Quelle suprise! UAH low-balling satellite temperature data by a factor of three

[Readers, this is something like the 5th time in the past 10 years that UAH (i.e., John Christy and Roy Spencer) has been caught lowballing their satellite temperature data.]  

A new study suggests that the University of Alabama at Huntsville is lowballing the warming of the atmosphere

by John Abraham, "Climate Consensus - The 97%," The Guardian, March 25, 2015

 Satellite orbiting Earth. Photograph: JG Photography / Alamy/Alamy

A very important study was just published in the Journal of Climate a few days ago. This paper, in my mind, makes a major step toward reconciling differences in satellite temperature records of the mid-troposphere region. As before, it is found that the scientists (and politicians) who have cast doubt on global warming in the past are shown to be outliers because of bias in their results.

The publication, authored by Stephen Po-Chedley and colleagues from the University of Washington, discusses some major sources of error in satellite records. For instance, after satellites are launched, they scan the Earth’s atmosphere and calibrate the atmospheric measurements using a warm target onboard the satellite and cold space. The accuracy with which the atmospheric measurements are calibrated can influence the inferred temperature of the atmosphere (called the warm-target bias). Additionally, over the years, multiple satellites have been launched and the selection of which satellite data are used can play a role. Finally, biases can occur because the satellite orbits drift during their lifetime, and the influence of diurnal temperature variation can affect the global temperature trends. 

Of these three errors, the last one (probably the most important one) was the focus of the just-published paper. 

It is known that there is a problem because there are multiple groups that create satellite temperature records, for instance: NOAA, Remote Sensing Systems (RSS), and the University of Alabama Huntsville (UAH). The problem is that their results don’t agree with each other. In particular, the UAH team, led by Dr. John Christy and Dr. Roy Spencer (who have discounted the importance and occurrence of climate change for years), presents results that differ quite a bit from the others. In fact, in the current paper, it is stated that “Despite using the same basic radiometer measurements, tropical TMT trend differences between these groups differ by a factor of three.”

An important aspect to this issue is that, for many reasons, it is expected that the tropospheric temperatures in the tropics will warm more than surface temperatures. This is called “tropospheric amplification.” According to two satellite groups, there is in fact such amplification. According to the UAH team, there is no amplification. The presence or absence of amplification is often used by some skeptics to discount the importance of global warming.

Why are there differences? Well, that’s part of what this paper tries to answer.

Each of the teams tries to deal with and correct for various satellite errors. For example, sometimes the diurnal cycle effect is removed using temperatures from climate models. The RSS and NOAA teams “apply a drift correction based on the diurnal cycle from a GCM (global climate model), whereas UAH produces a microwave-sounding-unit, mid-tropospheric-temperature, diurnal correction based on temperature comparisons between three co-orbiting satellites ... UAH does not yet correct the diurnal drift for satellites carrying Advanced Microwave Sounding Units because they attempt to use these satellites during periods when the diurnal drift is small.”

The present paper presents a calibration scheme that allowed a diurnal correction to be obtained from the satellite measurements themselves, in particular by solving for a common diurnal cycle correction using temperatures from all available satellites.

As the authors state in the paper, their new results agree with the two groups that show more warming. They disagree with UAH. As the authors state,

In general, our trends corrected with a GCM and trends corrected with our observationally derived diurnal cycle correction are similar to trends from NOAA and RSS ... the UAH ocean trend is notably lower than trends from the other datasets.

So, how do the trends compare? Well, the lowest trends (in degrees Celsius heating per decade) are from UAH, and they equal 0.029 °C per decade for the 1979–2012 period for the mid-troposphere region between 20° South and 20° North. The new results are almost 4 times higher at 0.114 °C per decade. The results using a diurnal correction from a climate model are in close agreement with the new findings (0.124 °C per decade). As additional support, the NOAA and RSS values are also close to the corrected results. The simple fact is – UAH is an outlier.

They also discovered that the results from RSS, NOAA, and the new study all show tropical amplification and are in agreement with the expected amplification from climate models. They state, “There is no significant discrepancy between observations and models for lapse rate change between the surface and the full troposphere.”

To summarize the amplification factor, the new study obtains a value of 1.4. If the diurnal cycle is eliminated using climate models, the result is 1.49. According to NOAA and RSS, the values are 1.31 and 1.10, respectively. Again, UAH is the outlier with an amplification factor of 0.56.

I wrote to Stephen Po-Chedley and asked for a summary. He told me,

We developed an observationally based diurnal cycle correction to remove the influence of satellite diurnal sampling drifts on long-term tropospheric temperature trends. This is important because other analyses (RSS and NOAA) used a model-derived diurnal cycle correction and questions have been raised about the validity of this bias correction. Trends from our work are in accord with trends from global circulation models and basic theory. 

We also found that the model-derived diurnal cycle correction used by RSS and NOAA is similar to our bias correction. The tropical tropospheric trend from the present study is 2.5 times that from another group, UAH. While this work shows that it is possible to understand discrepancies between MSU/AMSU datasets, there are still important differences between the datasets that need further scrutiny.

