The (missing) tropical hot spot

I’m wondering who even thought this was a good or important point for discussion. The intro text is itself misleading in a number of ways – on the facts and on the history, some of which I believe it has wrong (or at least is overly petty or without appropriate error bars). As far as I can tell from my study of the history of this, the first person to highlight tropical lapse rate changes with the name “hot spot” and claim it was a “fingerprint” of greenhouse warming was Christopher Monckton in an August 2007 article here:
http://scienceandpublicpolicy.org/monckton/greenhouse_warming_what_greenhouse_warming_.html

and as can be read there (compared with the rational discussion above), he clearly was very confused by FIgure 9.1 in IPCC AR4 WG1. It’s not a “spot”, for one thing – we’re talking about the entire equatorial band around the earth. And it’s hardly “hot” – the troposphere is still cooler than the surface; the issue is the ratio of a small relative temperature change – making it hard to measure (something John Christy seems to refuse to acknowledge in his comments which refer only to problems in models, not in observations). And it’s very definitely and absolutely and certainly not a “fingerprint” of greenhouse warming – the same pattern is there, as discussed clearly by Mears above, for every source of surface warming.

Furthermore, the ratio of tropical troposphere to surface changes *is* greater than one if you look over relatively short periods of time – as Mears also discussed here, as can be read clearly from Santer’s 2008 paper for instance. So the expected amplification from theory (“models” hardly does the reasoning here justice) seems to be very visible on short time scales. The discrepancy is *ONLY* with regard to the ratio of long-term temperature changes, over periods of greater than a decade.

For example, look at monthly temperature anomalies between a relative low in January 1989 and relative high in February 1998, a large temperature change over less than a decade. From some numbers I looked at a few years ago (apologies if corrections have changed this significantly) we have:

January 1989 to February 1998 change:
Hadley tropical surface temp: 0.970 C (-0.172 to 0.798)
RSS tropical TLT: 1.836 (-0.522 to 1.314)
RSS tropical TMT: 1.806 (-0.466 to 1.340)
UAH tropical T2LT: 1.83 (-0.52 to 1.31)
UAH tropical T2: 1.76 (-0.48 to 1.28)
so dividing by the 0.97 surface temperature difference gives an amplification of between 1.81 and 1.89. What explains this clear case of amplification over a little under a decade, but the apparent lack of amplification in the longer-term trends? If there’s no reasonable theoretical explanation for such a difference, the difficult of observational calibration over such long time-periods strongly suggests it’s not the theory that’s wrong.

So we have a case here of tricky and difficult observations being promoted in a wildly incorrect fashion by the flamboyant Mr. Monckton – and then echoed in venues such as this one – for what reason? The burden of proof here is with the satellite observations, not with basic physical theory. As has been pointed out earlier as well – surface temperature changes are well understood. If the tropical troposphere is warming less than expected, the logic points to higher climate sensitivity, not lower. So this whole mess has only downsides for those trying to avoid action.

A question for John & Carl, as the Satellite Temperature experts.

Could the Hot Spot be hiding in plain site in the existing data? Follow my chain of thought and see what you think.

The Upper Troposphere channel (TTS, MSU 3, AMSU 7) is showing essentially no warming or cooling. But this is the channel we would expect to see the hot spot appearing in.

TTS has a weighting function where nearly 1/2 the signal (perhaps 40%) for TTS originates in the lower stratosphere and the rest in the upper troposphere.

Channel TLS (MSU 4, AMSU 9) is associated with the lower stratosphere. Its weighting function is strongly located in the lower stratosphere and middle stratosphere with only a very small percentage (less than 10%) of its signal originating from the upper troposphere. And this channel is showing substantial cooling (more than 0.3 DegC/decade cooling which is stronger than the warming trend in the lower troposphere). Also the less commonly cited higher altitude channels for the rest of the stratosphere are all showing the stratosphere cooling as well.

So, if the stratosphere is cooling strongly then nearly half the signal for the TTS channel, the one that is relevant when looking for the hot spot, is coming from a region of strong cooling. If the overall signal for TTS is showing no warming or cooling, doesn’t this suggest that the part of the TTS signal that originates in the upper troposphere must actually be warming, in order to balance the stratospheric component to give a net flat-line trend?

Isn’t the hot-spot visible in the data if we understand what the data actually means?

Could an approach similar to that used to create the TLT synthetic channel, using a differencing algorithm applied to off-nadir samples taken from MSU 3/AMSU 7, be used to synthesize a true upper troposphere measurement? Alternatively, could an approach similar to that of Fu & Johansson be used to extract such a signal?

That might clear up this question once and for all.

It is worth noting that the statistical test used in Douglass et al. (2008) is obviously inappropriate as a perfect climate model is almost guaranteed to fail it! This is because the uncertainty is measured by the standard error of the mean, rather than the standard deviation, which falls to zero as the number of models in the ensemble goes to infinity. If we could visit parallel universes, we could construct a perfect climate model by observing the climate on those parallel Earths with identical forcings and climate physics, but which differed only in variations in initial conditions. We could perfectly characterise the remaining uncertainty by using an infinite ensemble of these parallel Earths (showing the range of outcomes that are consistent with the forcings). Clearly as the actual Earth is statistically interchangable with any of the parallel Earths, there is no reason to expect the climate on the actual Earth to be any closer to the ensemble mean than any randomly selected parallel Earth. However, as the Douglass et al test requires the observations to lie within +/- 2 standard errors of the mean, the perfect ensemble will fail the test unless the observations exactly match the ensemble mean as the standard error is zero (because it is an infinite ensemble). Had we used +/- twice the standard deviation, on the other hand, the perfect model would be very likely to pass the test. Having a test that becomes more and more difficult to pass as the size of the ensemble grows is clearly unreasonable. The spread of the ensemble is essentially an indication of the outcomes that are consistent with the forcings, given our ignorance of the initial conditions and our best understanding of the physics. Adding members to the ensemble does not reduce this uncertainty, but it does help to characterise it.

