Part 2 CFS: Where It’s Going

1. Part 2 CFS: Where It’s Going S. Lord, H-L Pan, S. Saha, D. Behringer, K. Mitchell And the NCEP (EMC-CPC CFSRR Team)

2. Overview • Current (CFS-v1) description and status • CFS Reanalysis and Reforecast (CFSRR  CFS-v2) • Atmosphere • Ocean • Land surface • Sea ice • Future development (CFS-v3) • Coupled A-O-L-S system • Long term Reanalysis strategy • Possibilities for Multi-Model Ensembles (MMEs) • Weather-Climate forecasting

3. Seasonal to Interannual Prediction at NCEP Operational System since August 2004 (CFS-v1) Ocean Model MOMv3 quasi-global 1ox1o (1/3o in tropics) 40 levels Climate Forecast System (CFS) Atmospheric Model GFS (2003) T62 (~200 km) 64 levels “Weather & Climate” Model Daily Coupling Reanalysis-2 3DVAR T62L28 OIv2 SST Levitus SSS clim. GODAS (2003) 3DVAR XBT TAO Triton Pirata Argo Salinity (syn.) TOPEX/Jason-1 Ocean reanalysis (1980-present) provides initial conditions for retrospective CFS forecasts used for calibration and research Funded by NCPO/OCO Stand-alone version with a 14-day lag updated routinely

4. Number of Temperature Observations per Month as a Function of Depth D. Behringer

5. CFS-v2 Highlights • High resolution data assimilation • Produces better initial conditions for operational hindcasts and forecasts (e.g. MJO) • Enables new products for the monthly forecast system • Enables additional hindcast research • Coupled data assimilation • Reduces “coupling shock” • Improves spin up character of the forecasts • Consistent analysis-reanalysis and forecast-reforecast for • Improved calibration and skill estimates • Provide basis for a future coupled A-O-L-S forecast system running operationally at NCEP (1 day to 1 year) • (currently in parallel testing for “GFS” 1-14 day prediction) Funded by NCPO/CDEP

6. CFSRR Components • Reanalysis • 31-year period (1979-2009 and continued in NCEP ops) • Atmosphere • Ocean • Land • Seaice • Coupled system (A-O-L-S) provides background for analysis • Produces consistent initial conditions for climate and weather forecasts • Reforecast • 28-year period (1982-2009 and continued in NCEP ops ) • Provides stable calibration and skill estimates for new operational seasonal system • Includes upgrades for A-O-L-S developed since CFS originally implemented in 2004 • Upgrades developed and tested for both climate and weather prediction • “Unified weather-climate” strategy (1 day to 1 year)

7. CFSRR Component Upgrades

8. Time One-day schematic of four 6-hourly cycles of CFSRR Global Reanalysis: Atmospheric Analysis 00Z GDAS 06Z GDAS 12Z GDAS 18Z GSI 00Z GSI 0Z GODAS 6Z GODAS 12Z GODAS 18Z GODAS 0Z GODAS Ocean Analysis 0Z GLDAS 6Z GLDAS 12Z GLDAS 18Z GLDAS 0Z GLDAS Land Analysis S. Saha and S. Moorthi

9. Testing with CMIP Runs (variable CO2) OBS is CPC Analysis (Fan and van den Dool, 2008) CTRL is CMIP run with 1988 CO2 settings (no variations in CO2, current operations) CO2 run is the ensemble mean of 3 NCEP CFS runs in CMIP mode • realistic CO2 and aerosols in both troposphere and stratosphere Processing: 25-month running mean applied to the time series of anomalies (deviations from their own climatologies)

10. 6hr Atmospheric Model GFS (2007) T382 64 levels GDAS GSI 24hr 6hr Land Model Ice Model LDAS Ice Ext Ocean Model MOMv4 fully global 1/2ox1/2o (1/4o in tropics) 40 levels 6hr GODAS 3DVAR CFSRR at NCEP Climate Forecast System V2

11. Future Development • What’s going on and what’s needed • Land surface • Ocean & Sea ice • Atmosphere

12. Noah LSM 4 soil layers (10, 30, 60, 100 cm) Frozen soil physics included Surface fluxes weighted by snow cover fraction Improved seasonal cycle of vegetation cover Spatially varying root depth Runoff and infiltration account for sub-grid variability in precipitation & soil moisture Improved soil & snow thermal conductivity Higher canopy resistance More OSU LSM 2 soil layers (10, 190 cm) No frozen soil physics Surface fluxes not weighted by snow fraction Vegetation fraction never less than 50 percent Spatially constant root depth Runoff & infiltration do not account for subgrid variability of precipitation & soil moisture Poor soil and snow thermal conductivity, especially for thin snowpack and moist soils Noah LSM replaces OSU LSM in new CFS Noah LSM replaced OSU LSM in operational NCEP medium-range Global Forecast System (GFS) in late May 2005 Some Noah LSM upgrades & assessments were result of collaborations with CPPA PIs K. Mitchell Funded by NCPO/CPPA

