Anthony DeAngelis [http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php] [http://www.hydro.com.au/handson/links/images/rain.gif]

Outline  Introduction  Data, Models, and Methodology  Results  Spatial Comparisons over United States  Analysis of Resolution  Ranking of Model Performance  Conclusions  Future Directions

Importance of Precipitation  Agriculture, water resources, power, etc.  Extreme Precipitation  Flooding takes 140 lives in the United States each year (USGS 2006 ).  Observational evidence of increases in the frequency and intensity of extreme precipitation throughout the world over 20 th century (e.g., Groisman et al. 2005 )  Model projections of future increases in heavy precipitation in response to increasing greenhouse gases (e.g., Pall et al. 2007 )  Quantification of future changes in precipitation relies on model simulations

How well do models simulate precipitation?  IPCC AR 4 –Fairly realistic mean precipitation by ensemble of PCMDI coupled models

Mean Precipitation 1980-1999 [IPCC AR 4, Ch 8, Fig. 8.5] CMAP Observations  Multi-model mean of AOGCMs 

How well do models simulate precipitation?  IPCC AR 4 –Fairly realistic mean precipitation by ensemble of PCMDI coupled models  Sun et al. 2007 –Overestimation of light precipitation and underestimation of heavy precipitation by CMIP 3 models

observations Sun et al. ( 2007) Figure 1 model average

How well do models simulate precipitation?  IPCC AR 4 –Fairly realistic mean precipitation by ensemble of PCMDI coupled models  Sun et al. ( 2007) –Overestimation of light precipitation and underestimation of heavy precipitation by CMIP 3 models  Kiktev et al. 2003 – HadAM 3 has little skill in simulating precipitation trends over 1950 - 1995  Higher resolution models perform better  Iorio et al. 2004 – NCAR CCM 3 – mean and extreme precipitation  Kimoto et al. 2005 – MIROC 3.0 – extreme precipitation  Models with embedded cloud resolving models or certain convective parameterizations perform better for extreme precipitation (Iorio et al. 2004, Emori et al. 2005 )

What did I do?  Compared 20 th century simulations from CMIP 3 models with observations over the contiguous United States  Looked at differences in spatial pattern of precipitation characteristics for individual models  Used a longer and consistent time period for comparison ( 1961 - 1998 ) than previous studies  Compared two gridded observational datasets  Assessed the role of resolution on model performance for all models collectively

Observational Data and Climate Models  Observations  Climate Prediction Center’s Daily United States Unified Precipitation (CPC) - 0.25 ° x 0.25 ° lon-lat ( 1948 - 1998 ) [Higgins et al. 2007]  David Robinson’s daily gridded precipitation (DAVR) - 1.0 ° x 1.0 ° lon-lat ( 1900 - 2003 ) [Dyer and Mote 2006]  Climate Models  20 th century simulations – forced with observed atmospheric composition  18 CMIP 3 models with daily precipitation from 1961 - 2000 and a standard (non 360 day) calendar  One ensemble member for each model  Meehl et al. ( 2007 )

