1. Some Approaches and Issues related to ISCCP-based Land Fluxes Eric F Wood Princeton University

2. 1.Overview of two models that we’re using for continental-scale ET retrievals, the “Surface Energy Budget System” (SEBS) based on Su (2002) and a Penman Montheith-based approach. 2.Quick-views of some surface radiation products over the U.S. (MODIS, CERES, ISCCP) 3.Some initial results 4.Some critical issues for Land Flux success. 5.Inferred ET (and surface budgets) from LSM, reanalysis and atmospheric satellite data Outine

3. Use the Surface Energy Balance Model (SEBS) to determine instantaneous  daily ET predictions (limited by surface temperature). SEBS Model Description Components of the radiation balance are used to determine the net radiation (R n ) – SW , α, ε, T s, LW  R n – G = H + LE Rn = (1- α) SW  + ε LW  - εσ The ground heat flux (G) is parameterized as a function of fractional cover – LAI/NDVI relationships, which needs to be improved

4. SEBS Vertical Extent (ASL-PBL) Viscous sublayer Transition layer Inertial sublayer Atmospheric Surface Layer (ASL) Planetary (Convective) Boundary Layer (PBL) Roughness sublayer ~ 10 1~2 m ~ 10 -1~1 m ~ 10 -3 m ~ 10 2-3 m Free Atmosphere Wind profile Blending height PBL height Interfacial sublayer Princeton University

5. Energy Balance Method - Turbulent Heat Fluxes Princeton University Use Similarity Theory for the Atmospheric Surface Layer Wind, air temperature, humidity (aerodynamic roughness, thermal dynamic roughness) H G0 LE Rn

6. SEBS Model Description CEOP observations used to assess ET predictions Forcing data from validation tower sites supplemented with MODIS data to produce estimates of surface fluxes.

7. Previous Tower Investigations – SMACEX 02 Examining the spatial equivalence for corn and soybean 5 tower sites3 tower sites High resolution/quality data produces good quality estimates – examine model accuracy

8. Previous Investigations – SMACEX 02 ~ 1020 m Ê = 380.0 W/m 2 σ = 35.7 W/m 2 Ê = 392.3 W/m 2 σ = 105.3 W/m 2 ~ 90 m Ê = 367.5 W/m 2 σ = 97.2 W/m 2 ~ 60 m

9. Penman-Monteith (P-M) Equation Rn – Net Radiation (W/m 2 ) G – Soil Heat Flux (W/m 2 )  a – Density of air (Kg/m 3 ) C p – Specific Heat of Air (J/Kg/ o C) e s – Saturated vapor pressure (Pa) e a – Vapor pressure of air (Pa) r a – Aerodynamic Resistance (s/m) r s – Surface Resistance (s/m)  – Slope of saturated vapor pressure (Pa/ o C)  – Psychrometric constant (Pa/ o C)

10. Datasets Data TypeVariableUnitSourcePlatformResolution Surface Meteorological Data Air temp. Pressure Wind Vapor Pressure  C KPa m/s KPa AIRS / ISCCP AIRS / ISCCP NLDAS AIRS Aqua / ISCCP NLDAS Aqua 45 km 12.5 km 45 km Radiative Energy Flux Incident SW Rad. Incident LW Rad. W/m 2 CERES ISCCP Aqua ISCCP 0.2 deg 2.5 deg Surface Temperature Composite Radiometric Temp. (Soil + Veg.) KMODIS ISCCP Aqua 1 – 5 km 2.5 deg Vegetation Parameters Emissivity Albedo LAI Veg. Type -------- MODIS / ISCCP MODIS Aqua/Terra Terra (UMD) 1 - 5 km / 0.2 deg 1 km / 0.2 deg 1 - 5 km 1 Km

