Was the record Amazon drought caused by warm seas?

The attribution of any event to a cause (whether it is global warming or a long term natural cycle or a recurring phenomena like El Nino) simply relies on a probabilistic argument – what is the probablity of such an event occuring in the absence of the supposed cause (P0), and how much more likely is it with the cause present (P). The attribution (in percent) is then 100*(1-P0/P) 100*(1-P/P0) (see this preprint for more details). However, the estimates of the probability are affected both by statistical sampling for recurring events, and by the lack of an identical planet without global warming to act as a control for the climate change case. So these probabilities often have to be estimated from a model. These problems mean that attributions can change as models get better, or when statistics get more significant.

In the case of a single event like Katrina, the noise in the system precludes any meangingful statement about the probabilities, although for the European heat wave in 2003 the signal was so strong, and the modelling good enough for an attribution of about 50% to human-related climate change (that is, such an event is estimated to have been twice as likely to have happened in the presence of global warming than otherwise) (Stott et al, 2004).

The signal-to-noise ratio improves when there are many events and we see a new pattern emerging, although changes always start with one first event. Thus, if we see more new record droughts following in rapid succession, then this unusual event might be more attributable. However, one should not forget the possibility that extreme events may be clustered in time, for instance if they are modulated by some slowly undulating external factor. These are factors that must go into estimating the background probability of any events.

Going back to the situation in the Amazonas region, it is often possible to attribute part of local rainfall variability to large-scale conditions, such as the general circulation or SST (similarly, the correlation for Carauar and S. Gab. do Cachoeira yield negative values for SST in the northern Tropical Atlantic while Manaus yields positive values in the south tropical Atlantic). This can be done through statistical analysis, such as correlation or regression as long as there is good empirical data is available. Thus, if there is a change in the large-scale conditions, then we can infer some of the local consequences. This can also be done using high-resolution nested local area models for a particular region. The former is known as ‘statistical’ or ‘empirical’ downscaling whereas the latter is known as ‘dynamical’ or ‘numerical’ downscaling.

The links between the varying SST gradients and the rainfall anomalies is well known, and shows up as an effect in the Amazonas region. Since it is the SST gradients that are important, this shows up in the correlation maps as a dipole in the tropical Atlantic. For most of the northeast Brazil, the annual rainfall is, according to these results, negatively correlated with the SST in the Carribean and the tropical north Atlantic, and positively correlated with SST in the southern tropical Atlantic. Others have also found that the correlation between SST and rainfall variations varies with time of the year.

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