Tropical tropospheric trends again

Back in December 2007, we quite heavily criticised the paper of Douglass et al (in press at IJoC) which purported to show that models and data were inconsistent when it came to the trends in the tropical troposphere. There were two strands to our critique: i) that the statistical test they used was not appropriate and ii) that they did not acknowledge the true structural uncertainty in the observations. Most subsequent discussion has been related to the statistical issue, but the second point is perhaps more important.

Even when Douglass et al was written, those authors were aware that there were serious biases in the radiosonde data (they had been reported in Sherwood et al, 2005 and elsewhere), and that there were multiple attempts to objectively address the problems and to come up with more homogeneous analyses. We mentioned the RAOBCORE project at the time and noted the big difference using their version 1.4 vs 1.2 made to the comparison (a difference nowhere mentioned in Douglass et al’s original accepted paper which only reported on v1.2 despite them being aware of the issue). However, there are at least three new papers in press that independently tackle the issue, and their results go a long towards addressing the problems.

The papers in question (all in press at the Journal of Climate) are from Lanzante and Free, Sherwood et al and Haimberger et al. Note that there are additionally at least two other papers on the way which are relevant but which are not yet publicly available – we’ll mention them when they appear.

First off, Lanzante and Free do an excellent job in really pinning down the biases (compared to satellites and models) of the standard homogenised radiosonde networks (RATPAC2 and HadAT2). These biases undoubtedly exist and the issue is to work out whether they are due to instrumental problems, sampling issues, errors in model physics or errors in model forcings (or all of the above!). In the global mean, there isn’t much of an issue for the mid-troposphere – the models and data track each other when you expect they would (the long term trends or after volcanoes, and don’t where you expect them not to, such as during La Niña/El Niño events which occur at different times in models and observations). Similarly the lower stratospheric trends and variability is reasonably matched except for the post-Pinatubo period where the sondes cool uniformly more than the models – possibly due to underestimation of the stratospheric ozone trend. However, the big discrepancies are in the tropics.

Lanzante and Free RATPAC

The errors due to the homogenisation procedures are unfortunately large (as also attested to by McCarthy et al (2008)) and could (according to L&F) be responsible for all of the difference from the moist adiabat (the expected amplification with height). This is because the distribution of sondes in the tropics is very sparse, there have been a lot of changes of instrumentation, and the known biases (related to solar heating for instance, giving warm biases during daylight) aren’t necessarily easy to correct. Sherwood et al, notably, discuss how to minimise three specific problems in any homogenisation procedure: missing change points, incorrectly detecting change points and problems with ‘bad neighbours’.

The differences from the expected profile are all towards cooling – and this leads to even more cooling in the stratosphere than the models predict as well as cooling in the troposphere (the bias most often remarked upon). The fact that the bias is the same sign throughout the column (despite the very different physics in each region) is a clue that this is unlikely to be real.

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