The thing that really concerns me though, is that Douglass and Christy (2013) discuss their earlier paper quite uncritically, despite the statistical shortcomings of that paper having been widely discussed, both on-line and in the peer-reviewed litterature.

Douglass DH, Christy JR, Pearson BD, Singer SF. A comparison of tropical temperature trends with model predictions. Int J Climatol 2008, 27:1693–1701

Douglass, D. and J.R. Christy, 2013: Reconciling observations of global temperature change: 2013. Energy and Env., 24 No. 3-4, 414-419.

I have a question about the last paragraph from Sherwood, about where the heat has been going in the past decade. If more of the heat has gone into the oceans, as seems likely, is it then appropriate to relax and think that the oceans are keeping us cool? Or will more heat in the oceans also cause a bit more sea level rise. Also: a small change of sea temperature could be a big problem for an ecosystem that may have evolved to depend on the past relative stability of ocean temperatures? So it may be that even if atmospheric temperature has not been rising much, we should still worry about the effects on the ocean ecosystem, and sea level.

As I understand it, the basic basic argument for a tropical hotspot would be the increase in evaporation due to an increase of surface temperature. It should be clear that evapotranspiration does not depend on surface air temperature, but on the surface “skin” temperature, which may differ considerably from the air temperature. We have studied the surface “skin” temperature, using Meteosat data for the period 1982-2006, and find a significant decrease in both land and ocean skin temperature. Why should we then expect a tropical hotspot?

See: Rosema A, Foppes S, van der Woerd J(2013) “Meteosat derived planetary temperature trend 1982-2006”, ENERGY & ENVIRONMENT, Volume 24, No. 3 & 4, 2013, pp 381-395

@ Chris and Andries
The way Mr Mears explains the hot spot is clearly illustrated in the link I mentioned before:
http://www.climatetheory.net/11-analysing-the-missing-tropical-hot-spot/

Using the graph in the link, assuming the same humidity instead of the same relative humidity of the parcel at the surface, a 10 degrees warmer parcel would start ascending over the DALR till 1 km high, and then follow the SALR.
This SALR curve would be very close to the 20C curve, resulting in a complete lack of warming at 10km.

So I don’t think that the (absolute) humidity at the surface can be disregarded.

Ross McKitrick seems to be implying that we should not trust the surface warming record, and should regard the atmospheric temperature record as a separate measure of climate change which somehow discounts global warming itself. He and others seem unwilling to accept the clear evidence that the surface and near-surface warming records are, collectively (that is including independent ocean surface, near-surface maritime, and 2-meter terrestrial records which at least over the 20th century are all quite consistent) far more trustworthy and solid than the dodgy free-atmosphere trends. I think we all agree that recent warming in the Tropics has been less than we would have expected no matter how it is measured, and I agree this merits further research (and indeed has spurred a flurry of efforts in the last year or two, so rest assured more papers will be coming out looking at this).

Ross McKitrick asserts consistency in the tropospheric observations because “the observational record involves two different systems (radiosondes and MSU) and the average balloon record does not differ from the average MSU series (MMH Table III)”. If ever there was an assertion about scientific observations that tells far more about the asserter than it does about reality, this is surely it. I urge the diligent on this thread to investigate this claim, and what it really implies about the consistency of tropospheric temperature trends, or in more common terminology, what their uncertainty is likely to be, a term that McKitrick notably avoids while talking about “averages”.

Carl Mears,

Thank you for your interesting answer.

From my side, I would make a different assumption even if I do not know enough about the source of the data and the tropical dynamics to be very assertive.

First, I don’t think that surface temperatures trends are very reliable. You noted that the boundary layer was complex, you can add it is very inhomogeneous in the horizontal plane. In addition, these inhomogeneities are not temporally stable and a series of puzzles are related to surface temperatures (various divergences and inconsistencies).

For these reasons, I prefer not to take into account the trend in 1000 hPa and analyze what happens from 850 hPa. It may not be statistically very significant, but in this case, the observed (relative) hot spot is even more pronounced than the modeled one (about 25% more at the tropopause).

On the other hand, the modeling of the initial effect of GHG (before feedback) postulates the invariance of the temperature gradient. This assumption is in contradiction with the general theory of heat transfer (flow distribution). In fact, we should expect an initial hot spot (not related to water vapor) in parallel with the increase of the average temperature.

The strong hot spot observed and the overvaluation of the surface warming by models are two characters that tend to confirm this theory.

First comments of Carl Mears on the two other blog posts:

It appears to me that the three of us agree fairly well about the basic facts, but differ on interpretation.
Like Steve, I am somewhat mystified about all the attention given to the tropical hotspot, as I don’t think it is very important for global warming theory, and it is relatively poorly observed. The uncertainties involved in the various types of observations are made worse by focusing on a ratio between two relatively small trend values.
I think all three of us agree that the observed temperature changes in the tropics (and globally) are less than predicted over the last 35 years. John uses this fact to argue that there are fundamental flaws in all climate models, and that there results should be excluded from influencing policy decisions. This goes much too far. First, many imperfect models are used to inform policy makers in many areas, including models of the economy, population growth, environmental toxins, new medicines, traffic flow, etc. etc. As pointed out by a commenter in this thread, policy makers are used to dealing with uncertain predictions. If we throw out all imperfect models, we will be reduced to consulting the pattern of tea leaves on the bottom of our cups to make decisions about the future. Second, as I argue below, there are many possible reasons for this discrepancy, and only a few substantially influence the long-term predictions.
Let’s return from this philosophical aside to a discussion of the difference between recent observations and climate model predictions. In my mind, the possible causes for this disagreement fall into 3 general categories.

