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In 2014, J. Zscheischler and co-authors conducted a continental-scale analysis on extreme events in gross primary productivity (GPP). The datasets used included a machine learning based construction, a semi-empirical, and two land surface models (OCN and LPJmL).

They found a few important phenomena:

(1) The 50 largest positive and negative GPP extremes accounted for most of the variations in continental GPP variation.

* That is, the extreme events, though limited in number, are very important for interannual variability in GPP.

(2) The spatial extents of the GPP extremes played a larger role on the impact of the event, than the duration or maximal GPP.

(3) Water scarcity was the most important cause of negative GPP extremes. Heat waves played a secondary role. In Europe, South America, and Oceania, fire was a third important factor.

* That is, GPP extremes happened most often when there is drought, followed by heat waves, and finally, in some continents, fires.

* It's interesting that the heat wave seemed to account for the GPP extremes best in Russia. Is this because the vegetation there are adapted to cold conditions?

bg.copernicus.org/articles/11/

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