Changes in precipitation over the La Plata Basin, projected by CLARIS-LPB regional models

Cavalcanti, Iracema Fonseca de Albuquerque; Silveira, Virginia P.

doi:10.3354/cr01388

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…All the models show negative changes for the northern SA and Chile, reaching values around -45%, compared with the baseline period . This result agrees with other authors' findings (Bambach et al 2022;Blázquez and Solman 2020;Llopart et al 2020;Cavalcanti and Silveira 2016;Gutiérrez et al 2021). On the contrary, over CARG and Uruguay, positive changes of precipitation (around 45%) are projected in some models, while over NEB the agreement among models is very poor.…”

Section: The Climate Change Signalsupporting

confidence: 93%

Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal

Blazquez

Solman

2023

Clim Dyn

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Precipitation and temperature biases from a set of Regional Climate Models from the CORDEX initiative have been analyzed with the aim of assessing the extent to which the biases may impact on the climate change signal. The analysis has been performed for the South American CORDEX domain. A large warm bias was found over central Argentina (CARG) for most of the models, mainly in the summer season. Results indicate that the possible origin of this bias is an overestimation of the incoming shortwave radiation, in agreement with an underestimation of the relative humidity at 850 hPa, variable that could be used to diagnose cloudiness. Regarding precipitation, the largest biases were found during summertime over north east of Brazil (NEB), where most of the models overestimate the precipitation, leading to wet biases over that region. This bias agrees with models' underestimation of both the moisture flux convergence and the relative humidity at lower levels of the atmosphere. This outcome suggests that the generation of more clouds in the models may drive the wet bias over NEB. The climate change signal could be affected by these systematic errors, considering that these biases may not be stationary. For both CARG and NEB regions, models with higher warm biases project higher warming levels, mainly in the summer season. In addition, it was found that these relationships are statistically significant with a confidence level of 95%, pointing out that biases are linearly linked with the climate change signal. For precipitation, the relationship between the biases and the projected precipitation changes are only statistically significant for the NEB region, where models with larger wet biases present the highest positive precipitation changes during the warm season. As in the case of biases, the analysis of the temperature and precipitation projections over some regions of South America suggests that they could be affected by clouds. The results found in this study point out that the analysis of the bias behavior could help in a better interpretation of the climate change signal.

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Section: The Climate Change Signalsupporting

confidence: 93%

Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal

Blazquez

Solman

2023

Clim Dyn

View full text Add to dashboard Cite

show abstract

Section: The Climate Change Signalsupporting

confidence: 93%

Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal

Blazquez

Solman

2022

Preprint

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Precipitation and temperature biases from a set of Regional Climate Models from the CORDEX initiative have been analyzed with the aim of assessing the extent to which the biases may impact on the climate change signal. The analysis has been performed for the South American CORDEX domain. A large warm bias was found over central Argentina (CARG) for most of the models, mainly in the summer season. Results indicate that the possible origin of this bias is an overestimation of the incoming shortwave radiation, in agreement with an underestimation of the relative humidity at 850 hPa, variable that could be used to diagnose cloudiness. Regarding precipitation, the largest biases were found during summertime over north east of Brazil (NEB), where most of the models overestimate the precipitation, leading to wet biases over that region. This bias agrees with models’ underestimation of both the moisture flux convergence and the relative humidity at lower levels of the atmosphere. This outcome suggests that the generation of more clouds in the models may drive the wet bias over NEB. The climate change signal could be affected by these systematic errors, considering that these biases may not be stationary. For both CARG and NEB regions, models with higher warm biases project higher warming levels, mainly in the summer season. In addition, it was found that these relationships are statistically significant with a confidence level of 95%, pointing out that biases are linearly linked with the climate change signal. For precipitation, the relationship between the biases and the projected precipitation changes are only statistically significant for the NEB region, where models with larger wet biases present the highest positive precipitation changes during the warm season. As in the case of biases, the analysis of the temperature and precipitation projections over some regions of South America suggests that they could be affected by clouds. The results found in this study point out that the analysis of the bias behavior could help in a better interpretation of the climate change signal.

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“…In recent years, several studies have been conducted using projections of future precipitation change over Brazil derived from global and regional climate models (RCM) (Alves and Marengo, 2010;Marengo et al, 2010a;Blázquez et al, 2012;Joetzjer et al, 2013;Chou et al, 2014a;Gulizia and Camilloni, 2015;Sánchez et al, 2015;Vera and Díaz, 2015;Cavalcanti and Silveira, 2016;Yoon, 2016;Ambrizzi et al, 2019;Solman and Blázquez, 2019;Díaz et al, 2020). They found a consistent pattern of intense rainfall increases in southern and southeastern Brazil and more dry spells and drought in Amazonia and Northeast Brazil.…”

Section: Introductionmentioning

confidence: 99%

Assessment of rainfall variability and future change in Brazil across multiple timescales

Alves

Chadwick

Moise

et al. 2020

Intl Journal of Climatology

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Rainfall variability change under global warming is a crucial issue that may have a substantial impact on society and the environment, as it can directly impact biodiversity, agriculture, and water resources. Observed precipitation trends and climate change projections over Brazil indicate that many sectors of society are potentially highly vulnerable to the impacts of climate change. The purpose of this study is to assess model projections of the change in rainfall variability at various temporal scales over sub-regions of Brazil. For this, daily data from 30 CMIP5 models for historical (1900-2005) and future (2050-2100) experiments under a high-emission scenario are used. We assess the change in precipitation variability, applying a band-pass filter to isolate variability on daily, weekly, monthly, intra-seasonal, and El Nino Southern Oscillation (ENSO) time scales. For historical climate, simulated precipitation is evaluated against observations to establish model reliability. The results show that models largely agree on increases in variability on all timescales in all subregions, except on ENSO timescales where models do not agree on the sign of future change. Brazil will experience more rainfall variability in the future that is, drier or more frequent dry periods and wetter wet periods on daily, weekly, monthly, and intra-seasonal timescales, even in sub-regions where future changes in mean rainfall are currently uncertain. This may provide useful information for climate change adaptation across, for example, the agriculture and water resource sectors in Brazil.

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Changes in precipitation over the La Plata Basin, projected by CLARIS-LPB regional models

Cited by 6 publications

References 37 publications

Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal

Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal

Temperature and precipitation biases in CORDEX RCM simulations over South America: possible origin and impacts on the regional climate change signal

Assessment of rainfall variability and future change in Brazil across multiple timescales

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