Bad Luck
Bad Forcings
Bad Model Physics

Bad Luck. By Bad Luck, I mean that the last decade is cooler than normal due to the random occurrence of some pattern of unforced internal variability. Most climate model simulation exhibit decade-long periods of little or no warming, as shown by Eastering and Wehner (2009). And in general, climate models, even though they tend to have too much year-to-year variability (as mentioned by John in his initial post), often show too little variability on multidecadal on longer scales. There is an interesting discussion of this topic in a recent issue of the AGU newsletter (Lovejoy, 2013). So, for multidecadal time periods, I would expect the real world to be bumpier (on multidecadal time acales) than a typical model simulation, and much bumpier than the mean of many simulations. Thus I think that there is some possibility part of the cause the current discrepancy may be just bad luck. Though the time period is getting long enough, and the discrepancy is getting large enough that we should be able to begin to understand something about what is going on. In other words, even if it is due to a random fluctuation, we should be able to see the fluctuation in other variables or parts of the system, such as heat flux into either the ocean or into space.

Bad Forcings. Forcings are the inputs to the climate system by processes external to Earth’s climate. These include anthropogenic modifications to the atmosphere (CO2, methane, various aerosols), volcanic aerosols, and changes in solar output. If the estimates of these forcings, which are used as input to the climate model simulations, are not correct we can hardly expect the climate model output to be correct. Is there any evidence for incorrect forcings over the past 35 years being used for model input? In fact, there is.
One example is radiative forcing due to stratospheric sulfate aerosols, which are little droplets of SO2 and water that scatter the incoming light from the sun. It is well accepted that increases in stratospheric aerosols warm the stratosphere and cool the surface and troposphere. Both effects can clearly be seen in the MSU temperature record after the colossal eruptions of El Chichon and Pinatubo. These eruptions spewed large amount of gaseous sulfur into the stratosphere, where it oxidized to form excess levels sulfate aerosols. These events, and others before 2000, are well represented in the stratospheric aerosols datasets used to drive the 20th century simulations for CMIP-5. After 2000, the level of stratospheric aerosols in the input datasets is allowed to decay to zero. In real life, however, observations indicate that the background level of stratospheric aerosols increased over the 2000-2010 period (Solomon et al, 2011), probably due to a large number of small volcanic eruptions (Neely et al, 2013). The effect is large enough to offset about 25% of the effect of increasing CO2 over this period (Solomon et al, 2011).
Other forcings with possible problems include solar output, stratospheric ozone, and black carbon aerosols. The sun has been in a quiet, low-output phase for longer than expected. This is not included in the CMIP-5 forcings, and thus model results should be expected to be slightly warmer than real life. Temperature changes in the upper troposphere and lower stratosphere have been shown to be very sensitive to the stratospheric ozone concentrations used (Solomon et al, 2012). These effects appear to extend below the tropical tropopause, low enough to affect tropospheric temperature trends and the tropospheric hotspot. The ozone dataset used in the CMIP-5 simulations is the one with the most conservative trends in ozone. If one of the other datasets had been used, the models would have shown less upper tropospheric warming. There are probably other similar problems that I am not aware of.
None of these effects are large enough to explain the model/measurement discrepancies by themselves, but they are each likely to be part of the cause. The cumulative effect of all has not been evaluated.

Bad Model Physics. It is also possible that Bad Model Physics could be part of the cause. Possible causes in this category include problems with cloud feedback, problems with the effects of tropospheric aerosols (and in particular the interaction of aerosols with cloud formation), and poorly-modeled interaction between the atmosphere and ocean. The first two are widely acknowledged to be major contributors to the uncertainty in model predictions. For the third, there is some evidence that heat is being subducted into the ocean at a rate higher than the models expect, though exactly where it is going is less clear (Balmaseda et al,, 2013, Levitus et al., 2012). In our own observations of ocean surface winds, we see trends in wind speed in the tropical pacific that are far larger than those predicted by models. These winds may serve to stir up the ocean, and remove heat from the surface. We do not know whether these effects represent part of the response to global warming, or a part of a pattern of decadal-scale random fluctuation.
Note that only some of the possible problems with model physics affect the long-term model predictions. Increased heat flux into the ocean only serves to delay the temperature increase (and increase the rate of sea level rise), while an error in cloud feedback could affect the long-term temperature rise.

In summary, there are a large number of possible explanations for the model/measurement discrepancy in recent temperature rise. Only a few of these, such as errors in cloud feedback, affect the long-term predictions, while others, such as errors in the natural forcings used as model input, or simulated ocean heat uptake do not. At this time, we simply do not know the exact cause or causes, but I strongly suspect that it is due to a combination of causes rather than one dominant cause.

References.

