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THE USE OF NWP TYPE SIMULATIONS TO TEST CONVECTIVE PARAMETERIZATIONS

THE USE OF NWP TYPE SIMULATIONS TO TEST CONVECTIVE PARAMETERIZATIONS. David Williamson National Center for Atmospheric Research. CCPP-ARM Parameterization Testbed (CAPT) Steve Klein, Jim Boyle, Ric Cederwall, Mike Fiorino, Jay Hnilo, Tom Phillips, Jerry Potter, Shaocheng Xie PCMDI / LLNL

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THE USE OF NWP TYPE SIMULATIONS TO TEST CONVECTIVE PARAMETERIZATIONS

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  1. THE USE OF NWP TYPE SIMULATIONS TO TEST CONVECTIVE PARAMETERIZATIONS David Williamson National Center for Atmospheric Research

  2. CCPP-ARM Parameterization Testbed (CAPT) Steve Klein, Jim Boyle, Ric Cederwall, Mike Fiorino, Jay Hnilo, Tom Phillips, Jerry Potter, Shaocheng Xie PCMDI / LLNL David Williamson, Jerry Olson NCAR WCRP / CAS WGNE TRANSPOSE AMIP Martin Miller, Christian Jakob ECMWF David Williamson NCAR Parameterization Modifications Guang Zhang Scripps Institute of Oceanography Richard Neale NCAR

  3. Forecasts with climate models • from operational analyses and reanalyses • at climate model resolution • Gain insight into parameterization errors • by comparing parameterized variables to estimates from • field campaigns (e.g. ARM) • when states fed to parameterizations are still close to • atmospheric analyses • Also useful just to examine model state errors

  4. Map fine resolution NWP analyses to coarse resolution • climate model grid • Spin-up land and parameterized variables to be consistent • with atmosphere forced to follow observed atmosphere • (or apply a Global Land Data Assimilation System) • Additional benefit: establish sensitivity of parameterization • behavior to different analyses

  5. SPIN-UP DURING FORECAST • NWP goal – make best possible forecast of evolving weather • Spin-up of precipitation is common problem • occurs because model is inconsistent with analyses • Precipitation ignored for first few hours of forecast • Our goal – gain insight into model errors • Spin-up is primary signal

  6. Forecasts with • Community Atmosphere Model (CAM3 and CAM2) • coupled to • Community Land Model (CLM3 and CLM2) • Initialized from ERA40 • TOGA-COARE IFA (November 1997) • CSU Verification data • Data sets for forcing and diagnosing SCM • (Ciesielski et al. , 2003) • ARM SGP (June/July 1997 IOP) • ARM Verification data • Data sets for forcing and diagnosing SCM and CRM • variational analysis (Zhang and Lin, 1997)

  7. TOGA COARE Intensive Flux Array (IFA) 1-30 November 1992 CSU data from http://tornado.atmos.colostate.edu/togadata/ifa_data.html

  8. TOGA COARE IFA Nov ‘92 Solid – CAM3, Dashed – CSU

  9. ARM SGP July ‘97 CAM2 CAM3 20 June – 13 July 1997

  10. FORECAST ERRORS SGP July ‘97 IFA Nov ‘92

  11. Temperature Balance Equation

  12. June-July ’97 SGP

  13. June-July ’97 SGP CONDENSATE FORMATION RAINFALL EVAPORATION FREEZING OF RAIN MELTING OF SNOW

  14. June-July ’97 SGP

  15. June-July ’97 SGP CONDENSATE FORMATION RAINFALL EVAPORATION FREEZING OF RAIN MELTING OF SNOW

  16. June-July ’97 SGP RAINFALL EVAPORATION CONDENSATE FORMATION

  17. June-July ’97 SGP CONDENSATE FORMATION RAINFALL EVAPORATION

  18. June-July ’97 SGP

  19. June-July ’97 SGP CAM3 CAM3 WITH ZHANG MOD CAM3 WITH NEALE MOD

  20. CONCLUSIONS • When Zhang is active, troposphere too warm • Errors larger in CAM3 than CAM2 (at SGP) • Convective time scale halved in CAM3 • Conversion between water and ice added to CAM3 • Rainfall evaporation dependence on cloud fraction in CAM3

  21. CONCLUSIONS • Composite over like process errors • Field campaign measurements essential • Need a large variety of cases • Do not tell what is wrong with model • Indicate which processes are producing wrong state • Does not imply incorrect formulation • Indicates where to look first • to determine why processes act incorrectly • Speculation (hypotheses) • for further experiments and examination

  22. POSTERS Willett, M., P. Bechtold, D. Williamson, J. Petch and S. Milton, 2006: Modelling the transition from suppressed to deep tropical convection: Comparison of global NWP and climate models with TOGA-COARE (GCSS WG4 Case5). Xie, S., S. Klein, J. Boyle, D. Williamson, and G. Zhang, 2006: Identifying Climate Model Deficiencies in Simulations of Tropical Intraseasonal Variability by Running Climate Model in Forecast Mode and Using Single-Column Model.

  23. Phillips, T. J., G. L. Potter, D. L. Williamson, R. T. Cederwall, J. S. Boyle, M. Fiorino, J. J. Hnilo, J. G. Olson, S. Xie, J. J. Yio, 2004: The CCPP-ARM Parameterization Testbed (CAPT): Where Climate Simulation Meets Weather Prediction, Bull. Amer. Meteor. Soc., 85, 1903-1915. Boyle, J., D. Williamson, R. Cederwall, M. Fiorino, J. Hnilo, J. Olson, T. Phillips, G. Potter and S. Xie, 2005: Diagnosis of Community Atmospheric Model 2 (CAM2) in numerical weather forecast configuration at Atmospheric Radiation Measurement (ARM) sites, J. Geophys. Res., 110 doi:10.1029/2004JD005042. Williamson, D. L., J. Boyle, R. Cederwall, M. Fiorino, J. Hnilo, J. Olson, T. Phillips, G. Potter and S. Xie, 2005: Moisture and Temperature budgets at the ARM Southern Great Plains Site in forecasts with the CAM2, J. Geophys. Res. , 110 doi:10.1029/2004JD005109. Williamson, D. L. and J. Olson, 2006: A comparison of forecast errors in CAM2 and CAM3 at the AMR Southern Great Plains Site, J. Climate, submitted.

  24. SGP June-July ’97 Forecast Errors June-July Climate Errors

  25. TOGA COARE IFA Nov ‘92

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