Introduction to Climate forecast System Version 2 (CFSV2) – AM, OM, LM, Sea-ice– GODAS and GLDAS

1. Introduction to Climate forecast System Version 2 (CFSV2) – AM, OM, LM, Sea-ice– GODAS and GLDAS Shrinivas Moorthi Acknowledgement; Many of the slides presented here are prepared by members of GCWMB branch and climate and land modeling teams. Shrinivas Moorthi

2. Disk management at the NCEP Super computers for developers The disk partitioning for development use at NCEP: /u/home - small individual user space meant for keeping small files such .bashrc, .profile, and some small scripts etc /global/save directory where source code, scripts and any important and hard to replace data. This disk is backed up daily. /global/noscrub - Bigger chunk of disk where files from forecasts can be saved for some time until archived – not backed up /ptmp - large temporary space where one can run model and the disk is scrubbed often based on use /stmp - large disk space which can also be used for running jobs, but scrubbed daily. Shrinivas Moorthi

3. Seasonal to Interannual Prediction at NCEP (CFS-v1) Operational August 2004 – March 2011 Ocean Model MOMv3 quasi-global 1ox1o (1/3o in tropics) 40 levels Climate Forecast System (CFS) Atmospheric Model GFS (2003) T62 (~200 km) 64 sigma levels “Weather & Climate” Model Daily Coupling Reanalysis-2 3DVAR T62L28 (1995 GFS) 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 Shrinivas Moorthi Stand-alone version with a 14-day lag updated routinely

4. 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) Shrinivas Moorthi

5. CFSRR Components • Reanalysis • 31-year period (1979-2010 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 • 29-year period (1982-2010 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 Shrinivas Moorthi

6. 6hr Atmospheric Model GFS (2008)* T382 64 levels GDAS GSI 24hr 6hr Land Model Ice Model GLDAS LIS Ice Ext Ocean Model MOMv4 fully global 1/2ox1/2o (1/4o in tropics) 40 levels 6hr GODAS 3DVAR CFSRR Climate Forecast System V2 Shrinivas Moorthi

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8. AM in CFSR • 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 • Use of historical and spatially varying CO2 and volcanic aerosols Shrinivas Moorthi

9. Why Enthalpy as a prognostic variable? Collaboration between Space Weather Prediction Center and EMC to develop whole atmosphere model (0-600km) coupled to global ionosphere plasmasphere model - to help predict potential communication and electrical grid disruption due to solar flares 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

10. The thermodynamic equation used in the operational GFS AM (sigma/p hybrid) 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

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

12. 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

13. AM configuration for CDAS(operational climate data assimilation system) • Operational CDAS associated with CFSV2 was implemented on March 30, 2011 • The vertical coordinate was changed from generalized coordinate of CFSR to sigma-pressure hybrid coordinate of operational GFS. • The vertical advection of tracers is based on the TVD scheme • Latest version of operational GSI is also used • Convective gravity wave drag and the changes related to marine stratus are retained • Other changes made following the current operational GFS are: Shrinivas Moorthi

14. AM configuration For CDAS • Resolution and ESMF • Eulerian T574L64 for fcst (0-9hr) • ESMF 3.1.0rp2 • Radiation and cloud • RRTM2 for Short Wave Radiation • RRTM1 Long Wave Radiation with hourly computation • Stratospheric aerosol SW and LW and tropospheric aerosol LW • Changing aerosol SW single scattering albedo from 0.90 in the operation to 0.99 • Changing SW aerosol asymmetry factor. Using new aerosol climatology. • Maximum/random cloud overlap • Time and spatially varying CO2 • Yang et al. (2008) scheme to treat the dependence of direct-beam surface albedo on solar zenith angle over snow-free land surface Shrinivas Moorthi

