Open AccessMesoscale vegetation‐atmosphere feedbacks in Amazonia
Author(s) -
Roy Somnath Baidya
Publication year - 2009
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2009jd012001
Subject(s) - mesoscale meteorology , deforestation (computer science) , amazon rainforest , vegetation (pathology) , environmental science , precipitation , hydrometeorology , climate model , climatology , climate change , ecosystem , atmospheric sciences , geography , meteorology , ecology , geology , computer science , biology , programming language , medicine , pathology
This paper investigates vegetation‐climate interactions in disturbed rain forests of Amazonia. The scientific objective of this paper is twofold. The first goal is to reconcile the discrepancy between the decrease in precipitation predicted by general circulation models and the observed increase in precipitation due to deforestation in Rondonia. Numerical experiments with the Regional Atmospheric Modeling System (RAMS) show that sharp gradients in land cover due to fishbone deforestation trigger organized mesoscale circulations, leading to more clouds and rain over the deforested patches. The second goal is to develop and implement a modeling framework to identify and explore the fundamental pathways involved in deforestation‐climate feedback over seasonal timescales. For this purpose, RAMS model outputs are combined with tower observations to develop a synthetic meteorological data set representing the impacts of deforestation on local hydrometeorology. A vegetation model forced by these data shows that extra rain promotes plant growth in the deforested patches during the water‐limited dry season. This phenomenon constitutes a seasonal‐scale “negative feedback” because accelerated vegetation recovery compensates for the effects of deforestation. This paper suggests that the regional climate observation infrastructure must be upgraded to resolve mesoscale feedbacks to accurately estimate the impact of deforestation in Amazonia. Moreover, these findings can significantly improve our understanding of ecosystem resiliency in disturbed tropical forests.