13. CFSRR Reanalysis Land Component:Global Land Data Assimilation System (GLDAS) • Applies same Noah LSM as in new CFS • Uses same native grid (T382 Gaussian) as CFSRR atmospheric analysis • Applies CFSRR atmospheric analysis forcing (except for precip) • hourly from previous 24-hours of atmospheric analysis • Precipitation forcing is from CPC analyses of observed precipitation • Model precipitation is blended in only at very high latitudes • GLDAS daily update of the CFSRR reanalysis soil moisture states • Reprocesses last 6-7 days to capture and apply most recent CPC precipitation analyses • Realtime GLDAS configuration will match reanalysis configuration • To sustain the relevance of the climatology of the retrospective reanalysis • Applies LIS: uses the computational infrastructure of the NASA Land Information System (LIS), which is highly parallelized

14. LIS Capabilities • Flexible choice of 7 different land models • Includes Noah LSM used operationally by NCEP and AFWA • Flexible domain and grid choice • Global: such as NCEP global model Gaussian grid • Regional: including very high resolution (~.1-1 km) • Data Assimilation • Based on Kalman Filter approaches • High performance parallel computing • Scales efficiently across multiple CPUs • Interoperable and portable • Executes on several computational platforms • NCEP and AFWA computers included • Being coupled to NWP & CRTM radiative transfer models • Coupling to WRF model has been demonstrated • Coupling to NCEP global GFS model is under development • Coupling to JCSDA CRTM radiative transfer model is nearing completion • Next-gen AFWA AGRMET model will utilize LIS with Noah • NCEP’s Global Land Data Assimilation utilizes LIS K. Mitchell, C. Peters-Lidard

15. Impact of Noah vs. OSU Land Models and GLDAS Initial Land States in 25-years of CFS Summer & Winter Reforecasts:Lessons Learned • Land surface model (LSM) for CFS forecast must be same as for supporting land data assimilation system (LDAS) • Impact of land surface upgrade on CFS seasonal precipitation forecast skill for is positive (but modest) • Significant only for summer season in neutral ENSO years (and then only small positive impact) • Essentially neutral impact for winter season and non-neutral ENSO summers • Differences in CFS precipitation skill over CONUS between neutral and non-neutral ENSO years exceeds skill differences between two different land configurations for same sample of years • Indicates that impact of SST anomaly is substantially greater than impact of land surface configuration

16. 2009+ Land Surface Model Development 1 - Unify all NCEP model land components to use MODIS-based hi-res global land use with IGBP classes 2 - Improve global fields of land surface characteristics (vegetation cover, albedo, emissivity) using satellite data (with Joint Center for Satellite Data Assimilation) 3 - Enhance land surface subgrid-variability with high-resolution sub-grid tiles 4 - Increase  number of soil layers (from 4 to about 10) 5 - Introduce dynamic seasonality of vegetation (to replace pre-specified seasonal cycle) 6 - Improve hydrology including addition of groundwater 7 - Add multi-layer treatment to snowpack physics 8 - Introduce carbon fluxes Items 5-8 are being transitioned from the CPPA-funded work of PI Prof Z.-L. Yang and Dr. G.-Y. Niu of U.Texas/Austin K. Mitchell

17. GODAS in the CFSRR • Operational in 2010 • MOMv4 (1/2o x 1/2o, 1/4o in the tropics, 40 levels) • Updated 3DVAR assimilation scheme • Temperature profiles (XBT, Argo, TAO, TRITON, PIRATA) • Synthetic salinity profiles derived from seasonal T-S relationship • TOPEX/Jason-1 Altimetry • Data window is asymmetrical extending from 10-days before the analysis date • Surface temperature relaxation to (or assimilation of) Reynolds new daily, 1/4o OIv2 SST • Surface salinity relaxation Levitus climatological SSS • Coupled atmosphere-ocean background • Current stand-alone operational GODAS will be upgraded in 2009 to the higher resolution MOMv4 and be available for comparison with the coupled version • Updated with new techniques and observations D. Behringer

18. In the west, assimilating Argo salinity corrects the bias at the surface and the depth of the undercurrent core and captures the complex structure at 165oE. In the east, assimilating Argo salinity reduces the bias at the surface and sharpens the profile below the thermocline at 110oW. Assimilating Argo Salinity Comparison with independent ADCP currents. ADCP GODAS GODAS-A/S D. Behringer