15. AM Configuration for CDAS • Gravity-Wave Drag Parameterization • Modified GWD routine to automatically scale mountain block and GWD stress with resolution. • Compared to the T382L64 GFS, the T574L64 GFS uses four times stronger mountain block and one half the strength of GWD. • Removal of negative water vapor • Using a positive-definite tracer transport scheme in the vertical to replace the operational central-differencing scheme to eliminate computationally-induced negative tracers. • Changing GSI factqmin and factqmax parameters to reduce negative water vapor and supersaturation points from analysis step. • Modifying cloud physics to limit the borrowing of water vapor that is used to fill negative cloud water to the maximum amount of available water vapor so as to prevent the model from producing negative water vapor. • Changing the minimum value of specific humidity in radiation in radiation calculation from 1.0e-5 in the operation to 1.0e-7 kg/kg. Shrinivas Moorthi

16. AM configuration For CDAS • Hurricane relocation • Running hurricane relocation at the 1760x880 forecast grid instead of the 1152x576 analysis grid • Posting GDAS pgb files first on Guassian grid (1760x880), then convert to 0.5-deg for hurricane relocation. • Post processing and Utility • Posting GFS forecast master pgb files on 0.5 deg, then copygb to 1-deg for postprocessing and archive. • Using a 20-bit and faster copygb instead of the operational 16-bit copygb • Using a new chgres which has double precision and has a fix in dry air mass (pdryini2=0) • Snow analysis • Using T574 compatible high-resolution snow analysis Shrinivas Moorthi

17. 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 Shrinivas Moorthi

18. Global Land Data Assimilation System (GLDAS) •GLDAS (running Noah LSM under NASA/Land Information System) forced with CFSv2/GDAS atmospheric data assimilation output and blended precipitation in a semi-coupled mode, versus no GLDAS in CFSv1, where CFSv2/GLDAS ingested into CFSv2/GDAS once every 24-hours. • In CFSv2/GLDAS, blended precipitation a function of satellite (CMAP; heaviest weight in tropics), surface gauge (heaviest in middle latitudes) and GDAS (modeled; high latitude), vs use of model precipitation comparison with CMAP product and corresponding adjustment to soil moisture in CFSv1. • Snow cycled in CFSv2/GLDAS if model within 0.5x to 2.0x of the observed value (IMS snow cover, and AFWA snow depth products), else adjusted to 0.5 or 2.0 of observed value. GDAS-CMAP precip Gauge locations IMS snow cover AFWA snow depth Shrinivas Moorthi

19. Land Information System Land Surface Characteristics Topography Land Cover Soil CFSR land analysis Soil Moisture Soil Temperature Snow Noah LSM Precipitation Forcing Non-precip Meteorological Forcing CFSR surface file gdas1.t00z.sfcanl Land Variables Soil Moisture Soil Temperature Snow Shrinivas Moorthi 19 Christa Peters-Lidard et al., NASA/GSFC/HSB

20. LAND SURFACE MODEL • 2 soil layers (10, 190 cm) • No frozen soil physics • Only one snowpack state (SWE) • 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 • 4 soil layers (10, 30, 60, 100 cm) • Frozen soil physics included • Add glacial ice treatment • Two snowpack states (SWE, density) • Surface fluxes weighted by • snow cover fraction • Improved seasonal cycle of vegetation • Spatially varying root depth • Runoff and infiltration account for sub-grid variability in precipitation & soil moisture • Improved thermal conduction in soil/snow • Higher canopy resistance • Improved evaporation treatment over bare soil and snowpack Shrinivas Moorthi

21. CFSR Soil Moisture Climatology Shrinivas Moorthi 21

22. CFSR Soil Moisture Climatology Shrinivas Moorthi 22

23. GODAS in the CFSRR • Operational in 2011 • 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 to the higher resolution MOMv4 and be available for comparison with the coupled version • Updated with new techniques and observations Shrinivas Moorthi D. Behringer

24. MOM4p0d Version The ocean is modeled with GFDL’s Modular Ocean Model Version 4.0d (MOM4p0d) The code has been rewritten from earlier versions and is now in Fortran 90. MOM4p0d supports 2-dimensional domain decomposition for improved efficiency in parallel environments as compared with earlier versions. MOM4p0d supports the Murray (1996) tripolar grid, providing an elegant solution to the problems associated with the convergence of a spherical coordinate grid in the Arctic. Domain and Resolution The domain is global (the previous version did not have an interactive Arctic Ocean). The grid is Arakawa B and the resolution is 1/2ox1/2o (1/4o within 10o of the equator). The vertical grid has 40 Z-levels with variable resolution (23 levels in the top 230 meters). Physics There is a fully interactive ice model. The equation of state is the McDougall et al. (2002) formulation. The non-local boundary layer parameterization, KPP, of Large et al. (1994) is used. Isoneutral lateral diffusion is used (Griffies et al., 1998) The formulation is Boussinesq and has a free surface. Shrinivas Moorthi 24