Model #Modeling GroupCountryModel IDSpatial Resolution (approximate - lon x lat) 1Bjerknes Centre for Climate ResearchNorwayBCCR BCM 2.02.81° x 2.81° 2Canadian Centre for Climate Modelling & AnalysisCanadaCCCMA CGCM 3.1 T473.75° x 3.75° 3Canadian Centre for Climate Modelling & AnalysisCanadaCCCMA CGCM 3.1 T632.81° x 2.81° 4Centre National de Recherches MétéorologiquesFranceCNRM CM 32.81° x 2.81° 5CSIRO Atmospheric ResearchAustraliaCSIRO MK 3.01.88° x 1.88° 6CSIRO Atmospheric ResearchAustraliaCSIRO MK 3.51.88° x 1.88° 7Geophysical Fluid Dynamics LaboratoryUSAGFDL CM 2.02.50° x 2.00° 8Geophysical Fluid Dynamics LaboratoryUSAGFDL CM 2.12.50° x 2.00° 9Goddard Institute for Space StudiesUSAGISS AOM4.00° x 3.00° 10Goddard Institute for Space StudiesUSAGISS E H5.00° x 3.91° 11Goddard Institute for Space StudiesUSAGISS E R5.00° x 3.91° 12Institute of Atmospheric PhysicsChinaIAP FGOALS 1.0 G2.81° x 3.00° 13Institute for Numerical MathematicsRussiaINM CM 3.05.00° x 4.00° 14Center for Climate System Research, National Institute for Environmental Studies, and Frontier Research Center for Global Change JapanMIROC 3.2 MEDRES2.81° x 2.81° 15Max Planck Institute for MeteorologyGermanyMPI ECHAM 51.88° x 1.88° 16Meteorological Research InstituteJapanMRI CGCM 2.3.22.81° x 2.81° 17National Center for Atmospheric ResearchUSANCAR CCSM 3.01.41° x 1.41° 18National Center for Atmospheric ResearchUSANCAR PCM 12.81° x 2.81° CMIP 3 Models Used More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.phphttp://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php

Spatial Comparisons  Linear re-gridding to 2.5 ° x 2.5 ° lon-lat  Typical model resolution that is fine enough to resolve the coastlines  Precipitation Quantities for 1961 - 1998  Mean  Frequency of wet days (precip. ≥ 0.254 mm/day)  Standard deviation for wet days divided by mean for wet days – precipitation variability  99 th percentile for all days  Generalized extreme value normalized scale parameter for yearly maximum daily precipitation distribution – extreme precipitation variability

Mean Precipitation 1961 - 1998 (mm/day) Convective parameterizations? [Iorio et al. 2004 ] Improper terrain representation? Agreement with IPCC AR 4

Mean Precipitation 1980-1999 [IPCC AR 4, Ch 8, Fig. 8.5] CMAP Observations  Multi-model mean of AOGCMs 

Mean for Wet Days 1961 - 1998 (mm/day)

Frequency of Wet Days 1961 - 1998 (days/year)

Normalized Standard Deviation for Wet Days 1961 - 1998 (dimensionless ) Could be related to too many wet days

99 th Percentile for All Days 1961 - 1998 (mm/day) Convective parameterizations again?

Seasonal 99 th Percentile  Model Average – Observations Average (%) Convection season- consistent with idea of bad convective representation

Example Generalized Extreme Value (GEV) Distribution Representative of New Jersey in Observations location parameter- center of distribution ( 45 mm/day) scale parameter- spread of distribution ( 9 mm/day) I plot scale/location ( 0.2 in this case)

GEV Normalized Scale Parameter for Yearly Maximum 1961 - 1998 (dimensionless) Not enough variability of precipitation extremes

GEV Normalized Scale Parameter for Yearly Maximum 1961 - 1998 (dimensionless)

Does Spatial Resolution Make a Difference?  Linear re-gridding to 5.0 ° x 4.0 ° lon-lat  Error- root mean square of absolute difference between each model and observations average (Iorio et al. 2004 )  Plot error against finite grid equivalent resolution (# of global grid cells)  Fit least squares linear regression to error vs. resolution plot

Error vs. Resolution Results  Statistically significant improvement in the frequency of wet days with higher resolution

