NCEP CFS Status and Future Plans (PowerPoint)

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					         Part 2
   CFS: Where It’s Going

S. Lord, H-L Pan, S. Saha, D. Behringer, K.

• 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
     Seasonal to Interannual Prediction at NCEP
   Operational System since August 2004 (CFS-v1)

         Ocean Model
                                   Climate              Atmospheric Model
                                   Forecast                GFS (2003)
                                   System                 T62 (~200 km)
      1ox1o (1/3o in tropics)
                                    (CFS)                   64 levels
            40 levels
                                     Daily                                     “Weather
                                    Coupling                                   & Climate”
            GODAS (2003)                               Reanalysis-2
                3DVAR                                    3DVAR
                  XBT                                    T62L28
                  TAO                                 OIv2 SST
                 Triton                            Levitus SSS clim.
                 Argo                 Ocean reanalysis (1980-present)
             Salinity (syn.)     provides initial conditions for retrospective
            TOPEX/Jason-1       CFS forecasts used for calibration and research
                                       Stand-alone version with a 14-day lag
Funded by NCPO/OCO                              updated routinely
Number of Temperature Observations per Month as a Function of Depth

                                                  D. Behringer   4
                 CFS-v2 Highlights
1. 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

2. Coupled data assimilation
    – Reduces “coupling shock”
    – Improves spin up character of the forecasts

3. Consistent analysis-reanalysis and forecast-reforecast for
    – Improved calibration and skill estimates

4. 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                                                               5
             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

• 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
   – “Unified weather-climate” strategy (1 day to 1 year)                  6
       CFSRR Component Upgrades
Component           Ops CFS                                2010 CFS
Atmosphere   1995 (R2) model              2008 model (upgrades to all physics)
             200 km/28 sigma levels       38 km/64 sigma-pressure levels
                                          Enthalpy-based thermodynamics
                                          Variable CO2 (historical data, future scenarios)
             R2 analysis                  GSI with simplified 4d-var (FOTO)
             Satellite retrievals         Radiances with bias-corrected spinup

Ocean        MOM-3                        MOM-4
             60N – 65 S                   Global domain
             1/3 x 1 deg.                 ¼ x ½ deg.
                                          Coupled sea ice forecast model

Ocean data   750 m depth                  2000 m

Land         No separate land property    Global Land Data Assim. Sys (GLDAS) driven
             analysis                     by observed precipitation
             1995 land model (2 levels)   2008 Noah model

Sea ice      Daily analysis               Daily hires analysis

Coupling     None                         Fully coupled background forecast (same as free
             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

                              Time                                    8
                                             S. Saha and S. Moorthi
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 9
   from their own climatologies)
                CFSRR at NCEP

              Climate Forecast System V2

GDAS    6hr      Atmospheric Model
 GSI                GFS (2007)
                   T382 64 levels
       24hr                                  6hr
LDAS          Land Model      Ice Model            Ice Ext
                     Ocean Model
                       MOMv4                 6hr
                      fully global                 3DVAR
               1/2ox1/2o (1/4o in tropics)
                       40 levels

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

 Noah LSM replaces OSU LSM in new CFS
 • Noah LSM                                 • OSU LSM
    – 4 soil layers (10, 30, 60, 100 cm)       – 2 soil layers (10, 190 cm)
    – Frozen soil physics included             – No frozen soil physics
    – Surface fluxes weighted by snow          – Surface fluxes not weighted by
       cover fraction                             snow fraction
    – Improved seasonal cycle of               – Vegetation fraction never less than
       vegetation cover                           50 percent
    – Spatially varying root depth             – Spatially constant root depth
    – Runoff and infiltration account for      – Runoff & infiltration do not account
       sub-grid variability in precipitation      for subgrid variability of
       & soil moisture                            precipitation & soil moisture
    – Improved soil & snow thermal             – Poor soil and snow thermal
       conductivity                               conductivity, especially for thin
                                                  snowpack and moist soils
    – Higher canopy resistance
    – More
              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
 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

• 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
                    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
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

 • 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                       15
           2009+ Land Surface Model
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

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
         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                       17
                                                         D. Behringer
In the west, assimilating                                    In the east, assimilating
Argo salinity corrects the                                   Argo salinity reduces
                              Assimilating Argo Salinity
bias at the surface and the                                  the bias at the surface
depth of the undercurrent                                    and sharpens the profile
                               Comparison with independent
core and captures the                                        below the thermocline
complex structure at                ADCP currents.           at 110oW.

