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Climate Hustle

A Response to the “Data or Dogma?” hearing

Posted on 17 January 2016 by Ben Santer, Carl Mears

Guest post (also available in PDF form) by:

Benjamin D. Santer, Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA.

Carl Mears, Remote Sensing Systems, Santa Rosa, CA.

On December 8, 2015, Senator Ted Cruz – the chairman of the Senate subcommittee on Space, Science, and Competitiveness –convened a hearing entitled “Data or Dogma?” The stated purpose of this event was to promote “…open inquiry in the debate over the magnitude of human impact on Earth’s climate” (1). In the course of the hearing, the chairman and several expert witnesses claimed that satellite temperature data falsify both “apocalyptic models” and findings of human effects on climate by “alarmist” scientists. Such accusations are serious but baseless. The hearing was more political theatrics than a deep dive into climate science.  

Satellite-derived temperature data were a key item of evidence at the hearing. One of the witnesses [a] for the majority side of the Senate subcommittee showed the changes (over roughly the last 35 years) in satellite- and weather balloon-based measurements of the temperature of the mid-troposphere (TMT), a layer of the atmosphere extending from the Earth’s surface to roughly 18 km (2). Satellite TMT measurements are available from late 1978 to present. Observed TMT data were compared with TMT estimates from a large number of model simulations. This comparison was ‘Exhibit A’ for the majority side of the subcommittee.

Senator Cruz used Exhibit A as the underpinning for the following chain of arguments: 1) Satellite TMT data do not show any significant warming over the last 18 years, and are more reliable than temperature measurements at Earth’s surface; 2) The apparent “pause” in tropospheric warming is independently corroborated by weather balloon temperatures; 3) Climate models show pronounced TMT increases over the “pause” period; and 4) The mismatch between modeled and observed tropospheric warming in the early 21st century has only one possible explanation – computer models are a factor of three too sensitive to human-caused changes in greenhouse gases (GHGs). Based on this chain of reasoning, Senator Cruz concluded that satellite data falsify all climate models, that the planet is not warming, and that humans do not impact climate.

This logic is wrong. First, satellites do not provide direct measurements of atmospheric temperature: they are not thermometers in space. The satellite TMT data plotted in Exhibit A were obtained from so-called Microwave Sounding Units (MSUs), which measure the microwave emissions of oxygen molecules from broad atmospheric layers (2-4).[b] Converting this information to estimates of temperature trends has substantial uncertainties.[c] The major uncertainties arise because the satellite TMT record is based on measurements made by over 10 different satellites, most of which experience orbital decay (5) and orbital drift (6-8) over their lifetimes. These orbital changes affect the measurements of microwave emissions, primarily due to gradual shifts in the time of day at which measurements are made. As the scientific literature clearly documents, the adjustments for such shifts in measurement time are large,[d] and involve many subjective decisions (2-4, 6-8). Further adjustments to the raw data are necessary for drifts in the on-board calibration of the microwave measurements (9, 10), and for the transition between earlier and more sophisticated versions of the MSUs.[e]

In navigating through this large labyrinth of necessary adjustments to the raw data, different plausible adjustment choices lead to a wide range of satellite TMT trends (2-10). This uncertainty has been extensively studied in the scientific literature, but was completely ignored in the discussion of Exhibit A by Senator Cruz and by witnesses for the majority side of the subcommittee (2-15). The majority side was also silent on the history of satellite temperature datasets. For example, there was no mention of the fact that one group’s analysis of satellite temperature data – an analysis indicating cooling of the global troposphere – was repeatedly found to be incorrect by other research groups (2, 3, 5-10).

Such corrective work is ongoing. Satellite estimates of atmospheric temperature change are still a work in progress (2, 3, 8), and the range of estimates produced by different groups remains large.[f] The same is true of weather balloon atmospheric temperature measurements (2, 11-13, 15-17).[g] Surface thermometer records also have well-studied uncertainties (2, 19, 20), but the estimated surface warming of roughly 0.9°C since 1880 has been independently confirmed by multiple research groups (2, 15, 19, 20).