11. Incoming Shortwave Radiation – CERES

12. Incoming Shortwave Radiation – ISCCP

13. Incoming Shortwave Radiation (Oklahoma)

14. Incoming Shortwave Radiation (Instantaneous) ISSCP (2.5deg) vs. CERES (upscaled to 2.5deg) May 1–Aug. 31, 2003, instantaneous (NASA/Aqua)

15. ISSCP (2.5deg) vs. CERES (upscaled to 2.5deg) May 2003 – August 2003, Aggregated to monthly from NASA/Aqua overpass times May 2003 July 2003 June 2003 Aug. 2003 Incoming Shortwave Radiation (Monthly)

16. Latent Heat Fluxes (Monthly Average) – SEBS

17. Latent Heat Fluxes (Monthly Average) – P-M

18. Latent Heat Fluxes (SEBS w. MODIS and ISCCP) (Monthly Instantaneous Average) APRIL 2003

19. Latent Heat Fluxes – Penman-Monteith (Monthly Instantaneous Average) APRIL 2003

20. Latent Heat Fluxes (SEBS w. MODIS and ISCCP) (Monthly Instantaneous Average)

21. APRIL 2003 Latent Heat Fluxes – Penman-Monteith (Monthly Instantaneous Average)

22. Critical Issues for LandFlux success 1.Scale – impact of coarse scale radiation, surface temperature, meteorology and properties. 2.Validation. Unconvinced that towers will do much for LandFlux. 3.Algorithm development/s, (multi-model merging of different retrievals?) Role of data assimilation? 4.Can we infer ET from other sources/models.

23. PGF50 1948-2000, 3hr, daily, 1.0deg P, T, Lw, Sw, q, p, w CRU 1901-2000, Monthly, 0.5deg P, T, Tmin, Tmax, Cld GPCP 1997-, Daily, 1.0deg P UW 1979-2000, Daily, 2.0deg P TRMM 2002-, 3hr, 0.25deg P SRB 1985-2000, 3hr, 1.0deg Lw, Sw NCEP/NCAR Reanalysis 1948-, 3hr, 6hr, daily, T62 P, T, Lw, Sw, q, p, w Reanalysis High temporal/low spatial resolution Observations Generally low temporal/high spatial resolution Bias-Corrected High temporal/high spatial resolution: Princeton Global Forcing 50-year data set (PGF50) Global Forcing Dataset (Sheffield et al. J Climate, 2006)

24. Global Mean Annual Runoff Ratio Seasonal (JJA) Surface Soil Moisture VIC Hydrology Model

25. Monthly time series (1979-2005) of Atmospheric-Land Water Budget over the Mississippi Airs sounding data USGS Gauge data Conv (mm) dw/dt (mm) Precip. (mm) Evap. (mm) Runoff (mm) ds/dt (mm)

26. Atmospheric-Land Water Budget over the Mississippi, 1998 Inferred P = E NARR – dw/dt NARR + conv NARR Inferred E = P NARR – dw/dt NARR + conv NARR Inferred ds/dt = conv NARR - dw/dt NARR - Q OBS NARR NLDAS Inferred Observed

27. Mean Seasonal Cycle of P-E

28. Mean Seasonal Cycle of ET

29. Mean Seasonal Cycle of Land Storage Anomaly

30. Mean Seasonal Cycle of Runoff

31. Mean Distribution of Atmospheric-Land Budgets 1979-1999: Evapotranspiration NARR Modeled VIC (NLDAS) Inferred from NARR Atmospheric Budget Higher NARR Modeled ET Low Inferred ET

32. Mean Distribution of Atmospheric-Land Budgets 1979-1999: NARR Convergence NARR dW/dt NLDAS Precip Low Inferred E a result of high Conv and high P NARR Precip

33. Mean Distribution of Atmospheric-Land Budgets 1979-1999: Observed Runoff and dS/dt from NLDAS (VIC) and Inferred from NARR Atmospheric Budget and Obs. Runoff Results in high ds/dt Observed runoff Inferred ds/dt from NARR And observed runoff NLDAS ds/dt