A Status Report on the Second Global Soil Wetness Project GSWP-2 Paul Dirmeyer and Xiang Gao Center for Ocean-Land-Atmosphere Studies Calverton, Maryland, USA

Context GLASS GCSS GABLS (AMMA) GAPP CliC COPES

GCM inter-comparisons Single column model analyses Land-surface model intercom- parisons (in situ) Global gridded model analyses GEWEX Global Land- Atmosphere System Study

Overview  The Global Soil Wetness project (GSWP) is an element of the Global Land-Atmosphere System Study (GLASS) a study of the GEWEX Modeling and Prediction Panel (GMPP), both contributing projects of the Global Energy and Water Cycle Experiment (GEWEX).  GSWP is charged with producing large-scale data sets of soil moisture, temperature, runoff, and surface fluxes by integrating one- way offline land surface schemes (LSSs) using externally specified surface forcing and standardized soil and vegetation distributions.  GSWP-2 is closely linked to the ISLSCP Initiative II data effort, and LSS simulations in GSWP-2 encompass the same core ten- year period as ISLSCP Initiative II ( ) and are conducted at a spatial resolution of 1°. hydro.iis.u-tokyo.ac.edu/GLASS/

Motivation  Soil wetness is an important component of the global energy and water balance.  Reservoir for the land surface hydrological cycle  Boundary condition for atmosphere  Controls the partitioning of land surface heat fluxes  Affects the status of overlying vegetation  Modulates the thermal properties of soil  Essential for climate predictability on seasonal-annual time scales  Soil Wetness is unknown over most of the globe  Difficult to measure in situ  Remote sensing techniques are partially effective  Few long-term climatologies of any kind exist  The same paradox exists for snow mass, soil heat content, the vertical fluxes of water and heat between land and atmosphere.

 Submitted  Imminent  Probably  Maybe  Bowed out GSWP-2 Modeling Status MODELInstitute Bucket University of Tokyo CLM-TOPUniversity of Texas at Austin CBM/CHASMMacquarie University, Australia CLASSMeteorological Service of Canada CLMNASA GSFC/HSB COLA-SSiBCOLA ECMWF HY-SSiBNASA GSFC/CRB ISBAMétéoFrance/CNRM LAPUTAMeteorological Research Institute, Japan Meteorological Agency LaDUSGS & NOAA/GFDL MATSIROFrontier RSGC MECMWFKNMI (Dutch MetOffice), Netherlands MosaicNASA GSFC/HSB MOSES-2Met Office, UK NOAHNOAA NCEP/EMC NSIPP-CatchmentNASA GSFC/NSIPP (GMAO) ORCHIDEE IPSL, France SiBUCKyoto University SlandUniversity of Maryland SPONSORInstitute of Geography, Russian Academy of Sciences SWAPInstitute of Water Problems, Russian Academy of Sciences VICU. Arizona VISA, CLM-TopUniversity of Texas at Austin

Sensitivity Experiments  Computing and storage burdens are not trivial  Three suites of experiments  A: 15 May 2004  B: 31 August  C: 15 October ExpDescription N1 Native Parameters (if applicable) P1Hybrid ERA-40 precipitation (instead of NCEP/DOE) P2NCEP/DOE hybrid with GPCC corrected for gauge undercatch (no satellite data) P3NCEP/DOE hybrid with GPCC (no undercatch correction) P4NCEP/DOE precipitation (no observational data) P5NCEP/DOE hybrid with Xie daily gauge precipitation R1NCEP/DOE radiation RSNCEP/DOE shortwave only RLNCEP/DOE longwave only R2ERA-40 radiation M1All NCEP meteorological data (no hybridization with observational data) M2All ECMWF meteorological data (no hybridization with observational data) V1U.Maryland vegetation class data I1Climatological vegetation A A B B B C C C A R3 ISCCP radiation C PE Hybrid ERA-40 precip. ERA-40 precipitation (no observational data)

P1 Glitch P1 correction! - The precipitation files to use for P1 were listed incorrectly. The files listed were not hybrid ERA-40 precipitation. They were the original ERA-40 precipitation. We have added a new experiment PE to represent what we had intended originally in P1.  We ask everyone doing P2 and/or P3 to perform PE as part of the precipitation suite. If you have already submitted Suite B, we ask for PE to be submitted as part of Suite C, with the 15 October deadline.  If you have already submitted P1, we will use it. It would be especially useful, though, if you also do P4. That will give us a direct comparison between the original ERA-40 and NCEP/DOE precipitation.

