Why Reanalysis "Data" Isn't

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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richard verney

What is the point of ‘reanalysis data’ when you have empirical observational data?
Why not and would it not be better to work from the empirical observational data?
I agree that the model creations (ie., the ‘reanalysis data’) appear way off target (although if they were to be averaged they may well provide a better fit with reality) and are another example as to how poor the models are. I guess nothing new there.

jens raunsø jensen

Hi Willis,
thanks for sharing this with us. I have not worked with the satellite or reanalysis data, but having just completed a step change analysis of nearly all complete and long term records in the GHCN (except North-America and Australia) I find strong support for the likely existence of real step changes in temperature records (hopefully I can post this later). If that is the case, the basic assumption of linearity underlying the widespread use of linear trend analysis (as also used in the XP2011) is violated and the resulting trend misleading. Given your insight and experience with the temperature records and analyses, would you have any thoughts on this fundamental issue, eg. should we discourage the use of linear regression as a method to identify “the pattern” in temperature records ?
Thanks … jens

Bloke down the pub

I suspect that error bars on the reanalysis data would be wider than any anomaly.

Data are readings taken from instruments. Period.
Missing data are forever missing.
Nothing produced by a model is “data”.
Rumplestiltskin is not a climate modeler.

RACookPE1978

Gee. That’s funny.
Last time I heard of temperature “data” for the Arctic, NASA-GISS’s Hansen was claiming +4 degrees across the whole Arctic landmass…… (Seems like he is plotting a huge “red” mass of hot air based on some 1200 km “averaging” and extrapolation scheme he came up with in some 1988 paper with a r^2 of 0.42 ….)
Now, is it +4.0 degrees? Or +0.4 degrees?

Ha! “Further funding is required …”
Here’s a simple Idea, Just scrap the Idea of ‘average global temperature’ problem solved!! And people like yourself can get on with real science.
All this averaging out of large amounts of data seems to show irrelevant results anyway, it does not relate very well to temperature nor can the result be used for any study related to causality which AGW enthusiasts do all the time, an environment with a variable range of temperature of between 0°C and 20°C can have any average temperature anywhere in-between at any giving time and it isn’t too hard to get the result you want just by mixing up and using different methods or by adding/subtracting favorable data.
Here’s an 8 day average temperature
1°C + 19°C + 3°C + 20°C + 2°C + 18°C + 0°C + 20°C :=83 /8 :=10.3 °C
Here’s another 8 day average temperature
1°C + 1°C + 1°C + 20°C + 1°C + 1°C + 0°C + 20°C :=44 /8 :=5.5 °C
Here’s a 16 day day average temperature from the above data calculated in two different ways
First 8 days + second 8 days 10.3 °C + 5.5 °C / 2 := 15.8°C
Complete 16 day
1°C + 19°C + 3°C + 20°C + 2°C + 18°C + 0°C + 20°C+1°C + 1°C + 1°C + 20°C + 1°C + 1°C + 0°C + 20°C :=128/16 :=8°C
Not a very scientific result to use or to show an accurate representation of the temperature of an environment over time, the margin of error on any variable temperature average is 50-100% Why? because it produces variable average results. if we model variable averages to trends such as Co2 the results become unusable as the rising trend of Co2 takes over and the the margin of error becomes 100% and all the data becomes unusable. Try to relate Solar effects to average global temperatures with a Co2 trend, It cant be done but reliable individual temperature readings fit very well.
Here’s my processors average temperature which is less variable over 8 days between 48°C and 50°C
49°C + 50°C + 48°C + 50°C + 49°C + 50°C + 49°C + 50°C :=395/8 := 49.3 °C
average temperature over 16 days
49°C + 50°C + 48°C + 50°C + 49°C + 50°C + 49°C + 50°C + 49°C + 50°C + 49°C + 50°C + 50°C + 48°C + 50°C + 49°C :=790/16 :=49.3°C
First 8 days + second 8 days 49.3 °C + 49.3°C:= 98.6/ 2 := 49.3°C
very accurate result!

Chuck Nolan

Further funding is required …
Willis have you contacted the “TEAM” to see if they could steer you towards some “Obama Money” to continue your research. They would be my first choice because they have really deep pockets. (Oh wait, their hands are in my pants pockets). Good luck.
If you get the chance tell Mann I want my data I paid for.

DirkH

Sparks says:
November 7, 2011 at 5:45 am
“Here’s a 16 day day average temperature from the above data calculated in two different ways
First 8 days + second 8 days 10.3 °C + 5.5 °C / 2 := 15.8°C

10.3 + 5.5 = 15.8
15.8 / 2 = 7.9
Forgot a bracket?

richard verney

DirkH says:
November 7, 2011 at 6:21 am
Sparks says:
November 7, 2011 at 5:45 am
//////////////////////////////////////////////////////////
Yes and isn;t 83 (first period) + 44 (second period) = 127 and not 128, so that the average of these periods should be 127/16 and not 128/16.
I have not checked the addition of the individual series.

