Measuring and Explaining GCM Simulation of Extreme Precipitation
Postdoctoral Scholar, UCLA Center for Climate Science
Tuesday, Mar 12, 2019, 11:00 am
DSRC Room 2A-305
Metrics are presented to evaluate GCM performance in the upper tail of the precipitation distribution, and for assessing agreement on projected precipitation shifts in distinct process-based regimes. Most GCMs perform better in inverse proportion to the convective fraction of simulated precipitation at a given grid cell, although some exhibit a more constant performance regardless of convective fraction. This behavior is unrelated to the overall convective fraction of the GCM or the extent to which convective parameterizations take over in the tropics. In general, projections of extremes are less reliable in the tropics, and vary between GCMs and locations in the extra-tropics, with the best performance in the extratropical winter hemisphere. Additionally, the full probability distribution function and its change is examined for a set of regions, dividing the globe based on GCM agreement on the dominant processes. Large-scale or convective-dominated regimes are separated, and the convective-dominated region is further divided based on the sign of the projected change in variance, which is associated with changes to extreme precipitation. Pooling across grid cells in each regime, unanimous agreement is found across GCMs for projected 21st century precipitation increasing monotonically with rain rate in the upper tail, in all regimes except where variance decreases. The adoption of these metrics is recommended for both model development as well as to inform the use of precipitation projections in climate impact studies. When interpreting precipitation projections it is important to properly capture the spread among GCMs and epistemic imperfections of the simulations.
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