= model average

Model #Modeling GroupCountryModel IDSpatial Resolution (approximate - lon x lat) 1Bjerknes Centre for Climate ResearchNorwayBCCR BCM 2.02.81° x 2.81° 2Canadian Centre for Climate Modelling & AnalysisCanadaCCCMA CGCM 3.1 T473.75° x 3.75° 3Canadian Centre for Climate Modelling & AnalysisCanadaCCCMA CGCM 3.1 T632.81° x 2.81° 4Centre National de Recherches MétéorologiquesFranceCNRM CM 32.81° x 2.81° 5CSIRO Atmospheric ResearchAustraliaCSIRO MK 3.01.88° x 1.88° 6CSIRO Atmospheric ResearchAustraliaCSIRO MK 3.51.88° x 1.88° 7Geophysical Fluid Dynamics LaboratoryUSAGFDL CM 2.02.50° x 2.00° 8Geophysical Fluid Dynamics LaboratoryUSAGFDL CM 2.12.50° x 2.00° 9Goddard Institute for Space StudiesUSAGISS AOM4.00° x 3.00° 10Goddard Institute for Space StudiesUSAGISS E H5.00° x 3.91° 11Goddard Institute for Space StudiesUSAGISS E R5.00° x 3.91° 12Institute of Atmospheric PhysicsChinaIAP FGOALS 1.0 G2.81° x 3.00° 13Institute for Numerical MathematicsRussiaINM CM 3.05.00° x 4.00° 14Center for Climate System Research, National Institute for Environmental Studies, and Frontier Research Center for Global Change JapanMIROC 3.2 MEDRES2.81° x 2.81° 15Max Planck Institute for MeteorologyGermanyMPI ECHAM 51.88° x 1.88° 16Meteorological Research InstituteJapanMRI CGCM 2.3.22.81° x 2.81° 17National Center for Atmospheric ResearchUSANCAR CCSM 3.01.41° x 1.41° 18National Center for Atmospheric ResearchUSANCAR PCM 12.81° x 2.81° CMIP 3 Models Used More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.phphttp://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php

Error vs. Resolution Results  Statistically significant improvement in the frequency of wet days with higher resolution  All other quantities showed decreasing error with higher resolution, but the linear fit was not statistically significant  All quantities showed low percentage of model error variability explained by the linear fit (r 2 )

= model average

Other potential reasons for variability in model error  Different vertical resolutions  Different grid types (e.g., spectral resolution vs. finite grid)  Different cloud and convective parameterizations  Different microphysics schemes  Different ocean components  Different radiation schemes

Ranking of Model Performance  Ratio: Root mean square error for each model divided by the average root mean square error for all models for each precipitation quantity  Eliminates biases from quantities with different units (e.g., mean precipitation, frequency of wet days)  Take average of ratio over precipitation quantities for each model and rank them

CMIP 3 Model Ranking All Precipitation QuantitiesMean and Frequency of Wet Days99 th Percentile and GEV Normalized Scale Model RankAverage ErrorModel RankAverage ErrorModel RankAverage Error 1. MPI ECHAM 5 0.6490 1. MPI ECHAM 5 0.5633 1. GFDL CM 2.1 0.6535 2. MRI CGCM 2.3.2 0.7728 2. NCAR CCSM 3.0 0.6823 2. GFDL CM 2.0 0.6872 3. NCAR CCSM 3.0 0.7810 3. MRI CGCM 2.3.2 0.7168 3. MPI ECHAM 5 0.7346 4. CSIRO MK 3.5 0.7853 4. CSIRO MK 3.5 0.7270 4. CCCMA CGCM 3.1 T63 0.7412 5. GFDL CM 2.1 0.8053 5. BCCR BCM 2.0 0.8786 5. Model Average 0.7457 6. Model Average 0.8186 6. Model Average 0.8916 6. CNRM CM 3 0.7762 7. CCCMA CGCM 3.1 T63 0.8261 7. CCCMA CGCM 3.1 T63 0.9109 7. CCCMA CGCM 3.1 T47 0.7999 8. BCCR BCM 2.0 0.9014 8. GISS E R 0.9231 8. MIROC 3.2 MEDRES 0.8275 9. GFDL CM 2.0 0.9120 9. GFDL CM 2.1 0.9571 9. MRI CGCM 2.3.2 0.8288 10. CCCMA CGCM 3.1 T47 0.9337 10. CSIRO MK 3.0 1.0342 10. CSIRO MK 3.5 0.8436 11. MIROC 3.2 MEDRES 0.9346 11. INM CM 3.0 1.0384 11. INM CM 3.0 0.8670 12. INM CM 3.0 0.9527 12. MIROC 3.2 MEDRES 1.0417 12. NCAR CCSM 3.0 0.8797 13. CSIRO MK 3.0 0.9709 13. CCCMA CGCM 3.1 T47 1.0674 13. CSIRO MK 3.0 0.9075 14. CNRM CM 3 0.9750 14. GFDL CM 2.0 1.136814. NCAR PCM 10.9147 15. NCAR PCM 11.0353 15. GISS E H 1.1532 15. BCCR BCM 2.0 0.9243 16. IAP FGOALS 1.0 G 1.185416. NCAR PCM 11.1558 16. IAP FGOALS 1.0 G 1.0807 17. GISS E H 1.2448 17. CNRM CM 3 1.1738 17. GISS AOM 1.1671 18. GISS AOM 1.3582 18. IAP FGOALS 1.0 G 1.2901 18. GISS E H 1.3364 19. GISS E R 1.9766 19. GISS AOM 1.5492 19. GISS E R 3.0301 More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.phphttp://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php

