1. Nov 7, 2007Shrinivas Moorthi1 Atmospheric Model for the Climate Forecast System Reanalysis and Retrospective Forecasts Shrinivas Moorthi

2. Nov 7, 2007Shrinivas Moorthi2 Thanks to Glenn White who prepared several slides in this presentation and YuTai Hou who prepared the slides related to radiation parameterization

3. Nov 7, 2007Shrinivas Moorthi3 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.

4. Nov 7, 2007Shrinivas Moorthi4 CDAS (R1)GFS AM (OPR) Vertical coordinateSigmaSigma/pressure Spectral resolutionT62T382 Horizontal resolution~210 km~35 km Vertical layers2864 Top level pressure~3 hPa0.266 hPa Layers above 100 hPa~7~24 Layers below 850 hPa~6~13 Lowest layer thickness~40 m~20 m Analysis schemeSSIGSI Satellite dataNESDIS temperature retrievals Radiances

5. Nov 7, 2007Shrinivas Moorthi5 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)

6. Nov 7, 2007Shrinivas Moorthi6 Comparison between AMs in R1, CFS (opr) and GFS (opr) R1 (T62L28)OPR CFS AM (T126L64) OPR GFS AM (T382L64) SAS ConvectionSAS Convection with momentum mixing Tiedtke Shallow convection Seasonal/zonal mean OzonePrognostic Ozone OSU LSM (2 layers) Noah LSM (4 layers) and sea ice model Diagnostic cloudsPrognostic cloud condensate Boundary layerNonlocal Boundary layerNon local boundary layer Graviry wave dragGravity wave dragGWD with Mountain Blocking GFDL IR radiation Random overlap GFDL IR radiation Random overlap RRTM IR radiation Max/random overlap NCEP SW -93 radiation (Chou ) Random overlap NCEP SW -95 radiation (Chou) Random overlap NCEP SW radiation (Chou) Random overlap Vertical diffusion Vertical diffusion with reduced background diffusion 2 nd order horz diffusion 6 th order horz diffusion Virtual Temperature

7. Nov 7, 2007Shrinivas Moorthi7 Operational CFS GFDL-LW Radiation vs. RRTM-LW Radiation GFDL RRTM Description:- 15 bands 16 bands - trans table look-up 140 cor-k terms - O 3,H 2 O,CO 2 O 3,H 2 O,CO 2,O 2,CH 4 CO, 4 CFCs Advantages/- comp efficient more comp efficient Disadvantages: - no aerosols effect aerosol effect capable - fixed CO 2 only varying CO 2 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

8. Nov 7, 2007Shrinivas Moorthi8 Clear sky LW cooling comparison for tropical, mid-latitude and subarctic winter profiles

9. Nov 7, 2007Shrinivas Moorthi9 Cloudy sky LW cooling comparison for tropical, mid-latitude and subarctic winter profiles

10. Nov 7, 2007Shrinivas Moorthi10 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

11. Nov 7, 2007Shrinivas Moorthi11 Options in GFS AM being considered for next operational model Enthalpy (C p T) as a prognostic variable in place of T v 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 CO 2 and volcanic aerosols

12. Nov 7, 2007Shrinivas Moorthi12 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  top /  sfc ~ 10 -13 Variation of specific heats in space and time needs to be accounted for

13. Nov 7, 2007Shrinivas Moorthi13 The thermodynamic equation used in the operational GFS AM has the form with ideal-gas law in the form where Here R d and R v are gas constants for dry air and water vapor and C pd, C pv are specific heats at constant pressure for dry air and water vapor.

14. Nov 7, 2007Shrinivas Moorthi14 The ideal-gas law is and defining enthalpy h as the thermodynamic energy equation can be re-written as The thermodynamic equation, derived from internal energy equation is (Akmaev, 2006 – Space Environmental Center) which has the same form as operational one

15. Nov 7, 2007Shrinivas Moorthi15 However, here R and C p 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 air sp. Hum ozone R i 287.05 461.50 173.2247 C pi 1004.6 1846.0 820.2391 Henry Juang of EMC implemented Enthalpy in the GFS AM

16. Nov 7, 2007Shrinivas Moorthi16 NCEP Operational SW Radiation vs. 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 - O 3,H 2 O,CO 2,O 2 O 3,H 2 O,CO 2,O 2,CH 4 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

