Global Land Atmosphere System Study (GLASS) progress

1. Global Land Atmosphere System Study (GLASS) progress Bart van den Hurk co-chair GLASS Future (1/1/11) chairs: Martin Best (MetOffice) and Joe Santanello (NASA) GLASS progress

2. The structure of GLASS PILDAS LUCID land-atmosphere coupling model data fusion LoCo metrics GLACE2 LandFlux GSWP3 PILPS-Urban benchmarking SnowMIP RAMI4PILPS ALMIP-2 GLASS progress

3. -2 The 2nd phase of the Global Land-Atmosphere Coupling Experiment Overall goal: Determine the degree to which realistic land surface (soil moisture) initialization contributes to forecast skill (rainfall, temperature) at 1-2 month leads GLASS progress

4. Setup GLACE2 • 10 GCMs • 100 start dates (1 Apr 1986 … 15 Aug 1995) • 10 members of 2 months runs • 2 series • series 1: initial soil from pseudo obs • series 2: random initial soil • Main Diagnostics: • difference in R2 relative to obs series 1 – series 2, averaged for 15-day lead periods GLASS progress

5. Publications • GRL-paper (Koster et al, 2009): main results for US • ClimDyn submitted (vdHurk et al, 2010): main results for US • Jhydromet submitted (Koster et al, 2010): extensive description, global evaluation with #precip gauges GLASS progress

6. GRL-paper GLASS progress

7. GLASS progress

8. Land contribution to potential predictability GLASS progress

9. Gain in skill as fraction of pot.pred. GLASS progress

10. J.Hydromet paper GLASS progress

11. Selecting extreme s.m. GLASS progress

12. Gauge density GLASS progress

13. Wet/dry quantiles GLASS progress

14. Main conclusions • Skill improvement in US better than in Europe (also potential predictability in US larger) • Skill declines with leadtime, but increases when extreme initial soil conditions are chosen. Assymetry in skill improvement seems present in some areas • Skill improves with better precipitation forcing • GLACE future proposal submitted to Dutch NSF (but failed…) GLASS progress

15. Project for the Intercomparison of Land Data Assimilation Systems (PILDAS) PILDAS-1 Experiment Plan Rolf Reichle* (NASA/GSFC) and Jean-François Mahfouf (Météo-France) *Email: Rolf.Reichle@nasa.gov Phone: +1-301-614-5693 GLASS progress

16. PILDAS objectives • Enable better communication among developers of land data assimilation systems (LDAS). • Develop and test a framework for LDAS comparison and evaluation. • Compare land assimilation methods. • Conduct sensitivity studies of assimilation input parameters (such as model and observation errors). • Provide guidance and priorities for future land assimilation research and applications. • Ultimately, produce enhanced global data sets of land surface fields. GLASS progress

17. Key findings (Kumar et al, 2009) • Identical twin experiments overestimate the skill contributed by the assimilation. • Stronger coupling between surface and root zone (VCS) leads to more efficient assimilation. • If assimilation system is properly set up, the skill improvement depends only weakly on the land model. • It is prudent to overestimate VCS in the assimilation model. GLASS progress

18. PILDAS-1 flowchart Phase A Phase B Phase C “LDAS” FORCING NOISE SFSM_i RZSM_i FLUX_i (truth) SFSM_i (obs) “TRUTH” FORCING LSM_i LDAS_k SFSM_k,i RZSM_k,i FLUX_k,i (assim) compare Skill_k,i Phase A: Generate truth for i=1:NTland models (participants). Phase B: Generate i=1:NTsets of synthetic observations (core). Phase C: Generate NA open loop and NA·NT assim. runs (participants). Phase D: Analyze results (all). Phase D GLASS progress

19. PILDAS-1 Phase D Skill metrics Phase D: • “Normalized Information Contribution” (Kumar et al. 2009) • “Vertical Coupling Strength” (Kumar et al. 2009) • Assimilation diagnostics (O-F mean, O-F variance etc.) GLASS progress

