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Atmospheric Model for the Climate Forecast System Reanalysis and Retrospective Forecasts

This presentation discusses the latest version of the Global Forecast System Atmospheric Model and its improvements for climate forecasting reanalysis and retrospective forecasts.

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Atmospheric Model for the Climate Forecast System Reanalysis and Retrospective Forecasts

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  1. Atmospheric Model for the Climate Forecast System Reanalysis and Retrospective Forecasts Shrinivas Moorthi Shrinivas Moorthi

  2. Thanks to Glenn White who prepared several slides in this presentation and YuTai Hou who prepared the slides related to radiation parameterization Shrinivas Moorthi

  3. GFS AM • Latest version of Global Forecast System (GFS) Atmospheric Model (AM) is being considered for CFSRR. • GFS AM - developed by the staff of Global Climate and Weather Modeling Branch of EMC. • The first reanalysis (NCEP/NCAR – R1) was based on the operational GFS AM of January 1995. • GFS AM has undergone major revisions since the first reanalysis. Shrinivas Moorthi

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  5. GFS AM improvements through reanalysis • Some specific problems found in NCEP/NCAR reanalysis, addressed in later AM changes • -- valley snow • -- wrong snow cover • -- wrong ocean albedo • -- SH paobs mislocated • -- ”pathological” problems in stratosphere • New reanalysis will find problems in GFS that will be addressed and produce improved GFS, improved future reanalysis and improved future CFS • We’ll keep doing it until we get it right (Glenn White) Shrinivas Moorthi

  6. Comparison between AMs in R1, CFS (opr) and GFS (opr) Shrinivas Moorthi

  7. Operational CFS GFDL-LW Radiationvs. RRTM-LW Radiation GFDLRRTM Description: - 15 bands 16 bands - trans table look-up 140 cor-k terms - O3,H2O,CO2 O3,H2O,CO2,O2,CH4 CO, 4 CFCs Advantages/ - comp efficient more comp efficient Disadvantages: - no aerosols effect aerosol effect capable - fixed CO2 only varying CO2 capable - fixed sfc emis varying emis capable - random cld ovlp random or max-ran - larger errors, especially improved accuracy at upper stratosphere, at upper stratosphere - simple cloud optical prop advanced cloud optical property property Shrinivas Moorthi

  8. Clear sky LW cooling comparison for tropical, mid-latitude and subarctic winter profiles Shrinivas Moorthi

  9. Cloudy sky LW cooling comparison for tropical, mid-latitude and subarctic winter profiles Shrinivas Moorthi

  10. The current operational GFS AM has Realistic moisture prediction with better depiction of no-rain areas Prognostic Ozone Prognostic cloud condensate Cloud cover only where cloud condensate > 0 Momentum mixing in deep convection Fast and accurate AER RRTM for IR radiation Mountain blocking parameterization Noah land model Sea-ice model Improved treatment of snow, ice, orography Better hurricane track prediction ESMF based modern computer algorithms Shrinivas Moorthi

  11. Options in GFS AM being considered for next operational model • Enthalpy (CpT) as a prognostic variable in place of Tv • AER RRTM shortwave radiation with maximum-random cloud overlap • IR and Solar radiation called every hour (Until now IR is called every 3 hours) • Use of historical and spatially varying CO2 and volcanic aerosols Shrinivas Moorthi

  12. Why Enthalpy as a prognostic variable? Collaboration between Space Environmental Center and EMC to develop whole atmosphere model (0-600km) to be coupled to global ionosphere plasmasphere model More accurate thermodynamic equation is essential since rtop/rsfc ~ 10-13 Variation of specific heats in space and time needs to be accounted for Shrinivas Moorthi

  13. The thermodynamic equation used in the operational GFS AM has the form where with ideal-gas law in the form Here Rdand Rvare gas constants for dry air and water vapor and Cpd, Cpv are specific heats at constant pressure for dry air and water vapor. Shrinivas Moorthi

  14. The ideal-gas law is The thermodynamic equation, derived from internal energy equation is (Akmaev, 2006 – Space Environmental Center) and defining enthalpyh as the thermodynamic energy equation can be re-written as which has the same form as operational one Shrinivas Moorthi

  15. However, here R and Cp are determined by their specific mixing ratios Currently, GFS AM has three tracers – specific humidity, ozone and cloud water. Ignoring cloud water, We use : dry airsp. Humozone Ri287.05461.50173.2247 Cpi1004.6 1846.0820.2391 Henry Juang of EMC implemented Enthalpy in the GFS AM Shrinivas Moorthi