P. Solar

Interesting post Willis, wise analysis.
It would be interesting to see the horizontal line recalculated for your stripped down versions. I presume this is the all latitudes average for all the data. It appears to be 0.14 K/decade
This is exactly what I just got from a totally different method looking at d2T/dt2 in HadCrut3
It needs writing up and checking but the bottom line was 1900 dT/dt trend of 0.2 K/century and second diff of 1.2 K/century^2 .
That would give a current value of 1.4 K / c as shown in that paper, and an average around 0.7 K/c for the last century, which seems to be a generally accepted value.
This sort of value is firmly in the DON’T PANIC zone.

Pamela Gray

If our degree of “agreement” were broadened, would not the correlation be near perfect? As in 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?

ferdberple

Isn’t the real question why the norther hemisphere is getting warmer and the southern hemisphere is getting colder if CO2 is well mixed?
How is it even possible that the southern hemisphere is getting colder? AGW certainly doesn’t explain that. Aerosols certainly don’t explain that unless China and India have suddenly shifted to the southern hemisphere.
This is a huge mystery completely unaccounted for in current theories of climate change. There is no way the southern hemisphere should be cooling while the northern hemisphere is warming over a 30 year timespan.
Why isn’t climate science all over this? It is a huge unexplained mystery in an area in which the science was supposed to be settled.

P. Solar

jens raunsø jensen says:
>>
would you have any thoughts on this fundamental issue, eg. should we discourage the use of linear regression as a method to identify “the pattern” in temperature records ?
Thanks … jens
>>
The post war drop is significant oddity in the 20c record. The problem seems to arise when pretend scientists decide to use this trough as point of reference when calculating temperature “trends”.
GH warming is a log function of gas concentration. CO2 concentration is reckoned to be rising in a roughly exponential way. That gives a linear increase in the “forcing”. A radiative forcing will produce a rate of change of temperature not a simple change of temp. Anyone trying to fit a linear model to temperature has not understood the first thing about the physics. They are trying to fit a straight line to a parabola.
Fitting a century long linear regression to dT/dt using HadCrut3 temps gives credible figures. It would appear that that sort of timescale may be sufficient to even out the steps.

barry

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.
Willis gives the impression that the satellite data are better than the rest. But this is not necessarily the case. There are 5 global temperature satellite data sets that I know of, and the decadal trend for each ranges from 0.14 to 2.0C/decade degrees over the last 30 years or so.
Problems abound with satellite data. From the study Willis is citing:

Unfortunately, similar to the shortcoming in the radiosonde observations, the number of satellite instruments and changes in design impact observational practices and the application of the data. For example, the MSU data come from 12 different satellites and the data quality is significantly affected by intersatellite biases, uncertainties in each instrument’s calibration coefficients, changes in instrument body temperature, drift in sampling of the diurnal cycle, roll biases and decay of orbital altitude (Christy and Spencer, 2000; Zou et al., 2008).

Choices must be made. This paper builds on others (written by, for example, Kevin Trenberth), that try to point out problems with the data so that they can be better dealt with.

ferdberple

Seriously, anyone have an “evidence based” explanation of why there is almost perfect agreement between all the datasets that the NH has been warming for 30 years while the SH has been cooling?
What feature of the the earth has been changing that could account for this? I’m not buying CFC’s and Ozone as a cause. The ozone hole “appeared” at the south pole when almost all the CFC use was in the northern hemisphere. Looking further at the graphs above, there really hasn’t been very much net heating once you add the SH (negative) data from the NH (positive) data.
It makes one wonder if GW is more a product of land versus sea temperature? Most of the land is in the NH, while most of the oceans are in the SH. Is this what we are seeing in this data? Evidence that GW is mostly connected to portions of the globe where there is land. In which case, AGW would be more likely due to land use, which cannot be well mixed, because the land doesn’t move very fast.

The reanalysis datasets do have their issues including that they include the likely flawed RSS and UAH data. A discussion is at the following URL:
http://www.skepticalscience.com/Eschenbach_satellite_part.html

Latitude

Willis, don’t know if you saw this…………..
Envirocensors Hide Explosive Japanese Satellite Data
Scorching new evidence of the environmental left’s scientific obstruction has surfaced in the squelching of reports of Japanese satellite data, which suggest that the underdeveloped world emits far more carbon dioxide than previously imagined, even more than many Western nations! If the claim is substantiated, it could turn the entire meme that industrialized civilization is endangering the planet on its head.
http://rogueoperator.wordpress.com/2011/11/07/envirocensors-hide-explosive-japanese-satellite-data/

ferdberple

How is it possible that the SH has been cooling for 30 years and CO2 is well mixed?