17. Nov 7, 2007Shrinivas Moorthi17 Clear sky SW heating comparison for tropical, mid-latitude and subarctic winter profiles

18. Nov 7, 2007Shrinivas Moorthi18 Cloudy sky SW heating comparison for tropical, mid-latitude and subarctic winter profiles

19. Nov 7, 2007Shrinivas Moorthi19 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

20. Nov 7, 2007Shrinivas Moorthi20 RRTM run shows reduced SST warm bias SST predicted in 50 year coupled simulation (winter) CTB sponsored Experiment run by S. Saha and Y. Hou

21. Nov 7, 2007Shrinivas Moorthi21 SST predicted in 50 year coupled simulation (summer) CTB sponsored Experiment run by S. Saha and Y. Hou

22. Nov 7, 2007Shrinivas Moorthi22 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

23. Nov 7, 2007Shrinivas Moorthi23 (unusually good month for GFS vs. ECMWF) GFS has useful skill 1.5 days longer than CDAS (Fang-Lin Yang) September 2007 No. Hemisphere 500 hPa height Anomaly correlation

24. Nov 7, 2007Shrinivas Moorthi24 October 2007 GFS has useful skill 1 day longer than CDAS

25. Nov 7, 2007Shrinivas Moorthi25 September 2007 Southern Hemisphere GFS has useful skill more than 1 day longer than CDAS

26. Nov 7, 2007Shrinivas Moorthi26 OPICDAS1CDAS2GDAS Global2.622.973.423.23 Land2.112.732.832.72 Ocean2.843.073.673.45 Precipitation JJA 2007

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28. Nov 7, 2007Shrinivas Moorthi28 GDAS has most similar pattern to independent estimate CDAS 1 and 2 have too much rain over southeast US

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30. Nov 7, 2007Shrinivas Moorthi30 CFS Reanalysis and Reforecast Scripts 9 (or 48) hr Coupled Model Forecast (first guess) New GFS + MOM4 with Sea Ice MPI-level Coupling fcst.sh Prep step Hurricane relocation Data preparation prep.sh GLDAS Global Land Data Assimi- lation lanl.sh GDAS Global Atmospheric Data Assimilation GSI anal.sh GODAS Global Ocean Data Assimilation oanl.sh Run Retrospective Forecast fcst.sh AM and OM Post post.sh Start here Copy IC files copy.sh Time 00Z ? Verify vrfy.sh Archive data arch.sh Retrospective Forecast? CFSRR website

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32. Nov 7, 2007Shrinivas Moorthi32 CDAS1CDAS2GDASK&TRangeSRB sh2416-27 lh8597877878-90 dsw199180200198185 usw4224253024 nsw157156175168142-174161 dlw346350345324348 ulw405406408390396 nlw-59-56-626640-7248 netrad9810011210299-119113 nhf-4-59 P2.973.423.232.692.69-3.1 E JJA07 Annual mean climatology

33. Nov 7, 2007Shrinivas Moorthi33 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

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35. Nov 7, 2007Shrinivas Moorthi35 GDAS has pattern most like Air Force estimate, but has too little stratus clouds in eastern ocean too far displaced from coast

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41. Nov 7, 2007Shrinivas Moorthi41 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

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48. Nov 7, 2007Shrinivas Moorthi48 GDAS has most reasonable pattern of surface short wave radiation But has too much in tropics CDAS1 has too high ocean surface albedo

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52. Nov 7, 2007Shrinivas Moorthi52 GDAS has more net heat flux into ocean than other Reanalyses COADS estimate substantially tuned to achieve balance

53. Nov 7, 2007Shrinivas Moorthi53 COADS climatological estimate Reanalyses one season

54. Nov 7, 2007Shrinivas Moorthi54 CFS Reanalysis and Reforecast Scripts 9 (or 48) hr Coupled Model Forecast (first guess) New GFS + MOM4 with Sea Ice MPI-level Coupling fcst.sh Prep step Hurricane relocation Data preparation prep.sh GLDAS Global Land Data Assimi- lation lanl.sh GDAS Global Atmospheric Data Assimilation GSI anal.sh GODAS Global Ocean Data Assimilation oanl.sh Run Retrospective Forecast fcst.sh AM and OM Post post.sh Start here Copy IC files copy.sh Time 00Z ? Verify vrfy.sh Archive data arch.sh Retrospective Forecast? CFSRR website