The hearing also failed to do justice to the complex issue of how to interpret differences between observed and model-simulated tropospheric warming over the last 18 years. Senator Cruz offered only one possible interpretation of these differences – the existence of large, fundamental errors in model physics (2, 21). In addition to this possibility, there are at least three other plausible explanations for the warming rate differences shown in Exhibit A: errors in the human (22-25), volcanic (26-30), and solar influences (24, 31) used as input to the model simulations; errors in the observations (discussed above) (2-20); and different sequences of internal climate variability in the simulations and observations (23, 24, 30, 32-36). We refer to these four explanations as “model physics errors”, “model input errors”, “observational errors”, and “different variability sequences”. They are not mutually exclusive. There is hard scientific evidence that all four of these factors are in play (2-20, 22-36).

“Model input errors” and “different variability sequences” require a little further explanation. Let’s assume that some higher extraterrestrial intelligence provided humanity with two valuable gifts: a perfect climate model, which captured all of the important physics in the real-world climate system, and a perfect observing system, which reliably measured atmospheric temperature changes over the last 18 years. Even with such benign alien intervention, temperature trends in the perfect model and perfect observations would diverge if there were errors in the inputs to the model simulations,[h] or if the purely random sequences of internal climate oscillations did not “line up” in the simulations and in reality (23, 24, 30, 32-36).

In short, “all models are too sensitive to CO2” is not the only valid explanation[i] for the model-data differences in Exhibit A (2, 11, 13, 18,22-24, 26, 30, 32-38). Dozens of peer-reviewed scientific studies show that the other three explanations presented here (“model input errors”, “observational errors”, and “different variability sequences”) are the primary reasons for most or all of the warming rate differences in Exhibit A.[j]  

But what if climate models really were a factor of three or more too sensitive to human-caused GHG increases, as claimed by the majority side of the subcommittee? The telltale signatures of such a serious climate sensitivity error would be evident in many different comparisons with observations, and not just over the last 18 years. We’d expect to see the imprint of this large error in comparisons with observed surface temperature changes over the 20th century (37-42), and in comparisons with the observed cooling after large volcanic eruptions (30, 43, 44). We don’t. There are many cases where observed changes are actually larger than the model expectations (41, 42), not smaller.

In assessing climate change and its causes, examining one individual 18-year period is poor statistical practice, and of limited usefulness. Analysts would not look at the record of stock trading on a particular day to gain reliable insights into long-term structural changes in the Dow Jones index. Looking at behavior over decades – or at the statistics of trading on all individual days – provides far greater diagnostic power. In the same way, climate scientists study changes over decades or longer (39-42, 45), or examine all possible trends of a particular length (23, 38, 46-48). Both strategies reduce the impact of large, year-to-year natural climate variability[k] on trend estimates. The message from this body of work? Don’t cherry-pick; look at all the evidence, not just the carefully selected evidence that supports a particular point of view.

In summary, the finding that human activities have had a discernible influence on global climate is not falsified by the supposedly “hard data” in Senator Cruz’s Exhibit A. The satellite data and weather balloon temperatures are not nearly as “hard” as they were portrayed in the hearing. Nor is a very large model error in the climate sensitivity to human-caused GHG increases the only or the most plausible explanation for the warming rate differences in Exhibit A. Indeed, when the observational temperature datasets in Exhibit A are examined over their full record lengths – and not just over the last 18 years – they provide strong, consistent scientific evidence of human effects on climate (41, 42, 48). So do many other independent observations of changes in temperature, the hydrological cycle, atmospheric circulation, and the cryosphere (41, 42).

Climate policy should be formulated on the basis of both the best-available scientific information and the best-possible analysis and interpretation. Sadly, neither was on display at the Senate hearing on “Data or Dogma?” There was no attempt to provide an accurate assessment of uncertainties in satellite data, or to give a complete and balanced analysis of the reasons for short-term differences between modeled and observed warming rates. Political theater trumped true “open inquiry”.

Climate change is a serious issue, demanding serious attention from our elected representatives in Washington. The American public deserves no less.  

Acknowledgments

We gratefully acknowledge the comments and valuable suggestions from Professor Susan Solomon (M.I.T.) and Dr. Mike MacCracken (The Climate Institute). 

