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Status report: GLASS panel meeting, Tucson, August 2003

Status report: GLASS panel meeting, Tucson, August 2003. Randal Koster Zhizhang Guo Paul Dirmeyer. time step n. Step forward the coupled AGCM-LSM. Step forward the coupled AGCM-LSM. Write the values of the land surface prognostic variables into file W1_STATES.

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Status report: GLASS panel meeting, Tucson, August 2003

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  1. Status report: GLASS panel meeting, Tucson, August 2003 Randal Koster Zhizhang Guo Paul Dirmeyer

  2. time step n Step forward the coupled AGCM-LSM Step forward the coupled AGCM-LSM Write the values of the land surface prognostic variables into file W1_STATES Write the values of the land surface prognostic variables into file W1_STATES GLACE: Global Land-Atmosphere CouplingExperiment This experiment is a broad follow-on to the four-model intercomparison study described by Koster et al. (2002)*, hereafter referred to as K02. *J. Hydrometeorology, 3, 363-375, 2002 K02 strategy, part 1: Establish a time series of surface conditions (Simulation W1) time step n+1 (Repeat without writing to obtain simulations W2 – W16)

  3. time step n K02 strategy, part 2: Run a 16-member ensemble, with each member forced to maintain the same time series of surface prognostic variables (Simulations R1 – R16) time step n+1 Step forward the coupled AGCM-LSM Step forward the coupled AGCM-LSM Throw out updated values of land surface prognostic variables; replace with values for time step n+1 from file W1_STATES Throw out updated values of land surface prognostic variables; replace with values for time step n from file W1_STATES

  4. Define a diagnostic W that describes the impact of the surface boundary on the generation of precipitation. All simulations in ensemble respond to the land surface boundary condition in the same way W is high Simulations in ensemble have no coherent response to the land surface boundary condition W is low

  5. Plan for GLACE K02 GLACE Step 1: 16-member ensemble, with prognostic states written out at each time step by one of the members. Step 2: 16-member ensemble, with all members forced to use the same time series of surface prognostic states. All simulations are run over July. Step 1: 16-member ensemble, with prognostic states written out at each time step by one of the members. Step 2: 16-member ensemble, with all members forced to use the same time series of surface prognostic states. Step 3: 16-member ensemble, with all members forced to use the same time series of deeper (root zone and below) soil moisture states. All simulations are run from June through August NEW

  6. Timetable for GLACE January, 2003: Experiment plan distributed February, 2003: Feedback from modeling groups. How many will participate? Do we have a critical mass? August, 2003: Deadline for finishing simulations August – Dec., 2003: Processing of results, preparation of paper. No physical workshop is planned. Preliminary findings will be continually communicated with participants, who will be encouraged to participate in the interpretation of the results.

  7. Participating Groups at Onset of Experiment Model Contact Status 1. BMRC with CHASM McAvaney/Pitman 2. CCM3 with BATS Hahmann 3. COLA with SSiB Dirmeyer 4. CSIRO w/ 2 land schemes Kowalczyk 5. ECMWF with TESSEL Viterbo 6. Env. Canada with CLASS Verseghy 7. GFDL with LM2p5 Gordon 8. GSFC(GLA) with SSiB Sud 9. Hadley Centre w/ MOSES2 Taylor 10. NCEP/EMC with NOAH Lu/Mitchell 11. NSIPP with Mosaic Koster 12. UCLA with SSiB Xue 13. U. Tokyo w/ MATSIRO Kanae/Oki 14. NCAR Bonan Polcher 15. LMDZ/ORCHIDEE

  8. Participating Groups Model Contact Status 1. BMRC with CHASM McAvaney/Pitman 2. CCM3 with BATS Hahmann 3. COLA with SSiB Dirmeyer 4. CSIRO w/ 2 land schemes Kowalczyk 5. ECMWF with TESSEL Viterbo 6. Env. Canada with CLASS Verseghy 7. GFDL with LM2p5 Gordon 8. GSFC(GLA) with SSiB Sud 9. Hadley Centre w/ MOSES2 Taylor 10. NCEP/EMC with NOAH Lu/Mitchell 11. NSIPP with Mosaic Koster 12. UCLA with SSiB Xue 13. U. Tokyo w/ MATSIRO Kanae/Oki 14. NCAR Bonan Dropped out Polcher 15. LMDZ/ORCHIDEE

  9. Participating Groups Model Contact Status 1. BMRC with CHASM McAvaney/Pitman 2. CCM3 with BATS Hahmann 3. COLA with SSiB Dirmeyer 4. CSIRO w/ 2 land schemes Kowalczyk 5. ECMWF with TESSEL Viterbo 6. Env. Canada with CLASS Verseghy submitted 7. GFDL with LM2p5 Gordon 8. GSFC(GLA) with SSiB Sud submitted 9. Hadley Centre w/ MOSES2 Taylor 10. NCEP/EMC with NOAH Lu/Mitchell 11. NSIPP with Mosaic Koster 12. UCLA with SSiB Xue 13. U. Tokyo w/ MATSIRO Kanae/Oki 14. NCAR Bonan dropped out Polcher 15. LMDZ/ORCHIDEE

