Thank you for your reply. You make some valid points. For what it is worth, I agree with your approach.

This may be off topic, but recently some people have been making claims that the climate models are on the “verge of failing”[Knappenberger] or that the IPPC models “are close to being refuted” [Pielke Senior]. I would be very interested in your thoughts on whether or not such confident conclusions/assertions are warranted.

In order to consider the body of evidence, readers should also consider Santer et al. (2011), Thorne et al. (2011a,b). In the unlikely chance you have not already read those papers then I highly recommend them as they supersede Christy et al. (2010) and do not necessarily support their findings. In fact, as I noted at SkepticalScience:

“……evidence that has been published the past year or so that showed model predictions are consistent with observations when one takes the inherent uncertainties into account or notes that there are still outstanding issues with the satellite tropospheric temperature estimates (e.g., Thorne et al. (2011a), Thorne et al. (2011b), Mears et al. (2011)).

Dr. Christy appears to be of the opinion that the satellite data (in particular the UAH data) are the metric to use against which to validate the models, but a recent paper by Mears et al. (2011) finds that there are probably unresolved issues with the satellite estimates. Christy and Spencer also have a long history of being overconfident in their product and dismissing concerns about biases in their product– I have documented that here.

From Thorne et al. (2011a):
“It is concluded that there is no reasonable evidence of a fundamental disagreement between tropospheric temperature trends from models and observations when uncertainties in both are treated comprehensively.”

From Santer et al. (2011):
“There is no timescale on which observed trends are statistically unusual (at the 5% level or better) relative to the multimodel sampling distribution of forced TLT trends. We conclude from this result that there is no inconsistency between observed near-global TLT trends (in the 10- to 32-year range examined here) and model estimates of the response to anthropogenic forcing.”

Again from Santer et al. (2011):
“Given the considerable technical challenges involved in adjusting satellite-based estimates of TLT changes for inhomogeneities [Mears et al., 2006, 2011b], a residual cool bias in the observations cannot be ruled out, and may also contribute to the offset between the model and observed average TLT trends.”

From Mears et al. (2011):
“As we move higher in the atmosphere, the radiosonde-satellite trend differences tend to increase. For TMT,
only about 50% of the radiosonde trends lie within our error bars, except in the northern extratropics, where the radiosonde network is the most spatially complete, and thus the adjusted data sets, which in almost all cases rely upon a neighbor constraint, are most likely to be reliable. Here the agreement remains good. For TMT, the STAR trends are consistently larger than RSS but generally just within our uncertainty bounds when diurnal adjustment uncertainties are included, while the UAH trends are less and tend to be outside our calculated error margins with the exception of the southern extratropics where the two data sets are in good agreement.”

Mears et al. conclude:
“It is clear from this comparison that many hitherto unexplained differences between the data sets, many of which have been previously documented, remain. Although the internal uncertainty estimates derived herein lead to consistency between a number of estimates there are nearly as many cases where differences between the RSS product and competing estimates cannot be reconciled as being caused solely by RSS data set internal uncertainties. An inescapable conclusion from this is that the methodological choices that we and others have made have lead to a substantial and significant impact upon the resulting estimates.”

Yes, of course, no model is perfect. But neither are the data, so we need to keep in mind that the satellite data (or radiosonde data) have their own issues and be open to the likelihood that those issues, when addressed, may bring the models and observations into even better agreement. As noted by several researchers it is not advisable to think that one dataset alone (e.g., UAH) represents the “truth”, because as noted by Thorne et al. (2011):
“No matter how august the responsible research group, one version of a dataset cannot give a measure of the structural uncertainty inherent in the information.”

My motivation in this post was the narrow one of reacting to Fu et al 2011, and I used RSS so as to relate to that paper. I did not mean the post to read as an endorsement of one product over the other. Here is a link to Christy et al 2010 to make it a bit easier for readers to access. Glancing at the figure at the top of my post, I don’t see anything from the model/RSS comparison to hint that there is less agreement after 1991, but this obviously requires a closer look. Biases in TLT ans SST data sets should be independent, so I would very much appreciate it if someone could point me to studies suggesting non-stationarity in their covariability, or non-stationarity in AMIP model biases, with the hope of indentifiying or supporting arguments for one product over another.

An idea I was trying to push in this post is the use of AMIP rather than fully coupled models so as to separate the effects of model/obs differences in SSTs from differences in vertical coupling. Avoiding the need to adjust for ENSOs or volcanoes is precisely my point

I personally think that it is more useful, when thinking about coupling of the surface with the troposphere in the tropics, to use only SSTs and not land temperatures. There are good reasons why we typically constrain only SSTs (and sea ice) in these AMIP runs. The difference in characteristic time scales in atmosphere and ocean makes it plausible that decoupling of the two doesn’t create too many problems (and I think the detailed agreement, at higher frequencies than the trend, in the figures in the post is evidence of this) — but land and atmosphere vary on the same time scales so decoupling them can cause a variety of problems. I am skeptical that specifying land temperatures somehow in this kind of model would be helpful in model/data comparisons. I would argue that it is better to think of land together with the troposphere as responding to SST variations–as discussed also in post #11, and when normalizing tropospheric trends with surface trends, I would rather see normalization by the SST trends only

With regard to the strong coupling between surface and troposphere, I was first exposed to this by Pan and Oort, 1983 based on radiosondes.