Footnotes

  1. Prof. John Christy from the University of Alabama at Huntsville.
  2. MSU estimates of the temperature of tropospheric layers also receive a small contribution from the temperature at Earth’s surface.
  3. This conversion process relies on an atmospheric radiation model to invert the observations of outgoing, temperature-dependent microwave emissions from oxygen molecules. Since oxygen molecules are present at all altitudes, the microwave flux that reaches the satellite is an integral of emissions from thick layers of the atmosphere.  
  4. At the end of the hearing, Senator Cruz questioned the reliability of thermometer measurements of land and ocean surface temperature, and highlighted the large adjustments to “raw” surface temperature measurements (adjustments which are necessary because of such factors as changes over time in thermometers and measurement practices). He did not mention that the surface temperature adjustments are typically much smaller than the adjustments to “raw” MSU data (2, 3, 8).
  5. This transition occurred in 1998, at the beginning of the 18-year “no significant warming” period highlighted by Senator Cruz.
  6. For example, over the longer 1979 to 2014 analysis period, tropospheric warming is a robust feature in all observational TMT datasets. For shorter, noisier periods (such as 1996 to 2014), the sign of the TMT trend is sensitive to dataset construction uncertainties.
  7. Disappointingly, Exhibit A neglects to show at least one weather balloon temperature dataset with substantial tropospheric warming over the last 18 years (18).
  8. Such as leaving out volcanic cooling influences that the real world experienced (23, 24, 26-30).
  9. The model results shown in Exhibit A are from so-called “historical climate change” simulations. These simulations involve changes in a number of different human and natural influences (e.g., human-caused changes in GHG levels and particulate pollution, and natural changes in solar and volcanic activity). They are not simulations with changes in GHG levels only, so it is incorrect to interpret the model-versus-observed differences in Exhibit A solely in terms of model sensitivity to GHG increases.   
  10. Another incorrect claim made at the hearing was that the mainstream scientific community had failed to show the kind of model-data comparisons presented in Exhibit A. Results similar to those in Exhibit A have been presented in many other peer-reviewed publications (2, 13, 18, 23, 24, 30, 32, 35, 38, 46, 47).
  11. Such as the variability associated with unusually large El Niño and La Niña events, which yield unusually warm or cool global-mean temperatures (respectively). The El Niño event during the winter of 1997 and spring of 1998 was likely the largest of the 20th century, and produced a large warming “spike” in surface and tropospheric temperatures.

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Comments

Comments 1 to 8:

  1. A good, informative post. Mears in particular must become frustrated seeing his work constantly misrepresented.

    With respect to ongoing research, I wonder if a series of high-resolution measurements in the 53-57 GHz band from an airborne microwave spectrometer (vertical looking up, vertical looking down and horizontal) under measured conditions of temperature, pressure and humidity might allow improved deconvolution of the satellite data. Most of the emission curves in the papers I've looked at have a very simplified, idealized look to them. (Maybe this has already been done, but if so I've missed it.)


    As an aside, I think 48 references in a short blog post must be close to some kind of record.

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  2. Cruz should have also been questioned on why he would use a graphic from "Steve Goddards" blog. Tony Heller even boasts about it on his blog (see "Ted Cruz used my graph").

    The 'hasn't warmed in 18 years etc" graph is Lord Monckton's deceptive graphic that does the rounds of contrarian blogs.

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  3. Thank you for posting this follow-up. I offer a critique of following paragraph:

    “Model input errors” and “different variability sequences” require a little further explanation. Let’s assume that some higher extraterrestrial intelligence provided humanity with two valuable gifts: a perfect climate model, which captured all of the important physics in the real-world climate system, and a perfect observing system, which reliably measured atmospheric temperature changes over the last 18 years. Even with such benign alien intervention, temperature trends in the perfect model and perfect observations would diverge if there were errors in the inputs to the model simulations,[h] or if the purely random sequences of internal climate oscillations did not “line up” in the simulations and in reality (23, 24, 30, 32-36).