In short, the Earth is warming, the warming is amplified in the troposphere, and those who claim otherwise are unlikely to be correct.

http://www.theguardian.com/environment/climate-consensus-97-per-cent/2015/mar/25/one-satellite-data-set-is-underestimating-global-warming

John Abraham: What's causing global warming? Look for the fingerprints

What's causing global warming? Look for the fingerprints

A new paper by Santer et al. (2013) finds patterns in the climate that indicate human-caused global warming

Benjamin Santer's study looked for fingerprints of human-caused climate change. Photograph: Ian Waldie/Getty Images

 

by John Abraham, Climate Consensus -- The 97%, The Guardian, September 16, 2013

Scientists are a skeptical bunch. We never accept claims without evidence and we spend large parts of our careers trying to show that other scientist's claims are wrong. This self scrutiny is one of our best traits, and it is a major reason why science advances over time. 

With this said, it often surprises people that scientists are in such strong agreement about human impacts on the Earth's climate. Many studies, including research by Doran and Zimmerman, Anderegg and colleagues, and more recently by my colleague's team, Cook et al., have shown conclusively that the world's climate scientists agree, to about 97%, that humans are significantly impacting the climate. But many people ask, how can they be so sure?

There are a number of reasons why we know humans are causing many of the changes we are seeing today. Among them, is the use of attribution studies, often called "fingerprinting." Scientists look at the patterns of climate change and ask, do they have the fingerprint of natural variation, or humans?

One of the most well-known climate change attribution scientists is Dr. Benjamin Santer. He and his team have developed tools to separate natural climate variations from human-induced changes by using a number of different tools. Their latest work was just published in the Proceedings of the National Academy of Sciences and is titled "Human and Natural Influences on the Changing Thermal Structure of the Atmosphere."

The method is somewhat complex; it involves the comparison of climate observations with the output of climate models. Specifically, they compared satellite observations from two different groups, with output from 20 climate models that participated in the most recent Coupled Model Intercomparison Project (CMIP-5). In the models, they calculated what the Earth would be like without us. The "world without us" scenarios have natural changes to the environment caused by volcanoes, the Sun, and internal climate variability (phenomena like El Niños and La Niñas). They wanted to know whether the "world without us" could have displayed the types of changes to the climate that we are seeing today.

Next, the scientists calculated what the Earth would be like if human emissions had occurred, but natural variations in volcanoes and the Sun had not. These "human only" simulations tell us what we expect the impact to be from greenhouse emissions alone; they give us an estimate of the human "fingerprint."

Finally, the models were used to estimate the amount of internal variability in the climate, without human impacts or forced changes from volcanoes and the sun. This third step quantifies the impact of things like El Niños, La Niñas, and other natural variations.

With these three calculations complete, the scientists then went to the observational record, extracting data from satellite measurements of the Earth's climate. They searched the measurements for the "human only effect" by comparing the measurements to the three sets of simulations. In particular, they looked at the signal-to-noise ratio, which helps tell them which of the three solutions ("world without us," "human only," or "natural variability") fit the observations best.

What did they find? Certain patterns emerge that are consistent with the "human only" scenario. For instance, the heating of the lower atmosphere and cooling of the upper atmosphere, which satellites clearly see, could only happen if human emissions were the culprit. But the study went further; they actually stacked the deck of cards in favor of nature. They used solar and volcanic variations much larger than those that actually occurred since 1979. The strategy was to see if even a worst case "world without us" could be made to look like the current measurements. But, even that didn't work. The human influence still stood out.

Perhaps the best summary is in the abstract of the paper.

"We show that a human-caused latitude/altitude pattern of atmospheric temperature change can be identified with high statistical confidence in satellite data. Results are robust to current uncertainties in models and observations … Our results provide clear evidence for a discernible human influence on the thermal structure of the atmosphere."

In climate science, as with most science, formal proofs are not possible. But I've read hundreds or perhaps thousands of scientific articles in my life, and this is about as convincing as it gets.

http://www.theguardian.com/environment/climate-consensus-97-per-cent/2013/sep/17/global-warming-fingerprints-santer-2013

Benjamin Santer et al., “Human and natural influences on the changing thermal structure of the atmosphere”

Fact sheet for “Human and natural influences on the changing thermal structure of the atmosphere” [1] [Sorry, this is screwed up -- had to copy from a pdf file, and some things did not make it.  Anyone wanting the pdf should send an email to me at apaixonada.por.rio@gmail.com ]
 
by Benjamin D. Santer, Jeffrey F. Painter, Céline Bonfils, Carl A. Mears, Susan Solomon, Tom M.L. Wigley, Peter J. Gleckler, Gavin A. Schmidt, Charles Doutriaux, Nathan P. Gillett, Karl E. Taylor, Peter W. Thorne, and Frank J. Wentz

To be published in Proceedings of the U.S. National Academy of Sciences, Online Early Edition,
Embargoed until September 16, 2013, 3:00 p.m., U.S. Eastern Time

Summary: Observational satellite data and the computer model response to human influence have a common pattern of changes in the thermal structure of the atmosphere. The key features of this pattern are global-scale tropospheric warming and stratospheric cooling over the 34-year satellite temperature record. We show that current climate models are highly unlikely to produce this distinctive signal pattern by internal variability alone, or in response to naturally forced changes in solar output and volcanic aerosol loadings. We detect a “human influence” signal in all cases, even if we test against natural variability estimates with much larger fluctuations in solar and volcanic influences than those we have observed since 1979. Our results highlight the very unusual
nature of observed changes in atmospheric temperature. [2]

