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Anthony DeAngelis

[http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php] [http://www.hydro.com.au/handson/links/images/rain.gif]. Anthony DeAngelis. Evaluation of Daily precipitation from Coupled Model Intercomparison Project Phase III (CMIP3) Models. Outline. Introduction Data, Models, and Methodology Results

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Anthony DeAngelis

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  1. [http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php] [http://www.hydro.com.au/handson/links/images/rain.gif] Anthony DeAngelis Evaluation of Daily precipitation from Coupled Model Intercomparison Project Phase III (CMIP3) Models

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

  3. 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 20th 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

  4. How well do models simulate precipitation? • IPCC AR4 –Fairly realistic mean precipitation by ensemble of PCMDI coupled models

  5. Mean Precipitation 1980-1999 CMAP Observations  Multi-model mean of AOGCMs  [IPCC AR4, Ch 8, Fig. 8.5]

  6. How well do models simulate precipitation? • IPCC AR4 –Fairly realistic mean precipitation by ensemble of PCMDI coupled models • Sun et al. 2007 –Overestimation of light precipitation and underestimation of heavy precipitation by CMIP3 models

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

  8. How well do models simulate precipitation? • IPCC AR4 –Fairly realistic mean precipitation by ensemble of PCMDI coupled models • Sun et al. (2007) –Overestimation of light precipitation and underestimation of heavy precipitation by CMIP3 models • Kiktev et al. 2003 – HadAM3 has little skill in simulating precipitation trends over 1950-1995 • Higher resolution models perform better • Iorio et al. 2004 – NCAR CCM3 – 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)

  9. What did I do? • Compared 20th century simulations from CMIP3 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

  10. 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 • 20th century simulations – forced with observed atmospheric composition • 18 CMIP3 models with daily precipitation from 1961-2000 and a standard (non 360 day) calendar • One ensemble member for each model • Meehl et al. (2007)

  11. CMIP3 Models Used More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php

  12. 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. ≥ 0.254mm/day) • Standard deviation for wet days divided by mean for wet days – precipitation variability • 99th percentile for all days • Generalized extreme value normalized scale parameter for yearly maximum daily precipitation distribution – extreme precipitation variability

  13. Mean Precipitation 1961-1998 (mm/day) Improper terrain representation? Convective parameterizations? [Iorio et al. 2004] Agreement with IPCC AR4

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

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

  16. 99th Percentile for All Days 1961-1998 (mm/day) Convective parameterizations again?

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

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

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

  20. 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

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

  22. = model average

  23. CMIP3 Models Used More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php

  24. 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 (r2)

  25. 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

  26. 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

  27. CMIP3 Model Ranking More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php

  28. CMIP3 Model Ranking More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php

  29. CMIP3 Model Ranking More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php

  30. CMIP3 Model Ranking More information: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php

  31. Conclusions • CMIP3 models underestimate mean and extreme precipitation amounts near the Gulf Coast • Convective parameterizations (Iorio et al. 2004) • CMIP3 models produce precipitation days too frequently, especially in the north and west • Higher resolution models perform much better • CMIP3 models have too little variability in all precipitation and extreme precipitation in the northern interior west • The MPI ECHAM5 is the best, the model average is better than the majority of individual models, and the GISS models are the worst with 20th century precipitation characteristics over the US

  32. Future Directions • Understand the reasons for differences in model performance • What makes the MPI ECHAM5 so good? • Evaluate the ability of CMIP3 models to simulate precipitation changes • Time period used here is too short for a reliable analysis • Expand the evaluation of CMIP3 precipitation to other regions

  33. 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/. • 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 theAtmosphere, 1, 85–88, doi:10.2151/sola.2005-023.

  34. 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/. • For more plots, see http://envsci.rutgers.edu/~toine379/extremeprecip/home

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