The 2 nd phase of the Global Land-Atmosphere Coupling Experiment Randal Koster GMAO, NASA/GSFC

GLACE-1 was a successful international modeling project that looked at soil moisture impacts on precipitation... Where precipitation responds to variations in soil moisture, according to the 12 GLACE models.

Motivation for GLACE-2 For soil moisture initialization to add to subseasonal or seasonal forecast skill, two criteria must be satisfied: 1.An initialized anomaly must be “remembered” into the forecast period, and 2.The atmosphere must be able to respond to the remembered anomaly. Addressed by GLACE Addressed by GLACE2: the full initialization forecast problem

Outline of Talk 1.Experiment overview 2.Technical issues 3.Diagnostic output 4.Analyses 5.Encouraged supplemental runs 6.Status

GLACE-2 : Experiment Overview Perform ensembles of retrospective seasonal forecasts Initialize land states with “observations”, using GSWP approach Prescribed, observed SSTs or the use of a coupled ocean model Initialize atmosphere with “observations”, via reanalysis Evaluate forecasts against observations Step 1:

GLACE-2 : Experiment Overview Perform ensembles of retrospective seasonal forecasts Initialize land states with “observations”, using GSWP approach Prescribed, observed SSTs or the use of a coupled ocean model Initialize atmosphere with “observations”, via reanalysis Evaluate forecasts against observations Step 2: “Randomize” land initialization!

GLACE-2 : Experiment Overview Step 3: Compare skill; isolate contribution of realistic land initialization. Forecast skill obtain in experiment using realistic land initialization Forecast skill obtained in identical experiment, except that land is not initialized to realistic values Forecast skill due to land initialization

FORECAST START DATES Apr different 10-member forecast ensembles. Each ensemble consists of 10 simulations, each running for 2 months Apr 15 May 1 May 15 Jun 1 Jun 15 Jul 1 Jul 15 Aug 1 Aug 15

Computational requirement: 100 forecast start dates 10 ensemble members per forecast 1/6 years (2 months) per simulation 2 experiments (including control) XXX = 333 years of simulation

Observed precipitation Wind speed, humidity, air temperature, etc. from reanalysis Observed radiation LSM A decade of LSM initial conditions for seasonal forecasts The resulting LSM initial conditions reflect observed antecedent atmospheric forcing. LAND STATE INITIALIZATION via offline simulations, a la GSWP A decade of offline integration

Outline of Talk 1.Experiment overview 2.Technical issues 3.Diagnostic output 4.Analyses 5.Encouraged supplemental runs 6.Status

Model resolution: Decided by participating groups. Land initialization: Best to be generated independently by each group using existing GSWP experimental design. Could be provided by GLACE-2 organizers, if proper scaling is performed. Atmospheric initialization: Reanalysis. Ocean boundary condition: GLACE-2 organizers will provide SST fields (persisted anomalies), or groups may use coupled ocean model.

Outline of Talk 1.Experiment overview 2.Technical issues 3.Diagnostic output 4.Analyses 5.Encouraged supplemental runs 6.Status

Required output diagnostics (to be provided to GLACE data center): 1. For each of the 4 15-day periods of each forecast simulation, provide global fields of: 15-day total precipitation 15-day average near-surface air temperature (in the lowest AGCM level) 15-day total evaporation 15-day average net radiation 15-day average vertically-integrated soil moisture content. 15-day average near-surface relative humidity

Required output diagnostics (to be provided to GLACE data center): 2. For each day of the first 30 days of certain forecast simulations, provide global fields of: vertically-integrated soil moisture content. This will allow us to analyze the decay with lead time of the information provided by soil moisture initialization. The forecast simulations chosen for this output are: Apr. 1, May 1, June 1, July 1, and Aug. 1 of 1986, 1988, 1990, 1992, and 1994.