Signal-to-noise analysis: A brief primer

Our PNAS paper describes results from a climate change detection and attribution study, in which we investigate the causes of temperature changes in Earth’s atmosphere. The focus of our study is on the vertical structure of atmospheric temperature change – in other words, on patterns of change that vary with latitude and with altitude. These patterns provide information about temperature changes in the troposphere and the stratosphere (see below):

Figure 1: This figure is from Synthesis and Assessment Product 1.1 of the U.S. Climate Change Science Program (Karl et al., 2006 1). It shows the approximate pressure and altitude boundaries of the troposphere and the stratosphere. The multi-colored line indicates the average dependence of temperature on altitude.

We rely on estimates of atmospheric temperature change from satellites and from computer models of the climate system (“climate models”). The satellite observations are made available by two different research groups; the simulation output is from as many as 20 of the models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP-5).

In the real world, many factors – both human and natural – are simultaneously acting on the climate system. We do not have a “control Earth,” on which there are no human-caused changes in atmospheric levels of greenhouse gases.

With climate models, however, it is possible to perform such controlled simulations. For example, we run climate models with our best estimates of the purely natural changes in volcanic activity and the Sun’s energy output over the last 1,000 years [2]. We can then ask whether these computer model estimates of the “world without us” produce climate-change patterns similar to the ones we have actually observed since 1979 [3]. The availability of “world without us” results allows us to examine – and to test – persistent claims that observed changes in climate are primarily due to natural causes, like an increase in solar irradiance, or the “recovery” of atmospheric temperature after large volcanic eruptions.

Our paper also considers simulations in which only human influences act on the climate system, and there are no changes in solar or volcanic influences. Examples of human influences include changes in atmospheric levels of greenhouse gases and particulate pollution. Such “human effects only” simulations are used to estimate the climate-change signal (also called the “fingerprint”) that we expect to see as a result of human activities [4].

Finally, the model simulation output gives us estimates of the year-to-year and decade-to-decade “noise” of internal climate variability, arising from such natural phenomena as the El Niño/Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). This internal variability (which we refer to as VINT) is unrelated to changes in the Sun, or to changes in volcanic activity.

We use a standard fingerprint method [5] to search for the model “human effects only” signal pattern [6] in the satellite observations. First, we quantify the changing strength of the signal pattern in observations. We then estimate the changes in signal strength that are caused by purely natural changes in climate.

Our signal detection method allows us to calculate so-called signal-to-noise (S/N) ratios. If the observed patterns of atmospheric temperature change are becoming increasingly similar to the model “human influence” fingerprint, and if the natural variability patterns are dissimilar to the fingerprint pattern, the S/N ratios will be large. S/N ratios larger than 3 show that there is highly significant correspondence between the model fingerprint and satellite data, and that natural climate variability is unlikely to explain this pattern match.

Our S/N ratios depend on the length of the temperature record. We focus on S/N ratios calculated over the full, 34-year period of the satellite data (1979 to 2012). Looking at long, multi-decade periods of record helps to reduce the impact of large, year-to-year natural variability, and more clearly reveals any underlying signal of human influences on climate. [4]

Question 1: What’s new about this research?

Two aspects are novel.

First, virtually all detection and attribution studies to date use computer model estimates of VINT (natural internal variability; see definition in the “primer”) to determine whether a human-caused climate change signal can be detected in observations. Here, we look at the signal detection issue in several different ways. We try to detect a human influence signal not only against the background noise of internal climate variability, but also against the natural variability information from the CMIP-5 “world without us” simulations. These simulations [7] give us estimates of the “total” natural variability of the climate system, VTOT, which arises from the combined effects of internal variability, fluctuations in the Sun’s energy output, and changes in the levels of volcanic particulates in the atmosphere. 

Second, most previous detection and attribution studies with temperature changes in a “slice” through the atmosphere [8] used results from only one or two climate models, and from a single observational temperature data set. We consider results from up to 20 climate models, and from two different observational data sets [9]. This enables us to determine whether previous claims of the positive detection of a human fingerprint in satellite temperature records are sensitive to current uncertainties in models and observations. We find that prior “positive detection” claims [10] are robust to the model and observational uncertainties considered here.

Question 2: What are your key findings?

In the satellite data, we’ve observed a pattern of large-scale warming of the lower atmosphere (the troposphere) and cooling of the stratosphere. Computer model estimates of the “human influence” fingerprint are broadly similar to the observed pattern (see Fig. 2). In sharp contrast, model simulations of internal and total natural variability cannot produce the same sustained, large-scale warming of the troposphere and cooling of the stratosphere. So in current climate models, natural causes alone are extremely unlikely to explain the observed changes in the thermal structure of the atmosphere.