25. GODAS – 3DVAR Version The Global Ocean Data Assimilation System (GODAS) is now based on MOM4p0d As was the case with MOM4p0d, the code has been completely rewritten from Fortran 77 to Fortran 90. Domain and Resolution The GODAS now has a global domain. The resolution has been increased to match the MOM4p0d configuration used in the CFSv2: 1/2ox1/2o (1/4o within 10o of the equator); 40 Z-levels. Functionality The analysis core of the GODAS (i.e. the 3DVAR) may be compiled either as an executable combing the analysis with MOM or an as executable containing only the analysis. The latter formulation is used with the CFSv2 where it reads the forecast from a restart file produced by the coupled CFSv2, does the analysis, and updates the restart file. An additional relaxation of surface temperature and salinity to observed fields is also under the control of the 3DVAR analysis. Data The data sets that can be assimilated are XBTs, tropical moorings (TAO, TRITON, PIRATA, RAMA), Argo floats, CTDs), altimetry (JASON-x). Shrinivas Moorthi 25

26. 6hr Atmospheric Model GFS (2008) T126 64 levels GDAS GSI 24hr 6hr Land Model Ice Mdl SIS LDAS Ice Ext Ocean Model MOMv4 fully global 1/2ox1/2o (1/4o in tropics) 40 levels 6hr GODAS 3DVAR GODAS in CFSv2 Climate Forecast System coupled in memory each 30 min Shrinivas Moorthi

27. MOMv4 Global Tripolar Grid 2 Arctic poles reside in landmass Higher resolution in equatorial zone The resolution is 1/2o X 1/2o increasing to 1/2o X 1/4o within 10o of the equator (resolution reduced 4X for display) Shrinivas Moorthi

28. Tripolar grid (Murray, 1996) 2 Arctic poles reside in landmass Arctic grid matches spherical coordinate grid at 65oN Shrinivas Moorthi After Griffies, 2007

29. 1980 1990 2000 The observing system XBT TAO TP/J-1 Argo Shrinivas Moorthi

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31. The changing number of temperature observations as a function of time and depth Shrinivas Moorthi

32. Sample annual distributions of T(z) as used by GODAS XBT-greenTAO-redArgo-blue Shrinivas Moorthi

33. The changing distribution of observations • Mostly XBTs (green) from fisheries, research cruises and shipping lines • Far more in Northern Hemisphere than in Southern Hemisphere • High concentration along coasts • Only a few tropical moorings (red) • About 60K profiles in 1985 • Argo float profiles (blue) now provide nearly full global coverage • Far more uniform distribution (>3200 floats, 120K profiles) • Moorings span the Pacific (TAO/TRITON), the Atlantic (PIRATA) and Indian Oceans (RAMA). (>100, ~36K profiles) • Fewer XBTs than in earlier decades (~30K profiles) Shrinivas Moorthi

34. International Argo deployments in 2000 GODAS assimilates all Argo and proto-Argo profiles. Shrinivas Moorthi

35. International Argo deployments as of October 31, 2007 Full Deployment GODAS assimilates all Argo and proto-Argo profiles. Shrinivas Moorthi

36. Commonality among versions of GODAS • during El Nino - La Nina shift of ‘97-’98 • All assimilate same data, incl. TAO • Altimetry withheld from these runs Two forced by NCEP-DOE R2, but use different models: MOMv3 vs. MOMv4 Two use the same model: MOMv4, but use different forcing: R2 vs. CFSR Two use the same model: MOMv4 and forcing: CFSR, but are uncoupled vs. coupled The solutions are most alike where there are data and differ most in the absence of data. Shrinivas Moorthi