Outline of Talk 1.Experiment overview 2.Technical issues 3.Diagnostic output 4.Analyses 5.Encouraged supplemental runs 6.Status

1. Determine maximum level of precipitation (and air temperature) predictability associated with land initialization. Results from pilot study of Koster et al. 2004

Regress full forecast against actual observations to retrieve r Determine forecast skill for precipitation (and air temperature) associated with land initialization.

Contribution to skill: P forecasts Contributions of land moisture initialization to the skill of subseasonal (one-month) forecasts of P and T Contribution to skill: T forecasts Koster et al., Journal of Hydrometeorology,5, pp , 2004 Results from pilot study of Koster et al. 2004

Outline of Talk 1.Experiment overview 2.Technical issues 3.Diagnostic output 4.Analyses 5.Encouraged supplemental runs 6.Status

Repeat Series 1 and Series 2 over a lengthier time period. Selected start dates from 50 years, using land conditions produced offline with Sheffield et al. (2006) data. Apr Apr 15 May 1 May 15 Jun 1 Jun 15 Jul 1 Jul 15 Aug 1 Aug 15 Choose dates of forecasts based on presence of large initial anomaly in region known to have high predictability.

ALSO... Austral summer forecasts. By shifting the GLACE-2 start dates by 6 months, participants may redo the experiment(s) for Austral summer. Data will be analyzed at the GLACE-2 data center. Idealized predictability analysis in absence of observations- based initializaiton.

Outline of Talk 1.Experiment overview 2.Technical issues 3.Diagnostic output 4.Analyses 5.Encouraged supplemental runs 6.Status

Timeline Spring, 2007: Obtained funding for GLACE-2! Summer 2007:Finish identifying interested modeling groups Summer 2007:Provide data to participants (meteorological forcing data, atmospheric initialization, SST conditions) Summer/Fall 2008:Simulations due Fall/Winter 2008: First analyses performed

Interested participants (so far) Model / GroupContact 1. GMAO (NASA/GSFC) R. Koster (old system and new system) 2. COLAP. Dirmeyer 3. NCEP GFS/CFSE. Wood, L. Luo 4. U. TokyoT. Yamada 5. ECHAM5S. Seneviratne 6. ECMWF IFSB. van den Hurk, H. Camargo 7. BMRCB. McAvaney

Do you want to participate? If so, contact Randy Koster at

Do you want to participate? If so, contact Randy Koster at Thank you!

Back-up slides

1. GLACE-2 will first perform an idealized analysis: For a given ensemble forecast, assume that the first ensemble member represents “nature”. STEP 1: Assume that the remaining ensemble members represent the “forecast”. STEP 2: Forecasted precipitation anomaly (mm/day) Ensemble member

For a given ensemble forecast, assume that the first ensemble member represents “nature”. STEP 1: Assume that the remaining ensemble members represent the “forecast”. STEP 2: STEP 3: Determine the degree to which the “forecast” agrees with the assumed “nature”. Precipitation anomaly (mm/day) Ensemble member To what extent does this anomaly… … agree with the average of these anomalies? GLACE-2 will first perform an idealized analysis:

Regress “forecast” against “observations” to retrieve r 2, a measure of forecast skill.

For a given ensemble forecast, assume that the first ensemble member represents “nature”. STEP 1: Assume that the remaining ensemble members represent the “forecast”. STEP 2: STEP 3: Determine the degree to which the “forecast” agrees with the assumed “nature”. This analysis effectively determines the degree to which atmospheric chaos foils the forecast, under the assumptions of “perfect” initialization, “perfect” validation data, and “perfect” model physics. STEP 4: Repeat multiple times, with each ensemble member in turn taken as “nature”. Average the resulting skill diagnostics. 1. GLACE-2 will first perform an idealized analysis:

Total number of required global fields: 100 x 10 x 4 x 6 x 2 = # of start dates # of ensemble members # of periods # of variables experiment and control

WI values for 7 different GSWP-2 models over a point in the U.S. Great Plains drought 1993 drought The unprocessed soil water diagnostics (shown here as degree of saturation) are not nearly as model-independent