This is true even if our signal detection approach uses total natural variability estimates from before the period of satellite temperature observations [11]. The “world without us” simulations sample changes in 5 volcanic and solar activity over the last 150 to 1,000 years. Many of these eruptions and solar irradiance changes are much larger [12] than the volcanic and solar changes we have observed since 1979. A remarkable aspect of our results is that even in this “worst case” signal detection situation, when we make signal identification difficult by using very large estimates of total natural variability, we still obtain consistent detection of a “human influence” fingerprint. [12] Examples include the major eruptions of Krakatoa in 1883 and Kuwae in 1452, and the large estimated changes in solar irradiance around the time of the Maunder Minimum (from roughly 1645 to 1715).
 
Satellite observations (Remote Sensing Systems)

Climate models (average of “human influence” simulations)

Figure 2: The vertical structure of changes in atmospheric temperature in satellite observations (top panel) and in computer model simulations performed as part of phase 5 of the Coupled Model Intercomparison Project (CMIP-5; bottom panel). As described in the PNAS paper, both panels provide a vertically smoothed picture of atmospheric temperature change. Information from only three atmospheric temperature layers – the lower stratosphere (TLS), the mid- to upper troposphere (TMT), and the lower troposphere (TLT) was used in generating the two plots. We show temperature changes in this “vertically smoothed” space because satellite-based estimates of atmospheric temperature change are available for TLS, TMT, and TLT, and because our signal detection study is performed with the zonally-averaged temperature changes for these three layers. All temperature changes are in the form of linear trends (in degrees Celsius) over the 408-month period from

Question 3: Is there evidence that the models you’ve used here systematically underestimate the total natural variability of atmospheric temperature?

If the CMIP-5 models analyzed here systematically underestimated the size of observed “total” natural variability, our S/N ratios would be spuriously inflated. In our previous work [13], we found no evidence that this is the case. To test the fidelity with which models simulate observed total natural variability, we compared modeled and observed temperature fluctuations on decadal timescales [14]. On average, the CMIP-5 models substantially overestimate the size of observed tropospheric temperature variability, suggesting that our S/N ratios are probably too conservative [15]. 

Question 4: Are there remaining problems?

Yes. Although we found a “pattern match” between the modeled and observed vertical structure of atmospheric temperature changes, most models have problems capturing the size of the observed changes. On average, the CMIP-5 models underestimate the observed cooling of the lower stratosphere, and overestimate the warming of the troposphere [16]. Some scientists have claimed that there is only one possible interpretation of such differences – that models are too sensitive to greenhouse gas increases. Such claims are incorrect. There are multiple interpretations of differences between modeled and observed temperature changes. Other possible explanations include: (A) residual errors in the observations; (B) an unusual sequence of natural climate fluctuations in the observations; and (C) the neglect or inaccurate specification of key “forcings” in model simulations of historical climate change. 

Results presented in our PNAS paper and elsewhere suggest that forcing errors make an important
contribution to the biases in model temperature trends [17].

References




1 Karl, T.R., S.J. Hassol, C.D. Miller, and W.L. Murray (eds.), 2006: Temperature Trends in the Lower Atmosphere: Steps for Understanding and Reconciling Differences. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, USA, 164 pp.  

2 Such simulations lack any human-caused changes in greenhouse gases or particulate pollution.

3 The period over which we have been monitoring atmospheric temperature from space.

4 Like the burning of fossil fuels.

5 Our fingerprint method has been successfully employed for the identification of human effects on surface and atmospheric temperature, upper ocean heat content, the height of the tropopause (the boundary between the troposphere and stratosphere), and atmospheric moisture over oceans.

6 As noted above, the signal is the latitude/altitude pattern of atmospheric temperature change.  

6 January 1979 to December 2012. The model results are an average of “human influence” simulations performed with 8 different CMIP-5 models. The y-axis shows atmospheric pressure (in hectoPascals).  

7 Which are referred to as “NAT” and “P1000” in our paper.

8 In other words, at the pattern of temperature change with latitude and altitude.

9 One of the two observational groups (Remote Sensing Systems in Santa Rosa) explored uncertainties in the
processing steps used to create the observations, and developed a set of four hundred plausible estimates of
observed atmospheric temperature change. We used this “ensemble of observations” in our detection study.

10 See, e.g., Santer, B.D., K.E. Taylor, T.M.L. Wigley, T.C. Johns, P.D. Jones, D.J. Karoly, J.F.B. Mitchell, A.H. Oort, J.E. Penner, V. Ramaswamy, M.D. Schwarzkopf, R.J. Stouffer, and S. Tett, 1996: A search for human influences on the thermal structure of the atmosphere. Nature, 382, 39-46.

11 The last 34 years. 

13 Santer, B.D., J.F. Painter, C.A. Mears, C. Doutriaux, P. Caldwell, J.M. Arblaster, P.J. Cameron-Smith, N.P. Gillett, P.J. Gleckler, J. Lanzante, J. Perlwitz, S. Solomon, P.A. Stott, K.E. Taylor, L. Terray, P.W. Thorne, M.F. Wehner, F.J. Wentz, T.M.L. Wigley, L.J. Wilcox, and C.-Z. Zou, 2013: Identifying human influences on atmospheric temperature. Proceedings of the National Academy of Sciences, 110, 26-33, doi: 10.1073/pnas.1210514109.

14 This analysis used digitally-filtered temperature data; the filtering highlighted temperature variability on timescales ranging from 5 to 20 years.

