ALBERT

Description: Bias correction can modify climate model-simulated precipitation changes without adverse affect on the ensemble mean Hydrology and Earth System Sciences Discussions, 10, 11585-11611, 2013 Author(s): E. P. Maurer and D. W. Pierce When applied to remove climate model biases in precipitation, quantile mapping can in some settings modify the simulated trends. This has important implications when the precipitation will be used to drive an impacts model that is sensitive to changes in precipitation. We use daily precipitation output from 12 general circulation models (GCMs) over the conterminous United States interpolated to a common 1° grid, and gridded observations aggregated to the same scale, to compare precipitation differences before and after quantile mapping bias correction. The change in seasonal mean (winter, DJF, and summer, JJA) precipitation between different 30-yr historical periods is compared to examine (1) the consensus among GCMs as to whether the bias correction tends to amplify or diminish their simulated precipitation trends, and (2) whether the modification of the change in precipitation tends to improve or degrade the correspondence to observed changes in precipitation for the same periods. In some cases, for a particular GCM, the trend modification can be as large as the original simulated change, though the areas where this occurs varies among GCMs so the ensemble median shows smaller trend modification. In specific locations and seasons the trend modification by quantile mapping improves correspondence with observed trends, and in others it degrades it. In the majority of the domain the ensemble median is for little effect on the correspondence of simulated precipitation trends with observed. This highlights the need to use an ensemble of GCMs rather than relying on a small number of models to estimate impacts.