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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2006
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
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| LEADER | 09940caa a22007097a 4500 | ||
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| 001 | PAPER-7064 | ||
| 003 | AR-BaUEN | ||
| 005 | 20230518203648.0 | ||
| 008 | 190411s2006 xx ||||fo|||| 00| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-33744770022 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 100 | 1 | |a Boulanger, J.-P. | |
| 245 | 1 | 0 | |a 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 |
| 260 | |c 2006 | ||
| 270 | 1 | 0 | |m Boulanger, J.-P.; Tour 45-55/Etage 4/Case 100 UPMC, LODYC, UMR CNRS/IRD/UPMC, 4 Place Jussieu, 75252 Paris Cedex 05, France; email: jpb@lodyc.jussieu.fr |
| 506 | |2 openaire |e Política editorial | ||
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| 520 | 3 | |a 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. |l eng | |
| 593 | |a Tour 45-55/Etage 4/Case 100 UPMC, LODYC, UMR CNRS/IRD/UPMC, 4 Place Jussieu, 75252 Paris Cedex 05, France | ||
| 593 | |a Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, University of Buenos Aires, Buenos Aires, Argentina | ||
| 690 | 1 | 0 | |a ARTIFICIAL NEURAL NETWORK |
| 690 | 1 | 0 | |a ATMOSPHERE-OCEAN COUPLING |
| 690 | 1 | 0 | |a ATMOSPHERIC GENERAL CIRCULATION MODEL |
| 690 | 1 | 0 | |a BAYESIAN ANALYSIS |
| 690 | 1 | 0 | |a CLIMATE CHANGE |
| 690 | 1 | 0 | |a CLIMATE PREDICTION |
| 690 | 1 | 0 | |a OCEANIC GENERAL CIRCULATION MODEL |
| 690 | 1 | 0 | |a SIMULATION |
| 651 | 4 | |a SOUTH AMERICA | |
| 700 | 1 | |a Martinez, F. | |
| 700 | 1 | |a Segura, E.C. | |
| 773 | 0 | |d 2006 |g v. 27 |h pp. 233-259 |k n. 2-3 |p Clim. Dyn. |x 09307575 |w (AR-BaUEN)CENRE-567 |t Climate Dynamics | |
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| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_09307575_v27_n2-3_p233_Boulanger |y Handle |
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