Challenges of regional climate modelling and validation
Seminar Room 1, Newton Institute
As attention shifts from broad global summaries of climate change to more specific regional results there is a need for statistics to analyze observations and model output that have significant variability and also to quantify the uncertainty in regional projections. This talk will survey some work on interpreting regional climate experiments. In large multi-model studies one challenge is to understand the contributions of different global and regional model combinations to the simulated climate. This is difficult because the individual runs tend to be short in length. Thus one is faced with the paradox of generating massive data sets that still demand statistical analysis to quantify significant features. We suggest some approaches based on functional data analysis that leverage sparse matrix techniques to handle large spatial fields.
(Joint work with Cari Kaufman, Stephen Sain and Linda Mearns.)
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