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Some Approaches and Issues related to ISCCP-based Land Fluxes

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

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Some Approaches and Issues related to ISCCP-based Land Fluxes

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

  2. Outine • 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. • Quick-views of some surface radiation products over the U.S. (MODIS, CERES, ISCCP) • Some initial results • Some critical issues for Land Flux success. • Inferred ET (and surface budgets) from LSM, reanalysis and atmospheric satellite data

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

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

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

  6. SEBS Model Description CEOP observations used to assess ET predictionsForcing 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 sites 3 tower sites High resolution/quality data produces good quality estimates – examine model accuracy

  8. ~ 1020 m Ê = 380.0 W/m2σ = 35.7 W/m2 ~ 90 m Ê = 392.3 W/m2σ = 105.3 W/m2 ~ 60 m Ê = 367.5 W/m2σ = 97.2 W/m2 Previous Investigations – SMACEX 02

  9. Rn – Net Radiation (W/m2) G – Soil Heat Flux (W/m2) a – Density of air (Kg/m3) Cp – Specific Heat of Air (J/Kg/oC) es – Saturated vapor pressure (Pa) ea – Vapor pressure of air (Pa) • ea – Vapor pressure of air (Pa) • ra – Aerodynamic Resistance (s/m) • rs – Surface Resistance (s/m) • – Slope of saturated vapor pressure (Pa/oC) • – Psychrometric constant (Pa/oC) Penman-Monteith (P-M) Equation

  10. Datasets

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

  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) APRIL 2003

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

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

  23. Global Forcing Dataset (Sheffield et al. J Climate, 2006) 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) CRU 1901-2000, Monthly, 0.5deg P, T, Tmin, Tmax, Cld GPCP 1997-, Daily, 1.0deg P UW 1979-2000, Daily, 2.0deg P NCEP/NCAR Reanalysis 1948-, 3hr, 6hr, daily, T62 P, T, Lw, Sw, q, p, w PGF50 1948-2000, 3hr, daily, 1.0deg P, T, Lw, Sw, q, p, w TRMM 2002-, 3hr, 0.25deg P SRB 1985-2000, 3hr, 1.0deg Lw, Sw

  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 Conv (mm) dw/dt (mm) Precip. (mm) Evap. (mm) Runoff (mm) ds/dt (mm) Airs sounding data USGS Gauge data

  26. NARR NLDAS Inferred Observed Atmospheric-Land Water Budget over the Mississippi, 1998 Inferred P = ENARR – dw/dtNARR + convNARR Inferred E = PNARR – dw/dtNARR + convNARR Inferred ds/dt = convNARR - dw/dtNARR - QOBS

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

  32. Low Inferred E a result of high Conv and high P Mean Distribution of Atmospheric-Land Budgets 1979-1999: NARR Convergence NARR dW/dt NLDAS Precip 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 Observed runoff Results in high ds/dt Inferred ds/dt from NARR And observed runoff NLDAS ds/dt

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