CMIP 3 Model Ranking All Precipitation Quantities Mean, Frequency of Wet Days, and Normalized Standard Deviation for Wet Days 99 th Percentile and GEV Normalized Scale Model RankAverage ErrorModel RankAverage ErrorModel RankAverage Error 1. MPI ECHAM 5 0.6408 1. MPI ECHAM 5 0.5783 1. GFDL CM 2.1 0.6535 2. CSIRO MK 3.5 0.7559 2. CSIRO MK 3.5 0.6974 2. GFDL CM 2.0 0.6872 3. MRI CGCM 2.3.2 0.7666 3. MRI CGCM 2.3.2 0.7251 3. MPI ECHAM 5 0.7346 4. GFDL CM 2.1 0.7954 4. NCAR CCSM 3.0 0.7448 4. CCCMA CGCM 3.1 T63 0.7412 5. NCAR CCSM 3.0 0.7987 5. Model Average 0.8621 5. Model Average 0.7457 6. Model Average 0.8155 6. MIROC 3.2 MEDRES 0.8895 6. CNRM CM 3 0.7762 7. CCCMA CGCM 3.1 T63 0.8457 7. GFDL CM 2.1 0.8901 7. CCCMA CGCM 3.1 T47 0.7999 8. MIROC 3.2 MEDRES 0.8647 8. CCCMA CGCM 3.1 T63 0.9154 8. MIROC 3.2 MEDRES 0.8275 9. CCCMA CGCM 3.1 T47 0.8886 9. CSIRO MK 3.0 0.932 9. MRI CGCM 2.3.2 0.8288 10. GFDL CM 2.0 0.8982 10. CCCMA CGCM 3.1 T47 0.9477 10. CSIRO MK 3.5 0.8436 11. INM CM 3.0 0.9161 11. INM CM 3.0 0.9489 11. INM CM 3.0 0.8670 12. CSIRO MK 3.0 0.9222 12. BCCR BCM 2.0 1.0024 12. NCAR CCSM 3.0 0.8797 13. BCCR BCM 2.0 0.9712 13. GFDL CM 2.0 1.0388 13. CSIRO MK 3.0 0.9075 14. CNRM CM 3 1.0737 14. GISS E R 1.096814. NCAR PCM 10.9147 15. NCAR PCM 11.1117 15. IAP FGOALS 1.0 G 1.215 15. BCCR BCM 2.0 0.9243 16. IAP FGOALS 1.0 G 1.161316. NCAR PCM 11.243 16. IAP FGOALS 1.0 G 1.0807 17. GISS E H 1.3447 17. CNRM CM 3 1.272 17. GISS AOM 1.1671 18. GISS AOM 1.3743 18. GISS E H 1.3503 18. GISS E H 1.3364 19. GISS E R 1.8701 19. GISS AOM 1.5125 19. GISS E R 3.0301 More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.phphttp://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php