15 In the lower stratosphere, the size of modeled and observed decadal variability is (on average) very similar.

16 Particularly in tropics and Southern Hemisphere (see Fig. 2).

17 Note that these biases have relatively small impact on the S/N results presented here. This is because the searched-for fingerprint patterns are normalized – thus reducing the effect of biases in the size of modeled temperature changes.

Benjamin D. Santer et al., "Separating signal and noise in atmospheric temperature changes: The importance of timescale," J. Geophys. Res., 2011, in press

Journal of Geophysical Research, in press; doi: 10.1029/2011JD016263

Separating signal and noise in atmospheric temperature changes: The importance of timescale

Benjamin D. Santer, Carl A. Mears, C. Doutriaux, Peter Martin Caldwell, Peter J. Gleckler, Tom M.L. Wigley, Susan Solomon, Nathan Gillett, Detelina P. Ivanova, Thomas R. Karl, John R. Lanzante, Gerald A. Meehl, Peter A. Stott, Karl E. Taylor, Peter Thorne, Michael F. Wehner and Frank J. Wentz

Abstract

We compare global-scale changes in satellite estimates of the temperature of the lower troposphere (TLT) with model simulations of forced and unforced TLT changes. While previous work has focused on a single period of record, we select analysis timescales ranging from 10 to 32 years, and then compare all possible observed TLT trends on each timescale with corresponding multi-model distributions of forced and unforced trends. We use observed estimates of the signal component of TLT changes and model estimates of climate noise to calculate timescale-dependent signal-to-noise ratios (S/N). These ratios are small (less than 1) on the 10-year timescale, increasing to more than 3.9 for 32-year trends. This large change in S/N is primarily due to a decrease in the amplitude of internally generated variability with increasing trend length. Because of the pronounced effect of interannual noise on decadal trends, a multi-model ensemble of anthropogenically-forced simulations displays many 10-year periods with little warming. A single decade of observational TLT data is therefore inadequate for identifying a slowly evolving anthropogenic warming signal. Our results show that temperature records of at least 17 years in length are required for identifying human effects on global-mean tropospheric temperature. 

Key points:

• Models run with human forcing can produce 10-year periods with little warming
• S/N ratios for tropospheric temp. are ~1 for 10-yr trends, ~4 for 32-yr trends
• Trends >17 yrs are required for identifying human effects on tropospheric temp.

Received 19 May 2011; accepted 21 August 2011. 

Citation: Santer, B. D., et al. (2011), Separating Signal and Noise in Atmospheric Temperature Changes: The Importance of Timescale, J. Geophys. Res., doi:10.1029/2011JD016263, in press.

http://www.agu.org/pubs/crossref/pip/2011JD016263.shtml

Of Satellites and Air – A Primer on Tropospheric temperature measurement by Satellite

Of Satellites and Air – A Primer on Tropospheric temperature measurement by Satellite

by Glenn Tamblyn, Skeptical Science, March 24, 2011

This post is an overview of the current state of Tropospheric temperature measurement via satellite. It is also the Advanced rebuttal to ‘the Troposphere isn’t warming’ sceptic argument.

The History of Tropospheric Temperature Measurement by Satellite

NASA has been building and launching the Tiros series (Television Infrared Observation Satellite) weather satellites since 1961. The satellites’ design has evolved over time. And after launch they are often operated by other agencies. The ones used for tropospheric temperature measurement are operated by the National Oceanographic and Atmospheric Administration (NOAA) beginning with Tiros N in October 1978, then NOAA 6 in June 1979, through to NOAA 19 in February 2009. These satellites are designed principally as weather satellites; their use in climatology is a secondary role. Also used in temperature measurement is the NASA AQUA satellite launched in May 2002 as a research satellite, part of the NASA A-Train.

A general overview of the spacecraft and their equipment and roles can be found here.

What the satellites do

These satellites have Microwave Sounding Units (MSU) that read the Brightness Temperature of microwave signals from below in 4 separate frequencies radiated by oxygen molecules. These frequencies tend to originate at different altitudes in the air column below the satellite and reflect the temperature at that altitude. I say "tend" because this isn’t exactly true and will matter later in the discussion. Using microwave signals associated with oxygen has the advantage that microwaves are not substantially blocked by the atmosphere, and oxygen is evenly distributed throughout the atmosphere, so its concentration only varies by negligible amounts. Thus, the temperature signal from oxygen is easy to detect, and is not going to be distorted by concentration changes. 

As the satellite orbits the Earth, the MSU continually scans a swathe below the spacecraft, at nadir (looking straight down) and to the limits of the instrument on each side. Also on each scan the MSU calibrates its readings by taking readings from two other sources – cold deep-space and an on-board, instrumented, hot reference source.

The satellites are Polar Orbiting & Sun Synchronous. Each makes around 14 orbits a day. Their orbit takes them nearly over the poles and the plane of the orbit lines up with the Sun. This is important because it ensures that each point on the Earth is always measured at the same time of day – Solar noon and Solar midnight.

On satellites up to NOAA-14, MSUs were used. On later satellites, including AQUA, Advanced Microwave Sounding Units (AMSU) were fitted. These are more advanced designs that scan in more detail and over more frequencies, but the basics of how they work are the same. In this discussion I will refer mainly to MSUs. The same concepts apply to the AMSUs. 