Eastering, D. R. and M. F. Wehner, “Is the Climate Warming or Cooling?”, Geophysical Research Letters, 36, L08706, doi:10.1029/2009GL037810, 2009.
Lovejoy, S., “What is Climate”, EOS, 94, number 1, January 2013.
Solomon, S., J.S. Daniel, R. R. Neely III, J. P.Vernier, E. G. Dutton, and L. W. Thomason, ‘The Persistently Variable “Background” Stratospheric Aerosol Layer and Global Climate Change’, Science 333, pp 866-870, 2011.
Solomon, S., P. J. Young, and B. Hassler, ‘Uncertainties in the evolution of stratospheric ozone
and implications for recent temperature changes in the tropical lower stratosphere’, Geophysical Research Letters, 39, L17706, doi:10.1029/2012GL052723, 2012.
M. A. Balmaseda, K. E. Trenberth, E. Kallen, ‘Distinctive climate signals in reanalysis of global ocean heat content’. Geophys. Res. Lett. 40, doi:10.1002/grl.50382 (2013).
S. Levitus, et al.,World ocean heat content and thermosteric sea level change (0-2000 m), 1955-2010. Geophys. Res. Lett. 39, L110603, doi:10.1029/2012GL051106 (2012).

First comments of John Christy on the two other blog posts:

For some readers of the science of climate, the topic of tropical surface and atmospheric temperature differences has become the “issue that would not die.” We have been discussing this for 15 years and yet resolution has not been achieved, either in the trends themselves or in the physical understanding of the problem. This has become a specific example of the fact that the science of climate change from human causes is not a “settled science.”

Comments on Mears Original Post

The information provided in Mears’s comment focuses on a little-used quantity to investigate the tropical temperature. After presenting the information, Mears arrives at the conclusion that the observational data are not yet accurate enough to prove or disprove the magnitude of the model-generated hot-spot as real (i.e. not accurate enough to falsify the dominant model response regarding the enhanced greenhouse effect). I agree with virtually all that Mears writes as background. However, I think the fundamental question examined here may be viewed from a larger perspective that draws on more information that then can lead to a less ambiguous conclusion.

Mears focuses on “observations” of a quantity known as the “Temperature Tropical Troposphere” TTT which is not actually observed, but is rather a derived quantity dependent upon the difference of two measurements (temperature of the mid-troposphere (TMT) and lower stratosphere (TLS)). The differencing process tends to increase the error opportunities for the derived product. Observations of the lower stratosphere (TLS) have greater uncertainty, and this contributes to the spread of the TTT results among the various datasets.

A more direct method that reduces observational error (especially of the more uncertain stratospheric portion) is simply to examine the temperature product of one channel which captures the bulk of the desired signal (mid-to-upper troposphere temperature) and which avoids the compounding of errors that a “difference” of products introduces. (The stratosphere only contributes about 7% to the TMT tropical signal.) Hence in my posting, the comparisons focused on the TMT product about which much more is known. [I calculated and discussed, but did not show by chart, that the results using the lower tropospheric temperature or TLT were very similar to those of TMT.] Mears also tends to discuss those datasets which are the “warmest”, i.e. RSS, STAR and MERRA.

However, there is information on why the datasets differ, and this can be used to infer a more confident assessment (see Christy et al. 2010 and 2011 for more information).

The following are a few examples of the knowledge we have of these datasets. STARv2.0 contains a spurious warming shift on 1 Jan 2001 which will be corrected in the new v3.0 to be released later this year. So, STAR’s results in Mears’s contribution overstate the warming. MERRA is an outlier dataset for temperature trends with problems, including a significant warm shift between 1990 and 1992 probably due to the inability to correct for infrared contamination from Mt. Pinatubo. As a result, MERRA produces the warmest tropospheric trend by far of all observational and reanalysis datasets, being almost +0.10 °C/decade warmer than the average of the balloons. HadAT2, using a more conservative methodology for detecting shifts in balloon measurements, likely has retained spurious upper troposphere/lower stratosphere cooling from radiosonde equipment changes over time which contributes to its relatively “cool” trend. ERA-I appears to be excellent in the lower troposphere, but with the inclusion of aircraft reports after 2002 experienced a spurious warming in the upper troposphere due to the previously too-cool analyzed values in that region (note: this also impacts the RAOBCORE and RICH datasets).

A minor controversy appeared last year when Po-Chedley and Fu (2012) allegedly found an error in our UAH TMT dataset when in fact the main source of their finding was based on an incorrect understanding of the UAH merging sequence of the satellites (Christy and Spencer 2013). It is understandable that Mears highlights RSS data since he is the source of the product, but evidence has been published to demonstrate the RSS TMT product likely has spurious tropical warming due to an apparent overcorrection of the diurnal cycle errors (e.g. Christy et al. 2010). [In a counter-intuitive result, this correction causes the RSS global TLT trend to be cooler than UAH’s.] I’m not clear as to why RATPAC (NOAA balloon dataset) nor JRA-25 (a Japanese reanalysis dataset) were not included in Mears analysis. Both are very near the overall averages shown in my earlier posting and are both cooler than RSS, STAR and MERRA. So, while none of the datasets can claim to be perfect, we can explain many of their differences and by averaging, reduce the independent errors.

Thus there are clear reasons for not highlighting RSS, MERRA or STAR as observational datasets. Rather, to take a more unbiased approach to the observations, I had simply calculated the mean of the two categories of datasets (satellite and balloon separately) to reduce the random error opportunities. In this way the impact of independent errors that lead to trends that are too warm or too cool may be limited. The fact the tropospheric trends from the average of two very different and independent set of monitoring systems, i.e. balloons and satellites, are within 0.01 °C/decade of each other, lends confidence to the result. [I did not include STAR due to the known shift in its temperature and the fact it uses the identical diurnal corrections as RSS – thus it is very similar to RSS but with a known spurious shift. However, even if STAR were included as an “independent” dataset, the significance of the results would not change.]

