1. Initialization of Land-Surface Schemes for Subseasonal Predictions. Paul Dirmeyer

2. Soil moisture decorrelation time-scales Land surface “memory” is concentrated in the sub- seasonal time-scale (0-3 months). Land surface “memory” is an ideal piece of potential predictability to be harvested for sub-seasonal forecasts.

3. Goal of land initialization Consistency across models (same anomalies) Consistency within model (between initial land state and land model)

4. Sources of initialization Observations Independent data sets (from a different model than yours) Consistent offline data set (from your land model) Coupled LDAS (your land and atmosphere models)

5. Snow Mass Cannot measure directly from satellite, although snow cover can be well determined. Must estimate coverage, e.g., Monthly Nimbus-7 SMMR data on a 30-minute latitude/longitude grid

6. Soil Temperature Very few and scattered soil temperature measurements. Big gaps exist in current “networks”.

7. Soil Wetness Very few and scattered soil wetness measurements. Some of the best long-term networks have decayed in last decade. Still gaps. In situ measurement s Global Soil Moisture Data Bank

8. Soil Wetness Remote sensing limited to very near surface and vegetation-sparse areas. Remote sensing L-band T B 0° look angle ½ ° resolution Courtesy Eleanor Burke Ruth DeFries via Tom Jackson

9. Independent data sets Combine meteorological observations (forcings) with a model of the land surface: Mintz and Serafini Schemm et al. Schnur and Lettenmaier Willmott and Matsura Huang et al. Dirmeyer and Tan (GOLD) Current Land Data Assimilation System (LDAS) products

10. Independent data sets Huang and van den Dool NCEP/CPC

11. Independent data sets Land Data Assimilation System (LDAS) – no assimilation N-LDAS

12. Independent data sets All have a basic shortcoming – the product is from someone else’s model Soil wetness is not easily transferable between models. Koster and Milly

13. Consistent offline data set Global Soil Wetness Project (GSWP) Historical Land Data Assimilation System (LDAS) Real Time Uses observed/analysis meteorological forcing to drive your land model uncoupled from atmospheric model. Generates land surface state variables and fluxes for best- possible atmosphere, but without feedback processes. True LDAS will also assimilate land surface state variable observations.

14. Global Soil Wetness Project (GSWP) Your model must be in the project to generate the initial conditions. COLA/SSIB

15. LDAS Approaches US (North American and Global)-LDAS approach: Assimilation of observations of soil temperature and moisture Strictly uncoupled from atmosphere Multi-model

16. LDAS Approaches European-LDAS approach: Relaxation of coupled PBL to observations Assimilation of observations to nudge soil moisture Multi-model precipitation radiation evaporation Soil moisture correction scheme Soil moisture content (sub)surface runoff Observations driving soil moisture correction Synops data METEOSAT/MSG Land surface parameterization scheme Boundary layer scheme AMSR (?)

17. Coupled LDAS Land model is coupled to its parent atmospheric model during integration. Shortcomings of atmospheric model fluxes (precipitation, radiation…) are overcome by some intervention: Replace downward fluxes (poor-man’s LDAS) Flux adjustment (similar to ocean-atmosphere) Empirical correction of state variables

18. ATMOSPHERIC CALCULATIONS Time step n ATMOSPHERIC CALCULATIONS Time step n+1 LAND CALCULATIONS Time step n LAND CALCULATIONS Time step n+1 Observed Precip. Observed Precip. Rad.T,q,… Precip. Rad.T,q,… Precip. E,H POOR MAN’S LDAS: A study of the impacts of soil moisture initialization on seasonal forecasts At every time step in a GCM simulation, the land surface model is forced with observed precipitation rather than GCM-generated precipitation. The observed global daily precipitation data comes from GPCP and covers the period 1997-2001 at a resolution of 1 o X 1 o (George Huffman, pers. Comm.) The daily precipitation is applied evenly over the day.

21. Compromises None of the above methods combine the key elements necessary for initialization of a broad, voluntary, multi- model experiment: Easy Cheap Fast

22. Compromises Possible Solution: Composite soil wetness: Interannual anomalies from an agreed-upon quasi- observed product Mean annual cycle from your land model (e.g., from AMIP-2, C20C, etc.)

23. Compromises Key questions: How to scale the soil wetness (and snow) anomalies to be consistent with your model? Water mass – but soil capacities may not agree

24. Compromises A better solution: Interannual standard deviation* * Origins of the Summer 2002 Continental U.S. Drought M. J. Fennessy, P. A. Dirmeyer, J. L. Kinter III, L. Marx and C. A. Schlosser 2002 Climate Diagnostics and Prediction Workshop, Vienna, VA. March 1, 2002 soil wetness anomaly (percent of saturation) for a) Huang et al. (1996) and b) COLA GCM initial condition. but AMIP coupled L-A variance may be poor Standard normal deviates:

25. Vegetation Good estimates of vegetation phenology exist from remote sensing (NDVI → LAI, Greenness ). Hindcast: climatology versus observed Forecast: climatology versus persisted anomaly