19. 2009+ GODAS Activities • Complete CFSRR • Evaluate ODA results • Add ARGO salinity • Improve climatological T-S relationships and synthetic salinity formulation • ENVISAT data? • Improve use of surface observations • Vertical correlations (mixed layer) • Situation-dependent error covariances (recursive filter formulation) • Investigate advanced ODA techniques • Experimental Ensemble Data Assimilation system (with GFDL) • Reduced Kalman filtering (with JPL) • Improved observation representativeness errors (with Bob Miller, OSU-JCSDA) • Impact of the GODAS mixed layer analysis on subseasonal forecasting with the CFS. Augustin Vintzileos (EMC) D. Behringer

20. SST 20oC DynHt U KF 3DVAR - A 20oC DynHt U SST KF 3DVAR - B Comparison of GODAS/KF and GODAS/3DVAR with TAO temperature and zonal velocity anomalies Re = [model explained variance] / [data variance] For points toward the top (GKF) and toward the right (G3DV) the models are closer to the data. For points above (below) the diagonal GKF (G3DV) is closer to the data. in collaboration with I Fukumori (JPL)

21. Sea Ice Analysis from CFSRR R. Grumbine

22. Atmospheric Model • Improve CFS climatology and predictive skill with improved physical parameterizations • Deep and/or shallow convection • Cloud/radiation/aerosol interaction and feedback • Boundary layer processes • Orographic forcing • Gravity wave drag • Stochastic forcing • Cryosphere

23. Shallow Cloud Development H.-L. Pan and J. Han • Use a bulk mass-flux parameterization • Based on the simplified Arakawa-Shubert (SAS) deep convection scheme, which is being operationally used in the NCEP GFS model • Separation of deep and shallow convection is determined by cloud depth (currently 150 mb) • Main difference between deep and shallow convection is specification of entrainment and detrainment rates • Only precipitating updraft in shallow convection scheme is considered; downdraft is ignored

24. Development based on LES studies Siebesma & Cuijpers (1995, JAS) Siebesma et al. (2003, JAS) LES studies

25. Combined Impact of Revised PBL & New Shallow Convection For CFS ISCCP Control Revised PBL & new shallow convection Cloud cover improved J. Han

26. Revised PBL + New shallow (Winter 2007) 500 hPa Height Anomaly Correlation NH(20N-80N) SH(20S-80S) Skill scores are better (1)

27. CONUS Precipitation skill score Winter 2007 12-36 hrs 36-60 hrs 60-84 hrs Skill scores are better (2)

28. Revised PBL + New shallow (Summer 2005) 500 hPa Height Anomaly Correlation NH(20N-80N) SH(20S-80S) Skill scores are better (3)

29. CONUS Precipitation skill score Summer 2005 12-36 hrs 36-60 hrs 60-84 hrs Skill scores are possibly better (4)

30. ENSO Signal Observed SST Anomaly Nino 3.4 OIV2 Control SST Anomaly 50 year CMIP Run ENSO too weak (early) Too strong later RESULT: no implementation for Weather or climate

31. Downward Shortwave Radiation at Ground 2S-2N Annual Mean 50 Year Run Year 1-20 Control Year 21-50 Year 1-20 Experiment Year 21-50 Can be Improved With Shallow-Deep Cloud Tuning Clouds Too Thick in SE Pacific (DSWR too small) Observed Observed DSWR from Visiting Scientist (Mechoso – UCLA, CPPA sponsored through VOCALS)

32. Five-member ensembles driven by Climatological SST forcing (1983-2002 avg) Phase (local time) of Maximum Precipitation (24-hour cycle) Myong-In Lee and Sieg Schubert (NASA/GMAO)

33. Impact of Diurnal SST (Xu Li)

34. T254 T126 T62 GDAS CDAS-2 RMS Error Growth Tropical Intraseasonal Forecasts (MJO) Resolution does not affect skill. Forecasts initialized by GDAS are better (a gain of ~3-5 days). Time evolution of mean energy at wave numbers 10-40 when CFS is initialized by R2 (red) or by GDAS (blue). Pattern Correlation drift A. Vintzileos

35. Ongoing Reanalysis Project • CFS will be upgraded every ~7 years • New forecast system • Upgrades from operations • New techniques • Higher resolution analysis • Aerosol and trace gas analysis • Carbon cycle • Hydrology, ground water, etc. • New observations from data mining • Satellite data treatment (e.g. bias correction) • Evolution to Integrated Earth System Analysis • Ongoing work to incorporate these improvements • Preparation for Reanalysis production phase • All additions carefully tested