20. PILDAS-1 experiment setup Tentative experiment setup (details TBD!): • Domain: Red-Arkansas river basin • Exchange grid: 0.25 deg lat/lon • Duration: 2002-2008 Forcing data will be provided and LDAS output is expected on the exchange grid. Participating systems may run on their native grids. Participating systems use native model parameters (land cover, soil texture, …). GLASS progress

21. PILDAS-1 logistics “Core group”: • Disseminates LDAS input data (forcing, synthetic obs). • Collects and post-processes LDAS output. • Coordinates analysis of results and publications. “Participants”: • Generate synthetic truth data and LDAS output. • Contribute to analysis of results. GLASS progress

22. PILDAS-1 concerns and open issues • Assimilate surface soil moisture or brightness temperature? • Make truth data available to all participant groups prior to their delivery of assimilation output? • Limit NT(# truth data sets, ie. # required assim. integrations)? • Require assimilation diagnostics (O-F) on exchange grid or native grid? • Infrastructure (web server, postdoc) and funding? • Dry-run with just two institutions. • Are systems ready and areresources (staff, computing) available for participation (ECMWF, CMC, …)? • Development of error checking and post-processing tools? GLASS progress

23. PILDAS-1 timeline Jun 2010: Disseminate experiment plan to select participants. Aug 2010: Refine experiment plan. Sep 2010: Disseminate experiment plan to potential participants. Dec 2010: Finalize domain and exchange grid. Prepare forcing data. Feb 2011: Conduct dry-run of entire experiment with 2 institutions. Mar 2011: Phase A – Truth integrations. Jun 2011: Phase B – Generation of synthetic observations. Aug 2011: Phase C – Data assimilation experiments. Oct 2011: Phase D – Analysis of experiments. Dec 2011: Draft publications. GLASS progress

24. PILDAS-1 potential participants Institution POC NASA/GMAO R. Reichle Meteo-France J.-F. Mahfouf ECMWF P. de Rosnay, G. Balsamo Environment Canada S. Belair UK Met Office M. Best, S. Pullen KNMI B. van den Hurk University of Colorado A. Slater Princeton University E. Wood Observatoire de Paris C. Jimenez University of Tokyo T. Koike University of Melbourne J. Walker NASA/HSB S. Kumar, C. Peters-Lidard, J. Santanello Argentina H. Lozza MIT D. Entekhabi, D. McLaughlin UCLA S. Margulis NCEP M. Ek NESDIS X. Zhan Colorado State University A. Jones USDA W. Crow CESBIO/SMOS Y. Kerr US Air Force Weather Agency J. Eylander University of Tokyo T. Oki ??? ??? Which of these groups have an assimilation system ready? Are we missing anyone? GLASS progress

25. Global Soil Wetness Project –phase 3 (GSWP3) • Intention • To extend GSWP2 offline land model data set (1986-1995) to ERA40 – present • To allow studies addressing • Trend analyses • Carbon/H2O coupling • Hydrological applications • Error propagation • To include multiple forcing data sets in order to span uncertainty range GLASS progress

26. GSWP3 - Status • Very good workshop in Tokyo June 2010 • Excellent scientific line-up • WATCH forcing data set (1900 – 2000), maybe to be extended with ERA-interim to present • Lead by Taikan Oki, involvement of NASA’s LIS • Pretty much a project “in development” • Many interested parties (including ECMWF) • (Too) many scientific goals • Not a very clear funding/time line (for me) GLASS progress

27. GLASS and WGNE • “Land topics” don’t seem to be at the heart of WGNE • Added value of strong links not entirely clear • Actual spin-off so far • Continued interest of land issues at ECMWF (Martin Miller) • WCRP Survey • ? • Expected benefit • Mutual development of benchmarks for model evaluation • Involvement in projects like PILDAS and GSWP3 GLASS progress