Abstract
Abstract
Three severe droughts impacted the Amazon in 2005, 2010, and 2015, leading to widespread above-average land surface temperature (LST) (i.e. positive thermal anomalies) over the southern Amazon in the dry season (Aug–Sep) of these years. Below-average dry-season incoming solar radiation (SW↓) and terrestrial water storage anomaly (TWSA) were simultaneously observed in 2005 and 2010, whereas the opposite was observed in 2015. We found that anomalies in precipitation (P), SW↓, and TWSA combined can well explain dry-season thermal anomalies during these droughts (average R2–0.51). We investigated the causes for opposing anomalies in dry-season SW↓ and TWSA, and found different hydro-climatological conditions preceding the drought-year dry seasons. In 2005 and 2010, P was considerably below average during the wet-to-dry transition season (May–July), causing below-average TWSA in the dry season that was favorable for fires. Increased atmospheric aerosols resulting from fires reduced solar radiation reaching the ground. In 2015, although below-average dry-season P was observed, it was above the average during the wet-to-dry transition season, leading to reduced fires and aerosols, and increased dry-season SW↓. To further examine the impact of opposite hydro-climatological processes on the drought severity, we compared dry-season LST during droughts with the maximum LST during non-drought years (i.e. LSTmax) for all grid cells, and a similar analysis was conducted for TWSA with the minimum TWSA (i.e. TWSAmin). Accordingly, the regions that suffered from concurrent thermal and water stress (i.e. LST > LSTmax and TWSA < TWSAmin) were identified. These regions are mainly observed over the southeast in 2005 and southern Amazon in 2010. In 2015, large-scale dry-season thermal stress was found over central and southeast Amazon with little water stress. This study underlines the complex interactions of different hydro-climatological components and the importance of understanding the evolution of droughts to better predict their possible impacts on the Amazon rainforest.
Funder
Australian Research Council Training Centre in Data and Analytics for Resources and Environments
Cited by
1 articles.
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