                          ADCP      GODAS        GODAS-A/S
                                                             D. Behringer      18
         2009+ GODAS Activities
• Complete CFSRR
    – Evaluate ODA results
• Add ARGO salinity
• Improve climatological T-S relationships and synthetic salinity
• 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-
• Impact of the GODAS mixed layer analysis on subseasonal
  forecasting with the CFS. Augustin Vintzileos (EMC)

                                                             D. Behringer
          Comparison of GODAS/KF and GODAS/3DVAR
         with TAO temperature and zonal velocity anomalies
                  Re = [model explained variance] / [data variance]

               SST                  20oC                DynHt                      U

           3DVAR - A

               SST                  20oC                DynHt                      U

           3DVAR - B

     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)
Sea Ice Analysis from CFSRR

                      R. Grumbine
           Atmospheric Model
•   Improve CFS climatology and predictive skill
    with improved physical parameterizations
    – Deep and/or shallow convection
    – Cloud/radiation/aerosol interaction and
    – Boundary layer processes
    – Orographic forcing
    – Gravity wave drag
    – Stochastic forcing
    – Cryosphere                                22
     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           23
                  Development based on LES studies

Siebesma & Cuijpers
  (1995, JAS)

Siebesma et al.
  (2003, JAS)

LES studies

                      Impact of
  ISCCP           PBL & New Shallow
                       For CFS


Revised PBL &
                Cloud cover improved
new shallow
                        J. Han
Revised PBL + New shallow (Winter 2007)
      500 hPa Height Anomaly Correlation

NH(20N-80N)                             SH(20S-80S)

          Skill scores are better (1)
CONUS Precipitation skill score Winter 2007

12-36 hrs            36-60 hrs             60-84 hrs

             Skill scores are better (2)               27
Revised PBL + New shallow (Summer 2005)
      500 hPa Height Anomaly Correlation

 NH(20N-80N)                             SH(20S-80S)

           Skill scores are better (3)
CONUS Precipitation skill score Summer 2005

12-36 hrs              36-60 hrs                   60-84 hrs

            Skill scores are possibly better (4)               29
                           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

                 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
                                     With Shallow-Deep
                                       Cloud Tuning
                                                            Clouds Too
                                                            Thick in SE
            Observed                                     (DSWR too small)

        Observed DSWR from Visiting Scientist (Mechoso
        – UCLA, CPPA sponsored through VOCALS)                      31
Phase (local time) of Maximum Precipitation (24-hour cycle)
         Myong-In Lee and Sieg Schubert (NASA/GMAO)

Five-member ensembles driven by Climatological SST forcing (1983-2002 avg)
Impact of Diurnal SST (Xu Li)
           Figure.1 The difference between two SST analysis (GSI – OPR)
           GSI: Tr analysis + NSST model (7-day mean, Aug. 24~30, 2007)
           OPR: Reynolds weekly analysis (Aug. 30, 2007)

                 Figure.2 Impact of NSST model on GFS predictive skill (OCN – CTL).
                 January 2008 (31 x 4 samples)

                     850 hPa Wind Tropics
      AC x 100

                                                                  Forecast days
  RMS Error Growth        Tropical Intraseasonal Forecasts (MJO)

                      Resolution does not                        T254
                      affect skill.                              T126
                      Forecasts initialized
                      by GDAS are better
                      (a gain of ~3-5                             GDAS
                      days).                                      CDAS-2

                              Time evolution of mean energy at wave
                              numbers 10-40 when CFS is initialized by
Pattern Correlation           R2 (red) or by GDAS (blue).