Someone directed me to your interesting post. I have some comments as this is a topic with which I’m all too familiar.

1. In several papers, summarized in Christy et al. 2010, we and others investigated the accuracy of the various tropical upper air temperature datasets in detail. It was shown that RSS contained spurious tropical warming in the 1990s due to the overcorrection for the diurnal cooling that characterizes the drifting afternoon spacecraft. RSS was clearly the outlier (see Fig. 4.) Thus, using RSS as the comparison does not represent the real observational evidence and portrays too much apparent agreement.

2. The comparison in the posting above does not contradict the evidence in our papers that the CMIP3 models overstate the amplification ratio. The HiRAMC180 comparison is using a model, but tightly constrained by real temperatures, i.e. an AMIP style run. The CMIP3 coupled model runs show more warming than actually occurred in this time period (globally about twice too much) with a tropical amplification factor around 1.37 (ratio of trends Tlt/Tsfc, see Fig 10 in Christy et al.)

3. In the runs of your model I see a TLT trend of +0.148 C/decade for 1979-2009. Observational tropical trends, as published, are +0.09 +/- 0.03 C/decade, producing an amplification ratio of 0.8 +/- 0.3 (Christy et al. 2010.) The HiRAMC180 model indicates a scaling ratio (using trend of Tsfc as +0.12 C/decade) of 1.23 – a little less than the typical GCM, but outside of the observed ratio.

4. The same comments apply to T2 (RSS has some extra warming not found in the other datasets except for STAR which was examined in detail in Christy et al. 2011 and found also to have instituted RSS’s diurnal correction, so suffers from the same problem as RSS.) Thus the red dots in your Fig. should be accompanied by many others further to the left (see our Fig. 10).

5. With much misinformation on this issue I want to indicate that any model/observation comparisons should be normalized (i.e. such as using the amplification ratio to eliminate variations due to volcanoes and ENSOs) and use the full tropical surface temperatures (rather than say SSTs only.)

6. Perhaps the first paper that recognized the tight coupling between tropospheric layers temperatures and the surface was Christy and McNider 1994 .

Thank you for the post and the opportunity to provide information that evidently was not used in your post.

Christy et al. 2010, What do observational datasets say about modeled tropospheric temperature trends since 1979? Rem. Sens., 2, doi:10.3390/rs2092148.

Although it was not presented in our GRL paper, we found a nearly constant T24/T2LT ratio on the interannual time scale from AR4 GCMs which agrees excellently with that from the MSU observations. This enforces your results and statements. Note that the tropical tropospheric temperature variability on the interannual time scale is dominated by the ENSO while in last thirty years the warming mainly occurs in the Atlantic ocean, and to less extent over Indian Ocean and West Pacific.

Qiang

In a paper by myself and David Neelin, we looked at some of these issues on somewhat shorter timescales, interannual and shorter over the last few decades. We compared profiles of temperature anomalies, regressed onto the free-tropospheric vertical average temperature anomalies, to a theoretical curve calculated using a series of reversible moist adiabats. The idea was to normalize by overall temperature change without relying on local boundary layer values, which may be somewhat decoupled from the free tropospheric changes. We got values that were extremely close to the moist adiabatic values for NCEP-NCAR reanalysis for basically all tropical regions and for monthly and daily anomalies (with the exception of the easterm Pacific for daily data). For daily AIRS satellite data (over two years) there were still good correlations but the regression coefficients were somewhat higher than the theoretical curve at midlevels and lower in the boundary layer; radiosondes during TOGA-COARE over the Pacific warm pool showed this behavior as well; monthly data looked closer to the moist adiabat, at least in the free troposphere.

Christopher E. Holloway and J. David Neelin, 2007:
J. Atmos. Sci., 64 (5), 1467-1487. doi:10.1175/JAS3907.1
http://journals.ametsoc.org/doi/abs/10.1175/JAS3907.1

We also looked at 3 models from CMIP3 used in the IPCC AR4, although this was not in the paper; it is in my Ph.D. dissertation, located on http://cms.ncas.ac.uk/~chollow/dissertation/ (Figure 2.14 on page 52). The models matched very closely to the moist-adiabatic curve for 25 years of the historical climate runs (this was over the warm pool but was also true for the tropics as a whole). We also did a simple profile of differences over a century of the climate change runs, and you can see almost the same shape of warming for that time scale.

We figured that the models, like the NCEP-NCAR reanalysis, are probably somewhat too constrained by their convective parameterizations, causing them to follow the moist-adiabatic curve too closely. But we know that the models are also doing this for shorter-term variability, and observations seem to validate a lot of this link to the moist adiabat on these shorter time scales.