Statistical prediction of terrestrial water storage changes in the Amazon Basin using tropical Pacific and North Atlantic sea surface temperature anomalies

Floods and droughts frequently affect the Ama- zon River basin, impacting transportation, agriculture, and ecosystem processes within several South American coun- tries. Here we examine how sea surface temperature (SST) anomalies influence interannual variability of terrestrial wa- ter storage anomalies (TWSAs) in different regions within the Amazon Basin and propose a statistical modeling frame- work for TWSA prediction on seasonal timescales. Three simple semi-empirical models forced by a linear combina- tion of lagged spatial averages of central Pacific and tropical North Atlantic climate indices (Nino 4 and TNAI) were cal- ibrated against a decade-long record of 3 , monthly TWSAs observed by the Gravity Recovery And Climate Experiment (GRACE) satellite mission. Nino 4 was the primary exter- nal forcing in the northeastern region of the Amazon Basin, whereas TNAI was dominant in central and western regions. A combined model using the two indices improved the fit sig- nificantly ( p < 0.05) for at least 64 % of the grid cells within the basin, compared to models forced solely with Nino 4 or TNAI. The combined model explained 66 % of the observed variance in the northeastern region, 39 % in the central and western region, and 43 % for the Amazon Basin as a whole, with a 3-month lead time between the climate indices and the predicted TWSAs. Model performance varied seasonally: it was higher than average during the wet season in the north- eastern Amazon and during the dry season in the central and western region. The predictive capability of the combined model was degraded with increasing lead times. Degradation rates were lower in the northeastern Amazon (where 49 % of the variance was explained using an 8-month lead time versus 69 % for a 1-month lead time) compared to the cen- tral and western Amazon (where 22 % of the variance was explained at 8 months versus 43 % at 1 month). These rela- tionships may contribute to an improved understanding of the climate processes regulating the spatial patterns of flood and drought risk in South America.

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