Assessment of a Regional Climate Change Scenario for Central Argentina: A Statistical Downscaling Approach
With the purpose of assessing the vulnerability of crop production in Argentina related to the impact of global warming, we estimated local changes of monthly mean precipitation for summer and winter months caused by CO2 doubling, at selected stations lying in central Argentina. A statistical downscaling approach was developed by means of empirical relationships between large-scale climatic variables from the NCEP re-analyses data set and local scale precipitation data. The method was tested against an independent set of observed data and subsequently applied to the Max Plank Institute (MPI) GCM control run. Despite the simplicity of the statistical approach developed, it was able to satisfactorily reproduce the spatial patterns of the regional precipitation field. The response of the climate system to the enhanced emission scenario simulated by the MPI model was used to infer the local climate change. A precipitation decrease over the region of interest is simulated by the MPI model for the increased CO2 scenario. Accordingly, with the GCM potential changes, the local precipitation decrease is higher in summer than in winter. This result has an important consequence for the rainfall regime over the region, namely that a higher decrease of rainfall is projected for the rainy season while a weaker decrease is projected for the dry season. Regional scenarios of climate change, including both rainfall and extreme temperatures were then used to assess the impact of climate change on crops (wheat, maize, sunflower and soybean) and pastures production in the Pampas region in order to evaluate the vulnerability of the system to global warming.
KeywordsCorn Assure Argentina Aires
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