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Activity Number:
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446
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #320761
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Title:
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A Statistical-Dynamical Approach to Probabilistic Decadal Climate Predictions
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Author(s):
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Francisco Beltran*
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Companies:
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Lawrence Livermore National Laboratory
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Keywords:
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Decadal prediction ;
Climate model blending ;
Climate prediction ;
Bayesian Inference
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Abstract:
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In this talk we present a statistical methodology to provide probabilistic climate predictions at the decadal time scale using Bayesian methods to blend different sources of climate information. The climate simulations used in this analysis stem from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database of multi-model ensembles for global climate simulations. In addition to observations for a given climate variable, we use historical and representative concentration pathways (RCP) simulations for long-term climate behavior and newly developed decadal simulations for short-term variability. Our Bayesian blending model provides estimates of possible source error in the climate models and produces more realistic forecasts. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344. LLNL-ABS-680228
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Authors who are presenting talks have a * after their name.