The simple numbers tell the story and can’t be overlooked. From 73 CMIP-5 model runs, the 1979-2012 mean tropical TMT trend is +0.26 °C/decade. The same trends calculated from observations, i.e. the mean of four balloon and mean of two satellite datasets, are slightly less than +0.06 °C/decade. Tropical TMT is a quantity explicitly tied to the response of models to the enhanced greenhouse effect (or any applied forcing). Because the sample of climate model runs is relatively large (N = 73) we have a very confident assessment of the model-mean value and its error range does not encompass the observations. In addition, the agreement of the means of two independent observational systems further indicates that we have a very good idea of the actual TMT trend. The mean of the models (often used as the “best estimate” in IPCC assessments) and observations differ by +0.20 °C/decade which is highly significant. And, we are not talking about 10 or 15-year trends – this is a 34-year period over which this discrepancy has grown. Regarding the highly significant nature of the differences in my initial posting, I failed to mention the many papers led by Ross McKitrick (e.g. McKitrick et al. 2010, McKitrick et al. 2011 and others) in which they demonstrate with more advanced statistical tools that the models and observations are indeed significantly different regarding tropical tropospheric temperature trends.

All in all there is little to argue with in the posting by Mears as it reflects a typically careful and clinical examination of the issue, and indeed allows for the conclusion I state above. However, the extra information shown above, I believe, enhances the confidence in the observational results that then leads to a more definitive statement that the models, on average, have significantly misrepresented the evolution over the past 34 years of a bulk quantity directly tied to the models’ response to the enhanced greenhouse effect.

Comments on Sherwood’s Original Post

Sherwood provides a more theoretical discussion of the topic, as well as bringing up some different problems with which climate models must also contend. He points to the likely tendency of models to tie the surface trends too tightly to upper tropospheric temperature and I agree. If I read the post correctly, however, Sherwood also expresses the opinions that (1) the observations are too error-prone for any definitive use, and (2) even if they were useful, the large disagreement between observations and models in the tropical troposphere would be largely inconsequential.

I hold very different opinions in that (1) with time and increased understanding, the observations of the troposphere from independent systems are converging to the true answer, and that (2) the fact the average model is accumulating heat in the upper atmosphere at a rate three times faster than the observations has serious implications for representing the entire climate system, including a mis-modeling of the surface temperature (see next paragraph). It also betrays a mis-handling of the hydrologic/convective processes of the troposphere – processes that are fundamental to understanding climate variability and change over time, as Sherwood notes. And, by utilizing TMT as I did, I avoided the uncertainties of the stratospheric correction to TTT that Sherwood rightly points out.

Sherwood displays a plot that shows how water vapor feedback and lapse-rate feedback tend to cancel. It is important to realize that this is generated from model output which I find difficult to accept as a proxy for the real world. For models in general, water vapor feedback doubles the surface warming. The lapse rate feedback mitigates this somewhat at the surface. Now, if I follow this train of thought correctly with the idea the upper temperature trend doesn’t matter, if the water vapor feedback in models was zero, is Sherwood claiming there would still be the extra surface warming? The observational evidence suggests the water vapor feedback is weak to non-existent for multi-decadal time scales which implies less warming than that depicted by models with their strong positive water vapor feedback.

Sherwood indicates that the surface temperature record is robust for climate purposes. I have three comments to make here. First, we and others have shown that the land surface record, as represented by daily mean temperature, is likely contaminated by a warming nighttime trend due to surface development around the world (e.g. Christy et al. 2006, McKitrick and Michaels 2007, Christy et al. 2009, McKitrick 2010, McKitrick and Nierenberg 2010, McNider 2012, Christy 2013.) Secondly, if the surface temperature is the most robust and important metric, we find that the 73 models shown in the earlier post, on average, produce a surface trend in the tropics that is almost twice too warm since 1979 even with the contaminated observational data (+0.19 vs. +0.11 °C/decade). Thus, even with the surface temperature metric, there are problems for models. Thirdly, the fundamental measure of greenhouse warming is the accumulation of joules in the climate system. The surface temperature is woefully inadequate to document this metric as the deep atmosphere and ocean represent the reservoirs that should be monitored to detect changes in this quantity.

When pointing out other model problems Sherwood notes, as an example, that no climate model has replicated the rapid north polar ice loss and that this is an interesting problem. However, none of the models have shown an increasing extent of sea ice in the southern hemisphere either – so we have a problem at both poles for which models have opposing answers and thus opposing issues to solve. There are other such examples of models overwarming the climate. However, as stated in my original post, the importance of the tropical atmospheric temperature is key to model fidelity to the real world because it involves the complicated and ubiquitous interrelationships among the various water components of the climate system.

I did not quite understand Sherwood’s closing comments challenging scientists to come up with a different theory regarding the hot spot. I understand the challenge regarding a new explanation for tropical features, but the implication is that the current theory should reign. However, it is clear that the current theory (as expressed in climate models) fails the test against observations and thus should not be granted any particular meritorious status. The current theory may be close to reality in some aspects but is missing something important since it diverges so far from reality. Other theories have been offered (i.e. negative cloud feedback as mentioned by Sherwood) which in fact more closely match the observed temperature changes.

As I indicated in the original post, I cannot say why any particular model departs so much from reality, but I believe the evidence is clear that the departures are real and significant which then exposes serious problems for the models as long-range forecasting tools.

John R. Christy
University of Alabama in Huntsville

References:

Christy, J.R., W.B. Norris, K. Redmond and K. Gallo, 2006: Methodology and results of calculating central California surface temperature trends: Evidence of human-induced climate change? J. Climate, 19, 548-563.

Christy, J.R., W.B. Norris and R.T. McNider, 2009: Surface temperature variations in East Africa and possible causes. J. Clim. 22, DOI: 10.1175/2008JCLI2726.1.

