Uncertainties in projections of climate extremes indices in South America via Bayesian inference
Historical simulations and projections of climate extremes indices of precipitation and temperature were analysed over South America until the end of the 21st century through 31 general circulation models (GCMs) under four Representative Concentration Pathways. Simulations were compared with reanaly...
- Autores:
-
Daniel Gouveia, Carolina
Rodrigues Torres, Roger
Marengo, José Antônio
Avila-Diaz, Alvaro
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2022
- Institución:
- Universidad de Ciencias Aplicadas y Ambientales U.D.C.A
- Repositorio:
- Repositorio Institucional UDCA
- Idioma:
- eng
- OAI Identifier:
- oai:repository.udca.edu.co:11158/5027
- Palabra clave:
- Análisis bayesiano
Radiación terrestre
Clima
Pronóstico del tiempo
Temperatura del aire
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es
| Summary: | Historical simulations and projections of climate extremes indices of precipitation and temperature were analysed over South America until the end of the 21st century through 31 general circulation models (GCMs) under four Representative Concentration Pathways. Simulations were compared with reanalysis data, and a Bayesian inference method was used to assess the uncertainties involved in the multi-model climate projections. Regarding the precipitation extremes indices, the GCMs' simulations reasonably approached the reanalysis data, but with heterogeneous biases, both in sign and in the location of the highest values. The temperature extremes indices presented the smallest biases when compared to precipitation. Projections show a gradual growth of precipitation extremes events as the analysed radiative forcing scenario increases, both in magnitude and extent, over a large part of South America. Projections also indicate a decrease in cold days and nights and an increase in warm days and nights, more pronounced in the equatorial region. Bayesian inference method smoothed changes in precipitation extremes events, both in magnitude and extent, compared to the simple GCMs' ensemble mean. There was no considerable variation in the temperature indices when applying the Bayesian inference. Finally, the probability density functions resulted in a predominance of multimodal and wide curves for the precipitation indices, showing great uncertainties in the GCMs' results, differently from those for the temperature indices, where the GCMs presented good agreement represented through unimodal and narrow curves. |
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