    It is my understanding, going all the way back to Lorenz (1963), Deterministic Nonperiodic Flow [1] that the more appropriate way to think of the weather/climate system is that it would NOT diverge from previous behavior if all the initial inputs were exactly the same. "Purely random" evokes the concept of a stochastic system where there is no such guarantee by definition.

    I understand that what Drs. Mears and Santer mean by "purely random" is that with the the real system, which is massive and complex, we do not have the observational fidelity OR computational ability to reliably predict short-term climate trends (i.e., weather) in advance due to the sensitivity a deterministc system has for initial conditions — therefore, it behaves as an "effectively random" system for the purposes of exactly timed, very precise prediction of future states.

    However, because it is an almost completely deterministic system, we can at least theoretically hope after the fact to suss out a causality chain for the various modes of internal variability and/or pertubations in external forcings which do contribute to constant change even absent our influences. I think this is a distinction which separates our argument from the magical thinking of the "climate is always changing (and nobody knows why)" crowd ...

    ... not that many of them see it that way since Lorenz (1963) is often abused as "proof" that climate cannot possibly be projected or predicted over the long-term because ... chaos.

    ---------------

    [1] http://eaps4.mit.edu/research/Lorenz/Deterministic_63.pdf

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  4. As a structural engineer I don’t claim to be any kind of expert in climate science. However it has become my favorite subject and the one thing I am most concerned about for my children and grandchildren. Therefore I do a lot of reading about the subject. Also as a structural engineer I don’t get to fudge the numbers based on my political beliefs or the results would be catastrophic.


    The subject of the pause in warming during this century has been completely aggravating to me. Living in East Texas I am completely surrounded by deniers that use this argument constantly. So I made a simple excel spreadsheet which showed the ‘degrees per decade’ rate of temperature from each year to 2015 using the NASA data at ‘http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt’ starting at 1975. At the year 2008 the number started getting very high since the time frame was so small and the temperature for those years was increasing rapidly. But before that the average degrees per decade increase was 0.273. The lowest rate of increase for the entire 40 year period was using 1998 which showed a rate of 0.124 degrees/decade because the temperature for 1998 was so high. If you use 1997 the rate is 0.206 and if you use 1999 the rate is 0.263 degrees/decade. This is the definition of cherry picking and went a long way towards debunking the argument of my peers that there had been no increase in temperature this century.


    I have a feeling the satellite data would show the same results.

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  5. It's great to see these posts on the satellite methods and their uncertainty. I've trying to find some intermediate-level explanations of how it works and now I've got them. Nice, and thanks.

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  6. Great post!

    A broader perspective:    Why the troposphere?

    To see if the Globe is warming, see the ocean heat content.

    To see if the climate is changing, see the global surface temperatures.

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  7. 6. Sailingfree       I get why the troposhere for a denier:

    The Earth's surface is warming. The stratosphere is cooling. 

    So somewhere in the troposphere, by interpolation, is a "Goldilocks Layer" with a level temperature graph.      So "No warming since forever!" can be truthfully proclaimed.

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  8. sailingfree @6, and unnecessarilly repeated @7, there is no such goldilocks layer.  The reason is that part of the stratospheric cooling has been due to the impact of CFCs destroying ozone.  When manufacture of CFCs was restricted, a regime in which both increasing CO2 and increasing CFCs combined to cool the stratosphere was replaced by one in which decreasing CFCs warmed the stratosphere while increasing CO2 cooled it slightly more.  That means the stratospheric trends vary significantly over time, while the tropospheric trends are more or less constant.  From that in turn it follows that the goldilocks layer in one time period is not the goldilocks layer in the second.  In the moderately near future we will have a third regime of near constant O3 (due to the lack of CFCs) coupled with increasing CO2.

    Further, as can be seen in this RATPAC data, the rate of cooling is different in different levels of the stratosphere:

    We can compare that with the weighting profile of the TMT MSU channel:

    We can then see that, first, in recent years the lower stratosphere has had a flat trend, or possibly even a slightly warming trend.  Second, we see that TMT only significantly samples the lower statosphere.  It follows that while lower stratospheric temperatures reduce the measured trend from 1979-2015, they have little effect on the measured trend from 1998-2015.

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