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The Ability of CMIP6 Models to Simulate 34-years of precipitation over the Brazilian Amazon
  • Corrie Monteverde
Corrie Monteverde
San Diego State University, San Diego State University

Corresponding Author:cmonteverde@sdsu.edu

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The Brazilian Amazon provides important hydrological cycle functions, including precipitation regimes that bring water tothe people and environment and are critical to moisture recycling and transport, and represents an important variable forclimate models to simulate accurately. This paper evaluates the performance of 13 Coupled Model Intercomparison Projectphase 6 (CMIP6) models. This is done by discussing results from spatial pattern mapping, Taylor diagram analysis and Taylorskill score, annual climatology comparison, and Empirical Orthogonal Function (EOF) analysis. Precipitation analysis shows1) This region displays a more uniform spatial distribution of precipitation with higher rainfall in the north-northwest anddrier conditions in the south. Models tend to underestimate northern values or overestimate the central to northwest averages.2) Southern Amazon has a more defined dry season (June, July, and August) and wet season (December, January, andFebruary) and models are able to simulate this well. Northern Amazon dry season tends to occur in August, September, andOctober and the wet season occurs in March, April, and May, and models are not able to capture the climatology as well.Models tend to produce too much rainfall at the start of the wet season and tend to either over- or under-estimate the dryseason, although ensemble means typically display the overall pattern more precisely. 3) EOF analysis of models are able tocapture the dominant mode of variability, which was the annual cycle or SAMS. 4) When all evaluation metrics are taken intoaccount the models that perform best are CESM2, MIROC6, MRIESM20, SAM0UNICON, and the ensemble mean. Thispaper supports research in determining the most up to date CMIP6 model performance of precipitation regime for 1981-2014for the Brazilian Amazon. Results will aid in understanding future projections of precipitation for the selected subset ofglobal climate models and allow scientists to construct reliable model ensembles, as precipitation plays a role in many sectorsof the economy, including the ecosystem, agriculture, energy, and water security.