An objective tropical Atlantic sea surface temperature gradient index for studies of south Amazon dry-season climate variability and change.
Lowe, Jason A.
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
The Royal Society
Copyright © 2008 The Royal Society. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Future changes in meridional sea surface temperature (SST) gradients in the tropical Atlantic could influence Amazon dry-season precipitation by shifting the patterns of moisture convergence and vertical motion. Unlike for the El Niño-Southern Oscillation, there are no standard indices for quantifying this gradient. Here we describe a method for identifying the SST gradient that is most closely associated with June-August precipitation over the south Amazon. We use an ensemble of atmospheric general circulation model (AGCM) integrations forced by observed SST from 1949 to 2005. A large number of tropical Atlantic SST gradient indices are generated randomly and temporal correlations are examined between these indices and June-August precipitation averaged over the Amazon Basin south of the equator. The indices correlating most strongly with June-August southern Amazon precipitation form a cluster of near-meridional orientation centred near the equator. The location of the southern component of the gradient is particularly well defined in a region off the Brazilian tropical coast, consistent with known physical mechanisms. The chosen index appears to capture much of the Atlantic SST influence on simulated southern Amazon dry-season precipitation, and is significantly correlated with observed southern Amazon precipitation. We examine the index in 36 different coupled atmosphere-ocean model projections of climate change under a simple compound 1% increase in CO2. Within the large spread of responses, we find a relationship between the projected trend in the index and the Amazon dry-season precipitation trends. Furthermore, the magnitude of the trend relationship is consistent with the inter-annual variability relationship found in the AGCM simulations. This suggests that the index would be of use in quantifying uncertainties in climate change in the region.
The authors are supported by the Joint Defra and MoD Programme (Defra) GA01101 (MoD) CBC/2B/0417_Annex C5. We acknowledge the modelling groups for making their simulations available for analysis, the Programme for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the CMIP3 model output, and the WCRP's Working Group on Coupled Modelling (WGCM) for organizing the model data analysis activity. The WCRP CMIP3 multi-model dataset is supported by the Office of Science, US Department of Energy. M.C. acknowledges further funding from the European Community ENSEMBLES (GOCE-CT-2003-505539) and DYNAMITE (GOCE-003903) projects under the Sixth Framework Programme.
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Vol. 363, pp. 1761 - 1766
Place of publication