Why Do Different Satellite Datasets Produce Different Global Temperature Trends?
January 6th, 2015 by Roy W. Spencer, Ph. D.
I thought it would be useful to again outline the basic reasons why different satellite global temperature datasets (say, UAH and RSS) produce somewhat different temperature trends.
They all stem from the fact that there is not a single satellite which has been operating continuously, in a stable orbit, measuring a constant layer of the atmosphere, at the same local time every day, with no instrumental calibration drifts.
Instead, what we have is multiple satellites (we use 14 of them for the UAH processing) with relatively short lifetimes (2 to 16+ years), most of which have decaying orbits which causes the local time of measurement to slowly change over the years, slightly different layers sampled by the earlier (pre-1998) MSU instruments compared to the later (post-1998) AMSU instruments, and some evidence of small calibration drifts in a few of the instruments.
An additional complication is that subsequent satellites are launched into alternating sun-synchronous orbit times, nominally 1:30 a.m. and p.m., then 7:30 a.m. and p.m., then back to 1:30 a.m. and p.m., etc. Furthermore, as the instruments scan across the Earth, the altitude in the atmosphere that is sampled changes as the Earth incidence angle of view changes.
All of these effects must be accounted for, and there is no demonstrably “best” method to handle any of them. For example, RSS uses a climate model to correct for the changing time of day the observations are made (the so-called diurnal drift problem), while we use an empirical approach. This correction is particularly difficult because it varies with geographic location, time of year, terrain altitude, etc. RSS does not use exactly the same satellites as we do, nor do they use the same formula for computing a lower tropospheric (“LT”) layer temperature from the different view angles of AMSU channel 5.
We have been working hard on producing our new Version 6 dataset, revamping virtually all of the processing steps, and it has taken much longer than expected. We have learned a lot over the years, but with only 2-3 people working part time with very little funding, progress is slow.
In just the last month, we have had what amounts to a paradigm shift on how to analyze the data. We are very hopeful that the resulting dataset will be demonstrably better than our current version. Only time will tell.
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Basically he is saying that ALL satellite temperature are models with various bodges to correct for satellite inconsistencies.
This generated a post:
David A says:
“So you are saying that all satellite temperatures are models with a few bodges added?”
Of course. The satellites aren’t measuring temperatures, they’re measuring microwaves. It takes a data model to convert those into temperatures…. He’s a description of RSS’s algorithm; it’s quite complex:
“Climate Algorithm Theoretical Basis Document (C-ATBD)”
RSS Version 3.3 MSU/AMSU-A Mean Layer Atmospheric Temperature
http://images.remss.com/papers/msu/MSU_AMSU_C-ATBD.pdf
It’s not clear to me why satellite temperatures are said to be clean and exact while surface measurements are not….
followed by a post from Spencer:
Roy Spencer says:
Franco, not in the case of microwave emission in the 50-60 GHz range, which depends on the concentration of molecular oxygen, which is extremely stable in space and time (unlike CO2).
I then tried to post a comment only to find I have seemingly been banned:
CO2 and O2 are linked as one would expect so O2 is not constant see the post hereThey all stem from the fact that there is not a single satellite which has been operating continuously, in a stable orbit, measuring a constant layer of the atmosphere, at the same local time every day, with no instrumental calibration drifts.
Instead, what we have is multiple satellites (we use 14 of them for the UAH processing) with relatively short lifetimes (2 to 16+ years), most of which have decaying orbits which causes the local time of measurement to slowly change over the years, slightly different layers sampled by the earlier (pre-1998) MSU instruments compared to the later (post-1998) AMSU instruments, and some evidence of small calibration drifts in a few of the instruments.
An additional complication is that subsequent satellites are launched into alternating sun-synchronous orbit times, nominally 1:30 a.m. and p.m., then 7:30 a.m. and p.m., then back to 1:30 a.m. and p.m., etc. Furthermore, as the instruments scan across the Earth, the altitude in the atmosphere that is sampled changes as the Earth incidence angle of view changes.
All of these effects must be accounted for, and there is no demonstrably “best” method to handle any of them. For example, RSS uses a climate model to correct for the changing time of day the observations are made (the so-called diurnal drift problem), while we use an empirical approach. This correction is particularly difficult because it varies with geographic location, time of year, terrain altitude, etc. RSS does not use exactly the same satellites as we do, nor do they use the same formula for computing a lower tropospheric (“LT”) layer temperature from the different view angles of AMSU channel 5.
We have been working hard on producing our new Version 6 dataset, revamping virtually all of the processing steps, and it has taken much longer than expected. We have learned a lot over the years, but with only 2-3 people working part time with very little funding, progress is slow.
In just the last month, we have had what amounts to a paradigm shift on how to analyze the data. We are very hopeful that the resulting dataset will be demonstrably better than our current version. Only time will tell.
----------------------
Basically he is saying that ALL satellite temperature are models with various bodges to correct for satellite inconsistencies.
This generated a post:
David A says:
“So you are saying that all satellite temperatures are models with a few bodges added?”