                                                               A. Vintzileos
    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
               Proposed Concept of Operations
                            Reanalysis Development and Production



Effort (%)

             60%                                     3-4 years
             50%                                                          Development

                            Production                                    Producion

                            2-3 years


                    1   2         3      4     5        6     7   8   9
Future Model Component Upgrades
Component                2010 CFS                             Possible Upgrades
             - AER RRTM shortwave & longwave radiation   - Fractional cloudiness (impacts surface solar
Atmosphere   - Variable CO2 & aerosols                   flux)
             - Maximum random cloud overlap              - Possible neural network emulation for
             Enthalpy-based thermodynamics               radiation (trained on hindcasts)
                                                         - Sigma-pressure-theta hybrid

             - Prognostic cloud water                    - Ferrier microphysics (impacts radiation and
             - Non-local PBL                             precipitation type)
             - Simplified Arakawa-Schubert conv.         - Shallow convection (mass flux)
                                                         - Convective gravity wave

                                                         - Conservative, positive definite tracer advection

             - Global Land Data Assim. Sys (GLDAS)       - Dynamic vegetation (impacts drought)
Land         driven by observed precipitation            - Groundwater (impacts soil wetness)

             - MOM-4                                     -Ocean ensemble (HYCOM + MOM ?)
Ocean                                                    -Salinity assimilation
                                                         - Situation-dependent background errors and
                                                         other advanced techniques

             Comprehensive Testing in Weather and Climate Modes
             • Daily data assimilation and 15 day forecasts
             • LDAS for balanced land states
             • CMIP runs (> 50 years)                                                               37
             • Sample seasonal runs (May & October)
 Multi-Model Ensemble Strategy
• International MME (IMME) with EUROSIP is under
   – Operational delivery
   – Consolidated products
   – Use for official duty only
• Full set of hindcasts required for bias correction and skill
• National MME
   – COLA is generating hindcasts for NCAR system
   – Issues are
       • developing concept of operations (how partners will
       • identifying metrics for value added (e.g. consolidation)
       • building computing resources (particularly for reforecasts)
         into computer acquisitions
                IMME Status (1)
• Goal: produce operational ensemble products from CFS
  and EUROSIP seasonal climate products
   – 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

                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
   – NCEP will distribute its combined products to the E-Partners as
     soon as possible each month, using ECMWF as the distributing
   – 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

Weather-Climate Forecasting

                            NCEP Production Suite
                              NCEP Production Suite
                  Weather, Ocean, & Climate Forecast Systems
                 Weather, OceanLand & Climate Forecast Systems
                                                       Version 3.0 April 9, 2004

                                                   Current - 2007
                                                   Current (2007)                                 RUC
               100                                                                                FIREWX
                                      NAM anal                                                    WAVES

                                                             GFS anal

Percent Used

                                                                        GFS                       GFSfcst

                60                               NAM                                              GFSanal

                40                                                                                ETAfcst
                20                                     RTOFS                                      SREF
                                                          CFS                                     Air Quality
                 0                                                                                OCEAN
                     0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00
                                 6 Hour Cycle: Four Times/Day                                     Seasonal
                              Global Model Suite
                             Daily 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
 •             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

Forecast          Membership      Runs/day     Number of         Horizontal        Forecast    Initialization   Computing
Product          refresh period               members per     resolution (ratio,    Length      technique        Resource
                                             refresh period    current value)                                     ratio

Daily-hires         4x/day           4             1              1.0, T382        15 days         GSI             1.0

Weekly               daily           80           80              0.5, T170        15 days     ET breeding         2.5

Monthly             weekly           8            56              0.5, T170        60 days          ??             1.0

Seasonal           monthly           2            60             0.33, T126         1 year    Lagged analysis     0.44
                                                                                                 4x daily                43
                            NCEP Production Suite
                              NCEP Production Suite
                  Weather, Ocean, & Climate Forecast Systems
                 Weather, OceanLand & Climate Forecast Systems
                                                    Version 3.0 April 9, 2004

                                      Next Generation Prototype
                                                                                              Rap Refresh
               100                        GENS/NAEFS                    HENS

                80      Reforecast

Percent Used

                60       CFS                NAM                                               GFSanal
                          RTOFS                                                               GFSens
                40                                                              WAV           ETAfcst
                          AQ     Hydro / NIDIS/FF                         AQ                  Regional
                                             CFS & MFS
                                         CFS & MFS & GODAS                                    ETAanal
                20                        RDAS                                                SREF
                                                      GDAS                                    Air Quality
                 0                                                                            OCEAN
                     0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00
                                6 Hour Cycle: Four Times/Day                                         44
• 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

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.
  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
     • Coupled NWP models
     • Stand-alone land surface applications

                                       K. Mitchell, C. Peters-Lidard
  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.
    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
•   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.

