Tropical SSTs: Natural variations or Global warming?

Does the AMO even exist as a climate phenomenon absent the complications in detecting the signal in actual observations? Here the answer is probably yes. Within coupled models, enhanced multi-decadal variability with an apparent origin in Atlantic ocean-atmosphere dynamics, does occur. This was shown in work published in the early 1990s by Delworth and collaborators using the GFDL coupled ocean-atmosphere model (see the update and review in Delworth and Mann, 2000), and in more recent work by Knight et al (2005 and 2006) with the Hadley Centre coupled model. This AMO signal, in the model simulations, is associated with oscillatory variations in the meridional overturning circulation such that when the overturning is stronger than normal, there is a warming pattern in the North Atlantic (and vice versa). However, the warming in the model simulations is largely confined to the extratropical North Atlantic, with only a small (roughly 0.1 C maximum) projection onto the Main Development Region (MDR) for Atlantic hurricanes. The model simulation results therefore appear consistent with those analyses of observations which find that the AMO signal does not have a substantial projection onto tropical Atlantic SST. This does not mean that the AMO could not in principle influence tropical Atlantic Hurricane activity. In fact, detailed analyses of both the GFDL and Hadley Centre simulations indicate that the AMO is associated with moderate changes in wind sheer in the tropical Atlantic, which could potentially influence Atlantic hurricane activity. However, as discussed earlier, a number of studies find that it is the SSTs that have played the primary role in the observed increases in hurricane intensities in recent decades.

An alternative approach to the problem is a formal ‘detection and attribution’ analysis which seeks to establish the role of a potentially forced signal in the midst of climate ‘noise’. This is where the new Santer et al paper comes in. Here, the authors examine the model simulations for the 20th Century that were coordinated for the IPCC AR4 and which now form a very valuable database that can be used in addressing issues such as those which concern us here. For each of the models, the trends in key Atlantic and Pacific regions can be compared in the runs with and without forcing. Assuming for the moment that the models produce a reasonable approximation for the naturally occurring decadal variability, it can easily be seen whether a) the trends in the models are similar to those in the real world, and b) to what extent they can be explained by the forcings. In the Santer et al study, they find that the model trends when driven with the 20th Century forcings do match the observations, and moreover, are clearly larger than can be explained by internal variations (see the figure extracted from Figure 2 of their paper). Interestingly, the study also supports the observation-based finding of Emanuel and Mann (2006) that sulphate aerosols are likely to have masked a significant component of the late 20th century tropical Atlantic greenhouse warming.

But do the models produce a reasonable amplitude of internal variability? This is a difficult question to answer because it can’t be easily deduced from the climate record (since there are many forcings, some natural, some anthropogenic) that are potentially obscuring the internal signal. However, over the period when we have good data, we can certainly check whether the models amplitude of variability is in the ball park of the observations. Santer et al did this as well, and find that indeed, there is no reason to think that models as a whole are systematically underestimate the internal component. One of the advantages of the IPCC AR4 data is that with so many models participating (22 models here), there will be a range of results – some models have more variability than observed, others less. A robust conclusion can therefore be drawn if the signals are clear regardless of the magnitude of any one models’ representation of the internal variability. (Santer et al have posted an illuminating Q&A on their study that discusses this point further.)

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