Tropical tropospheric trends again

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.

Thus different groups have been trying to use different techniques to improve the homogenisation. Haimberger et al propose two approaches – one is to use the reanalysis datasets (ERA-40) as a guide, and a second is an objective way of looking for break points in the individual timeseries. The first approach (adopted in the RAOBCORE products) can bring new information because the reanalysis assimilates a lot more data than just the sondes temperatures – satellite products for water vapour, temperature, winds etc. – and tries to come up with a consistent picture. Unfortunately, the reanalysis itself has jumps as new sources of data (new satellites for instance) get assimilated, and so there are limits to how much use it is – and since they assimilate MSU data as well, the results aren’t necessarily independent of the uncertain MSU products either.

Haimberger et al tropical trends

The second approach is denoted RICH (Radiosonde Innovation Composite Homogenization). This uses a similar method as HadAT2 to detect change points but uses the RAOBCORE product as a background. This works because the problems that arise in the reanalysis are not going to be correlated to the problems in an individual sonde. As can be seen this produces significantly different results from the standard products (like HadAT). RICH also seems more physically consistent than RAOBCORE, particularly in the lower troposphere.

Haimberger et al spatial distribution of trends

The results from RICH (shown here for the individual locations), show up quite dramatically why the tropics have much greater uncertainty than the Northern Hemisphere mid-latitudes. The number and density of stations there is dramatically less.

Sherwood et al tropical trendsSherwood et al (pdf) take a different, iterative, approach that involves using the wind shear measurements (taken at the same time as the temperatures) as a consistency check. These measurements can be linked (via the thermal wind equation to temperature gradients) but are not subject to the same problems in instrumentation. They specifically avoid using the satellite data in the process, so that this can serve as a check on the final product.

So what do these authors find? Each of the different, and independent, methodologies ends up finding pretty much the same thing. If you do a better job on the homogenisation, you end up with answers closer to the expected moist adiabatic amplification of the trend with height. It bears stating again that the expected amplification has nothing to do with the greenhouse effect – it is just a function of the surface warming. Note also that this result was nowhere built in to the analyses and so to find that the most consistent readings produce just that is a validation of all the approaches.

Sherwood et al zonal mean trends

To conclude, the structural uncertainty in the radiosonde data is large, and while these attempts to improve the homogenisation are a step in the right direction, the degree of adjustment is a concern. The bottom line is that the observations may well be closer to the model data than preliminary analyses suggested but that the structural uncertainty remains high. Coming to dramatic conclusions based on any of this remains unwise.

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