Some Science

The paper by Grody (1983; section 2) contains a discussion of the science of microwave sounding, and in particular the existence of weighting functions derived from solving the Radiative Transfer Equation. “…the temperature weighting function…defines the contribution of temperature at different altitudes to the brightness temperature.” These functions are produced by summing the contribution at each frequency of microwave emissions from multiple levels in the atmosphere, taking into account the radiating behaviour of the atmosphere, pressure, temperature, path length, etc.  

Grody (1983) section 2

Here, as is common with atmospheric measurements, altitudes are given as pressures rather than kilometres. The dotted lines are the weighting functions for the extreme side parts of the scans while the solid lines are for the nadir view. The fact that the weighting functions at different frequencies have very different profiles with regard to altitude is what allows us to measure temperatures at these different altitudes. Each frequency obtains most of its signal from a band of altitude. This altitude behaviour adds a major complication to measurement however; more on this later… 

The signal received by the MSU in its target frequency is made up of three components: signals from the atmosphere radiated up to the satellite, signals from the atmosphere radiated down and reflected off the Earth’s surface, and signals emitted by the Earth itself.

The 4 frequencies are designated MSU Channels 1 (50.30 GHz), 2 (53.74 GHz), 3 (54.96 GHz), and 4 (57.95 GHz). Because the peak of the weighting function for Channel 1 is so close to the surface, this has a rather high component being emitted from the surface and is not very useful for tropospheric temperature measurement because of this surface ‘contamination.’ For the other channels, the surface component is much smaller but still needs to be allowed for.  And this differs over land and sea. 

Some Nomenclature

A range of terms are used in the following discussion so I will summarise their meaning in the table below.

Terminology	Meaning
T1, MSU Channel 1	Real channel peaking near ground level. Seldom used
T2, MSU Channel 2, TMT	Real channel peaking in mid to lower Troposphere. Stratospheric bias not removed
T3 MSU Channel 3, TTS, TUT	Real channel peaking in mid to upper troposphere. Stratospheric bias not removed
T4, MSU Channel 4, TLS	Real channel peaking in lower stratosphere
TLT	Synthetic channel derived from T2, peaking in lower troposphere. Stratospheric bias is removed

 

 

 

 

 

 

  

 

 

 

 

Who Analyzes the data

Data from the various instruments onboard the NOAA satellites are distributed to a wide range of organisations for various purposes. For climatological temperature measurement the two main groups performing this regular analysis of the data from the MSUs and providing temperature products are at the University of Alabama, Huntsville (UAH) and at Remote Sensing Systems in California (RSS). A number of other research groups have also done analyses of the data, but to investigate the methodology, not to produce regular temperature series products. To produce a long-term temperature series from the satellite data these groups need to address a number of issues: 

Satellite Problems

NOAA-B 1980 failed to achieve orbit. NOAA-13 had a catastrophic power failure two weeks after launch. NOAA-9 only had a relatively short overlap (3 months) with its follow-on satellite NOAA-10. Questions have been raised about the calibration of NOAA-16.

Overlap between satellites

Each satellite has slightly different calibrations, orbits etc. To get a long-term temperature series, you need to ‘splice’ together the data from various satellites, launched and de-activated at different times. You need enough overlap between the operating lives of each satellite to compare their results to establish a common baseline. Many of the satellites have had quite long lives, so another factor is degradation of the equipment and ‘drift’ in their calibrations. There is then the question of whether to use a new satellite’s data with its overlap issues or continue using an older satellite with its ageing issues. Some commentators have suggested that a major part of the discrepancy between the UAH and the RSS products is due to the different methods they have used to handle the limited overlap of NOAA-9 and NOAA-10, perhaps as high as 65% of the difference. The following graph shows the difference between UAH and RSS temperature series for channel T2. The divergence is noticeable from around 1987 when NOAA-9 and NOAA-10 had their limited overlap period. The upper line on each graph is the difference between RSS and UAH. These graphs only go to 2004.

 

Switching from MSU to AMSU

Since the AMSU has a different number of channels at slightly different frequencies, this makes ‘splicing’ their data to that of the older MSUs more complex.  

Orbital Decay

The NOAA satellites do not have propulsion systems to correct for decay in their orbit due to friction from the very top of the atmosphere. So their altitude slowly drops over time. This has an effect on the readings, in much the same way as changing the scan angle alters the weighting function. This must be compensated for. Orbital decay is not always even. Changes in solar activity cause the Earths atmosphere to bulge and contract, changing the decay rates. However, the AQUA satellite does have propulsion, so does not suffer as much from these problems. 

Instrument Body Effect

This is the problem of the satellite experiencing varying heating and cooling as it travels around its orbit. The hot target is meant to be fixed to a single temperature, but actually they experience some change over the life of the satellite. Also the body of the MSU warms and cools, and this can affect the readings it takes. Some of this IBE can be adjusted for after launch by analyses that compare between satellites. Also, over long periods these variations will tend to average out. But not all errors can be removed. 