Conclusions  CMIP 3 models underestimate mean and extreme precipitation amounts near the Gulf Coast  Convective parameterizations (Iorio et al. 2004 )  CMIP 3 models produce precipitation days too frequently, especially in the north and west  Higher resolution models perform much better  CMIP 3 models have too little variability in all precipitation and extreme precipitation in the northern interior west  The MPI ECHAM 5 is the best, the model average is better than the majority of individual models, and the GISS models are the worst with 20 th century precipitation characteristics over the US

Future Directions  Understand the reasons for differences in model performance  What makes the MPI ECHAM 5 so good?  Evaluate the ability of CMIP 3 models to simulate precipitation changes  Time period used here is too short for a reliable analysis  Expand the evaluation of CMIP 3 precipitation to other regions

References  Dyer, J. L., and T. L. Mote, 2006: Spatial variability and patterns of snow depth over North America, Geophys. Res. Lett., 33, L16503, doi:10.1029/2006GL027258.  Emori, S., A. Hasegawa, T. Suzuki, and K. Dairaku, 2005: Validation, parameterization dependence and future projection of daily precipitation simulated with an atmospheric GCM, Geophys. Res. Lett., 32, L06708, doi:10.1029/2004GL022306.  Groisman, P. Y., R. W. Knight, D. R. Easterling, T. R. Karl, G. C. Hegerl, and V. N. Razuvaev, 2005: Trends in intense precipitation in the climate record, J. Clim., 18, 1326-1350.  Higgins, R. W., W. Shi, E. Yarosh, and R. Joyce, 2000: Improved United States precipitationquality control system and analysis. NCEP/Climate Prediction Center Atlas No. 7, published online at http://www.cpc.ncep.noaa.gov/research_papers/ncep_cpc_atlas/7/.http://www.cpc.ncep.noaa.gov/research_papers/ncep_cpc_atlas/7/  Iorio, J. P., P. B. Duffy, B. Govindasamy, S. L. Thompson, M. Khairoutdinov, and D. Randall, 2004: Effects of model resolution and subgrid scale physics on the simulation of precipitation in the continental United State, Clim. Dyn., 23, 243–258, doi:10.1007/s00382-004-0440-y.  Kiktev, D., D. M. H. Sexton, L. Alexander, and C. K. Folland, 2003: Comparison of modeled and observed trends in indices of daily climate extremes, J. Clim., 16, 3560–3571.  Kimoto, M., N. Yasutomi, C. Yokoyama, and S. Emori, 2005: Projected changes in precipitation characteristics near Japan under the global warming, Scientific Online Letters on the Atmosphere, 1, 85–88, doi:10.2151/sola.2005-023.

References Continued  Meehl, G. A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. F. B. Mitchell, R. J. Stouffer, and K. E. Taylor, 2007: The WCRP CMIP3 multi-model dataset: A new era in climate change research, Bull. Amer. Meteor. Soc., 88, 1383-1394.  Pall, P., M. R. Allen, and D. A. Stone, 2007: Testing the Clausius-Clapeyron constraint on changes in extreme precipitation under CO2 warming, Clim. Dyn., 28, 351-363.  Randall, D. A. and Coauthors, 2007: Climate Models and Their Evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.  Sun, Y., S. Solomon, A. Dai, and R. W. Portmann, 2007: How often will it rain? J. Clim., 20, 4801-4818.  United States Department of the Interior, United States Geological Survey, 2006: Fact Sheet: Flood Hazards- A National Threat. Available at http://pubs.usgs.gov/fs/2006/3026/.http://pubs.usgs.gov/fs/2006/3026/  For more plots, see http://envsci.rutgers.edu/~toine379/extremeprecip/homehttp://envsci.rutgers.edu/~toine379/extremeprecip/home

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