Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America

Projections for South America of future climate change conditions in mean state and seasonal cycle for temperature during the twenty-first century are discussed. Our analysis includes one simulation of seven Atmospheric-Ocean Global Circulation Models, which participated in the Intergovernmental Pan...

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Autores principales: Martínez, Fernando Luis, Segura, Enrique Carlos
Publicado: 2006
Materias:
artificial neural network
atmosphere-ocean coupling
atmospheric general circulation model
Bayesian analysis
climate change
climate prediction
oceanic general circulation model
simulation
South America
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09307575_v27_n2-3_p233_Boulanger
http://hdl.handle.net/20.500.12110/paper_09307575_v27_n2-3_p233_Boulanger
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_09307575_v27_n2-3_p233_Boulanger
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spelling paper:paper_09307575_v27_n2-3_p233_Boulanger2023-06-08T15:52:37Z Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America Martínez, Fernando Luis Segura, Enrique Carlos artificial neural network atmosphere-ocean coupling atmospheric general circulation model Bayesian analysis climate change climate prediction oceanic general circulation model simulation South America Projections for South America of future climate change conditions in mean state and seasonal cycle for temperature during the twenty-first century are discussed. Our analysis includes one simulation of seven Atmospheric-Ocean Global Circulation Models, which participated in the Intergovernmental Panel on Climate Change Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three Special Report on Emissions Scenarios (SRES) A2, A1B, and B1. We developed a statistical method based on neural networks and Bayesian statistics to evaluate the models' skills in simulating late twentieth century temperature over continental areas. Some criteria [model weight indices (MWIs)] are computed allowing comparing over such large regions how each model captures the temperature large scale structures and contributes to the multi-model combination. As the study demonstrates, the use of neural networks, optimized by Bayesian statistics, leads to two major results. First, the MWIs can be interpreted as optimal weights for a linear combination of the climate models. Second, the comparison between the neural network projection of twenty-first century conditions and a linear combination of such conditions allows the identification of the regions, which will most probably change, according to model biases and model ensemble variance. Model simulations in the southern tip of South America and along the Chilean and Peruvian coasts or in the northern coasts of South America (Venezuela, Guiana) are particularly poor. Overall, our results present an upper bound of potential temperature warming for each scenario. Spatially, in SRES A2, our major findings are that Tropical South America could warm up by about 4°C, while southern South America (SSA) would also undergo a near 2-3°C average warming. Interestingly, this annual mean temperature trend is modulated by the seasonal cycle in a contrasted way according to the regions. In SSA, the amplitude of the seasonal cycle tends to increase, while in northern South America, the amplitude of the seasonal cycle would be reduced leading to much milder winters. We show that all the scenarios have similar patterns and only differ in amplitude. SRES A1B differ from SRES A2 mainly for the late twenty-first century, reaching more or less an 80-90% amplitude compared to SRES A2. SRES B1, however, diverges from the other scenarios as soon as 2025. For the late twenty-first century, SRES B1 displays amplitudes, which are about half those of SRES A2. © Springer-Verlag 2006. Fil:Martinez, F. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Segura, E.C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2006 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09307575_v27_n2-3_p233_Boulanger http://hdl.handle.net/20.500.12110/paper_09307575_v27_n2-3_p233_Boulanger
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic artificial neural network
atmosphere-ocean coupling
atmospheric general circulation model
Bayesian analysis
climate change
climate prediction
oceanic general circulation model
simulation
South America
spellingShingle artificial neural network
atmosphere-ocean coupling
atmospheric general circulation model
Bayesian analysis
climate change
climate prediction
oceanic general circulation model
simulation
South America
Martínez, Fernando Luis
Segura, Enrique Carlos
Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America
topic_facet artificial neural network
atmosphere-ocean coupling
atmospheric general circulation model
Bayesian analysis
climate change
climate prediction
oceanic general circulation model
simulation
South America
description Projections for South America of future climate change conditions in mean state and seasonal cycle for temperature during the twenty-first century are discussed. Our analysis includes one simulation of seven Atmospheric-Ocean Global Circulation Models, which participated in the Intergovernmental Panel on Climate Change Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three Special Report on Emissions Scenarios (SRES) A2, A1B, and B1. We developed a statistical method based on neural networks and Bayesian statistics to evaluate the models' skills in simulating late twentieth century temperature over continental areas. Some criteria [model weight indices (MWIs)] are computed allowing comparing over such large regions how each model captures the temperature large scale structures and contributes to the multi-model combination. As the study demonstrates, the use of neural networks, optimized by Bayesian statistics, leads to two major results. First, the MWIs can be interpreted as optimal weights for a linear combination of the climate models. Second, the comparison between the neural network projection of twenty-first century conditions and a linear combination of such conditions allows the identification of the regions, which will most probably change, according to model biases and model ensemble variance. Model simulations in the southern tip of South America and along the Chilean and Peruvian coasts or in the northern coasts of South America (Venezuela, Guiana) are particularly poor. Overall, our results present an upper bound of potential temperature warming for each scenario. Spatially, in SRES A2, our major findings are that Tropical South America could warm up by about 4°C, while southern South America (SSA) would also undergo a near 2-3°C average warming. Interestingly, this annual mean temperature trend is modulated by the seasonal cycle in a contrasted way according to the regions. In SSA, the amplitude of the seasonal cycle tends to increase, while in northern South America, the amplitude of the seasonal cycle would be reduced leading to much milder winters. We show that all the scenarios have similar patterns and only differ in amplitude. SRES A1B differ from SRES A2 mainly for the late twenty-first century, reaching more or less an 80-90% amplitude compared to SRES A2. SRES B1, however, diverges from the other scenarios as soon as 2025. For the late twenty-first century, SRES B1 displays amplitudes, which are about half those of SRES A2. © Springer-Verlag 2006.
author Martínez, Fernando Luis
Segura, Enrique Carlos
author_facet Martínez, Fernando Luis
Segura, Enrique Carlos
author_sort Martínez, Fernando Luis
title Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America
title_short Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America
title_full Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America
title_fullStr Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America
title_full_unstemmed Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America
title_sort projection of future climate change conditions using ipcc simulations, neural networks and bayesian statistics. part 1: temperature mean state and seasonal cycle in south america
publishDate 2006
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09307575_v27_n2-3_p233_Boulanger
http://hdl.handle.net/20.500.12110/paper_09307575_v27_n2-3_p233_Boulanger
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AT seguraenriquecarlos projectionoffutureclimatechangeconditionsusingipccsimulationsneuralnetworksandbayesianstatisticspart1temperaturemeanstateandseasonalcycleinsouthamerica
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