Diurnal Drift

Earlier I mentioned that the satellites are in Sun Synchronous orbits and are meant to stay aligned with the Sun so that they always cross the equator at the same time – the Local Equator Crossing Time (LECT). If they don’t then the normal daily temperature cycles below (the Diurnal cycle) will start to add a false bias to the data. To stay Sun Synchronous the satellite’s orbit has a small precession, just less than one degree per day. However, this precession isn’t perfectly accurate, and small drifts in this can introduce a ‘diurnal drift’ for each satellite, slowly changing its LECT. Drifts of up to 0.5 hr/year have been observed. So a Diurnal Drift correction is needed for each satellite. The two groups (UAH and RSS) have used different methods to achieve this.  

UAH use data from view angles to left and right of nadir at fixed times in the orbit to look at different times of day below. These data allow a calculation that can remove the effect of the drift rate. The approach is simple, but the calculations can magnify the effects of other uncertainties. 

RSS take the approach of using a high-resolution climate model to simulate the expected daily variations beneath the satellite and use this to remove the diurnal drift. The modelled simulation is validated against the actual daily temperature ranges observed by the satellite. This method uses a simulation but has much less sampling noise compared to the UAH method. 

And as the following graph shows, the net effect is that the UAH method has added a cooling bias over time while RSS’s adds a warming bias to the raw temperature data.

 

Figure 2: Diurnal drift corrections by UAH and RSS. UAH corrections add an overall cooling effect. RSS corrections added an overall warming effect. Both teams show strongest corrections in the tropics but in opposite directions.

Sea Ice and Summer Melt Pools

A complicating factor in the polar regions is surface emissions from ice. These make up the normal surface emissions that have to be allowed for in calculating temperature, but with the large seasonal variations in sea ice extent, no single surface factor for these regions can be used. Similarly the appearance of melt pools on the ice in summer confuses the picture since these emit like water, not ice. For this reason the temperature products don’t go all the way to the poles.

Stratospheric Biasing: Why T2 isn’t what it seems

Twice I have mentioned that the way the microwave signal is generated at different altitudes in the atmosphere is important. Go back and look at the first figure of weighting functions. The horizontal line at 200 mbar marks the approximate starting height of the stratosphere. (this actually varies from 1l km near the pole to 17 km at the equator). Look at how much of each curve is above this line. And recall that one of the major effects of AGW is a cooling of the stratosphere. So stratospheric cooling adds a cooling bias to the microwave signals. The signal the satellite measures underestimates the tropospheric temperature. This is most an issue with channels T2 and T3. For T2, around 15% of the signal originates in the stratosphere, and since the stratosphere has cooled much more than the troposphere has warmed, the effect of this is more than 15% of the reading. T3 is split almost 50/50 between the two layers. T4 on the other hand gets most of its signal from the stratosphere with very little from the troposphere. As a result, T2 and T3 significantly underestimate the warming that has occurred in their nominal altitude band. Without some form of correction, they are almost useless.  

T2 vs. TLT

The problems with stratospheric cool biasing were recognised early and in 1992, Spencer and Christy at UAH introduced a new temperature product to remove the stratospheric bias and focus more on the lower troposphere. This removed most of the bias by mathematically combining readings from multiple view angles on the same scan to produce a reading weighted more strongly to the lower troposphere. This was originally called MSU2LT, and later with the addition of AMSU readings it was called MSUTLT. This method produces the lower troposphere weighting expected but is vulnerable to significantly increased sampling errors – essentially taking the difference between two samples will magnify the sample errors. Also, by looking at an East/West swathe, they are sensitive to temperature variations across the swathe. They are also more sensitive to direct surface emissions since they are magnifying the lower level signal.  

However, this approach was a significant advance in reading lower tropospheric temperatures. In 2005, RSS also introduced a TLT product using the same nadir/side scan approach, and in this work they introduced the different Diurnal Drift compensation described above. 

Fu et al. (2004, 2005)

In 2004/2005, Qiang Fu, Celeste Johanson et al. published an alternative method for removing the stratospheric bias from the T2 signal. Since the T4 channel is predominantly stratospheric in origin, they removed a proportion of the T4 signal from the T2 signal to remove the stratospheric bias. In order to determine how much to remove, they used radiosonde data to establish a vertical temperature profile for the atmosphere. Then they determine by a least squares regression technique the appropriate weight to give to T2 and T4. They performed this on global, hemispheric and tropical zones on both the UAH and RSS data, to calculate a temperature series for each between 850 and 300 hPa, producing the following trend values:   

 

Note that these values were produced in 2004, before RSS had added their Diurnal Drift compensation. And the figure below shows the modified weighting function from Fu et al. 

Their weighting function has a broader weighting over the entire troposphere than the TLT products so is likely to be more representative of the overall troposphere. NOAA maintains a comparison temperature record for UAH, RSS and the Fu et al. adjustments to them here.

The technique of Fu et al. has limitations. It depends on an independent source for the vertical temperature profile it uses – the radiosonde record. This record suffers from limited geographic coverage and has its own issues with data quality. Also the profile may alter over time. And since it uses profiles averaged over large regions, it is not useful for estimating regional trends other than very approximately. However, it provides an important validation of the broad results from the TLT products.

