Authors: JeanPhilippe Boulanger Fernando Martinez Enrique C Segura
Publish Date: 2006/08/30
Volume: 28, Issue: 2-3, Pages: 255-271
Abstract
Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community and the use of large ensemble of simulations is considered as a significant step toward that goal The present paper thus discusses a new methodology based on neural network to mix ensemble of climate model simulations Our analysis consists of one simulation of seven Atmosphere–Ocean Global Climate Models which participated in the IPCC Project and provided at least one simulation for the twentieth century 20c3m and one simulation for each of three SRES scenarios A2 A1B and B1 Our statistical method based on neural networks and Bayesian statistics computes a transfer function between models and observations Such a transfer function was then used to project future conditions and to derive what we would call the optimal ensemble combination for twentyfirst century climate change projections Our approach is therefore based on one statement and one hypothesis The statement is that an optimal ensemble projection should be built by giving larger weights to models which have more skill in representing present climate conditions The hypothesis is that our method based on neural network is actually weighting the models that way While the statement is actually an open question which answer may vary according to the region or climate signal under study our results demonstrate that the neural network approach indeed allows to weighting models according to their skills As such our method is an improvement of existing Bayesian methods developed to mix ensembles of simulations However the general low skill of climate models in simulating precipitation mean climatology implies that the final projection maps whatever the method used to compute them may significantly change in the future as models improve Therefore the projection results for late twentyfirst century conditions are presented as possible projections based on the “stateoftheart” of present climate modeling First various criteria were computed making it possible to evaluate the models’ skills in simulating late twentieth century precipitation over continental areas as well as their divergence in projecting climate change conditions Despite the relatively poor skill of most of the climate models in simulating presentday large scale precipitation patterns we identified two types of models the climate models with moderatetonormal ie close to observations precipitation amplitudes over the Amazonian basin and the climate models with a low precipitation in that region and too high a precipitation on the equatorial Pacific coast Under SRES A2 greenhouse gas forcing the neural network simulates an increase in precipitation over the La Plata basin coherent with the mean model ensemble projection Over the Amazonian basin a decrease in precipitation is projected However the models strongly diverge and the neural network was found to give more weight to models which better simulate presentday climate conditions In the southern tip of the continent the models poorly simulate presentday climate However they display a fairly good convergence when simulating climate change response with a weak increase south of 45°S and a decrease in Chile between 30 and 45°S Other scenarios A1B and B1 strongly resemble the SRES A2 trends but with weaker amplitudesWe wish to thank the Institut de Recherche pour le Développement IRD the Institut PierreSimon Laplace IPSL the Centre National de la Recherche Scientifique CNRS Programme ATIP2002 for their financial support crucial for the development of the authors’ collaboration We are also grateful to the European Commission for funding the CLARIS Project Project 001454 in whose framework the present study was undertaken We are grateful to the University of Buenos Aires and the “Department of Atmosphere and Ocean Sciences” for welcoming JeanPhilippe Boulanger We thank Tim Mitchell and David Viner for providing the CRU TS20 datasets Finally we wish to thank the European project CLARIS http//wwwclariseuorg for facilitating the access to the IPCC simulation outputs We acknowledge the international modeling groups for providing their data for analysis the Program for Climate Model Diagnosis and Intercomparison PCMDI for collecting and archiving the model data the JSC/CLIVAR Working Group on Coupled Modelling WGCM and their Coupled Model Intercomparison Project CMIP and Climate Simulation Panel for organizing the model data analysis activity and the IPCC WG1 TSU for technical support The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Office of Science US Department of Energy Special thanks are addressed to Alfredo Rolla for his strong support in downloading all the IPCC model outputs
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