Rainy Season Migration across the Northeast Coast of Brazil Related to Sea Surface Temperature Patterns

Accurate regional seasonal forecasts of the rainy season are essential for the implementation of effective socioeconomic activities and policy. However, current characteristics of the period of occurrence of the rainy season in the Eastern Northeast Brazil (ENEB) region demonstrated that maximum pre...

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Bibliographic Details
Published in:Atmosphere 2024-06, Vol.15 (6), p.713
Main Authors: Santos Pereira, Marcos Paulo, Couto, Fabiana, Schumacher, Vanúcia, Fabrício Daniel dos Santos Silva, Helber Barros Gomes, Djane Fonseca da Silva, Heliofábio Barros Gomes, Rafaela Lisboa Costa, Justino, Flávio B, Herdies, Dirceu Luís
Format: Article
Language:English
Subjects:
atmosphere
Climate change
Climatic analysis
Datasets
El Nino
Forecasting
Homeless people
Maximum precipitation
ocean
Oceans
Precipitation
Rain
Rainy season
Regions
Sea surface
Sea surface temperature
seasonal forecast
Seasonal forecasting
Seasons
Statistical analysis
Statistical methods
Surface temperature
Temperature patterns
Wet season
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Summary:Accurate regional seasonal forecasts of the rainy season are essential for the implementation of effective socioeconomic activities and policy. However, current characteristics of the period of occurrence of the rainy season in the Eastern Northeast Brazil (ENEB) region demonstrated that maximum precipitation varies substantially depending on the period analyzed. From 1972 to 2002, the rainy season occurred during the June–July–August (JJA) quarter, while from 1981 to 2011, it occurred in the April–May–June (AMJ) quarter. To access how these differences may be due to different patterns of sea surface temperature (SST), using observed precipitation and SST data from NOAA for the period from 1982 to 2018, this study identified the spatial patterns of inter-annual changes in Pacific and Atlantic SST related to the occurrence of the ENEB rainy seasons. We focus on the statistical method of symmetric mean absolute percentage error (sMAPE) for forecasting these periods based on SST information. Our results revealed five different quarterly periods (FMA, MAM, AMJ, MJJ, JJA) to the rainy season, in which MJJ is more prevalent. The sMAPE values of the SST patterns are inversely proportional to precipitation in the ENEB. Hence, it may be concluded that our climate analysis demonstrates that seasonal SST patterns can be used for forecasting the period of the rainy season.
ISSN:2073-4433
DOI:10.3390/atmos15060713