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Improving Climate Projections Using "Intelligent" EnsemblesRecent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.
Document ID
20160007438
Acquisition Source
Langley Research Center
Document Type
Presentation
Authors
Baker, Noel C.
(Oak Ridge Associated Universities, Inc. Hampton, VA, United States)
Taylor, Patrick C.
(NASA Langley Research Center Hampton, VA, United States)
Date Acquired
June 10, 2016
Publication Date
May 5, 2015
Subject Category
Meteorology And Climatology
Report/Patent Number
NF1676L-21455
Meeting Information
Meeting: CERES Science Team Meeting
Location: Hampton, VA
Country: United States
Start Date: May 5, 2015
End Date: May 7, 2015
Sponsors: NASA Langley Research Center
Funding Number(s)
WBS: WBS 652528.02.01
Distribution Limits
Public
Copyright
Public Use Permitted.
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