The Contribution of Soil Moisture Information to Forecast Skill: Two Studies Randal Koster and Sarith Mahanama Global Modeling and Assimilation Office, NASA/GSFC Ben Livneh Dept. of Civil and Env. Engineering, U. Washington With contributions from the GLACE-2 team, Dennis Lettenmaier, Rolf Reichle, and Qing Liu Direct questions to:

Long-term question: To what extent might hydrological prediction benefit from satellite-based soil moisture data (e.g., from SMAP or SMOS)? This talk: Describe two recent studies quantifying the benefits to prediction of model-based soil moisture data (produced with observed met. data); make inferences regarding satellite data.

Study #1 Subseasonal air temperature and precipitation forecasts GLACE-2: A quantification of the impact of realistic soil moisture initialization on the prediction of precipitation and air temperature at subseasonal timescales.

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 P, T forecasts against observations Series 1: In a subseasonal forecast system (GCM), 4

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 P, T forecasts against observations Series 2: In a subseasonal forecast system (GCM), “Randomize” land initialization! 5

GLACE-2 : Experiment Overview Step 3: Compare skill in two sets of forecasts; isolate contribution of realistic land initialization. Forecast skill, Series 1 Forecast skill, Series 2 Forecast skill due to land initialization 6 Examine 60 independent subseasonal forecasts during JJA (10 ensemble members each)  month simulations.

Participant List Group/ModelPoints of Contact 1. NASA/GSFC (USA): GMAO seasonal forecast system (old and new) 2. COLA (USA): COLA GCM, NCAR/CAM GCM 3. Princeton (USA): NCEP GCM 4. IACS (Switzerland): ECHAM GCM 5. KNMI (Netherlands): ECMWF 6. ECMWF 7. GFDL (USA): GFDL system 8. U. Gothenburg (Sweden): NCAR 9. CCSR/NIES/FRCGC (Japan): CCSR GCM 10. FSU/COAPS 11. CCCma # models S. Seneviratne, E. Davin E. Wood, L. Luo P. Dirmeyer, Z. Guo R. Koster, S. Mahanama2 B. van den Hurk T. Gordon J.-H. Jeong T. Yamada models 1 G. Balsamo, F. Doblas-Reyes M. Boisserie1 1B. Merryfield 7

Temperature forecasts : Increase in skill (r 2 ) during JJA due to land initialization (Multi-model results, conditioned on strength of local initial soil moisture anomaly) Extreme terciles all dates Extreme quintiles Extreme deciles days days days 8 Forecast skill: r 2 with land ICs vs r 2 w/o land ICs Dates for conditioning vary w/location

Study #2 Seasonal streamflow prediction A quantification, using multiple land models, of the degree to which soil moisture and snow initialization contribute to streamflow forecast skill at seasonal timescales.

10 Experiment: 1. Perform multi-decadal offline simulation covering CONUS, using observations-based meteorological data. Determine streamflows in various basins for MAMJJ and compare against (naturalized) streamflow observations. 2. Repeat, but doing forecasts: Simulate MAMJJ streamflow knowing only soil moisture and snow conditions on January 1. (Use climatological met forcing for January – July.) Compare forecasts to observations. (Not a synthetic study!) 3. Repeat, knowing only snow conditions on January Repeat, knowing only soil moisture conditions on January 1.

MAMJJ Streamflow Forecast Skill (r 2 ) a. CTRL: Forcings, initial snow, initial SM known (not true forecasts) b. Exp1: Initial snow, initial SM known c. Exp2: Initial snow knownd. Exp3: Initial SM known belongs to 5 ( also 1&2)belongs to 2 (also 1) Skill of model simulation of MAMJJ streamflow given: -- Realistic January 1 initial conditions -- “Perfect” prediction of forcing during forecast period Skill (r 2 ) Skill of model simulation of MAMJJ streamflow given: -- Realistic January 1 initial conditions -- No skill in prediction of forcing during forecast period (use climatology)

MAMJJ Streamflow Forecast Skill (r 2 ) a. CTRL: Forcings, initial snow, initial SM known (not true forecasts) b. Exp1: Initial snow, initial SM known c. Exp2: Initial snow knownd. Exp3: Initial SM known belongs to 5 ( also 1&2)belongs to 2 (also 1) Skill (r 2 ) “Snow initialization only” test: snow important toward northwest of study area. “Soil moisture initialization only” test: SM more important toward southeast of study area.

Oct. 1 initialization Jan. 1 initialization Apr. 1 initialization July 1 initialization Synthetic study results: Lead, in months, for which some significant (95% confidence level) streamflow forecast skill is obtained from soil moisture initialization Number of Lead Months Soil moisture initialized Snow initialized

These two studies show that accurate soil moisture initialization can lead to improvements in: subseasonal air temperature forecasts seasonal streamflow forecasts What are the implications for satellite- derived soil moisture data?

15 In the global GLACE-2 analysis, the skill levels obtained are clearly connected to the accuracy of the soil moisture initialization, indicating that improved soil moisture estimates can lead to improved forecasts. Land-Derived Skill (r 2 ) for Air Temperature Forecasts Rain-gauge density (# gauges / 2 o x2.5 o grid cell) Measure of Underlying Model “Predictability” Surrogate for soil moisture accuracy

16 In the global GLACE-2 analysis, the skill levels obtained are clearly connected to the accuracy of the soil moisture initialization, indicating that improved soil moisture estimates can lead to improved forecasts. Land-Derived Skill (r 2 ) for Air Temperature Forecasts Rain-gauge density (# gauges / 2 o x2.5 o grid cell) Measure of Underlying Model “Predictability” SMAP or SMOS would effectively increase the ordinates of the dots… … suggesting that we’d get more skill for these locations with SMAP or SMOS data

SMAP data coverage Rain gauge density: a reasonable surrogate metric for the accuracy of soil moisture initial conditions in a forecast SMAP data will be available over most of the world (the white areas), allowing first-order increases in soil moisture accuracy in many regions. (Of course, GPM will help, as well…) Rain gauge density GLACE-2 cutoff

Rain gauge density Even in regions of high rain gauge density, data assimilation studies* with AMSR-based and SMMR-based soil moisture data show that satellite-based products improve the estimation of soil moisture over that obtained with the rainfall forcing alone. SMAP and SMOS will improve over AMSR and SMMR, providing even higher accuracy than indicated here. *Reichle et al., JGR, 112, 2007 Reichle and Koster, GRL, 32, 2005 Liu et al., Journal Hydromet., submitted. From Liu et al. (submitted) Skill (r)

19 Summary Accurate soil moisture initialization (as derived from met forcing, particularly precipitation) does contribute significantly to skill in temperature and streamflow forecasts. (Koster et al., GRL, 2010; Koster et al., Nature Geosci., 2010) Forecasts are indeed found to improve with the accuracy of the soil moisture initialization. Given AMSR & SMMR experience, SMAP & SMOS should contribute significantly to the accuracy of initialization. Inference: SMAP & SMOS should contribute to forecast skill. Obvious next challenge: quantify these contributions!