1. Interannual Variability of Great Plains Summer Rainfall in Reanalyses and NCAR and NASA AMIP-like Simulations Alfredo Ruiz-Barradas Sumant Nigam Department of Atmospheric and Oceanic Science University of Maryland 5 th International Scientific Conference on the Global Energy and Water Cycle Orange County, California, USA June 20-24, 2005

2. Motivation To better know the structure and mechanisms of precipitation variability in nature and models At issue: –Model validation –Relative contributions of local (evaporation) and remote (moisture fluxes) water sources –SST-Circulation-Hydroclimate linkages

3. Outline The data sets. Precipitation variability over the Great Plains. Structure of hydroclimate fields and their relative contributions associated with precipitation anomalies. Implications on the surface energy balance. Conclusions. References.

4. Data Sets North American Regional Reanalysis (NARR): 1979-1998. ECMWF Global Reanalysis (ERA-40): 1958-1998. NCEP Global Reanalysis (NCEP): 1950-1998 AMIP integrations from: –NCAR’s Community Atmospheric Model, version 3.0 (CAM3.0): 1950-1998 –NASA’s Seasonal-to-Interannual Prediction Project Model (NSIPP): 1950-1998. CPC’s US-Mexico retrospective precipitation analysis (US-Mexico): 1950-1998 COLA’s Global Offline Land Surface Data set (GOLD): 1979-1998

5. Standard Deviation of monthly rainfall during summer (JJA ) Blue box is used to define the Great Plains Precipitation (GPP) Index: Area-averaged precipitation anomalies. NARR assimilates very well precipitation Quasi-realistic variability in global reanalyses Models better than global reanalyses

6. Smoothed GPP Indices during the warm-season months 1993 19881950s1970s Monthly JJA STD (mm/day) US-Mexico NARR ERA-40 NCEP CAM3.0 NSIPP 0.90 0.81 0.66 1.21 0.96 0.99 Correlations wrt US-Mexico US-Mexico NARR ERA-40 NCEP CAM3.0 NSIPP Monthly JJA 1 0.99 0.71 0.53 0.11 -0.09 Smoothed 1 0.99 0.55 0.33 0.25 0.06

7. Warm-season regressions of monthly GPP indices on PRECIPITATION 0.8 0.9 0.6 1.2 1.0 Regionally confined anomalies in NARR & US-Mexico Sub-continental scale anomalies in ERA-40 and NCEP Simulated anomalies are closer to observations than global reanalyses

8. Warm-season regressions of monthly GPP indices on STATIONARY MOISTURE FLUXES 0.8 0.6 0.4 0.3 0.4 NARR Southerly moisture fluxes from the Gulf of Mexico and Caribbean Sea converging over central US. Westerly moisture fluxes from southwestern states Global Reanalyses ERA-40,especially, has both pathways Models CAM3.0 has very weak transport from the Gulf of Mexico NSIPP has stronger fluxes from the Gulf of Mexico None of the models has westerly fluxes

9. Warm-season regressions of monthly GPP indices on TRANSIENT MOISTURE FLUXES 0.0 0.1 -0.0 Transients carry moisture from the southeast to the northwest of the region, especially in NARR and ERA-40.

10. Warm-season regressions of monthly GPP indices on TOTAL MOISTURE FLUXES 0.7 0.6 0.5 0.4 Total moisture fluxes keep the circulation features from the stationary component. Maximum of MFC is now centered in the region

11. Warm-season regressions of monthly GPP indices on EVAPORATION 0.2 0.1 -0.10.2 0.80.7 CI=1/3 of that in P & MFC NARR and GOLD have similar structure and amplitude of anomalies Reanalyses EVAPORATION anomalies are ~a third of MFC anomalies (except in NCEP). Simulated EVAPORATION anomalies are ~twice the MFC anomalies!!

12. Correlation between July’s rainfall and preceding and succeeding monthly rainfall. US-Mexico Low dependence on previous months rainfall. CAM3.0 Dependence of previous months rainfall is comparable to reanalyses. Reanalyses Moderate dependence on previous months rainfall. NSIPP Very high dependence on previous months rainfall

13. Warm-season regressions of monthly GPP index on SURFACE RADIATION & TEMPERATURE 0.4 -0.8 -2.0 -1.6 -1.4 SW anomalies are very close in NARR and both models, however, LH anomalies are ~ 3x larger in models: NARR CAM3.0 NSIPP SW -5.1 -5.2 -4.9 LH -5.8 -22.1 -19.9 Large evaporation in models induces large surface cooling, decreased upward LW (increased LW anomalies), increased SH from the atm to the sfc and a total negative surface energy balance: NARR CAM3.0 NSIPP LW 4.5 10.4 10.0 SH 6.8 14.8 13.6 EB 0.4 -2.0 -1.6 T -0.8 -2.0 -1.4

14. Conclusions Reanalyses suggest that remote water sources (moisture fluxes) dominate over local water sources (evaporation) in the generation of interannual rainfall variability over the Great Plains during the warm-season. Models put a premium on local water sources (precipitation recycling). Deficient simulation of moisture pathways feeding the Great Plains. In consequence: regional hydroclimate simulations and predictions remain challenging for global models (at least in the context of variability over the Great Plains).

15. References Nigam, S., and A. Ruiz-Barradas, 2005: Seasonal hydroclimate variability over North America in ERA-40, Regional Reanalysis and AMIP simulations. Submitted to J. Climate. Ruiz-Barradas, A., and S. Nigam, 2005a: Warm-season Precipitation Variability over the US Great Plains in Observations, NCEP and ERA-40 Reanalyses, and NCAR and NASA Atmospheric Simulations. J. Climate., 18, 1808-1829. ______, ______, 2005b: Great Plains Hydroclimate Variability: The View from the North American Regional Reanalysis. Submitted, J. Climate.