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Initialization of the Noah Land Surface Model and its Coupling to CFS

Initialization of the Noah Land Surface Model and its Coupling to CFS. Ken Mitchell, Rongqian Yang, Jesse Meng and EMC Land Team Environmental Modeling Center (EMC) National Centers for Environmental Prediction NOAA Annual Climate Diagnostics and Prediction Workshop Boulder, CO

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Initialization of the Noah Land Surface Model and its Coupling to CFS

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  1. Initialization of the Noah Land Surface Model and its Coupling to CFS Ken Mitchell, Rongqian Yang, Jesse Meng and EMC Land Team Environmental Modeling Center (EMC) National Centers for Environmental Prediction NOAA Annual Climate Diagnostics and Prediction Workshop Boulder, CO 23-27 October 2006 Much of this work sponsored by the CPPA program of the NOAA Climate Program Office

  2. Outline of Presentation • The Noah Land Surface Model (Noah LSM) • The Global Land Data Assimilation System with Noah LSM (GLDAS/Noah) • CFS summer forecasts (N.H. summer) • Impact of Noah LSM without GLDAS/Noah I.C.s • Impact of Noah LSM with GLDAS/Noah I.C.s • CFS winter forecasts (N.H. winter) • CONUS focus

  3. Dynamical Ensemble Hydrological PredictionThe Coupled and Uncoupled ApproachesA) Coupled B) Uncoupled precipitation Atmospheric Model (GFS) Bias-corrected Precipitation Forecasts (ensemble) Post Processor (Bias Correction) Precipitation Fluxes Land Surface Model (Noah) Land Surface Model (Noah) Runoff (ensemble) Runoff (ensemble) River Routing Model River Routing Model Stream Flow (ensemble) Stream Flow (ensemble) Post Processor Post processor Final Product Final Product This presentation is about improving the coupled land/atmosphere approach via the CFS

  4. CFS Improvement Thrusts(See also earlier presentation by Suru Saha this session) • Higher resolution • T126 vs T62 (about 1-deg vs. 2-deg) • Improved physics • Atmosphere • Ocean • Sea ice • Land • Improved initial analysis / data assimilation • Atmosphere • Ocean • Land • Stochastic forcing

  5. Improving CFS Land Physics • Current Ops CFS applies OSU LSM • OSU LSM: Oregon State University (late 1980’s) • H. Pan, L. Mahrt, M. Ek, J. Kim, P. Rusher, others • Next-Gen CFS in CTB applies Noah LSM • History of Noah LSM • Development led by EMC (1990s, 2000s) • Descendant of OSU LSM (but with many extensions) • Available as a community LSM from NCEP public server (1-d column model) • Key partners: • Federal: NWS/OHD, Air Force, NESDIS/ORA, NASA/HSB, NCAR/RAP, CPC • Universities: OSU, Princeton, Rutgers, U. Oklahoma, U. Arizona • Implementation History at NCEP: • Eta mesoscale model (Jan 1996) • Regional Reanalysis and R-CDAS (2004) • GFS global model (May 2005) • Also implemented in • EMC N. American Land Data Assimilation System (NLDAS) • EMC and NASA/HSB Global Land Data Assimilation System (GLDAS) • NASA/HSB Land Information System(LIS) • Air Force GLDAS (AGRMET) • WRF (via public repository at NCAR) and NCAR/RAP HR-LDAS

  6. Noah LSM versus OSU LSM in NCEP Global Model • 4 soil layers (10,30,60,100 cm) vs. 2 soil layers (10, 190 cm) • land surface evaporation:reduced high bias in warm-season • vegetation cover:improved properties and seasonality • improved seasonal cycle of green vegetation fraction • spatially varying root depth (1-2 m) vs. constant 2 m • add frozen soil physics (freeze/thaw latent heat, limit infiltration) • snowpack physics improvements:greatly reduced early melt bias • add snow density state variable (retain SWE) • retain some snowmelt in snowpack and allow refreezing • refine functions for snow cover fraction and snow albedo • add patchy snow cover treatments to • snow sublimation, sensible & ground heat flux, skin temp • improved numerics/robustness for very shallow snow • transpiration: refine soil moisture threshold for stress onset • direct soil evaporation: revise dependence on soil moisture • smaller ground heat flux bias • especially: wet soil, under snowpack, under dense vegetation • new functions for soil thermal diffusivity and soil heat capacity

  7. LAND DATA ASSIMILATION SYSTEMS: •Three Broad Approaches • 1) Coupled Land/Atmosphere 4DDA • precipitation forcing at land surface is from parent atmospheric model • Precipitation may have large bias: >large soil moisture bias • Soil moisture may be nudged to reduce impact of precipitation bias • Exp. 1: based on external soil moisture climatology: • NCEP/NCAR Global Reanalysis 1 • Exp. 2: based on model-minus-observed precip differences • NCEP/DOE Global Reanalysis 2 (GR2) • GR2 provides initial land states for ops CFS/OSU • 2)Uncoupled Land 4DDA (land model only) • observed precipitation used directly in land surface forcing • should execute same LSM on same grid & terrain as coupled model • Exp: EMC uncoupled GLDAS • GLDAS provides initial land states for CTB tests of CFS/Noah • 3)Hybrid Land 4DDA e.g.Regional Reanalysis • Coupled land/atmosphere, but: • observed precipitation is assimilated for driving the land surface

