Land Surface Fluxes in Coupled Land/Atmosphere Analysis Systems

Land Surface Fluxes in Coupled Land/Atmosphere Analysis Systems

Michael Bosilovich, NASA GSFC

And Collaborators

LandFlux Workshop, May 2007

Main Discussion Points

Surface Temperature AssimilationCoupled Analysis of Skin TemperatureValidation: Station Obs, CEOP Fluxes

MERRASurface data products, usefulness for LandFlux

Multi-Model Analysis for CEOP (MAC)7 operational analyses or reanalysesGoal: uncertainty in the physical processes


Ts in Coupled Analysis

Motivation: surface skin temperature (Ts) is a critical state because

it reflects the surface radiative properties and energy budget and can dictate convective initiation.

Reliable Ts field from the operational GMAO DAS (Global Modeling and Assimilation Office Data Assimilation System) is a key requirement from scientific instrument team users.

Method: NCAR Community Land Model (CLM) version 2 land-sfc

model (Dai et al. 2002; Zeng et al. 2002; Bonan et al. 2002) and GEOS4 DAS (Bloom et al. 2005).

ISCCP 3 hourly, 30Km Skin Temperature Ts analysis and coupled bias correction


Data Assimilation Method

PSAS–Analysis Increment (Cohn et al 1998)Incremental Bias Correction – Dee and da

Silva (1998)IBC expanded to consider Diurnal CycleInclude Incremental forcing at every time

step

BGTLETHTRRRRt

TC SSSSLLSS

S )()()(

Bosilovich et al. (2007, JMSJ)


2m Air Temp, Mean BiasJuly 2001


2m Diurnal Temp RangeJuly 2001


LBA Fluxes: CEOP EOP1

a b

c d

e f

CTL Rondonia

CTL Manaus

EXP2 Rondonia

EXP2 Manaus


BALTEX Fluxes: CEOP EOP1

CTL Lindenberg

CTL Cabauw

EXP2 Lindenberg

EXP2 Cabauw

l k

j i


Coupled Ts Analysis Summary

Including Skin T analysis (bias correction) improved air temperature, and in limited comparisons sensible heat flux

Several (possibly systematic) degradations in Latent Heating were noted

Needs diurnally resolved Ts, and likely multivariate analysis (soil moisture, cloud)

This method was tested in GEOS4, but does not directly carry over to GEOS5


MERRA

Modern Era Retrospective-analysis for Research and Applications

GEOS5 – NSIPP GCM Physics, Semi-Lagrangian dynamical core, GSI analysis

Catchment Land surface model(Koster et al)1979-2008 (Possibly longer)½°×⅔° spatial resolution (72 vertical levels)No Land Data Assimilation


Incremental Analysis Update

IAU reduces Spin Down/Up features, allowing hourly output (and analysis tendencies in output)


Atmospheric Water Budget

QIAU – Incremental Analysis Update of 3 Dimensional Water Vapor

Provides an estimate of error/uncertainty in the background modeling

Systematic component of QIAU can be related back to E, P (multiple regression, Schubert and Chang, 1996)

IAUQqVPEt

q


MERRA Surface Diagnostics

Two Dimensional data will be at 1 hourly frequencies

Surface MeteorologyVertical IntegralsRadiation (sfc, TOA, clear sky, all sky)Fluxes and transfer coefficientsLand data (not including lakes/coasts)Lowest Model level forcing


SGP Elk Falls – JUL 2004

GEOS5 Beta 9 Experiment

July 2004


SGP Lamont - JUL2004

Underestimate of LE at Lamont (central facility)


MODIS LST Day


MODIS LST Night


Station temperature comparison

2Degree experimentNCDC Summary of

the DayJuly 2001


Global Precipitation


Dec 2005 03ZDec 15-31 Average


Dec 2005 06Z


Dec 2005 09Z


Dec 2005 12Z


Dec 2005 15Z


Dec 2005 18Z


Dec 2005 21Z


Dec 2005 00Z


Dec 2005 03Z


GEOS5 Hourly Evaporation


Multi-Model Analysis for CEOP

Seven analysis data sets have been contributed to CEOPNCEP, ECPC, CPTEC, MSC, UKMO, JMA, BMRC and GMAO

We will pull together like variables form all the systems into a superensemble with mean and variance

We want to define the range of uncertainty in the physical aspects of the analyses, e.g. surface fluxes and radiation


Ensemble Characteristics

CEOP EOP 3 and 4 (2003 and 2004)Monthly averages to start, then daily and

diurnal cycle Regrid to 1.25° × 1.25°For Monthly, provide the individual members

contribution to the ensemble as well (might be too much at daily frequencies)


2D Surface Variables

Description Units BMRC CPTEC ECPCRII ECPCSFM GMAO JMA MSC NCEP UKMOSurface Pressure Pa × × × × × × × × ×Mean Sea Level Pressure Pa × × × × × ×Surface Air Temperature K × × × × × × × × ×Surface Air Moisture kg kg-1 × × × × × × × ×Surface Eastward Wind m s-1 × × × × × × × ×Surface Northward Wind m s-1 × × × × × × × ×Precipitation kg m-2 s-1 × × × × × × × × ×Convective Precipitation kg m-2 s-1 × × ×Surface Runoff kg m-2 × × × × × ×Liquid equivalent snow depth kg m-2 × × × × × ×Latent Heat Flux W m-2 × × × × × × × ×Sensible Heat Flux W m-2 × × × × × × × ×Surface Incoming Shortwave W m-2 × × × × × × × × ×Surface Incoming Longwave W m-2 × × × × × × × × ×Surface Reflected Shortwave W m-2 × × × × × × × × ×Surface Outgoing Longwave W m-2 × × × × × × × × ×Surface Skin Temperature W m-2 × × × × × ×TOA Longwave Outgoing W m-2 × × × × × × × ×TOA Shortwave Incoming W m-2 × × × × × ×TOA Shortwave Outgoing W m-2 × × × × ×Total Cloud Cover (0-1) × × × × × × × × ×Total Column Water Vapor kg m-2 × × × × × × ×Total Column Condensed Water kg m-2 × × × ×

Also, H, Q, T, U, V at 850, 700, 500, 300 and 200 mb


Zonal Precipitation

JJA(land only)


Zonal Latent Heat

JJA(land only)

JJA(GSSTF obs. only)

JJA(land only)

JJA(GSSTF obs. only)


Bondville LH Time Series


Bondville SH Time Series


Precipitation Anomalies


Multi-model Analysis Summary

7 data sets downloaded and being ensembled in version 1 (GMAO and BMRC near to providing data)

White paper describing the ensemble methods and decisions available for comment

Could provide a sense of the model variability in surface fluxes

Documents

Land Surface Fluxes in Coupled Land/Atmosphere Analysis Systems