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Numerical Simulation Scaling from the Leaf to the Region: ACASA/MM5/WRF at UCDavis

Numerical Simulation Scaling from the Leaf to the Region: ACASA/MM5/WRF at UCDavis. Kyaw Tha Paw U, ( aus: om ay: OD;) Liyi Xu, Dave Pyles Biomicrometeorology Group, University of California, Davis For presentation at National Central University, Taiwan 11 am Friday December 19, 2008.

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Numerical Simulation Scaling from the Leaf to the Region: ACASA/MM5/WRF at UCDavis

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  1. Numerical Simulation Scaling from the Leaf to the Region: ACASA/MM5/WRF at UCDavis Kyaw Tha Paw U, (aus: om ay: OD;) Liyi Xu, Dave PylesBiomicrometeorology Group, University of California, Davis For presentation at National Central University, Taiwan 11 am Friday December 19, 2008

  2. Statement of the Problem How to create process-based models to scale from leaf level to regional scale level

  3. Justification Generally agreed that detailed process-based models will be more successful in predicting climate change interactions with ecosystems (land surfaces)

  4. Justification Probability that detailed process-based models will be more successful in short to medium scale regional forecasts because of improved descriptions of energy and mass (water) exchange with ecosystems (land surfaces)

  5. Modeling to Scale Up Leaf Physiology Parameterization (Ball-Berry,Von Caemmerer-Farquhar) Layer or Discretize Canopy, Equations for Transfer Between Layers Abstract Plants As Parts of Elements In Canopies Continue Process to Large Scales

  6. Radiation 3rd-order Turbulence OBS or WRF/MM5 thermal IR PBL visible & near-IR Precipitation heat, H2O, CO2 momentum 20 levels surface-layer Physiology ACASA Soil Model layered heat and water transport soil 15 levels UCDAdvanced Canopy-Atmosphere-Soil Algorithm

  7. Sdj=(Sdo)(Idj) Idj= Sbj=(Sbo)(Ibj) Ibj=exp(-kLAI) visible & near-IR Sbo Lj= Lj+1 Idj+1 + Tl,j+1(1-Idj+1) thermal IR Lj+1 Lj ACASA Radiation (3 Wavebands) UCDAdvanced Canopy-Atmosphere-Soil Algorithm

  8. Radiation thermal IR visible & near-IR Energy budget of soil surface, leaves, other canopy elements 20 levels surface-layer ACASA soil 15 levels UCDAdvanced Canopy-Atmosphere-Soil Algorithm

  9. Radiation 9 Angle classes sunlit 1 Class shaded Branches & Trunks Soil Radiative Characteristics of each item thermal IR visible & near-IR Energy budget of soil surface, leaves, other canopy elements 20 levels surface-layer ACASA soil 15 levels UCDAdvanced Canopy-Atmosphere-Soil Algorithm

  10. SURFACE ENERGY BALANCE Rn = H + LE + G + S + M Rn= net radiation H= sensible heat flux LE= latent heat flux G= ground heat flux S= canopy heat storage M= respiration & photosynthesis

  11. Rn = H + LE + G + S + M Exact Energy budget solution Paw U & Gao 1988 Saturation vapor pressure = 4th order polynomial; then above equation solved analytically with “Quartic” solution Temperature of all each canopy type element calculated

  12. Radiation thermal IR visible & near-IR 20 levels surface-layer Physiology ACASA soil 15 levels UCDAdvanced Canopy-Atmosphere-Soil Algorithm

  13. Physiological Plant Response: Function of Light, Temperature, Humidity, soil water, carbon dioxide concentration; controls LE and energy budget Farquhar-von Caemmerer Photosynthesis --An Vc=carboxylation rate Vo=photorespiration rate Rd= dark respiration rate Reference: Farquhar, G. D. and von Caemmerer, S., 1982: Modeling photosynthetic response to environmental conditions. Encyclopedia of Plant Physiology II, 12b, O.L. Lange, P. S. Nobel, C. B. Osmond, and H. Ziegler, Eds., Springer-Verlag, Berlin, Germany, 747 pp. Stomatal Control: Modified Ball-Berry Model