  8. CFS/Noah CFS/OSU ► Choice of LandInitial Conditions GLDAS/Noah GLDAS/Noah GLDAS/Noah Climatology GLDAS/Noah Climatology ► GR2/OSU GR2/OSU GR2/OSU Climatology GR2/OSU Climatology CFS Land Experiments (8)Land Experiments of CFS T126 with CFS/Noah and CFS/OSU Choice of Land Model Intended Ops ► Current Ops “GR2” denotes NCEP/DOE Global Reanalysis 2 • Experiment Goal: • 10 years x 2 seasons (winter/summer) x 10 members x 8 Experiments • Experiments Completed to date: • 2 years X 8-10 members x the 3 experiments denoted above by “►” • -- Summer: 1999 (wet U.S. monsoon), 2000 (dry U.S. monsoon) • -- Winter: 1983 (strong El’Nino), 1989 (significant La’Nina)

  9. How do GLDAS/Noah and GR2/OSU land states compare? See Session 1 Poster by Jesse Meng et al. Some examples shown next in which GLDAS is designated as “LIS” LIS denotes the Land Information System infrastructure for land data assimilation that EMC has transitioned to NCEP test beds via partnership with the LIS development group in the NASA/GSFC Hydrological Sciences Branch.

  10. 2-m total soil moisture [%]01 May Climatology LIS/Noah GR2/OSU

  11. GR2/OSU LIS/Noah Soil Moisture and Precip AnomaliesMay 1999

  12. Illinois 2-meter Soil Moisture [mm] 1985-2004 Total Vtype 12 Climatology Anomaly

  13. 2-m total soil moisture [%]01 May Climatology LIS/Noah GR2/OSU

  14. 2-m total soil moisture [%]01 May 1999 Anomaly LIS/Noah GR2/OSU

  15. 2-m total soil moisture [%]30 Dec Climatology LIS/Noah GR2/OSU

  16. 2-m total soil moisture [%]30 Dec 1982 Anomaly LIS/Noah GR2/OSU

  17. Summer:1999 (wet U.S. monsoon) vs. 2000 (dry U.S. monsoon) CFS/Noah/GLDAS vs. CFS/OSU/GR2 and CFS/Noah/GR2 10-members each (initialized from late April and early May)

  18. Observed Monthly Precipitation Anomaly Left Column: 1999 Right Column: 2000 Top Row: July 1999: Wetter Monsoon Bottom Row: August

  19. Interannual Difference: 1999-minus-2000July Total Precipitation Anomalies (mm)10-member Ensemble Mean T126 CFS / Noah / GLDAS T126 CFS / OSU / GR2 T126 CFS / Noah / GR2 CFS with Noah is superior: Provided Noah-consistent initial land states provided!!

  20. Verifying NARR Analysis Interannual Difference: 1999-minus-2000JULY mean 2m Temperature Anomalies (K)10-member Ensemble Mean T126 CFS / Noah / GLDAS T126 CFS / OSU / GR2 Both CFS/Noah and CFS/OSU have wrong sign in southwest and midwest, but amplitde of CFS/Noah error is substantially less than CFS/OSU.

  21. Interannual Difference: 1999-minus-2000July mean 500 mb Height Anomalies (m)10-member Ensemble Mean T126 CFS / Noah / GLDAS T126 CFS / OSU / GR2 Verifying NARR Analysis No clear advantage for either CFS/Noah/GLDAS or CFS/OSU/GR2

  22. Winter:1983 (El’Nino) vs. 1989 (La’Nina) CFS/Noah/GLDAS vs. CFS/OSU/GR2 8-members each (initialized from late November and early December)

  23. Interannual Difference: 1983-minus-1989Jan-Feb-Mar mean SST Anomalies (K) OBSERVED T126 CFS / OSU / GR2 Interannual difference in CFS predicted winter mean SST agreed well with observed.

  24. Interannual Difference: 1983-minus-1989Jan-Feb (JF) Precipitation Anomalies (mm)8-member Ensemble Mean T126 CFS / Noah / GLDAS T126 CFS / OSU / GR2 Verifying NARR Analysis CFS/Noah/GLDAS not much different from CFS/OSU/GR2. CFS/Noah/GLDAS does show some indication of some improvement around southern west coast.

  25. Interannual Difference: 1983-minus-1989Jan-Feb (JF) 2m Temperature Anomalies (K)8-member Ensemble Mean T126 CFS / Noah / GLDAS T126 CFS / OSU / GR2 Verifying NARR Analysis Neither CFS/Noah/GLDAS or CFS/OSU/GR2 shows any particular advantage over the other. CFS/OSU better in some regions, CFS/Noah better in other regions.

  26. Interannual Difference: 1983-minus-1989Jan-Feb (JF) 500 Mb Height Anomalies (m)8-member Ensemble Mean T126 CFS / Noah / GLDAS T126 CFS / OSU / GR2 Verifying NARR Analysis Both CFS/Noah and CFS/OSU have rather poor (albeit different) height anomaly pattern compared to observed. CFS/Noah shows some slight advantage along west coast and Southeast coast

  27. Conclusions • The Noah LSM exhibits a promising preliminary indication of improving CFS summer season forecasts of precipitation and 2m air temperature over CONUS • Provided Noah LSM compatible initial land states are provided by GLDAS/Noah • The Noah LSM does not appear to improve CFS winter season forecasts of precipitation and height fields • Much more follow-on work is needed • Finish 10-year CFS/Noah and CFS/OSU climatology • Assess other years and other regions besides CONUS • Examine entire Jun-Jul-Aug period, not just July • Investigate entire atmospheric and land water budget • atmospheric moisture convergence versus surface evaporation

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