@PhDThesis{Santos:2015:TrUmAm,
author = "Santos, Jos{\'e} Guilherme Martins dos",
title = "Transporte de umidade na Amaz{\^o}nia e sua rela{\c{c}}{\~a}o
com a temperatura da superf{\'{\i}}cie do mar dos oceanos
adjacentes utilizando as simula{\c{c}}{\~o}es do CMIP5",
school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
year = "2015",
address = "S{\~a}o Jos{\'e} dos Campos",
month = "2015-05-12",
keywords = "bacia Amaz{\^o}nica, balan{\c{c}}o de umidade, modelos
num{\'e}ricos, precipita{\c{c}}{\~a}o, rean{\'a}lises, Amazon
basin, moisture balance, numerical models, precipitation,
reanalysis.",
abstract = "Os estudos sobre modelagem num{\'e}rica na Amaz{\^o}nia mostram
que os modelos n{\~a}o conseguem capturar aspectos importantes da
variabilidade do clima nesta regi{\~a}o, por isso, {\'e}
importante entender as raz{\~o}es que causam essa dificuldade.
Neste trabalho s{\~a}o utilizados os resultados de Modelos de
Circula{\c{c}}{\~a}o Geral do Coupled Model Intercomparison
Project Phase 5 (CMIP5) com o objetivo de avaliar as
interrela{\c{c}}{\~o}es entre a precipita{\c{c}}{\~a}o
regional, a converg{\^e}ncia de umidade e a Temperatura da
Superf{\'{\i}}cie do Mar (TSM) nos oceanos adjacentes, para
estudar como a falha na representa{\c{c}}{\~a}o por parte dos
modelos pode traduzir-se em bias na precipita{\c{c}}{\~a}o
simulada na Amaz{\^o}nia. Utilizando dados de rean{\'a}lise
(GPCP, CMAP, TSM, ERAI e evapotranspira{\c{c}}{\~a}o) e 21
simula{\c{c}}{\~o}es num{\'e}ricas do CMIP5 durante o clima
atual (1979-2005), em junho, julho e agosto (JJA) e dezembro,
janeiro e fevereiro (DJF), respectivamente, para representar as
caracter{\'{\i}}sticas das esta{\c{c}}{\~o}es seca e chuvosa,
s{\~a}o avaliados como os modelos simulam a
precipita{\c{c}}{\~a}o, o transporte e a converg{\^e}ncia de
umidade, e a velocidade vertical em diferentes regi{\~o}es da
Amaz{\^o}nia. Assim, {\'e} poss{\'{\i}}vel identificar
{\'a}reas que s{\~a}o mais ou menos influenciadas pela TSM dos
oceanos adjacentes. Os resultados mostraram que a maioria dos
modelos do CMIP5 tiveram dificuldade em representar adequadamente
alguns aspectos observados. As an{\'a}lises regionais de
precipita{\c{c}}{\~a}o mostraram que na m{\'e}dia a
subestimativa e o erro padr{\~a}o da m{\'e}dia (SEM) no
per{\'{\i}}odo seco (JJA) foram maiores do que no
per{\'{\i}}odo chuvoso. Verificou-se que a TSM do Atl{\^a}ntico
e do Pac{\'{\i}}fico tropical modularam o setor norte da
Amaz{\^o}nia durante JJA devido a influ{\^e}ncia do gradiente
inter-hermisf{\'e}rico, enquanto em DJF a TSM do
Pac{\'{\i}}fico influenciou somente a parte leste desta
regi{\~a}o devido a influ{\^e}ncia do El
Niņo-Oscila{\c{c}}{\~a}o Sul (ENOS). A an{\'a}lise do
transporte de umidade em JJA mostrou que ela preferencialmente
entra na Amaz{\^o}nia atrav{\'e}s da borda leste pela Alta
Subtropical do Atl{\^a}ntico Sul (ASAS). Por outro lado, em DJF a
entrada ocorreu tanto pela borda norte quanto pela leste via
ventos al{\'{\i}}sios e ASAS. O balan{\c{c}}o de umidade anual
foi positivo e a floresta funcionou como sumidouro
(converg{\^e}ncia) de umidade atmosf{\'e}rica na
esta{\c{c}}{\~a}o chuvosa e fonte (diverg{\^e}ncia) na
esta{\c{c}}{\~a}o seca. Na m{\'e}dia anual, a Amaz{\^o}nia
atuou como sumidouro (converg{\^e}ncia) de umidade
atmosf{\'e}rica e fonte para as regi{\~o}es Sul e Sudeste do
Brasil e norte da Argentina sendo a umidade transportada pelo Jato
de Baixos N{\'{\i}}veis. Al{\'e}m disso, os resultados
mostraram que durante DJF as simula{\c{c}}{\~o}es no setor
nordeste da Amaz{\^o}nia apresentaram um bias na
precipita{\c{c}}{\~a}o e subestimativa da converg{\^e}ncia de
umidade devido a influ{\^e}ncia do bias na TSM do
Pac{\'{\i}}fico. Durante JJA, um bias na
precipita{\c{c}}{\~a}o foi observado no setor sudoeste associado
tamb{\'e}m com um bias negativo de converg{\^e}ncia de umidade,
mas com menor influ{\^e}ncia da TSM dos oceanos adjacentes. A
dificuldade em representar os mecanismos produtores de
precipita{\c{c}}{\~a}o na Amaz{\^o}nia por parte dos modelos e
em simular adequadamente a variabilidade da TSM nos oceanos
Pac{\'{\i}}fico e Atl{\^a}ntico podem ser respons{\'a}veis por
essas subestimativas na Amaz{\^o}nia. Algumas
limita{\c{c}}{\~o}es associadas aos modelos foram apresentadas,
como por exemplo, dificuldade em simular a intensidade do
padr{\~a}o de circula{\c{c}}{\~a}o zonal e a
representa{\c{c}}{\~a}o da ZCIT mais intensa nos oceanos
adjacentes que contribuiu para condi{\c{c}}{\~o}es secas na
Amaz{\^o}nia. Com base na avalia{\c{c}}{\~a}o das
vari{\'a}veis precipita{\c{c}}{\~a}o, TSM e
circula{\c{c}}{\~a}o foram selecionados os melhores (ACCESS1-0,
BCC-CSM1.1, CNRM-CM5, HADGEM2-CC, HADGEM2-ES, MIROC5 e MIROC-ESM)
e os piores modelos (CSIRO-MK3-6-0, FGOALS-G2, GISS-E2-R, INM-CM4,
MRI-CGCM3). Alguns pontos foram sugeridos como os fatores
respons{\'a}veis para que um modelo seja melhor do que o outro em
representar a precipita{\c{c}}{\~a}o, isto {\'e}, mecanismos
produtores de precipita{\c{c}}{\~a}o, representa{\c{c}}{\~a}o
satisfat{\'o}ria do ciclo anual de precipita{\c{c}}{\~a}o e a
variabilidade da TSM dos oceanos adjacentes. ABSTRACT: Studies on
numerical modeling in Amazonia show that the models fail to
capture important aspects of climate variability in this region
and it is important to understand the reasons for this drawback.