  10. Participating Groups Model Contact Status 1. BMRC with CHASM McAvaney/Pitman finished, not yet submitted 2. CCM3 with BATS Hahmann 3. COLA with SSiB Dirmeyer 1 finished, not yet submitted 4. CSIRO w/ 2 land schemes Kowalczyk 5. ECMWF with TESSEL Viterbo 6. Env. Canada with CLASS Verseghy submitted 7. GFDL with LM2p5 Gordon 8. GSFC(GLA) with SSiB Sud submitted 9. Hadley Centre w/ MOSES2 Taylor 10. NCEP/EMC with NOAH Lu/Mitchell 11. NSIPP with Mosaic Koster finished, not yet submitted 12. UCLA with SSiB Xue 13. U. Tokyo w/ MATSIRO Kanae/Oki 14. NCAR Bonan dropped out Polcher 15. LMDZ/ORCHIDEE

  11. Participating Groups Model Contact Status 1. BMRC with CHASM McAvaney/Pitman finished, not yet submitted 2. CCM3 with BATS Hahmann In progress 3. COLA with SSiB Dirmeyer 1 finished, not yet submitted 4. CSIRO w/ 2 land schemes Kowalczyk 5. ECMWF with TESSEL Viterbo 6. Env. Canada with CLASS Verseghy submitted 7. GFDL with LM2p5 Gordon In progress 8. GSFC(GLA) with SSiB Sud submitted 9. Hadley Centre w/ MOSES2 Taylor 10. NCEP/EMC with NOAH Lu/Mitchell In progress 11. NSIPP with Mosaic Koster finished, not yet submitted 12. UCLA with SSiB Xue In progress In progress 13. U. Tokyo w/ MATSIRO Kanae/Oki 14. NCAR Bonan dropped out Polcher 15. LMDZ/ORCHIDEE

  12. Ωp (R - W) NSIPP

  13. Ωp (S - W) NSIPP

  14. Past experience suggests that W is strongly related to relative humidity. Dots show locations where W is high

  15. Ωp(S - W) verses Relative Humidity NSIPP

  16. In principle, imposing land surface boundary states should decrease the intra-ensemble variance of the atmospheric fields. corresponding pdf when land boundary is specified pdf of precipitation at a given point, across ensemble members s2P (S) We are examining this in GLACE by looking at the variance ratio: s2P (W)

  17. Variance(S)/Variance(W) NSIPP

  18. ΩT (S - W)

  19. Experiment website: http://glace.gsfc.nasa.gov

  20. UPDATED TIMELINE: -- Put preliminary results on web; initiate discussion (Sept.) -- Gather remaining submissions (Sept., Oct.) -- Process all data (Sept., Oct., Nov.) -- Write draft of paper; discuss it interactively on the web, iterate on text (Oct. - Jan.) -- Submit paper (February)

  21. Soil Moisture Memory Analysis (an update) Basic idea: GLACE addresses one part of the “land impacts on seasonal prediction” question: the degree to which the atmosphere responds predictably to a land surface moisture anomaly. The proposed memory analysis addresses the second part: the degree to which land surface moisture anomalies can be predicted in the coupled system.

  22. Eni = cwni + d Rni Qni = awni + b Pni aPn cRn cov(wn,Fn) Cs Cs wn2 Approach Combine the water balance equation: Cs wn+1,i = Cs wni + Pni - Eni - Qni with approximate equations for evaporation and runoff to get an equation for the autocorrelation of soil moisture: swn , , , r = f ( ) sw,n+1 Koster and Suarez, J. Hydrometeorology, 2, 558-570, 2001.

  23. Intercomparison Study: Variations in Soil Moisture Memory Among Current GCMs Question: Can we characterize soil moisture memory in different AGCMs? Can we explain why the memory is large in some AGCMs and small in others? Can we explain the different geographical distributions of memory in the different AGCMs? Difficult approach: Have each AGCM group perform a long-term simulation; have them provide GLASS with relevant outputs Easy approach: Mine relevant data from AMIP archives. Do the study with existing data, using memory equation! Outlook for study: Tom Phillips of AMIP has expressed a great interest in this study and has offered us access to the relevant data. A post-doc with the appropriate background will be joining our team at GSFC in October 2003, and she has expressed a strong interest in tackling this problem. Some added GLACE diagnostics allow additional memory analysis.

  24. SEASONAL FORECASTING STUDIES (an update)

  25. Earlier approach: Perform a multi-year integration to generate initial conditions. GCM-generated radiation, wind speed, air temperature, etc. Observed precipitation “Realistic” initial soil moisture conditions for forecasts that reflect observed antecedent precipitation. (J. Hydrometeorology, 2003) Mosaic LSM New (“LDAS”) Approach: Perform an improved multi-year integration. Wind speed, humidity, air temperature, etc. from reanalysis Observed precipitation Observed radiation Initial conditions that are even more realistic. Mosaic LSM

  26. AMIP: without land initialization Simulated versus observed precipitation anomalies (JJA, mm/day) 1:1 line fitted line OBSERVED Area studied PREDICTED Scaled LDAS: with land initialization OBSERVED PREDICTED

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