1. Global and SE U.S. Assessment of Precipitation: Comparison of Model Simulations with Reanalysis-based Observations Eduardo Ponce Mojica Polytechnic University of Puerto Rico Dr. Auroop R. Ganguly Computational Sciences and Engineering Division August 2009

2. 2Managed by UT-Battelle for the U.S. Department of Energy Overview Introduction –Climate change –Precipitation Objectives Resources –Climate models –Climate observations Methodology Conclusions Future work

3. 3Managed by UT-Battelle for the U.S. Department of Energy Background Climate changes have been a BIG concern for the past decades –Global warming –Climate extremes –Anthropogenic effects Processes and materials derived from human activities Atmospheric concentration of greenhouse gases

4. 4Managed by UT-Battelle for the U.S. Department of Energy Introduction Climate changes may cause or worsen precipitation events –Floods –Droughts –Precipitation extremes Long-duration Short-duration

5. 5Managed by UT-Battelle for the U.S. Department of Energy Introduction Precipitation is difficult to predict –Too many parameters to take into account Ocean circulation Land surface Sea ice Concentration of atmospheric gases Electromagnetic radiation –Complex meteorological physics Mass and energy transfer Radiant exchange

6. 6Managed by UT-Battelle for the U.S. Department of Energy Introduction Precipitation events may be studied for a specific region, or across the whole Earth Southeast United StatesEarth

7. 7Managed by UT-Battelle for the U.S. Department of Energy Objectives Compare two climate models with observations Use statistical analyses to describe models Obtain uncertainties from climate model and observations

8. 8Managed by UT-Battelle for the U.S. Department of Energy What is …? Climate Precipitation

9. 9Managed by UT-Battelle for the U.S. Department of Energy What is climate? Phenomena occurring in the atmosphere in a long period of time –Ranges from months to thousand or million of years Composed of numerous meteorological elements –Temperature –Atmospheric pressure –Wind –Rainfall –Evapotranspiration Affected by latitude and longitude

10. 10Managed by UT-Battelle for the U.S. Department of Energy What is precipitation? Products due to condensation of atmospheric water vapour deposited on Earth's surface –Rain –Ice pellets –Snow –Hail

11. 11Managed by UT-Battelle for the U.S. Department of Energy Resources Climate models simulations Climate observations data MATLAB –Statistical analysis –Graph global and regional data Microsoft Excel –Construction of data plots –Construction of data tables CCSM3 HadCM3 NCEP1

12. 12Managed by UT-Battelle for the U.S. Department of Energy Climate models Community Climate System Model, version 3 (CCSM3) –United States –United States Department of Energy (DOE) Earth System Grid (ESG) Hadley Centre Coupled Model, version 3 (HadCM3) –United Kingdom –Intergovernmental Panel on Climate Change (IPCC) Project for Climate Model Diagnosis and Intercomparison (PCMDI)

13. 13Managed by UT-Battelle for the U.S. Department of Energy Climate observations National Centers for Environmental Prediction, reanalysis 1 (NCEP1) –United States –National Oceanic and Atmospheric Administration (NOAA)

14. 14Managed by UT-Battelle for the U.S. Department of Energy Methodology Interpolate climate models data –Different latitudes and longitudes precision –CCSM3 with NCEP1 –HadCM3 with NCEP1 Interpolated model 94 x 192 CCSM3/ HadCM3 128 x 256 NCEP1 94 X 192

15. 15Managed by UT-Battelle for the U.S. Department of Energy Methodology Case study regions –Global –Southeast United States Latitudes: 24 ° N – 41 ° N Longitudes: 95 ° W - 74 ° W Time range (1948 – 1999)

16. 16Managed by UT-Battelle for the U.S. Department of Energy Methodology Apply statistical methods –Mean –Standard deviation –Skewness –Median –Bias = observations - models

17. 17Managed by UT-Battelle for the U.S. Department of Energy Climate graphs Global mean – NCEP1 and CCSM3 NCEP1 average precipitation rate in mm/s from 1948 to 1999 CCSM3 average precipitation rate in mm/s from 1948 to 1999

18. 18Managed by UT-Battelle for the U.S. Department of Energy Climate graphs Global mean – NCEP1 and HadCM3 NCEP1 average precipitation rate in mm/s from 1948 to 1999 HadCM3 average precipitation rate in mm/s from 1948 to 1999

19. 19Managed by UT-Battelle for the U.S. Department of Energy Climate graphs SE U.S. mean – NCEP1 and CCSM3 NCEP1 Southeastern U.S. average precipitation rate in mm/s from 1948 to 1999 CCSM3 Southeastern U.S. average precipitation rate in mm/s from 1948 to 1999

20. 20Managed by UT-Battelle for the U.S. Department of Energy Climate graphs SE U.S. mean – NCEP1 and HadCM3 NCEP1 Southeastern U.S. average precipitation rate in mm/s from 1948 to 1999 HadCM3 Southeastern U.S. average precipitation rate in mm/s from 1948 to 1999

21. 21Managed by UT-Battelle for the U.S. Department of Energy Climate graphs CCSM3 and HadCM3 SE U.S. bias graphs Southeastern U.S. average of biased precipitation rate in mm/s between the CCSM3 and the NCEP1 from 1948 to 1999 Southeastern U.S. average of biased precipitation rate in mm/s between the HadCM3 and the NCEP1 from 1948 to 1999

22. 22Managed by UT-Battelle for the U.S. Department of Energy Plots

23. 23Managed by UT-Battelle for the U.S. Department of Energy Plots

24. 24Managed by UT-Battelle for the U.S. Department of Energy Plots

25. 25Managed by UT-Battelle for the U.S. Department of Energy Results Global scope –CCSM3 more accurate Southeast United States –HadCM3 more accurate

26. 26Managed by UT-Battelle for the U.S. Department of Energy Research conclusions Global scope –CCSM3 over predicts precipitation rate –HadCM3 over predicts precipitation rate –CCSM3 more accurate model Southeast U.S. –CCSM3 under predicts precipitation rate –HadCM3 under predicts precipitation rate –HadCM3 more accurate model Study small regions with climate models –Reduces uncertainties –Outputs statistics more accurately

27. 27Managed by UT-Battelle for the U.S. Department of Energy Future research Test accuracy of CCSM3 and HadCM3 in other regions Propose safety measures for high precipitation areas Simulate precipitation rates from 2000 to 2100

28. 28Managed by UT-Battelle for the U.S. Department of Energy Bibliography Auroop R. Ganguly, Shih-Chieh Kao, Karsten Steinhaeuser, Esther S. Parish, Marcia L. Branstetter, David J. Erickson III, and Nagendra Singh. Uncertainties in the Assessments of Climate Change Impacts on Regional Hydrology and Water Resources. (2009: In Review). Intergovernmental Panel on Climate Change (IPCC). Fourth Assessment Report: 2007.

29. 29Managed by UT-Battelle for the U.S. Department of Energy Acknowledgments Special thanks go to… The Research Alliance in Math and Science program, sponsored by the Office of Advanced Scientific Computing Research, U.S. Department of Energy Dr. Auroop R. Ganguly for the opportunity to work on this project. Shih-Chieh Kao, Karsten Steinhaeuser, the GIST Group, and Rashida E. Askia for their continued support Debbie McCoy, who made provisions for this research experience along with exceptional professional support

30. 30Managed by UT-Battelle for the U.S. Department of Energy QUESTIONS