By Alfredo Ruiz-Barradas and Sumant Nigam Department of Meteorology

1. Interannual Variability of Warm-Season Rainfall over the US Great Plains in NASA/NSIPP and NCAR/CAM2.0 AMIP Simulations By Alfredo Ruiz-Barradas and Sumant Nigam Department of Meteorology University of Maryland December 11, 2003

2. Goal • To assess interannual variability of precipitation over North America in AMIP-like runs of CAM2.0 and NSIPP models during summer months (June, July, August).

3. Outline • Data • JJA Climatology • Interannual Variability • Remarks

5. From TV News: it seems we have “the flood of the century” every year…

6. Data • Precipitation: • Retrospective US and Mexico analysis. • Hulme (University of East Anglia) data set. • Xie/Arkin precipitation data set. • NCEP & ERA40 Reanalyses. • SST from Hadley Center. • NCEP & ERA40 Reanalyses. • AMIP simulations (ens05 & mean) from the NSIPP model. • AMIP simulation (case newsstamip06) from the CAM model.

7. Data • Reanalysis and simulations extrapolated to a 5°2.5 grid on 17 pressure levels. • Monthly climatology for the 1950-1998 period. • Monthly anomalies wrt 1950-1998 climatology. • JJA is the mean of June, July, August. • Assessment through: • Standard Deviation • Precipitation Index • Multivariate analysis

8. CLIMATOLOGY OF MOISTURE FLUXES

14. Remarks: Climatology • Vertically Integrated Moisture Fluxes: 1) Observations in agreement: mean southerly moisture fluxes from MFD in the Gulf of Mexico and Caribbean Sea toward MFC in the GP; output of moisture fluxes by transients from the GP region to the N and NE. 2) Simulations reproduce observed features at different extent with NSIPP and CAM having problems to capture both MFC and southerly moisture flux. • Precipitation: 1) No-reanalysis data sets agree very well. 2) NCEP Reanalysis overestimate precipitation; ERA-40 is reasonably well. 3) Shifted maximum in simulations: W in NSIPP, E in CAM.

15. INTERANNUAL VARIABILITY OF PRECIPITATION

21. Remarks: Variability • Precipitation: 1) No-reanalysis data sets agree very well. 2) NCEP has larger variability than observations; ERA-40 has reasonably variability but maximum is to the W of the GP. 3) Maximum of STD is shifted to the W in NSIPP and to the E in CAM. • Indices: 1) ERA40 has larger correlation with no-reanalysis indices than NCEP has. 2) Simulations disagree with each other and with verifying no-reanalysis observations. 3) Simulations suggest that precip over the GP region is largely of convective nature. However ERA-40 indicates that large-sacle precipitation is equally important!!

22. REGRESSING INDICES

27. Remarks: Regressions • GP precip anomalies are associated with mean southerly MF from the Gulf of Mexico and Caribbean Sea, as well as mean MFC. Transients enhance precip in the N and reduce it in the S of the region. • Simulations disagree between them, NSIPP is closer to observations but with max of precip to the W of the max of MFC; CAM however shows MF from the Pacific!! • GP precip anomalies are linked to Pacific SSTs in both observations and simulations. • A wave-train with lows over the oceans and central US present in observations is weakly captured in simulations.

28. MULTIVARIATE ANALYSIS Precip+SfcTmp+SfcPress

32. JJA vs MJJA or JJAS REOF OF SST+(700)

34. Remarks • Multivariate analysis indicates: • Great Plains precipitation variability is the main mode of summer variability in observations; • This is however not the case in both model simulations; • Wet/dry events are cold/warm events in both observed and simulated summers. • Part of the GP precip variability seems to be forced by the atmosphere. Transition months affect the structure of what is defined as “summer”.