with radiosonde and and renanalyses of mid-tropospheric temps. While the mean temperature trend is positive, most data sets show slight cooling south of 40S.
RSS discuss CH2 issues in this 2003 paper, similarly showing mid-tropospheric cooling trends over the Southern oceans and Antarctica.
http://www.remss.com/papers/msu/A_Reanalysis_of_the_MSU_Channel_2_Tropospheric_Temperature_Record.pdf
Just to stress, ‘mid-tropospheric’ is a bit of a misnomer, and despite Willis’ suggestion that the various data sets are “all purporting to be measuring the same thing,” they do not measure the same thing. MSU measures radiance, radiosondes measure temperature, and the reanalyses try to combine datasets. In the case of the satellite data, the radiance measurements come from a deep section of the atmosphere which includes the lower stratosphere (which is cooling). There are bound to be discrepancies.
As to why there are negative trends South of 40S in the mid-troposphere – beats me. I wonder if it might have something to do with ozone depletion, or it may simply be the natural thermal characteristics of the planet in that region of the sky, even with warming at the surface.
A few quotes from the study Willis has shared with us;
You’re not confused at all. More water coverage should mean less extremes. But it may also mean more clouds leading to a cooling.
I personaly don’t think it’s a mystery at all. The two hemispheres do mirror each other in some aspects of climate (i.e. less ice in Arctic, more ice in Antarctic) so if one is warming for whatever reason, the other may well cool.
The atmosphere is a gas afterall and will behave as such. Think about high pressure and low pressure and air movements.