Climate variability over South America‐regional and large scale features simulated by the Brazilian Atmospheric Model (BAM‐v0)

Abstract

The reliability of climate prediction by a global model is directly related to the ability to simulate the observed climate variability and the main teleconnection patterns. Precipitation anomalies in certain regions are strongly affected by these features, and it is important to know if models are able to reproduce such patterns and influences. The main objective of this article is to analyse some global features of the Brazilian Atmospheric Model with simplified physics (BAM‐v0), and to discuss several aspects of climate variability over South America. Especially, the ability of the model in simulating the main teleconnection patterns that affect South America and the precipitation variability in several regions of Brazil associated with the Pacific and Atlantic Sea Surface Temperature. The model is the atmospheric component of the Brazilian Earth System Model‐Ocean–Atmosphere (BESM), which can be used to long integrations due to the simplified physics, considering computer limitations. Climate variability is investigated through analyses of variance and correlations, and teleconnections such as Southern Annular Mode (SAM) and Pacific South American (PSA) are obtained from EOF analyses. El Niño Southern Oscillation (ENSO) features are analysed through the Southern Oscillation Index and precipitation anomalies. BAM‐v0, even at coarse resolution, represents many climate variability features. It captures the influences of tropical Pacific and Atlantic Oceans on Northeast Brazil precipitation and reproduces the influences of ENSO over South America. SAM and PSA teleconnections are well simulated. Observed features of the South America Monsoon System are captured by the model, although the intensities of precipitation variability need to be improved. There are some deficiencies related to global budget, precipitation variance in some regions of the globe and precipitation anomalies in certain regions of South America. Identification of model deficiencies and variability analyses are important to model development and contribute to climate prediction improvements.

Evaluation of climate simulations produced with the Brazilian global atmospheric model version 1.2

“…The two BAM-1.2 horizontal resolutions used in this study are triangular quadratic truncation at 126 waves (TQ126, corresponding to a grid of approximately 1.0° in latitude and longitude) and at 62 waves (TQ62, corresponding to a grid of approximately 1.8° in latitude and longitude), both with 42 (L42) sigma vertical levels (32 in the troposphere and 10 in the stratosphere) and a model top at 2hPa. The coarser resolution (TQ62) was chosen because it was used in previous BAM studies Raia, 2017 andCavalcanti et al 2020) and it was computationally efficient. The other resolution (TQ126) was chosen because it allows almost doubling the spatial refinement of the simulations, therefore providing more detailed information, potentially leading to improved representation of some regional climate features.…”

“…This model evolved recently from the first version 1.0 (BAM-1.0, Figueroa et al 2016) to the current version 1.2 (BAM-1.2), which is evaluated in this paper when run for performing climate AMIP-type simulations. Cavalcanti and Raia (2017) and Cavalcanti et al (2020) investigated the ability of a predecessor BAM version with simplified and fast physical parameterizations (known as BAM version 0.0, BAM-0.0) in simulating the lifecycle of the South American monsoon system and climate variability over South America, respectively. Guimarães et al (2020) defined a configuration and performed the first assessment of BAM-1.2 for sub-seasonal predictions, which is the same version currently used at CPTEC for global operational numerical weather prediction.…”

Impactos das queimadas na Amazônia no tempo em São Paulo na tarde do dia 19 de agosto de 2019

“…A circulação atmosférica em 700 hPa, através do JBN, parece ser a responsável pelo transporte do material particulado em suspenção para as maiores latitudes. De fato, simulações do modelo BRAMS (Cavalcanti et al, 2019) Outra forma de mostrar a trajetória das parcelas de ar que chegam numa determinada região é usando o modelo HYSPLIT (Tadini et al, 2020). Nesse, é definido um local e o modelo determina a trajetória (caminho percorrido) das parcelas de ar que chegam naquele ponto.…”

Teleconnection patterns in the Southern Hemisphere represented by ECMWF and NCEP S2S project models and influences on South America precipitation

“…Although the polar configuration is well represented, the models indicate less intense centres at middle latitudes and reproduce only the observed centres over South Atlantic, South Indian and Southwest Pacific, while ERA5 re‐analysis shows two centres over South Indian Ocean, producing a hemispheric configuration of wave four, and does not have the centre over Southeast Pacific as shown in some model results. However, in intra‐seasonal or seasonal timescales, the first mode of variability in December–January–February at 500 hPa or 200 hPa, from NCEP‐NCAR or ERA Interim re‐analysis, shows centres over South Atlantic, Indian, Southwest Pacific oceans and also over Southeast Pacific (Cavalcanti et al, 2020; Grainger et al, 2011; Osman & Vera, 2020). Another difference is the higher explained variance in the models than in the re‐analysis, which presents around 23% of the total, while the models show variances of around 31% in the second week (NCEP) to 47% in the fourth week (ECMWF).…”

“…Global models are able to represent the main teleconnections that affect South America in climate simulations, for example, coupled climate models were able to represent the Southern Annular Mode (Grainger et al, 2013; Vera & Silvestri, 2009). SAM and PSA were also well simulated by the Brazilian Global Atmospheric Model (Cavalcanti et al, 2020). This model reproduces the precipitation dipole between Southeast and South Brazil in the summer season related to the SACZ variability (Cavalcanti et al, 2017).…”