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\begin{document}

\title{The Ability of CMIP6 Models to Simulate 34-years of precipitation over
the Brazilian Amazon}



\author[1,1]{Corrie Monteverde}%
\affil[1]{San Diego State University}%


\vspace{-1em}



  
  \date{May 28, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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