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

K02 strategy, part 1: Establish a time series of surface conditions (Simulation W1) Step forward the coupled AGCM-LSM Write the values of the land surface prognostic variables into file W1_STATES Step forward the coupled AGCM-LSM Write the values of the land surface prognostic variables into file W1_STATES time step ntime step n+1 (Repeat without writing to obtain simulations W2 – W16) GLACE: Global Land-Atmosphere Coupling Experiment 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, , 2002

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) Step forward the coupled AGCM-LSM Throw out updated values of land surface prognostic variables; replace with values for time step n from file W1_STATES Step forward the coupled AGCM-LSM time step ntime step n+1 Throw out updated values of land surface prognostic variables; replace with values for time step n+1 from file W1_STATES

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

Plan for GLACE K02 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. NEW 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. 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

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.

Bonan14. NCAR Kanae/Oki13. U. Tokyo w/ MATSIRO Xue12. UCLA with SSiB Koster11. NSIPP with Mosaic Lu/Mitchell10. NCEP/EMC with NOAH Taylor9. Hadley Centre w/ MOSES2 Sud8. GSFC(GLA) with SSiB Gordon7. GFDL with LM2p5 Verseghy6. Env. Canada with CLASS Viterbo5. ECMWF with TESSEL Kowalczyk4. CSIRO w/ 2 land schemes Dirmeyer3. COLA with SSiB Hahmann2. CCM3 with BATS McAvaney/Pitman1. BMRC with CHASM ContactModel Participating Groups at Onset of Experiment Status 15. LMDZ/ORCHIDEE Polcher

Bonan14. NCAR Kanae/Oki13. U. Tokyo w/ MATSIRO Xue12. UCLA with SSiB Koster11. NSIPP with Mosaic Lu/Mitchell10. NCEP/EMC with NOAH Taylor9. Hadley Centre w/ MOSES2 Sud8. GSFC(GLA) with SSiB Gordon7. GFDL with LM2p5 Verseghy6. Env. Canada with CLASS Viterbo5. ECMWF with TESSEL Kowalczyk4. CSIRO w/ 2 land schemes Dirmeyer3. COLA with SSiB Hahmann2. CCM3 with BATS McAvaney/Pitman1. BMRC with CHASM ContactModel Participating Groups Status Dropped out 15. LMDZ/ORCHIDEE Polcher

Bonan14. NCAR Kanae/Oki13. U. Tokyo w/ MATSIRO Xue12. UCLA with SSiB Koster11. NSIPP with Mosaic Lu/Mitchell10. NCEP/EMC with NOAH Taylor9. Hadley Centre w/ MOSES2 Sud8. GSFC(GLA) with SSiB Gordon7. GFDL with LM2p5 Verseghy6. Env. Canada with CLASS Viterbo5. ECMWF with TESSEL Kowalczyk4. CSIRO w/ 2 land schemes Dirmeyer3. COLA with SSiB Hahmann2. CCM3 with BATS McAvaney/Pitman1. BMRC with CHASM ContactModel Participating Groups Status dropped out submitted 15. LMDZ/ORCHIDEE Polcher

Bonan14. NCAR Kanae/Oki13. U. Tokyo w/ MATSIRO Xue12. UCLA with SSiB Koster11. NSIPP with Mosaic Lu/Mitchell10. NCEP/EMC with NOAH Taylor9. Hadley Centre w/ MOSES2 Sud8. GSFC(GLA) with SSiB Gordon7. GFDL with LM2p5 Verseghy6. Env. Canada with CLASS Viterbo5. ECMWF with TESSEL Kowalczyk4. CSIRO w/ 2 land schemes Dirmeyer3. COLA with SSiB Hahmann2. CCM3 with BATS McAvaney/Pitman1. BMRC with CHASM ContactModel Participating Groups Status dropped out submitted finished, not yet submitted 15. LMDZ/ORCHIDEE Polcher 1 finished, not yet submitted

Bonan14. NCAR Kanae/Oki13. U. Tokyo w/ MATSIRO Xue12. UCLA with SSiB Koster11. NSIPP with Mosaic Lu/Mitchell10. NCEP/EMC with NOAH Taylor9. Hadley Centre w/ MOSES2 Sud8. GSFC(GLA) with SSiB Gordon7. GFDL with LM2p5 Verseghy6. Env. Canada with CLASS Viterbo5. ECMWF with TESSEL Kowalczyk4. CSIRO w/ 2 land schemes Dirmeyer3. COLA with SSiB Hahmann2. CCM3 with BATS McAvaney/Pitman1. BMRC with CHASM ContactModel Participating Groups Status dropped out submitted finished, not yet submitted In progress 15. LMDZ/ORCHIDEE Polcher 1 finished, not yet submitted

Ω p (R - W) NSIPP

Ω p (S - W) NSIPP

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

Ω p (S - W) verses Relative Humidity NSIPP

In principle, imposing land surface boundary states should decrease the intra-ensemble variance of the atmospheric fields. pdf of precipitation at a given point, across ensemble members corresponding pdf when land boundary is specified We are examining this in GLACE by looking at the variance ratio:  2 P (S)  2 P (W)

Variance(S)/Variance(W) NSIPP

Ω T (S - W)

Experiment website:

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)

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.

Approach Combine the water balance equation: C s w n+1,i = C s w ni + P ni - E ni - Q ni with approximate equations for evaporation and runoff to get an equation for the autocorrelation of soil moisture: = cw ni + d E ni R ni = aw ni + b Q ni P ni cR n CsCs aP n CsCs  = f (,,,  wn  w,n+1 ) cov(w n,F n ) wn2wn2 Koster and Suarez, J. Hydrometeorology, 2, , 2001.

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.

SEASONAL FORECASTING STUDIES (an update)

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

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