  14. Radiation Turbulence OBS or MM5 or WRF thermal IR PBL + FT visible & near-IR heat, H2O, CO2 momentum 20 levels surface-layer Physiology ACASA soil 15 levels UCDAdvanced Canopy-Atmosphere-Soil Algorithm

  15. Turbulence: Layer to Layer Interaction Equations Down-Gradient (K-theory) Transfer Flux = transport coefficient * gradient

  16. Layer to Layer Interaction Equations Down-Gradient (K-theory) Transfer Not Appropriate in Plant Canopies, where many cases of Non-gradient or “counter-gradient” transfer

  17. Layer to Layer Interaction Equations Down-Gradient (K-theory) Transfer Lagrangian Statistical Simulations

  18. Layer to Layer Interaction Equations Down-Gradient (K-theory) Transfer Lagrangian Statistical Simulations Good for Scalar Transport but can’t simulate velocity field, need to input velocity statistics before simulations

  19. Layer to Layer Interaction Equations Down-Gradient (K-theory) Transfer Lagrangian StatisticalSimulations Large Eddy Simulation

  20. Layer to Layer Interaction Equations Down-Gradient (K-theory) Transfer Lagrangian StatisticalSimulations Large Eddy Simulation Can visualize turbulent flows at 10 Hz Large Computational Demands, cannot be run with regional scale model

  21. Layer to Layer Interaction Equations Down-Gradient (K-theory) Transfer Lagrangian StatisticalSimulations Large Eddy Simulation Higher-Order Closure Transfer

  22. Layer to Layer Interaction Equations Down-Gradient (K-theory) Transfer Lagrangian StatisticalSimulations Large Eddy Simulation Higher-Order Closure Transfer Extra Parameters But shown to work well in Plant Canopies

  23. Radiation 3rd-order Turbulence OBS or MM5 thermal IR PBL + FT visible & near-IR heat, H2O, CO2 momentum 20 levels surface-layer Physiology ACASA soil 15 levels UCDAdvanced Canopy-Atmosphere-Soil Algorithm

  24. Humidity: Reynolds Stress: Example of Second-Moment Equations for Turbulence

  25. Momentum Humidity Third Moment Equations using Quasi-Gaussian Approximation, for Turbulence

  26. Radiation 3rd-order Turbulence OBS or MM5 thermal IR PBL + FT visible & near-IR Precipitation heat, H2O, CO2 momentum 20 levels surface-layer Physiology ACASA soil 15 levels UCDAdvanced Canopy-Atmosphere-Soil Algorithm

  27. Radiation 3rd-order Turbulence OBS or MM5 thermal IR PBL + FT visible & near-IR Precipitation heat, H2O, CO2 momentum 20 levels surface-layer Physiology ACASA Snowpack hydrology Soil Model layered heat and water transport soil 15 levels UCDAdvanced Canopy-Atmosphere-Soil Algorithm

  28. ACASA INPUT: Atmospheric: Top of Domain: Mean Velocity Temperature, humidity, CO2Concentration Shortwave, Longwave Radiation downwards Physiological/Biological: Parameters for Photosynthesis & Stomates Leaf Area Index Profile Leaf radiative parameters Leaf size, angle distribution Soil physical characteristics