37. Commonality among versions of GODAS • during El Nino - La Nina shift of ‘97-’98 • Velocity data are not assimilated • Altimetry withheld from these runs Two forced by NCEP-DOE R2, but use different models: MOMv3 vs. MOMv4 Two use the same model: MOMv4, but use different forcing: R2 vs. CFSR Two use the same model: MOMv4 and forcing: CFSR, but are uncoupled vs. coupled Solutions show greater differences in currents than in temperature. Forced MOMv4 solutions are most similar. Similarity between the MOMv3 analysis and the CFSR is coincidental. Shrinivas Moorthi

38. Commonality among versions of GODAS • during El Nino - La Nina shift of ‘97-’98 • Altimetry withheld from these runs Two forced by NCEP-DOE R2, but use different models: MOMv3 vs. MOMv4 Two use the same model: MOMv4, but use different forcing: R2 vs. CFSR Two use the same model: MOMv4 and forcing: CFSR, but are uncoupled vs. coupled Strong similarities among the model runs and the observations. The CFSR is weakest in the cold tongue area. The positive signal at 20oN in the uncoupled runs is present in the CFSR and TOPEX, but too weak to be seen at 10cm interval. Shrinivas Moorthi

39. GODAS compared with surface drifter derived SST MOMv4 based GODAS 1/2o resolution Global MOMv3 based GODAS 1o resolution Quasi-global AOML surface drifter based SST climatology Independent data (Lumpkin et al.) Shrinivas Moorthi

40. GODAS compared with independent surface drifter velocities MOMv4 based GODAS 1/2o resolution Global GODAS has eastward flow on and north of equator in Indian Ocean MOMv3 based GODAS 1o resolution Quasi-global Drifters show stronger flow in western boundary and Southern Ocean AOML surface drifter based climatology Independent Lumpkin et al. The agreement is very good given that GODAS does not directly assimilate velocity observations and the drifter velocities are derived from the lagrangian motion of the drifters. Shrinivas Moorthi

41. GODAS compared with tide gauges and TOPEX/Jason-1 Shrinivas Moorthi For these experiments tide gauges and TOPEX/Jason-1 are independent

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45. Equatorial salinity section in the Pacific (vertical bars show positions of time-series below). Assimilating Argo Salinity GODAS Salinity variability due to correlation with temperature. GODAS-A/S Salinity variability introduced by observations. Shrinivas Moorthi

46. In the east, assimilating Argo salinity reduces the bias at the surface and sharpens the profile below the thermocline at 110oW. 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. Assimilating Argo Salinity Comparison with independent ADCP currents. ADCP GODASGODAS-A/S Shrinivas Moorthi

47. INCOIS – NCEP Collaboration Co-Principal Investigators: M. Ravichandran (INCOIS), D.Behringer (NCEP) The collaboration was established in November of 2009 for the purpose of transferring a copy of NCEP’s Global Ocean Data Assimilation System (GODAS) to INCOIS. The GODAS will provide INCOIS with a real-time analysis of the physical state of the Indian Ocean through the assimilation of data sets from a variety of platforms (ships, moorings, autonomous drifting buoys, satellite). In return, NCEP will benefit from an ongoing expert evaluation of the GODAS performance in the Indian Ocean, leading to model and system improvements. GODAS code and sample forcing and assimilation data suitable for testing were transferred to INCOIS in January, 2010. INCOIS had GODAS up and running by the end of March and had finished a long experiment (2003 – present) by the end of April. INCOIS is currently exploring the sensitivity of the system to the wind forcing (NCEP vs QuikSCAT). Shrinivas Moorthi

48. SEA ICE Model in CFSV2 Xingren Wu EMC/NCEP and IMSG Shrinivas Moorthi

49. NSIDC Arctic sea ice hits record low in 2007 9/16/2007 Shrinivas Moorthi

50. Outline • Sea Ice • Sea Ice in the Weather and Climate System • Sea Ice in the NCEP Forecast System • - Analysis/Assimilation • - Forecast: GFS, CFS • Sea Ice in the CFS Reanalysis Shrinivas Moorthi