Diagnosis of North American Hydroclimate Variability in IPCC’s Climate Simulations Alfredo Ruiz–Barradas 1 and Sumant Nigam University of Maryland ----o---- Breckenridge, CO June 19-21, 2007 Abstract The annual cycle of precipitation, as well as interannual variability of North American hydroclimate during summer months are analyzed in IPCC’s coupled simulations of the 20th century climate. The state-of-the-art general circulation models, participating in the 4th Assessment Report for the IPCC, included in the present study are the American’s CCSM3, PCM, GISS-EH, and GFDL-CM2.1; the British UMKO-HadCM3, and the Japanese MIROC3.2(hires). Data sets with proven high quality such as NCEP’s North American Regional Reanalysis (NARR), and CPC’s US-Mexico (US-MEX) precipitation analysis are used as targets for simulations. While models capture winter precipitation very well over the US northwest, they are challenged over the US southeast in the same season, and over central US and Mexico during summer. Models’ potential in simulating interannual hydroclimate variability over North America during the warm-season is varied and limited to the central US. Models like PCM, and in particular UKMO-HadCM3, exhibit reasonably well the observed distribution and relative importance of remote versus local contributions to precipitation variability over the region. However, in models like CCSM3 and GFDL-CM2.1 local contributions dominate over remote ones, in contrast with warm- season observations. The significance of SST linkages, in the context of interannual variability of precipitation over the Great Plains, is highlighted by a coherent basin-scale structure resembling the Pacific Decadal variability pattern in observations; the UKMO-HadCM3 model is the one that best reproduces such SST structure. Figure 1: First harmonic of climatological precipitation (vectors), and mean annual precipitation (background) in NARR and model simulations, Vectors pointing to the north indicate a maximum on 1 July. Only 1, 2, 3, 4, 6, 9 mm/day isohyets are displayed. Figure 2: Standard deviation of monthly summer (JJA) precipitation in NARR and model simulations. Contour interval is 0.3 mm/day. The blue box defines the Great Plains region (90 ° -100 ° W,35 ° -45°N). Figure 3: Histogram of precipitation events over the Great Plains region, as portrayed by the Great Plains Precipitation (GPP) Index during summer months in US- MEX analysis and model simulations, Figure 4: Correlation between July’s GPP anomalies with May, June, July and August monthly precipitation anomalies, Error bars in June represent the standard error when calculating correlations. Summary Climatological winter precipitation is well simulated over the US northwest, but not over the southeast during the same season, or central US during summer..Large precipitation variability in models arises as consequence of the occurrence of rare extreme wet or dry events. On the other hand, reduced precipitation variability is consequence of the lack of those extreme events, and the increased number of small wet and dry events..The relative importance of processes contributing to the generation of interannual variability of precipitation is varied among models. PCM, and in particular UKMO-HadCM3, exhibit reasonably well the observed distribution and relative importance of remote versus local contributions to precipitation variability over the region. In CCSM3 and GFDL-CM2.1 local contributions dominate over remote ones, in contrast with observations; in the other extreme are models like GISS-EH and MIROC3.2(hires). CCSM3, and GFDL-CM2.1 both prioritize the local recycling of precipitation over convergence of remote moisture fluxes, however, the land- surface memory in GFDL-CM2.1 is stronger than in CCSM3. In both models large negative air temperature anomalies arise as consequence of the strong recycling of precipitation. A coherent basin-scale correlation structure, resembling the Pacific Decadal variability pattern in SST observations, is associated with GPP variability; the UKMO-HadCM3 model is the one that best reproduces such SST structure. Figure 6: Warm-season regressions of GPP Indices on vertically integrated moisture fluxes and associated convergence anomalies from NARR and model simulations. Contour interval is 0.3 mm/day. 1 u Figure 5: Warm-season regressions of GPP Indices on precipitation anomalies from NARR and model simulations. Contour interval is 0.3 mm/day. Figure 8: Warm-season regressions of GPP Indices on surface air temperature anomalies from NARR and model simulations. Contour interval is 0.3K. Figure 9: Warm-season SST correlations of smoothed GPP Indices from US-MEX analysis/Hadley and model simulations. Contour interval is 0.1. The index was smoothed using a filter on seasonal mean anomalies. Figure 7: Warm-season regressions of GPP Indices on evaporation anomalies from NARR and model simulations. Contour interval is 0.1 mm/day.

References Ruiz-Barradas, A. S. Nigam, 2006: IPCC's 20th Century Climate Simulations: Varied Representations of North American Hydroclimate Variability. J. Climate, 19, Ruiz-Barradas. A., S. Nigam, 2006: Great Plains hydroclimate variability: the view from North American Regional Reanalysis. J. Climate, 19, Nigam, S., and A. Ruiz-Barradas, 2006: Seasonal Hydroclimate Variability over North America in Global and Regional Reanalyses and AMIP Simulations: A Mixed Assessment. J. Climate, 19, Ruiz-Barradas, A., S. Nigam, 2005: Warm-season rainfall variability over the US Great Plains in observations, NCEP and ERA-40 reanalyses, and NCAR and NASA atmospheric model simulations. J. Climate, 18,