R3 Added ISCCP radiationISCCP radiation (thanks to Yuanchong Zhang and Bill Rossow for providing us with this data). This is an observationally- based alternative to the SRB radiation used in the baseline simulation.  It does not have the problems at the month boundaries that SRB does  It uses a different set of retrieval and QC algorithms than SRB. You may wish to try this as an alternative to SRB or reanalysis radiation, but see the FAQ page for information on how the time averaging has been performed for this product (it is different than the other radiations). Please see the ISCCP web site for more information on this product.FAQ pageISCCP web site

Multi-Model Analysis  daily mean fluxes, state variables at 1° over land (excl. Antarctica)  Consider all available land models (~16)  Now investigating methods for compositing (can we do better than simple average?)  Target: complete product by end of 2004

Unbiased Forecast Variants  Let {X i (t), i=1,…M} denote an ensemble of soil wetness forecasts produced by M models at a fixed location; an arbitrary linear combination of these forecasts is given by: Regression-improved individual forecast R Regression-improved multimodel ensemble Mean forecast R EM Arithmetic Average C Regression-improved multimodel forecast R all Kharin, V. V., and F. W. Zweirs, 2002, J. Climate, 15,

Skill Score Comparison for C, R EM, and R all 18 years, deep layer(s), 6 models

Transferability  First step (½ Illinois to other ½ Illinois)  Individual models and simple compositing is unaffected.  More complex compositing shows a small loss in skill.  Real test – Illinois to China…

Remote Sensing Application  To develop and test large-scale validation and assimilation techniques over land, by coupling the land surface models with the “validated” state-of-the art L-band microwave emission model (L-MEB*) to simulate prognostic brightness temperature observable remotely from satellite microwave radiometers. Land Surface Model - 0~5cm soil moisture - 0~5cm soil T. - 50/100cm soil T. - Vegetation canopy T. - Canopy interception - LAI, Air T. - Landmask - Soil texture class (sand% and clay%) - Elevation - Vegetation type Microwave Emission Model Brightness Temperature [*acknowledgement: Jean-Pierre Wigneron (INRA), Thierry Pellarin (CNRM), and Jean-Christophe Calvet (CNRM)]

L-MEB Model Validation Data ExperimentLocationTime & Date VegetationSoilInstrument & platform ConfigurationImage Size (pixel size) Monsoon’90 Walnut Gulch Watershed, AZ (31°44.617’N, 110°3.083’W) 16 ~ 18 UTC, 7/31/1990 ~ 8/9/1990 Mixed grass- shrub rangeland Sandy Loams PBMR, NASA C-130 Aircraft Monoangular (  =8º) H pol- arization only 8 x 20 km 2 (180 m) Washita’92 Little Washita Watershed, OK (34°57.624’N, 97° ’W) 16 ~ 19 UTC, 6/10/1992 ~ 6/18/1992 Rangeland, Pasture Variable ESTAR, NASA C-130 aircraft Monoangular (  =0º) H pol- arization only 18 x 46 km 2 (200 m) SGP’97 Little Washita Watershed, OK (34°57.624’N, 97° ’W) 15 ~ 18 UTC, 6/18/1997 ~ 7/17/1997 Rangeland, Pasture Variable ESTAR, NASA P3B aircraft Monoangular (  =0º) H pol- arization only irregular (800 m) Portos’91 INRA, Avignon, France, (43°55N, 4°53E) 7/24/1991 ~ 9/30/1991 Soybean Silty Clay Loam PORTOS, Crane broom Multiangular H & V pola- Rization N/A (point- based) Portos’93 INRA, Avignon, France, (43°55N, 4°53E) 4/19/1993 ~ 7/8/1993 Wheat Silty Clay Loam PORTOS, Crane broom Multiangular H & V pola- Rization N/A (point- based)

Validation of L-MEB Model Use observed soil moisture, soil temperature, etc. as inputs to L-MEB

Statistics of L-MEB Validation Experiment Regression StatisticsError Statistics InterceptSlopeRBias (K)RMSE (K) Sample Size (site x day) Monsoon ’ (8 x 6) Washita ’ (10 x 8) SGP (15 x 15)* Porots ’ (28 x 1) Portos ’ (26 x 1) All

Coupled LSS-MEB Validation (Washita’92, OK) Use LSS soil moisture, soil temperature, etc. as inputs to L-MEB

Coupled LSS-MEB Validation (Soil Characteristics Comparison)

Coupled LSS-MEB Validation (Precipitation Comparison) Grid 1 Grid 2

Microwave Analogs Example global 1° map of the synthetic L-band H-polarized brightness temperature corresponding to the incidence angle and equator crossing time of HYDROS Satellite for June 01, 1992.

Presenting GSWP-2  Session at AMS Annual Mtg., Hydrology Conf.  San Diego, CA, USA – January 2005  GEWEX 5 th Int’l. Science Conf.  Costa Mesa, CA, USA – June 2005  Abstract submission deadline 16 January 2005  EGU (Apr 2005 – Vienna)  Spring AGU (May 2005 – New Orleans)  AMS (Jan 2006 – Atlanta)

Journal Special Issue/Section? Do we want to do a special issue?  J. Geophys. Res. (efficient – no delays)  J. Hydrometeor. (better targeted audience)  Glob. Planet. Change (easier?) ……

Proposal for Baseline, etc.  COLA = Continue with multi-model analysis based on B0 simulations  Climatology (12 months), Monthly (120), Daily  Call it “GSWP-2 Version 1.0”  Japan = Produce a new baseline forcing (including spin up)  Improved based on problems found, solutions suggested  Call it “B1”, release, and encourage modelers to submit in 2005  Sensitivity studies continue based on B0  Sensitivity studies not as sensitive to systematic errors as analysis  COLA = Produce a multi-model analysis based on B1 simulations  Call it “Version 2.0”