Notes

Outline

Initialization of the Land Surface in Seasonal-to-Interannual Predictions Jeffrey P. Walker Universities Space Research Association NASA/Goddard Space Flight Center http://land.gsfc.nasa.gov/~cejpw

Overview Development of an optimal methodology for initializing the land surface in NASA’s  seasonal-to-interannual prediction project (NSIPP) The importance of the soil moisture land surface states in seasonal-to-interannual predictions The initialization approach A demonstration of the approach using a synthetic study Future directions

Soil Moisture v Sea Surface Temp Knowledge of soil moisture has a greater impact on the predictability of summertime precipitation over land at mid-latitudes than Sea Surface Temperature (SST).

Precipitation Predictability Index

Importance of Soil Moisture

Initialization Approach Provide global land surface initialization states for the retrospective period of 1979 to present. A two-tiered approach. Tier 1: Spin-up the land surface model off-line from the GCM using the best atmospheric forcing data available. Minimizes forcing bias! Tier 2:Assimilate passive microwave remote sensing observations of near-surface soil moisture from SMMR and SSM/I. Minimizes model and forcing bias!

Tier 1: Better Forcing Re-Analysis Atmospheric Forcing Data Sets ECMWF Re-analysis Advanced Global Data  4x/day, 01/79 - 12/93 1.125 degrees  (Gaussian) NCEP/NCAR Re-analysis 4x/day, 1948-1999 2.5 x 2.5 degrees Bias Correct Using Monthly Mean Observational Data Sets Observational Data Sets NCAR Northern Hemisphere Sea Level Pressure  01/1899 – present; 5 x 5 degrees Climate Research Unit (University of East Anglia) Temperature and Precipitation 01/01 - 12/98; 0.5 x 0.5 degrees Center for Climatic Research (University of Delaware) Terrestrial Temperature and Precipitation 01/50 - 12/96 ; 0.5 x 0.5 degree Global Precipitation Climatology Project (GPCP) 01/86 - 03/95; 2.5 x 2.5 degree Langley Eight Year Shortwave and Longwave Surface Radiation Budget 07/83 - 06/91 - in process of being extended; 2.5 x 2.5 degree

The Catchment Discretization Hydrologic unit is the catchment Level 5 Pfafstetter » 4500 km2

Re-analysis: Grid to Catchment

Observations: Grid to Catchment

Bias Correction: Air Temperature

ISLSCP v ECMWF

Tier 2: Assimilation

SMMR Polarization Ratio (Mean)

SMMR Polarization Ratio (Std Dev)

Why Assimilate Soil Moisture? Accurate soil moisture forecasts are necessary for accurate predictions of precipitation. LSM spin-up does not guarantee correct soil moisture initialization. Errors in soil moisture forecasts result from: The memory of errors in LSM soil moisture initialization. Errors in atmospheric forcing data. Errors in LSM physics. Errors in soil and topographic data.

What is Data Assimilation ? The process of finding the model representation which is most consistent with the observations.

Catchment Hydrological Regimes

The Catchment-Based LSM srfexc = srfexc – es – srflow + i + a rzexc  =  rzexc – ev + srflow – rzflow + b catdef = catdef + et – rzflow + baseflow + c where a, b and c are correction terms to            ensure mass balance Using a first-order Taylor series expansion:

Numerical Experiments “Truth” Data Set Spin-up catchment-based LSM for 1987 using ISLSCP forcing data. Run the simulation for 1987 using ISLSCP forcing data and output surface moisture data as “observations”. Open Loop Data Set Degrade the soil moisture prognostic variable spin-up states to make the catchments artificially wet. Run the simulation for 1987 using ISLSCP forcing data. Assimilated Data Set Degrade the soil moisture prognostic variable spin-up states to make the catchments artificially wet (as for the open loop). Run the simulation for 1987 using ISLSCP forcing data. Assimilate the surface “observations” once every 3 days using the Kalman-filter.

Soil Moisture: January 30

Soil Moisture: July 31

Soil Moisture: December 29

“Errors” in Assimilated Moisture: 1

“Errors” in Assimilated Moisture: 2 Soil Depth

Monthly Evapotranspiration

Monthly Runoff

Summary A surface measurement of soil moisture may be used to correct the entire soil moisture profile. Assimilation of soil moisture has a positive impact on the water and energy budgets. Soil moisture assimilation performs best for regions with shallower soils; particularly depths less than 3m.

Near-Term Goals Identify the maximum level of error which surface observations can have before the assimilation is no longer useful. Identify the minimum frequency of surface observations before there is a significant degradation of the assimilation results. Assimilate Dr. Owe’s surface soil moisture data from SMMR over North America and evaluate. Move towards a global implementation of the assimilation.

Importance of Snow In the northern hemisphere the snow cover ranges from 7% to 40% during the annual cycle. The high albedo, low thermal conductivity and large spatial/temporal variability impact both the energy and water budgets. Snow adjacent to bare soil causes mesoscale wind circulations. Direct replacement with observations does not account for model bias.

Snow Assimilation: NCEP ETA

Snow Assimilation: NSIPP Development of a Kalman filtering based snow assimilation strategy which overcomes the current limitations with assimilation of snow water equivalent, snow depth, and snow cover. Investigate the utility of novel snow observation products in such an assimilation strategy. Such observations include snow melt signature and fractional snow cover. Provide a basis for global implementation of an assimilation scheme for snow observation products, both for near-real-time forecasting and for the accurate initialization of seasonal-to-interannual predictions in the NSIPP fully-coupled GCM.