  29. ACASA INPUT:

  30. Observed vs. simulated wind direction & speed for different stability regimes (6/9/1998 - 8/7/1999)

  31. Sensitivity of model wind direction to model Complexity & vegetation structure daytime cases (6/9/1998 - 8/7/1999)

  32. Sensitivity of model wind direction to model Complexity & vegetation structure nighttime cases (6/9/1998 - 8/7/1999)

  33. Observed vs. simulated air temperature for different stability regimes (6/9/1998 - 8/7/1999)

  34. Mean Observed and simulated [CO2] profiles May 20 – Aug 26, 2002 • Legend: • Rswd < 150 Wm-2 • 150 < Rswd < 300 Wm-2 • 300 < Rswd < 450 Wm-2 • 450 < Rswd < 600 Wm-2 • Rswd > 600 Wm-2 2 1 4 3 5

  35. Radiation 3rd-order Turbulence OBS or MM5/WRF thermal IR PBL + FT visible & near-IR heat, H2O, CO2 momentum 20 levels surface-layer Physiology ACASA soil 15 levels UCDAdvanced Canopy-Atmosphere-Soil Algorithm

  36. Basic properties of the MM5/ACAS Land Use Categories Seattle Portland Central Valley Los Angeles

  37. z z WR WR WR BL BL BL (c) (b) (a) SO SO SO (a) NCEP/NCAR reanalysis MM5- ACASA MM5- BATS T, u T, u T, u WR WR WR BL BL BL (e) (d) (f) SO SO SO MM5- ACASA MM5- BATS NCEP/NCAR reanalysis q q q WR WR WR BL BL BL (g) SO (i) SO (h) SO Figure 2: (a) NCEP Reanalysis topography, MM5 topography, (c) MM5 land use categories. Plots (d), (e), and (f) are Reanalysis, MM5-ACASA, and MM5-BATS near-surface air temperature with wind vectors, respectively. Plots (g), (h), and (i) are Reanalysis, MM5-ACASA and MM5-BATS near-surface specific humidity, respectively. Wind vector arrows appearing in plots (e) and (f) are for every four MM5 horizontal grid points.

  38. MM5- ACASA MM5- BATS MM5- LECMWF H H H WR WR WR BL BL BL (a) SO (b) SO (c) SO (d) MM5- ACASA MM5- BATS MM5- LECMWF LE LE LE WR WR WR BL BL BL (d) SO SO (e) SO (f) MM5- ACASA MM5- ACASA MM5- LECMWF NEE WR WR WR BL BL BL (h) (i) (g) SO SO SO Precipitation Figure 3: Average July 21-31 fluxes and accumulated precipitation for western North America. Plots (a)-(c) are MM5-ACASA, MM5-BATS, and MM5-LECMWF H (W m-2) values, respectively. Plots (d)-(f) are the same as (a)-(c) but for LE (W m-2). Plot (g) is the average July 21-31 MM5-ACASA NEE, or CO2 flux density (μmol m-2 s-1). Plots (h) and (I) are July 21-31 MM5-ACASA and MM5-LECMWF accumulated precipitation (cm) for western North America, respectively

  39. Average CO2 Fluxes July 21-31, 1998 Large CO2 Uptake Large CO2 Release

  40. Land use topography Average Surface Fluxes Latent Heat(Wm-2) July 21-31, 1998 MM5/BATS MM5/ACASA

  41. MM5 Simulation, Aug 4-5 1992 Clear skies Eta PBL scheme NCEP/NCAR Reanalysis for ICs & BCs 40 vertical layers (20 below 0.85 sigma) Washington Triple nesting as shown: D01: dx, dy = 8.1 km D02: dx, dy = 2.7 km D03: dx, dy = 0.9 km Oregon

  42. D03 land use map (dark green = coniferous forest)

  43. MM5 Inner domain ~70m winds, 10pm local time Head of white arrow shows WRCCRF site location Color shows terrain height (m) 500 mb winds show nearly uniform southwesterlies Sensible Heat Flux Mostly –40 to 0 W m-2

  44. MM5 Inner domain ~70m winds, 3pm local time Head of white arrow shows WRCCRF site location Color shading under vectors shows terrain height (m) 500 mb winds show nearly uniform southwesterlies Sensible Heat Flux Mostly 150 to 400 W m-2

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