Precipitation patterns over northern Brazil basins: climatology, trends, and associated mechanisms

Shimizu, M. H.; Anochi, Juliana Aparecida; Kayano, Mary Toshie

doi:10.1007/s00704-021-03841-4

Cited by 11 publications

(17 citation statements)

References 44 publications

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“…The NEB region provides good skill score results practically throughout the year. The excellent performance of ECMWF-SEAS5 in this Brazilian sector shows that its skillfulness does not derive only from the strong Pacific influence on the region but also from other modes of variability such as the Tropical Atlantic Dipole [13,32,[53][54][55][56][57].…”

Section: Temperaturementioning

confidence: 99%

Evaluation of ECMWF-SEAS5 Seasonal Temperature and Precipitation Predictions over South America

Ferreira

Reboita

Drumond

2022

Climate

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Nowadays, a challenge in Climate Science is the seasonal forecast and knowledge of the model’s performance in different regions. The challenge in South America reflects its huge territory; some models present a good performance, and others do not. Nevertheless, reliable seasonal climate forecasts can benefit numerous decision-making processes related to agriculture, energy generation, and extreme events mitigation. Thus, given the few works assessing the ECMWF-SEAS5 performance in South America, this study investigated the quality of its seasonal temperature and precipitation predictions over the continent. For this purpose, predictions from all members of the hindcasts (1993–2016) and forecasts (2017–2021) ensemble were used, considering the four yearly seasons. The analyses included seasonal mean fields, bias correction, anomaly correlations, statistical indicators, and seasonality index. The best system’s performance occurred in regions strongly influenced by teleconnection effects, such as northern South America and northeastern Brazil, in which ECMWF-SEAS5 even reproduced the extreme precipitation anomalies that happened in recent decades. Moreover, the system indicated a moderate capability of seasonal predictions in medium and low predictability regions. In summary, the results show that ECMWF-SEAS5 climate forecasts are potentially helpful and should be considered to plan various strategic activities better.

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Section: Temperaturementioning

confidence: 99%

Evaluation of ECMWF-SEAS5 Seasonal Temperature and Precipitation Predictions over South America

Ferreira

Reboita

Drumond

2022

Climate

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“…In the coastal sector of R6 (centered at 5 • S-10.5 • S and 35 • W-42 • W), ECMWF-SEAS5 shows more evident underestimates, with larger dry biases in May through November. The system's ability to reproduce rainfall in the first semester, especially from March to May, stands out, which may be associated with the good predictability of the strong relationship between the tropical Atlantic SST and rainfall over the NEB in this period [121,[207][208][209][210][211].…”

Section: Cluster 5-region 5 (R5)mentioning

confidence: 99%

“…ITCZ is the most relevant atmospheric system in the northern NEB and semiarid region. In the rainy season between February and May, ITCZ is in its southernmost position, around 4 • S, thus being the primary dynamic mechanism responsible for the rainfall [1,[207][208][209][210][211][212]. However, ITCZ migrates northward in the other seasons, and its southernmost descending branch reaches R6, inhibiting precipitation in the sector [1].…”

Section: Cluster 5-region 5 (R5)mentioning

confidence: 99%

“…Such variability is attributed to teleconnection mechanisms, such as the SST variation in the tropical Pacific and Atlantic. Additionally, during El Niño events, the subsidence cell acting on the region inhibits convective activity over the western tropical Atlantic and reduces precipitation in the NEB [32][33][34]93,207,211,[228][229][230].…”

Section: Cluster 5-region 5 (R5)mentioning

confidence: 99%

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A New Look into the South America Precipitation Regimes: Observation and Forecast

Ferreira

Reboita

2022

Atmosphere

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South America is a vast continent characterized by diverse atmospheric phenomena and climate regimes. In this context, seasonal climate predictions are helpful for decision-making in several relevant socioeconomic segments in this territory, such as agriculture and energy generation. Thus, the present work evaluates the performance of ECMWF-SEAS5 in simulating the South American precipitation regimes by applying a non-hierarchical clustering technique. In addition, the study describes the main atmospheric systems that cause precipitation in each cluster and updates a previous work performed in South America in 2010. As a result, ECMWF-SEAS5 simulates (with good correspondence) the eight climate regimes identified in the analysis of precipitation from the Climate Prediction Center (CPC). Moreover, ECMWF-SEAS5 has a satisfactory ability in representing the rainfall regime in low and medium climate predictability regions, such as central and southern South America. ECMWF-SEAS5 has good performance in the climate characterization of South America and it gives us confidence in using its seasonal climate predictions throughout the continent.

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“…Due to vast social and environmental vulnerabilities, besides an agricultural-based economy, Brazil (and SA) frequently suffers negative impacts caused by current climate variability and extreme climate events and could be highly affected by the projected future climate change (Baettig et al, 2007;Meehl et al, 2007;Torres et al, 2012;Torres and Marengo, 2013;Masson-Delmotte et al, 2021). In a recent study, Shimizu et al (2022) used a multi-model analysis with the Detection and Attribution experiments from CMIP6 to better understand the physical processes related to precipitation trends over North and Northeast Brazil, but no assessment of historical runs was presented. Pereima et al (2022) compared different CMIP5 and CMIP6 ensemble performances in simulating precipitation in Southern Brazil.…”

Section: Introductionmentioning

confidence: 99%

Assessment of CMIP6 models' performance in simulating present-day climate in Brazil

et al. 2022

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Brazil is one of the most vulnerable regions to extreme climate events, especially in recent decades, where these events posed a substantial threat to the socio-ecological system. This work underpins the provision of actionable information for society's response to climate variability and change. It provides a comprehensive assessment of the skill of the state-of-art Coupled Model Intercomparison Project, Phase 6 (CMIP6) models in simulating regional climate variability over Brazil during the present-day period. Different statistical analyses were employed to identify systematic biases and to choose the best subset of models to reduce uncertainties. The results show that models perform better for winter than summer precipitation, consistent with previous results in the literature. In both seasons, the worst performances were found for Northeast Brazil. Results also show that the models present deficiencies in simulating temperature over Amazonian regions. A good overall performance for precipitation and temperature in the La Plata Basin was found, in agreement with previous studies. Finally, the models with the highest ability in simulating monthly rainfall, aggregating all five Brazilian regions, were HadGEM3-GC31-MM, ACCESS-ESM1-5, IPSL-CM6A-LR, IPSL-CM6A-LR-INCA, and INM-CM4-8, while for monthly temperatures, they were CMCC-ESM2, CMCC-CM2-SR5, MRI-ESM2-0, BCC-ESM1, and HadGEM3-GC31-MM. The application of these results spans both past and possible future climates, supporting climate impact studies and providing information to climate policy and adaptation activities.

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Precipitation patterns over northern Brazil basins: climatology, trends, and associated mechanisms

Cited by 11 publications

References 44 publications

Evaluation of ECMWF-SEAS5 Seasonal Temperature and Precipitation Predictions over South America

Evaluation of ECMWF-SEAS5 Seasonal Temperature and Precipitation Predictions over South America

A New Look into the South America Precipitation Regimes: Observation and Forecast

Assessment of CMIP6 models' performance in simulating present-day climate in Brazil

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