1. Using the National Multi-Model Ensemble (NMME) System Johnna Infanti Advisor: Ben Kirtman

2. Introduction Data and Methods Results Summary and Discussion Future Work Introduction http://droughtmonitor.unl.edu/

3. Observed (CMAP) Southeastern US Precipitation Area averaged precipitation anomalies (92W-75W, 30N-38N) from 1982 to 2009 Introduction Data and Methods Results Summary and Discussion Future Work

4. Observed Tropical Pacific SST Anomaly Link Introduction Data and Methods Results Summary and Discussion Future Work Mo et al., 2008

5. Observational Data CMAP Precipitation Monthly averaged precipitation rate values (mm/day) Data is available from 1979 to 2009 2.5 degree latitude x 2.5 degree longitude global grid NCDC Sea Surface Temperature Monthly average SST values in degrees C Data available from 1982 to 2010 2 degree grid Introduction Data and Methods Results Summary and Discussion Future Work

6. 1981-20091982-20091981-20101982-2010 Forecast Providers NMME CFSv1 CFSv2 GFDL- CM2.2 IRI- ECHAM4-f IRI- ECHAM4-a CCSM3.0 NASA Introduction Data and Methods 15 EM24 EM10 EM12 EM 6 EM10 EM 10 Month Lead 8 Month Lead12 Month Lead9 Month Lead Saha (Saha et al. 2006) Rosati (Zhang et al. 2007) DeWitt (DeWitt, 2005) Kirtman (Kirtman & Min, 2009) Schubert (Vernieres et al. 2011) Results Summary and Discussion Future Work

7. Winter (December) Forecast initialization for 2005, 2006, 2007 Summer (August) Forecast initialization for 2005, 2006, 2007 Area Average Precipitation Anomalies for All Models (colored lines) vs. Observed CMAP Precipitation (green and brown bars) Introduction Summary and Discussion Future Work Data and Methods Results

8. FMA 2006 Observed Precipitation Anomaly vs. Grand Ensemble Mean of NMME Models Introduction Summary and Discussion Future Work Data and Methods Results Observed (CMAP) Precip Anomaly Forecast Initialized in Aug2005 (Long Lead Time) Forecast Initialized in Dec2005 (Short Lead Time) FMA 2006 Seasonal Average

9. FMA 2006 Observed SST Anomaly vs. Grand Ensemble Mean of NMME Models Introduction Summary and Discussion Future Work Data and Methods Results Observed (NCDC) SST Anomaly Forecast Initialized in Aug2005 (Long Lead Time) Forecast Initialized in Dec2005 (Short Lead Time) FMA 2006 Seasonal Average

10. FMA 2007 Observed Precipitation Anomaly vs. Grand Ensemble Mean of NMME Models Introduction Summary and Discussion Future Work Data and Methods Results Observed (CMAP) Precip Anomaly Forecast Initialized in Aug2006 (Long Lead Time) Forecast Initialized in Dec2006 (Short Lead Time) FMA 2007 Seasonal Average

11. FMA 2007 Observed SST Anomaly vs. Grand Ensemble Mean of NMME Models Introduction Summary and Discussion Future Work Data and Methods Results Forecast Initialized in Aug2006 (Long Lead Time) Forecast Initialized in Dec2006 (Short Lead Time) Observed (NCDC) SST Anomaly FMA 2007 Seasonal Average

12. Observed Regression Introduction Summary and Discussion Future Work Data and Methods Results Observed FMA SE US area average precipitation anomalies regressed onto FMA SST

13. Summary of Results NMME did predict a drought in FMA2006 for the 2 initial conditions, but it was too far south, too weak, and the spatial structure did not match the observed drought The prediction for FMA2007 showed a positive precipitation anomaly over the southeastern US, thus the drought did not persist in the models as it did in observations There is a possibility that the discrepancy between the observations and the NMME system in winter tropical Pacific SSTs may have caused the major differences in the drought in 2007, and may partially explain the 2006 differences We think that the focused area of variability seen in the regression image may be linked to the precipitation variability in the SEUS region Introduction Summary and Discussion Future Work Data and Methods Results

14. Future Work Given the large differences in the prediction models, we would like to investigate the winter tropical Pacific SST link more closely Will use coupled ocean atmosphere model to attempt to isolate the SST signal to see if we can improve the prediction, or if the persistence of the drought is truly unpredictable Plans to study land-surface interaction experiments to determine how much of an effect the land-surface has on the drought Introduction Future Work Data and Methods Results Summary and Discussion

15. Thank you to Ben Kirtman, Dughong Min, and the institutional members of the NMME system

16. References Archive web images: US Drought Monitor Archive: http://droughtmonitor.unl.edu/http://droughtmonitor.unl.edu/ IRI ENSO Model Forecast Archive: http://iri.columbia.edu/climate/ENSO/currentinfo/archive/index.html http://iri.columbia.edu/climate/ENSO/currentinfo/archive/index.html NOAA Climate Test Bed: www.cpc.ncep.noaa.gov/products/ctb/www.cpc.ncep.noaa.gov/products/ctb/ Seager, R., A. Taznova, and J. Nakamura, 2009: Drought in the Southeastern United States: Causes, Variability over the Last Millennium, and Potential for Future Hydroclimate Change, J. Climate, 22, 5021-5045. Mo, K. C., and J. E. Schemm, 2008: Relationships between ENSO and drought over the southeastern United States, Geophys. Res. Lett., 35, L15701, doi:10.1029/2008GL; 034656. Manuel, J., 2008: Drought in the Southeast: Lessons for water Management. Environ: Health Perspect., 116, A168-A171. Cocke, S., T. E. LaRow and D.W. Shin, 2007: Seasonal rainfall predictions over the southeast United States using the Florida State University nested regional model. J. Geophys Res., 112, D04106, doi:10.1029/2006JD007535 Ropelewski, C. F., and M. S. Halpert, 1986: North American precipitation and temperature patterns associated with the El Nino/ Southern Oscillation (ENSO). Mon. Wea. Rev., 114, 2352-2362. Ropelewski, C. F., and M. S. Halpert, 1989: Precipitation patterns associated with the high index phase of the Southern Oscillation. J. Climate, 2, 268-284.