  Science Plan for the CFS (IV)
• Gravity wave drag
  Gravity waves generated by the sub-grid scale orography and/or
  cumulus convection transport wave energy from the troposphere to
  the stratosphere and mesosphere and thus control the climate of
  those regions.
• Stochastic forcing
  Stochastic forcing is not in the CFS at this time, but is important for
  parameterizing random, unresolved physical forcing.
• Cryosphere
  The cryosphere (glaciers, frozen land, sea ice) plays a crucial role in
  determining the earth's climate. Modeling of sea-ice and its
  interaction with the ocean and atmosphere, and modeling frozen
  land and its interaction with the atmosphere are all important to

   Science Plan for the CFS (V)
• Testing procedures are key to the road to making model
• While transition to operation for MMEs requires only
  seasonal hindcasts to be evaluated, it is done because
  we expect the team maintaining the MME models to do
  their own rigorous tests.
• Tests in data assimilation modes and evaluated with
  forecasts are crucial for weather forecasts.
• Tests in multi-year coupled simulations and seasonal
  hindcasts are crucial for climate forecasts
• CTB computer resource is not sufficient and NCEP
  computer must be used when full-scale testing is needed

•   Insufficient EMC staff to collaborate with external investigators,
    train their staff (often post-docs) on use of the CFS, and develop
    new parameterization codes suitable for the CFS for the broad
    spectrum of possible areas listed above (O2R);
•   Insufficient computing resources for experimentation and
    transition changes to the CFS;
•   Insufficient EMC and NCEP Central Operations (NCO) staff to
    support the R2O (implementation) process;
•   Insufficient knowledge within the research community about the
    tests needed to complete an implementation

  We built a new shallow convection
      scheme a few years ago
• 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
             We build it based on LES studies

Siebesma & Cuijpers
  (1995, JAS)

Siebesma et al.
  (2003, JAS)

LES studies

Cloud water cross-section
looks better
PBL & Low clouds combined
(CFS run)



                            Cloud cover looks
      Revised PBL &
      new shallow
Revised PBL + New shallow (Winter, 2007)
      500 hPa Height Anomaly Correlation

NH(20N-80N)                           SH(20S-80S)

           Skill scores were better
    Precipitation skill score over US continent

12-36 hrs             36-60 hrs           60-84 hrs

              Skill scores were better                59
Revised PBL + New shallow (Summer, 2005)
       500 hPa Height Anomaly Correlation

 NH(20N-80N)                          SH(20S-80S)

           Skill scores were better
    Precipitation skill score over US continent

12-36 hrs             36-60 hrs                  60-84 hrs

             Skill scores were slightly better               61

Observed ENSO signal   62
 NINO3.4 set22

Multi-year simulation of the control looks ok
             NINO3.4 set28b

The test version showed too weak ENSO in early years and too strong   64
ENSO in later years. RESULTS : no implementation
                                                       Srb2 is observation

With a VOCALS grant from CPPA, Mechoso worked
with us to examine these runs. This is the downward
short wave radiation reaching ground for the control

There is too much radiation reaching ground for the
new package over western Pacific but too little over
central Pacific. More changes will have to be made.
Cloud water cross-section
looks better
  Climate Requirements for NCEP’s
   Next Operational System (2011)
Application        Operational System      Computing   Requirement Generator
                                            X factor
Seasonal-Monthly   CFS                        32       Climate Prediction Center
                   Monthly fcst system        10+              “

                   GLDAS, NLDAS               2.5*     NIDIS, CPC

                   Reforecast                  9*             “

• Ratio of ops:R2O computing                             + Extension of Week2 system
     – Currently 1:1.3                                   * New system

     – Requesting
                                  Climate (and other) computing requirements
          • 2011: 1:2.0              total a factor of 3X in additional funding
          • 2013: 1:3.0          (above Moore’s Law – constant $$ capability)      68
          • 2015: 1:4.0
NOAA Computing Resources and
 Operational Requirements for
 Climate Forecasting at NCEP

     • Research
       – Including CFSRR
     • Operations

             Climate R&D Computing
                 for Week2 to S/I
 NOAA R&D Computer through November 2007
                                                   November 2007 - June 2008

      30                  30

                                                                               CTB & CDEV



                                                Enables CFSRR to
                                               execute ¾ of required
                                                  production rate

                           June 2008+
New Power6 system for CTB, CDEV, JCSDA, MTB (same % as previous)                   70
                CFSRR will use all Power5 system

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