Of course. The satellites aren’t measuring temperatures, they’re measuring microwaves. It takes a data model to convert those into temperatures…. He’s a description of RSS’s algorithm; it’s quite complex:
“Climate Algorithm Theoretical Basis Document (C-ATBD)”
RSS Version 3.3 MSU/AMSU-A Mean Layer Atmospheric Temperature
http://images.remss.com/papers/msu/MSU_AMSU_C-ATBD.pdf
It’s not clear to me why satellite temperatures are said to be clean and exact while surface measurements are not….
followed by a post from Spencer:
Roy Spencer says:
Franco, not in the case of microwave emission in the 50-60 GHz range, which depends on the concentration of molecular oxygen, which is extremely stable in space and time (unlike CO2).
I then tried to post a comment only to find I have seemingly been banned:
climateandstuff.blogspot.co.uk/2012/06/further-thoughts-on-co2-cycle.html
(repeat post as 1st got trashed.
This linked to a post on this blog with the following plot:
O2 is not constant over a year and continually falls over the period shown (it's locked to co2 cycle!). If Spencer believes O2 is constant then perhaps this is a source of error in his UAH measurements!!
Additional info on oxygen plot:
http://bluemoon.ucsd.edu/publications/ralph/Keeling_et_al_Tellus_07.pdf
The primary O2/N2 reference gases (or ‘primaries’) used at Scripps consist of 12 tanks filled between 1986 and 1989, and an additional set of six filled between 1993 and 1994, as summarized
in .....
The large size of the atmospheric O2 reservoir makes measurements of the relatively small changes in O2 concentration challenging. Resolving a land biotic sink of 2 Pg C requires the
ability to detect a change of ∼1.8 × 1014 moles in the global O2 abundance, which corresponds to 0.000 49% of the total burden of O2 in the atmosphere. Changes in O2 concentration are
typically expressed in terms of the relative change in O2/N2 ratio δ(O2/N2) = (O2/N2)sample/ O2/N2)reference − 1, where δ(O2/N2) is multiplied by 10e6 and expressed in ‘per meg’ units. In these units, a change of 1.8×10e14 moles in the globalO2 abundance corresponds to a change of 4.9 per meg. In spite of the measurement challenge, there are now at least six independent
O2 measurement techniques in use that have demonstrated a precision at the level 6 per meg or better (Keeling, 1988a; Bender et al., 1994; Manning et al., 1999; Tohjima, 2000; Stephens et al., 2003; Stephens et al., 2006), and these methods are being variously applied for flask or in situ measurements by at least 12 scientific institutions.
I am not sure whether I understand the axis with the O2/N2 ratio. Why is is negative? A logarithm?
ReplyDeleteN2 is constant. Thus if the ratio had changed from -100 to 400, that would mean an enormous decrease in the oxygen concentration, which is clearly not physical. Thus I must be understanding something wrong.
As the plot suggests the main reason for fluctuations in O2 are the fluctuations in CO2. The annual cycle of CO2 is much, much smaller than 1%; the mean concentration of CO2 already is. The O2 percentage in the atmosphere is almost 20%. Thus one would expect that the fluctuations in O2 are much smaller than the mean concentration of O2. To me it seems a reasonable assumption to keep it constant in the temperature retrieval. This retrieval has much bigger other problems.
It is really funny that the mitigation sceptics are so fond of their satellite measurements. These tropospheric temperatures are the most heavy temperature dataset we have. Normally the mitigation sceptics complain about adjustments. For political reason the tropospheric temperatures are naturally better for them, the dataset is short and the trend is relatively small. However, normally the mitigation sceptics claim to be the only true guardians of science. Something there does not fit together.
Hi Victor
ReplyDeletefrom a document from the site reference on the plot:
http://bluemoon.ucsd.edu/publications/ralph/Keeling_et_al_Tellus_07.pdf
The primary O2/N2 reference gases (or ‘primaries’) used at
Scripps consist of 12 tanks filled between 1986 and 1989, and
an additional set of six filled between 1993 and 1994, as summarized
in .....
The large size of the atmospheric O2 reservoir makes measurements
of the relatively small changes in O2 concentration
challenging. Resolving a land biotic sink of 2 Pg C requires the
ability to detect a change of ∼1.8 × 1014 moles in the global
O2 abundance, which corresponds to 0.000 49% of the total burden
of O2 in the atmosphere. Changes in O2 concentration are
typically expressed in terms of the relative change in O2/N2 ratio
δ(O2/N2) = (O2/N2)sample/(O2/N2)reference − 1,
where δ(O2/N2) is multiplied by 10e6 and expressed in ‘per meg’
units. In these units, a change of 1.8×10e14 moles in the globalO2
abundance corresponds to a change of 4.9 per meg. In spite of the
measurement challenge, there are now at least six independent
O2 measurement techniques in use that have demonstrated a
precision at the level 6 per meg or better (Keeling, 1988a; Bender
et al., 1994; Manning et al., 1999; Tohjima, 2000; Stephens et
al., 2003; Stephens et al., 2006), and these methods are being
variously applied for flask or in situ measurements by at least
12 scientific institutions.
Hopefully this clears up the sizing problem