This work used the general circulation models of the Coupled Model
Intercomparison Project Phase 5 (CMIP5) results to evaluate the
inter- relations between regional precipitation, moisture
convergence and Sea Surface Temperature (SST) in the adjacent
oceans, to assess how flaws in the representation of these
processes can translate into biases in simulated rainfall in
Amazonia. Using reanalysis (GPCP, CMAP, ERSST.v3, ERAI and
evapotranspiration) and 21 numerical simulations from CMIP5 during
the present climate (1979-2005) in June, July and August (JJA) and
December, January and February (DJF), respectively, to represent
dry and wet season characteristics, are evaluate how the models
simulate precipitation, moisture transport and convergence, and
vertical velocity in different regions of Amazonia. Thus, it is
possible to identify areas of Amazonia that are more or less
influenced by adjacent ocean SSTs. The results showed that most of
the CMIP5 models have poor skill in adequately representing some
aspects observed. The regional rainfall analysis showed that on
average the underestimation in the dry season (JJA) were higher
than in the rainy season. It was found that Atlantic and Pacific
SSTs modulate the northern sector of Amazonia during JJA due to
the influence of the inter-hermispheric gradient, while in DJF
Pacific SST only influences the eastern sector of the region due
to the influence of the El Niņo-Southern Oscillation (ENSO). The
analysis of moisture transport in JJA showed that moisture
preferentially enters the Amazon through the eastern edge by
Atlantic Subtropical High (ASH). On the other hand, in the DJF
entry was either from northern edge or the east via trade winds
and ASH. The anual moisture balance was positive and the forest
was considered as a sink (convergence) of atmospheric moisture
during the rainy season and a source (divergence) in the dry
season. In the annual average, the Amazon was considered as a sink
(convergence) of local moisture to the atmosphere and a source for
the South and Southeast regions of Brazil and northern Argentina
transported by Low Level Jet. Additionally, the results showed
that during DJF the simulations in northeast sector of Amazonia
showed a bias in precipitation and an underestimation of moisture
convergence due to the influence of biases in the Pacific SST. On
the other hand, during JJA, a strong precipitation bias was
observed in the southwest sector associated, also with a negative
bias of moisture convergence, but with weaker influence of SSTs of
adjacent oceans. The poor representation of precipitation
mechanisms in Amazonia by the models and the difficulty of
adequately representing the variability of SSTs in the Pacific and
Atlantic oceans may be responsible for these underestimates in
Amazonia. Some limitations associated with the models were
presented as for example, difficulty in simulating the intensity
of zonal circulation pattern and the representation of more
intense ITCZ adjacent oceans that contributed to dry conditions in
the Amazon. Based on the assessment of rainfall, SST and
circulation were selected the best (ACCESS1-0, BCC-CSM1.1,
CNRM-CM5, HadGEM2-CC, HadGEM2-ES, MIROC5 and MIROC-ESM) and the
worst models (CSIRO-MK3-6-0, FGOALS-G2, GISS-E2-R, INM-CM4,
MRI-CGCM3). Some items have been suggested as factors responsible
for such a model is better than another represent precipitation,
i.e., precipitation mechanisms producers, satisfactory
representation of the annual cycle ofprecipitation and the
variability of SST adjacent oceans. Some points have been
suggested as factors responsible for one model is better than the
other to represent the precipitation, thats is, precipitation
mechanisms, satisfactory representation of the annual cycle of
precipitation and variability of SST adjacent oceans.",
committee = "Herdies, Dirceu Luis (presidente) and Randow, Celso Von
(orientador) and Oliveira, Gilvan Sampaio de (orientador) and
Satyamurty, Prakki and Calheiros, S{\^a}mia Regina Garcia",
copyholder = "SID/SCD",
englishtitle = "Moisture transport in Amazon and its relationship with the sea
surface temperature of adjacent oceans using the CMIP5
simulations",
language = "pt",
pages = "130",
ibi = "8JMKD3MGP8W/3J3GFRL",
url = "http://urlib.net/ibi/8JMKD3MGP8W/3J3GFRL",
targetfile = "publicacao.pdf",
urlaccessdate = "22 maio 2024"
}