DirkH says:
November 7, 2011 at 6:21 am
Thanks, I had it right the first time before someone came in and interrupted me, I’ve been rushing about all day, probably should have waited until later and put a bit more time into my comment. but I hope you still understand where I was going with it despite the error. It’s a travesty. 🙂

ferdberple

jens raunsø jensen says:
>>
would you have any thoughts on this fundamental issue, eg. should we discourage the use of linear regression as a method to identify “the pattern” in temperature records ?
Thanks … jens
>>
Most would agree that today’s temperature is not independent of yesterdays (autocorrelation).
Here is what wikipedia has to say:
Autocorrelation violates the ordinary least squares (OLS) assumption that the error terms are uncorrelated. While it does not bias the OLS coefficient estimates, the standard errors tend to be underestimated (and the t-scores overestimated) when the autocorrelations of the errors at low lags are positive.
In other words, linear regression can under-estimate data errors and natural variability.

ferdberple

Latitude says:
November 7, 2011 at 7:22 am
Envirocensors Hide Explosive Japanese Satellite Data
http://rogueoperator.wordpress.com/2011/11/07/envirocensors-hide-explosive-japanese-satellite-data/
I’d read about the Japanese findings and wondered why they were not being widely reported. This is huge because it shows that land use, not industrialization is driving CO2 emissions. Definitely not a message that the IPCC, WWF, and REDD wants anyone to hear.

I think it would be a fairer presentation of the analysis for the x-Axis to be plotted
Not as 90S to 90 N as equidistant increments.
rather the increments should be cos(of latitude) so that
0 to 10N is the widest increment and 80N to 90N is quite narrow.
This would visually reflect their contribution to their areas on the earth.
It is harder to label Latitude in the chart Excel with X = cos(latitude) than incrementing by latitude. But it might be effective to do it at least once.

Carrick

Robert:

The reanalysis datasets do have their issues including that they include the likely flawed RSS and UAH data.

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.

Another excellent article by Willis. As always, I gave it 5 stars.☺
Pamela Gray says:
“If our degree of ‘agreement’ were broadened, would not the correlation be near perfect? As in let’s round to the nearest whole degree and call anything less noise.”
Pamela rightly points out the elephant in the room: click
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.

commieBob

ferd berple says:
November 7, 2011 at 6:50 am
Isn’t the real question why the norther hemisphere is getting warmer and the southern hemisphere is getting colder if CO2 is well mixed?

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.

For example, climate of Southern Hemisphere locations is often more moderate when compared to similar places in the Northern Hemisphere. This fact is primarily due to the presence of large amounts of heat energy stored in the oceans.

http://www.physicalgeography.net/fundamentals/8o.html

Bomber_the_Cat

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”.

Olen

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.

Willis Eschenbach

jens raunsø jensen says:
November 7, 2011 at 4:51 am

Hi Willis,
thanks for sharing this with us. I have not worked with the satellite or reanalysis data, but having just completed a step change analysis of nearly all complete and long term records in the GHCN (except North-America and Australia) I find strong support for the likely existence of real step changes in temperature records (hopefully I can post this later). If that is the case, the basic assumption of linearity underlying the widespread use of linear trend analysis (as also used in the XP2011) is violated and the resulting trend misleading. Given your insight and experience with the temperature records and analyses, would you have any thoughts on this fundamental issue, eg. should we discourage the use of linear regression as a method to identify “the pattern” in temperature records ?
Thanks … jens

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.

JPeden

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.

Willis Eschenbach

Robert says:
November 7, 2011 at 8:32 am

… 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).

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.

Willis Eschenbach

Robert says:
November 7, 2011 at 8:32 am

I very much condemn the argument seen here and elsewhere quite frequently that the satellite records are the definitive “gatekeeper” of global temperature trends.

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:

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.

and I closed by saying that:

There is still a lot of variation in the different measurements of the global tropospheric temperature.

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.

Willis Eschenbach

ndcodeblue says:
November 7, 2011 at 9:56 am

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.

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.

Septic Matthew

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?

G. Karst

Pamela Gray says:
November 7, 2011 at 6:43 am
If our degree of “agreement” were broadened, would not the correlation be near perfect? As in 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?

Sounds more accurate and precise… to me, climatically speaking! GK

BioBob

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.

Philip Bradley

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

kwik

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.

Philip Bradley

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.

P. Solar

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

Justthinkin

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)?

Bomber_the_Cat

“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?

Stephen Singer

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

barry

fred berple,

Seriously, anyone have an “evidence based” explanation of why there is almost perfect agreement between all the datasets that the NH has been warming for 30 years while the SH has been cooling?

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;

The results show that the spread increases significantly with atmospheric height. The spread in the reanalysis datasets is much larger than the radiosondes in the stratosphere. In contrast, the spread in both reanalysis and radiosonde datasets is very small and shows the trend in better agreement with each other in the troposphere….
Based on the tropospheric temperature (TCH2), in contrast, all the datasets indicate (Fig. 3) a significant warming in the troposphere, except for the Antarctic….
The statistically significant test shows that the warming trends exceed the confidence at the 99% level over the northern middle-high latitudes in all datasets. In contrast, all trends over the southern middle-high latitudes are not statistically significant. Over the tropics, there are trends in the RSS, STAR of the MSU, the RAOBCORE of the radiosonde and the MERRA, NCEP-CFSR of the reanalysis going through the significant test at the 99% level.

commieBob says:
November 7, 2011 at 8:22 am
ferd berple says:
November 7, 2011 at 6:50 am
Isn’t the real question why the norther hemisphere is getting warmer and the southern hemisphere is getting colder if CO2 is well mixed?
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.

Justthinkin says:
November 7, 2011 at 12:39 pm
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)?

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.