Christy, J.R., B. Herman, R. Pielke, Sr., P. Klotzbach, R.T. McNider, J.J. Hnilo, R.W. Spencer, T. Chase and D. Douglass, 2010: What do observational datasets say about modeled tropospheric temperature trends since 1979? Remote Sens. 2, 2138-2169. Doi:10.3390/rs2092148.

Christy, J.R., R.W. Spencer and W.B Norris, 2011: The role of remote sensing in monitoring global bulk tropospheric temperatures. Int. J. Remote Sens. 32, 671-685, DOI:10.1080/01431161.2010.517803.

Christy, J.R. and R.W. Spencer, 2013: Comments on “A bias in the midtropospheric channel warm target factor on the NOAA-9 Microwave Sounding Unit.” J. Atmos. Oceanic Techno., 30, 1006-1013. Doi:10.1175/JTECH-D-12-00107.1.

Christy, J.R., 2013: Monthly temperature observations for Uganda. J. Applied Meteor. Clim. (in press).

McKitrick, R.R. and P.J. Michaels, 2007: Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data. J. Geophys. Res., 112:D24S09. DOI:10.1029/2007JD008465.

McKitrick, R.R., S. McIntyre and C. Herman, (2010): Panel and multivariate methods for tests of trend equivalence in climate data sets. Atmos. Sci. Lett., 11(4), 270-277. doi: 10.1002/asl.290.

McKitrick, R.R. and N. Nierenberg, 2010: Socioeconomic patterns in climate data. J. Econ. Soc. Meas. 35:149-175. DOI:10.3233/JEM-2010-0336.

McKitrick, R.R., S. McIntyre and C. Herman, (2011): Corrigendium. Atmos. Sci. Lett., 12(4), 386-388. doi: 10.1002asl.360.

McNider, R.T., G.J. Steeneveld, A.A.M. Holtslag, R.A. Pielke Sr., S. Mackaro, A. Pour-Biazar, J. Walters, U. Nair, and J.R. Christy, 2012: Response and sensitivity of the nocturnal boundary layer over land to added longwave radiative forcing. J. Geophys. Res., 117, D14106, doi:10.1029/2012JD017578.

Po-Chedley, S. and Q. Fu, 2012: A bias in the midtropospheric channel warm target factor on the NOAA-9 Microwave Sounding Unit. J. Atmos. Oceanic. Technol., 29, 646-652.

Regarding the decreased trend at 850hPa compared to the surface, could it be partially related to radiosondes being launched nearly exclusively from land? It has been discussed in numerous papers that part of the land-sea warming contrast is caused by decreased evapotranspiration due to increased CO2. Even on islands this might enhance near-surface warming.

I would guess the CMIP5 comparisons use complete sampling of the tropics (?) though even attempting to mask for radiosonde sampling might not work since GCM grid cells are too coarse for many of the islands used.

Regarding the question of feedback between water vapor and the lapse rate:

The anti-correlation between the lapse rate and water vapor feedback is well understood physically (see e.g., Ingram, 2010), since outgoing radiation changes are largely determined by the relative humidity structure, but this partial cancellation has been known for decades, and alternative ways to setup feedback definitions, such as keeping relative humidity fixed while warming the troposphere (instead of the usual base state in which just the temperature is allowed to adjust and specific humidity is held fixed, see e.g., Held and Shell, 2012) yield insight into the framework behind this cancellation. But I think John Christy is trying to dodge any acknowledgment that we know something about climate.

For the feedback, it’s useful in this context to decompose the tropical water vapor feedback into two components, one of which is the enhanced water vapor that you’d get throughout the column if you warmed the tropical troposphere by a uniform amount. I’ll call that WV(w). The second component to the water vapor feedback would be any “extra water” that you get on top of the uniform-warming assumption by amplifying the upper tropospheric surface temperature. I’ll call that WV(a). So the total water vapor feedback, WV, would be WV = WV(w)+WV(a).

Let LR be the lapse rate feedback. Ignoring cloud feedbacks, and any other surface albedo changes, the total feedback in the tropics would roughly be feedback = WV + LR = WV(w) + WV(a) + LR.

In general, we’d expect WV(w) and WV(a) > 0 (positive feedbacks) and LR |LR| > WV(a). It is the WV(a) component that is partially cancelled by the lapse rate feedback, and since |LR| > WV(a), any departure from the moist adiabat to something closer to a uniform warming situation would result in a positive feedback (since the negative lapse rate feedback is larger than this second water vapor contribution). Nonetheless, the total water vapor feedback, bringing in W(w), makes the sum positive regardless.

There is now clear evidence for positive water vapor feedback in observations, even on longer timescales (see e.g., Soden et al., 2005, Science Shi and Bates, 2011, JGR), despite Christy’s assertion otherwise. This is also true in the lower troposphere and on interannual timescales, with many papers on this (e.g., Dai et al., 2011, J. Climate, and see some of Andrew Dessler’s papers). Consistent water vapor responses are also seen in response to Pinatubo.

Based on emails from both Steven Sherwood and John Christy, and based on Carl Mears’ blogpost, I can report that all three agree that

1) Yes, amplified warming in the tropical troposphere is expected.

And that

2) No, the hot spot in the tropics is not specific to a greenhouse mechanism.

Notice that I changed the wording of question/statement 2 here, because the word “fingerprint” was interpreted differently by John Christy than how we meant it.