36. Proposed Concept of Operations Development Phase 3-4 years Production Phase 2-3 years

37. Future Model Component Upgrades • Comprehensive Testing in Weather and Climate Modes • Daily data assimilation and 15 day forecasts • LDAS for balanced land states • CMIP runs (> 50 years) • Sample seasonal runs (May & October)

38. Multi-Model Ensemble Strategy • International MME (IMME) with EUROSIP is under negotiation • Operational delivery • Consolidated products • Use for official duty only • Full set of hindcasts required for bias correction and skill masking • National MME • COLA is generating hindcasts for NCAR system • Issues are • developing concept of operations (how partners will participate) • identifying metrics for value added (e.g. consolidation) • building computing resources (particularly for reforecasts) into computer acquisitions

39. IMME Status (1) • Goal: produce operational ensemble products from CFS and EUROSIP seasonal climate products • EUROSIP • ECMWF • Met Office • Meteo France • Prospectus has been submitted to EUROSIP Counsel • Covers • Licensing • Commercial interest and revenue sharing • Consistent with EUROSIP general provisions • Formal Memorandum of Understanding will be drafted

40. IMME Status (2) • Some tenets of a potential agreement • E-partners and NCEP will be free to process individual forecasts into combined IMME products with their own procedures • NCEP will distribute its combined IMME product to its internal users for official duty use in time to meet NCEP forecast schedules • NCEP will distribute its combined products to the E-Partners as soon as possible each month, using ECMWF as the distributing agent • NCEP and E-partners will coordinate distribution of IMME products to their users on a regular monthly schedule • Product delivery will not compromise any organization’s operational delivery schedules and commitments • NCEP wishes to join the EUROSIP Steering Group as a non-voting member and will participate in future meetings

41. Weather-Climate Forecasting

42. NCEP Production Suite Weather, Ocean, Land & Climate Forecast Systems Current - 2007 Current (2007) GDAS NAM anal GFS anal GFS SREF HUR NAM GENS/NAEFS RDAS AQ RTOFS CFS

43. Global Model SuiteDaily to S/I Forecasts • All forecasts are Atmosphere-Land-Ocean coupled • All systems are ensemble-based except daily, high-resolution run • All forecasts initialized with LDAS, GODAS, GSI from GFS initial conditions • Physics and dynamics packages may vary • Anticipated that the weekly forecast will have most rapid implementations and code changes, seasonal configuration may be one (or at most two) versions behind weekly

44. Reforecast GENS/NAEFS HENS HUR SREF RTOFS NAM RTOFS GFS WAV AQ Hydro / NIDIS/FF AQ CFS & MFS & GODAS RDAS GDAS NCEP Production Suite Weather, Ocean, Land & Climate Forecast Systems Next Generation Prototype Rap Refresh CFS MFS Global Regional CFS & MFS Hydro

45. Summary • CFSRR  CFS-v2 • High resolution reanalysis • CO2 trend • Upgrades models and data assimilation • Foundation for coupled “earth-system” reanalysis • Beginning scientific development of CFS-v3 • Fully coupled A-O-L-S system for IESA • Advanced data assimilation techniques • Building a MME system with International and US contributions • Focusing on Weather-Climate forecasting • 1 day to 3 years

46. ThanksQuestions?

47. Comparison of GODAS/M4 and GODAS/M3 with TAO temperature and zonal velocity In the thermocline both GM4 and GM3 are warm at 140w, while GM4 is warm and GM3 is cold at 110w. The undercurrent is stronger than observed in GM4 and weaker in GM3. The vertical structure at 165e is better in GM4 than in GM3.

48. Land Information System (LIS) • NOAA-NASA-USAF collaboration • K. Mitchell (NOAA) • C. Peters-Lidard (NASA) • J. Eylander (USAF) • LIS hosts • Land surface models • Land surface data assimilation and provides • Regional or global land surface conditions for use in • Coupled NWP models • Stand-alone land surface applications K. Mitchell, C. Peters-Lidard

49. Science Plan for the CFS (II) • Most effective way to improve the CFS (climate) GFS/CFS (weather) as one package • We want to improve weather and climate forecasts by making physically based improvements to the atmospheric model parameterization packages. • We have been successful when we apply rigorous tests to physically based parameterization improvements to both weather and climate models and want to continue along this way.

50. Science plan for the CFS (III) • Deep and/or shallow convection These processes transport sub-grid scale heat and moisture vertically, which is especially important for climate prediction. • Boundary layer processes As the CFS is a coupled model, the boundary layer is critical for communication of the ocean and land conditions with the atmosphere. • Cloud/radiation/aerosol interaction and feedback Clouds and aerosols modulate the sources and sinks of the thermal energy in to the earth system. This interaction is crucial on climate time scales. • Orographic forcing Orography determines many climate variables through form-drag, mountain blocking, and land/sea contrast.