  16. NCEP Operational SW Radiationvs. New RRTM SW Radiation NCEPRRTM Description: - 8 uv+vis, 1-nir 5 uv+vis, 9-nir bnds - 38 k-dis terms 112 cor-k terms - O3,H2O,CO2,O2 O3,H2O,CO2,O2,CH4 Advantages: - Comp. Efficient Accu. (use ARM’s data) clr-sky - 10-30 w/m2 reduction cld-sky - adv. scheme Disadvantages: - large errors Comp. slow, 4 times clear-sky - und est slower than opr sw cloudy-sky - over est YuTai Hou of EMC implemented RRTM in the GFS AM Shrinivas Moorthi

  17. Clear sky SW heating comparison for tropical, mid-latitude and subarctic winter profiles Shrinivas Moorthi

  18. Cloudy sky SW heating comparison for tropical, mid-latitude and subarctic winter profiles Shrinivas Moorthi

  19. Coupling of GFS to MOM3 (MOM4) In the operational CFS, AM and OM are coupled daily with AM and OM running sequentially In the new CFS, the coupling is MPI-level (developed by Dmitry Shenin) – AM, OM and the coupler run simultaneously Coupling frequency is flexible up to the OM time step Same AM code can run in coupled or standalone mode Coupler details for MOM4 will be presented later in this meeting Shrinivas Moorthi

  20. SST predicted in 50 year coupled simulation (winter) RRTM run shows reduced SST warm bias CTB sponsored Experiment run by S. Saha and Y. Hou Shrinivas Moorthi

  21. SST predicted in 50 year coupled simulation (summer) CTB sponsored Experiment run by S. Saha and Y. Hou Shrinivas Moorthi

  22. Jack Woolen and others have spent years improving the data base of conventional observations --much more complete than before --errors better understood Great deal of experience now with satellite bias corrections Experienced with changes in observations in last 10 years Knowledge is being applied to new reanalysis GFS produces much more skilled forecasts than CDAS --GFS has proven track record in forecasting hurricane tracks and in seasonal forecasts as CFS, indicating that GFS produces much more realistic tropical atmosphere than CDAS in both analyses and forecasts Shrinivas Moorthi

  23. (Fang-Lin Yang) September 2007 No. Hemisphere 500 hPa height Anomaly correlation (unusually good month for GFS vs. ECMWF) GFS has useful skill 1.5 days longer than CDAS Shrinivas Moorthi

  24. October 2007 GFS has useful skill 1 day longer than CDAS Shrinivas Moorthi

  25. September 2007 Southern Hemisphere GFS has useful skill more than 1 day longer than CDAS Shrinivas Moorthi

  26. Precipitation JJA 2007 Shrinivas Moorthi

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  28. GDAS has most similar pattern to independent estimate CDAS 1 and 2 have too much rain over southeast US Shrinivas Moorthi

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  30. CFS Reanalysis and Reforecast Scripts AM and OM Post post.sh Start here Copy IC files copy.sh 9 (or 48) hr Coupled Model Forecast (first guess) New GFS + MOM4 with Sea Ice MPI-level Coupling fcst.sh Verify vrfy.sh CFSRR website Prep step Hurricane relocation Data preparation prep.sh GODAS Global Ocean Data Assimilation oanl.sh Archive data arch.sh Retrospective Forecast? Time 00Z ? GDAS Global Atmospheric Data Assimilation GSI anal.sh GLDAS Global Land Data Assimi- lation lanl.sh Run Retrospective Forecast fcst.sh Shrinivas Moorthi

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  32. JJA07 Annual mean climatology Shrinivas Moorthi

  33. CDAS1 had wrong ocean albedo, reflected too much short wave CDAS2 too low sensible heat flux GDAS too much downward short wave, more net heat flux into ocean than CDAS1 or CDAS2 Shrinivas Moorthi

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  35. GDAS has pattern most like Air Force estimate, but has too little stratus clouds in eastern ocean too far displaced from coast Shrinivas Moorthi

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  41. GDAS has less evporation, more sensible heat flux over Continents than CDAS1 or 2 COADS estimate based on little data in Southern Hemisphere Latent heat estimate smaller than any of reanalyses—may reflect Too weak COADS (COADS fluxes tend to give net heat flux into Ocean) or too strong hyrdological cycle in reanalyses Shrinivas Moorthi

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  48. GDAS has most reasonable pattern of surface short wave radiation But has too much in tropics CDAS1 has too high ocean surface albedo Shrinivas Moorthi

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