In his email to us, John Christy wrote regarding Q1: “Yes, the hot spot is expected via the traditional view that the lapse rate feedback operates on both short and long time scales.” Regarding Q2 he wrote: “it [the hot spot] is broader than just the enhanced greenhouse effect because any thermal forcing should elicit a response such as the “expected” hot spot.” Further elaborations in the email exchange, e.g. regarding whether to call this a fingerprint, involved interpretations as to the meaning of (a lack of) a hot spot, which we will defer for the moment.

The next issue that we’ll take up is encapsulated in Q3:

3) Is there a significant difference between modelled and observed amplification of surface trends in the tropical troposphere (i.e. between the modelled and the observed hot spot)?

John Christy reasons that if the means of two different subsets of the data are roughly the same, then we know the answer, even if there is large scatter within each subset. That is very interesting: according to that reasoning there is no longer any doubt about equilibrium climate sensitivity, because the average of the models and of various estimates based on past data are each around 3C (e.g., IPCC 2007).

As for his reasons for rejecting various datasets, they seem like subjective, a posteriori rationalisations. Every dataset shows rapid changes somewhere or another which look like they could be artificial, or has some design limitation. Is there any peer-reviewed paper using objective criteria to show that the datasets John rejects are truly worse than the others? My 2008 paper showed that the warming trends from the UAH version of MSU TMT at the time were significantly smaller than those from radiosonde data, in a fairly consistent manner across different parts of the globe, while the other two analyses available at the time were consistent with the sondes (please compare the comprehensive global approach in that paper with the pick-and-choose methods one sometimes sees). This was never either refuted or acknowledged by John who continues to maintain that his products are the ones to believe.

As Mears indicates, he, along with Sherwood, are “mystified” that so much attention is drawn to the tropical hot spot, or lack thereof, because they suspect it is not germane to the global warming issue. Others are even more than “mystified” and seek to completely shut off debate which reminds me of the line “… move along now, nothing to see here” used in several movies including Men In Black and The Naked Gun where secretive and embarrassed authorities try to divert a curious public from observing an obvious disaster caused by said authorities. Seriously, in my opinion, there IS something critically important to see here.

The “hot spot”, as I stated earlier, represents an integration of much of our understanding of the energy cycle of the climate system. It is the energy cycle that must be well-characterized before attempting to forecast the climate response to a very slight increase in total energy forcing due to the enhanced greenhouse effect. The tropical atmosphere represents about 30% of the global atmospheric mass, holds a significant role of the planetary hydrologic cycle, and is the entry point for about half of the Earth’s solar energy. If the processes that combine to create the observed tropical structure, variations and change are not understood and replicated well, then we cannot claim we know enough about the system to make confident predictions. Thus, I agree with the instigators of this blogpost, by saying “…DON’T move along now, because there IS something to see here.”

I will address issues as I came across them while reading the posts, so this may appear as a set of disjoint paragraphs. But without reading the somewhat boring details of my following comments, in summary, I don’t believe there is disagreement regarding the basic statement that tropical tropospheric trends of observations and models are significantly different. This means we have some serious questions to explore regarding the energy cycle that have not been well-characterized in the climate modeling establishment to date.

Why RCP8.5?
My use of a particular CMIP-5 scenario (here RCP8.5) is irrelevant when dealing with the observational period (a question from Sherwood). All of these RCP scenarios used the same forcing to 2006, then continued on until a prescribed forcing was achieved for each scenario. The differing scenarios don’t lead to different results until after 2030 when the lowest level is approached and that scenario’s forcing is fixed at that level. Thus the use of any of the forcing scenarios is fine as long as one deals with pre-2030 output.

Dataset numbers
The idea that the average of two completely different sets of measurements gives the same result is quite helpful in my view (a question from Sherwood). [As an aside, Sherwood describes the average of the balloons and satellites as “roughly” the same when in fact their average is different by only 0.01 C/decade, so “roughly” is not an accurate characterization.]

To be more specific about the numbers, we can say the trend of the radiosonde-average tropical TMT annual anomalies is +0.047 ± 0.035 °C/decade where the error is large enough to incorporate all four of the realizations. This trend value is smaller than assumed in my original posting because I had erroneously inserted the lower troposphere (TLT) rather than TMT in one of the datasets. It is curious to me that the latest version of RICH displays the warmest trend of the balloon datasets whereas in earlier versions, RAOBCORE was more positive than RICH. I suspect there may be an unwanted artifact that arises from the use of the ECMWF first-guess in the adjustment process. There is an obvious spurious warm trend in RAOBCORE at the 100 hPa pressure level [see light green circle in my original Fig. 2 at 100 hPa where the trend warms dramatically from 150 hPa to 100 hPa when all other evidence indicates the trend should be less at 100 hPa than 150 hPa], but as this has a small impact on TMT (not so TUT retrieval) we shall not address it.

Averaging the satellites (UAH and RSS) we have +0.059 ±0.031 °C/decade where again the error range encompasses both datasets. Recently I have begun to use the average of RSS and UAH as a useful product in climate discussions as this reduces independent errors that are present in each of our datasets (e.g. McKitrick et al. 2010). Of the six datasets used here, RSS displays the warmest trend and RATPAC the coolest (see Christy et al. 2010 and 2011 for quantitative discussions on dataset differences – information which demonstrates that my decisions regarding dataset selection were based on evidence and were not “subjective, a posteriori rationalizations.”). Taking all datasets together, this provides an average trend between +0.05 and +0.06 °C/decade.

[The appeal to the climate sensitivity analogy regarding dataset differences is actually very interesting (Sherwood question) as recent estimates of its value have fallen well below the IPCC AR4 estimate with no less than 10 recent papers indicating central estimates between 1.0 and 2.1 °C. This is evidence that the calculation of climate sensitivity has considerable uncertainty and that IPCC estimates are likely too high for a number of reasons. By comparison, the observational estimates of TMT shown here are highly consistent.]

