Optimal selection of climate projections from large ensembles (UCAR NCAR, Clark)
How best to develop an optimal subset of climate projections to holistically sample the mean and uncertainty in future climate projections.
Need and Benefit
The climate research community continues to build ever-larger ensembles of climate projections. Because recent research has shown the importance of assessing uncertainty from a large number of sources, e.g. global models [Meehl et al., 2005; Knutti and Sedlacek, 2013], initial conditions [Deser et al., 2012; Kay et al., 2014], downscaling methods [Gutmann et al., 2012; Mearns et al., 2013], and hydrologic models [Addor et al., 2014; Vano et al., 2014; Mendoza et al., 2015], and because computational advances permit more such models to be combined, the resulting ensemble size will increase from hundreds to thousands to tens or hundreds of thousands of projections. These advances have far outstripped the ability of the applications community to comprehensively characterize and understand the effect of the plethora of available projections. As a result, there is an urgent need to develop strategies to sample from such a vast array of climate projections while minimizing any loss of information. This will have the immediate benefit of providing the applications community with a more manageable collection of projections so that meaningful decisions can be made.
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Bureau of Reclamation Review
The following documents were reviewed by experts in fields relating to this project's study and findings. The results were determined to be achieved using valid means.
Using Quantitative Hydrologic Storylines to Assess Climate Impacts (final, PDF, 615KB)
By Kenneth Nowak
Publication completed on September 30, 2016