In further work here, Fu et al. used a similar technique to their 2004 study, but instead of using radiosonde data, they performed a correlation directly between T2 and T4 directly to produce a mid-troposphere result (T_TT) and between T2 and the less frequently used T3 channel to produce a lower troposphere series (T_TLT), removing the stratospheric bias from both and showing results for the tropics. The resulting trends are shown below.

 

The data for this only covers 1987 to 2003 since the T3 channels on earlier satellites were unreliable prior to 1987. They also critique the UAH data, suggesting that their results are un-physical. However, since their data only go to 2003, it does not include more recent corrections by UAH. 

Vinnikov and Grody 

In 2005, Vinnikov and Grody (V&G) published another analysis of MSU data trends. Based on their previous work, it used a quite different, frequency and statistically based method to determine the underlying trends for the MSU measurements. They also considered additional issues related to calibration errors. Instead of assuming that there is a linear calibration error associated with the hot target calibration, they allow for this calibration varying over the satellites orbit due to external factors. They show that they can calculate this effect based just on latitude/longitude variation of the reading without needing to look at any time dependency.  

In their earlier work, they had put a figure on trends of 0.22-0.26 °C/decade. In this work, they are estimating 0.20 °C/decade. The following graphs show measured surface, and their calculated TMT trends vs. latitude, and the same values calculated by climate models. The key discrepancies are at the poles with the modelled Northern Surface temps being much higher than measured Northern Surface temps, but modelled and measured Northern Troposphere values agreeing well. Surface temperature products do not cover the Northern polar region or extrapolate from lower latitude measurements. Southern polar values also disagree, but this is commonly ascribed to the effects of the ozone hole, which climate models do not include yet. There is significant agreement at mid and tropical latitudes.

 

  

Zou et al.

In 2010, Zou et al. published a new analysis method to produce low level MSU data for T2. This deals with removing many of the other calibration and inter-satellite correlation issues. Their method uses Synchronous Nadir Overpasses – points in time where two satellites are able to observe the same point below. This happens more commonly at high latitudes. Using this, they are able to evaluate most of the onboard inter-satellite calibration issues since the satellites are receiving the same signal from below. The main outstanding areas that their analysis does not address are Diurnal Drift and stratospheric cooling bias. They are not really trying to do this, instead producing a lower level data set to which others could apply further work. Their results are shown below. The data from their analysis can be obtained here.

 

Monthly anomaly time series and trends for the global mean TMT, TUT and TLS, where TMT, TUT, and TLS represent deep-layer temperatures at mid-troposphere, upper-troposphere, and lower-stratosphere.

So who is right?

So which teams analysis method is correct? Throughout the history of tropospheric temperature measurement, the UAH analysis has always been lower than RSS for all temperature products. However, as time has gone by, they have been drawing closer together. Currently their TLT trends are RSS 0.147 and UAH 0.138 which are down from earlier trends due to the slow down in warming in recent years. The convergence of their results may be due to the diminishing impact of the overlap problems between NOAA-9/NOAA-10. By comparison, the Fu et al. method applied to RSS TMT and UAH TMT give RSS/FU 0.153 °C/decade, UAH/FU 0.112 °C/decade. Vinnikov and Grody have given around 0.20 °C/decade, while Zou et al. give 0.137 °C/decade; both without stratospheric bias adjustment. 

Which of the techniques of UAH or RSS are correct? Both have weaknesses – UAH use comparisons between different view angles from one scan in two different parts of their analysis, magnifying the sensitivity to errors. RSS use a short-term climate model rather than just data. Commentators seem to prefer the RSS analysis. Neither applies the lat/long dependent analysis of hot source calibration used by V&G, so this could well increase their trends somewhat. And applying the Fu et al. technique to V&G or Zou may give more divergent results again.  

Perhaps what can be said is that the UAH/RSS approach probably straddles the result their methods would find. Other methods suggest higher values. So a reasonable estimate at this point is that warming lies somewhere between the mid estimate of UAH/RSS and the figures that would be produced by V&G & Zou if stratospheric cool biasing were removed. This suggests a long-term trend of around 0.15-0.18 °C/decade for the lower troposphere, much in line with the surface trends. And similar or higher for the mid-troposphere based on the fact that Fu et al. is looking at the entire troposphere, and V&G are showing higher tropospheric than surface warming through the mid and tropical latitudes. 

So these various analyses clearly show that the troposphere IS warming, as determined from multiple sources. And if anyone quotes satellite temperature data to make a point with you, make sure you ask them which series they are referring to. If they simply say ‘the satellite data from UAH,’ they may not know what they are talking about.

Further Reading

The IPCC had this to say about the satellite record (section 3.4.1.2).

And Scott Church tells you even more than that up to 2005.

Addendum

This post also has relevance to the ‘There's no Tropospheric hot spot’ argument. Look at some of the graphs above. Fu et al. (2004, 2005) showed greater warming in the troposphere than the surface for the Tropics and Southern Hemisphere for their adjustments to the RSS data. And Vinnikov and Grody also show greater warming in the troposphere compared to surface records and also in agreement with models in the Tropics and Southern Hemisphere. Whereas the analyses by UAH, RSS and Zou are not able to show reliably what has happened in the mid and upper troposphere.

http://www.skepticalscience.com/Primer-Tropospheric-temperature-measurement-Satellite.html