Dataset Selection
The comment about selecting or deselecting datasets is a minor issue. First, all four of the updated radiosonde datasets were indeed used, so there is no issue there. Even though there is some dependence of RAOBCORE and RICH, as they are produced by the same investigator, their individual construction processes appear to be sufficiently different that I utilized them as independent realizations.

Secondly, due to obvious issues with the inability to seamlessly incorporate new observing systems into the assimilation process, the Reanlayses (JRA, ERA-I, MERRA) were not used at all (see Sakamoto and Christy 2009 and Christy et al. 2011– again, the reasons for not using Reanalyses are not “subjective” but based on published information). Additionally, a quick examination of The State of the Climate 2012 (BAMS 2013) indicates the three Reanalyses have much greater spread than the simple observational datasets. Here, regarding Reanalyses, I agree with Mears.

Thirdly, the only observational satellite dataset not utilized was STAR TMT for the reasons stated, i.e. (a) it has a glitch in 2001 that the authors recognize and (b) it uses the same diurnal corrections as RSS, and thus is not an independent realization of satellite temperatures. I included RSS even though it has been demonstrated that the dataset contains a warming shift in the 1990s in the tropics relative to all other datasets (including surface datasets) that suggests errors in the diurnal correction (Christy et al. 2010). Thus, RSS, with a likely spurious warming due to diurnal overcorrections, is accepted, but to compound this by adding STAR (with the same diurnal correction issue) will lead to double counting a documented problem. The differences in the additional adjustments of bias and calibration are small by comparison.

However, if it settles anything, adding STAR into the mix does not change the ultimate conclusion to which I had arrived. Since the STAR shift is known to its authors, I calculated the value relative to RSS (since both STAR and RSS use the same diurnal correction and the AMSUs were in use in 2001, this focuses on the shift apart from other adjustments) as +0.056 °C. Subtracting this shift at 1 Jan 2001, now produces a time series almost identical with RSS with a difference in trend of only +0.004 °C/decade. So, using the three datasets as independent realizations (a poor assumption as noted) we have a satellite result of +0.071 °C/decade.

Mears states the new STAR product (v3.0) differs from RSS in the tropics by an unspecified amount but from Mears numbers would appear to be about +0.010 to 0.015 °C/decade warmer than RSS. We have no information about how STAR has applied adjustments so no independent assessment has yet been performed which may reveal problems. In any case, the calculation done in the previous paragraph with an estimate for STARv3.0 doesn’t change any of the basic numbers and result. Thus, even if we throw out the radiosonde datasets, a mean satellite trend of +0.07 °C/decade is still highly significantly different from +0.26 °C/decade found in the models. I don’t see any other conclusion that can be justified and I note that the other authors essentially agree with this finding.

The use of TTT
The issue of TTT vs. TMT is discussed by Mears who prefers TTT. One objection he expresses is the “considerable weight” that the stratosphere exerts on TMT. Actually, that weight is only 7 percent, hardly “considerable” in my opinion. Now, if TTT were a directly-measured quantity, I would agree that it produces a purer tropical tropospheric signal than TMT. However, it is not directly measured and contains error that is larger than TMT alone. Too, one wonders why models should be exempt from getting the tiny portion of the stratosphere that resides in TMT correct to begin with?

With regard to TTT, I calculated the quantity for all 73 CMIP-5 models with the mean of the tropical TTT 1979-2012 trends of +0.32 °C/decade. The mean of the models’ TMT trends was +0.27 °C/decade (this is the mean of the trends whereas the trend of the annual means is +0.26 °C/decade). Throughout the individual comparisons, a consistent result, and an artifact of the retrieval scheme, was that for models, TTT was warmer than TMT by an amount that was inconsistent with the TMT trend. Indeed for many models with a wide range of TMT trends of +0.13 to +0.45, the addition to TMT to calculate TTT was between +0.04 and +0.05 °C/decade. Note too that the trends of TTT from the satellite datasets provided by Mears are also about 0.04 to 0.05°C/decade warmer than TMT.
Therefore, from the models, the average ratio of TTT to TMT was 1.18, meaning very little new information is provided by the retrieval as far as the models go. The satellite ratios are much larger (1.35 to 1.82) than the models simply because the denominators (i.e. observed TMT trends) are so much smaller.
So, whatever one thinks about TTT, and I think the errors are larger than estimated by Mears, one still must come to the conclusion that the trend of TMT is a problem for models to replicate. This seems to be accepted by Mears and Sherwood too.

Readers Comments
Reader GavinCawley: The results of Douglass et al. 2007, which are actually less remarkable than shown in my initial post here, still stand. The confusion created by reading later papers and blogs is the misunderstanding of the question that was addressed. We asked the question this way, ”If climate models had the same surface temperature trend as the real world, would their upper air temperature trends agree with the real world?” This was clearly stated in the paper. This condition of a required agreement in surface trends allowed us to directly compare the models and observations, and we found that the climate models, on average, were significantly different than observations. We discussed problems with the criticisms of our 2007 paper in Douglass and Christy 2013, such as the improper use of datasets known to be obsolete and the comparison of upper air trends even though surface trends between models and observations were not consistent (i.e. apples to oranges).

Reader Phi: The 1000 hPa temperature is taken from the NCDC surface temperature dataset. We did not have values at 950 hPa.

Reader Paul Matthews: Agreed. There are other papers too that are now pointing out the obvious. I’m wondering how the IPCC AR5 will discuss the issue as the earlier drafts were less than forthcoming.