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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

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

outline of presentation
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
slide3
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

cfs improvement thrusts see also earlier presentation by suru saha this session
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
improving cfs land physics
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
slide7

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
land data assimilation systems
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
cfs land experiments 8 land experiments of cfs t126 with cfs noah and cfs osu

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)
how do gldas noah and gr2 osu land states compare

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.

illinois 2 meter soil moisture mm 1985 2004
Illinois 2-meter Soil Moisture [mm] 1985-2004

Total

Vtype 12

Climatology

Anomaly

summer 1999 wet u s monsoon vs 2000 dry u s monsoon

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)

slide19

Observed Monthly

Precipitation Anomaly

Left Column:

1999

Right Column:

2000

Top Row:

July

1999:

Wetter Monsoon

Bottom Row:

August

slide20
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!!

interannual difference 1999 minus 2000 july mean 2m temperature anomalies k 10 member ensemble mean

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.

interannual difference 1999 minus 2000 july mean 500 mb height anomalies m 10 member ensemble mean
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

winter 1983 el nino vs 1989 la nina

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)

interannual difference 1983 minus 1989 jan feb mar mean sst anomalies k
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.

interannual difference 1983 minus 1989 jan feb jf precipitation anomalies mm 8 member ensemble mean
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.

interannual difference 1983 minus 1989 jan feb jf 2m temperature anomalies k 8 member ensemble mean
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.

interannual difference 1983 minus 1989 jan feb jf 500 mb height anomalies m 8 member ensemble mean
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

conclusions
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