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Jeremy Pal Filippo Giorgi, Raquel Francisco, Elfatih Eltahir

Part I: Representation of the Effects of Sub-grid Scale Topography and Landuse on the Simulation of Surface Climate and Hydrology Part II: The Effects of Soil Moisture on the Simulation of Surface Climate and Hydrology. Jeremy Pal Filippo Giorgi, Raquel Francisco, Elfatih Eltahir.

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Jeremy Pal Filippo Giorgi, Raquel Francisco, Elfatih Eltahir

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  1. Part I: Representation of the Effects of Sub-grid Scale Topography and Landuse on the Simulation of Surface Climate and HydrologyPart II: The Effects of Soil Moisture on the Simulation of Surface Climate and Hydrology Jeremy Pal Filippo Giorgi, Raquel Francisco, Elfatih Eltahir

  2. Part I: Representation of the Effects of Sub-grid Scale Topography and Landuse on the Simulation of Surface Climate and HydrologyPart II: The Effects of Soil Moisture on the Simulation of Surface Climate and Hydrology

  3. Subgrid Topography and Landuse Scheme • Land surfaces are characterized by pronounced spatial heterogeneity that span a wide range of scales (down to 100s of meters). • Topography and landuse exert a strong forcing on atmospheric circulations and land-atmosphere exchanges. • Current climate models cannot capture the full range of scales, thus intermediate techniques can be used. 60-km 10-km

  4. 10-km Topography • Coarse Domain: • ~250 grid points • Medium Domain: • ~9,000 grid points • Fine Domain: • ~325,000 grid points 360-km Topography 60-km Topography

  5. 360-km Landuse 60-km Landuse 10-km Landuse • Coarse Domain: • ~250 grid points • Medium Domain: • ~9,000 grid points • Fine Domain: • ~325,000 grid points

  6. Mean Landuse and Elevation 60-km General Methodology • Define a regular fine scale sub-grid for each coarse scale model grid-box. • Landuse, topography, and soil texture are characterized on the fine grid. • Disaggregate climatic fields from the coarse grid to the fine grid (e.g. temperature, water vapor, precipitation). • Disaggregation technique based on the elevation differences between the coarse grid and the fine grid. • Perform BATS surface physics computations on the fine grid. • Reaggregate the surface fields from the fine grid to the coarse grid.

  7. Methodology: Disaggregation • Temperature disaggregated according to the subgrid elevation difference: sg = subgrid; i,j = subgrid cell; overbar coarse grid T = near surface air temperature; h = topographical elevation GT = average atmospheric lapse rate = 6.5 °C/km

  8. Methodology: Disaggregation • Temperature disaggregated according to the subgrid elevation difference: sg = subgrid; i,j = subgrid cell; overbar coarse grid T = near surface air temperature; h = topographical elevation GT= average atmospheric lapse rate = 6.5 °C/km • Relative humidity is held constant (more or less).

  9. Height, temperature, and moisture conserved. • For example: Methodology: Disaggregation • Temperature disaggregated according to the subgrid elevation difference: sg = subgrid; i,j = subgrid cell; overbar coarse grid T = near surface air temperature; h = topographical elevation GT= average atmospheric lapse rate = 6.5 °C/km • Relative humidity is held constant (more or less).

  10. Methodology: Disaggregation • Temperature disaggregated according to the subgrid elevation difference: sg = subgrid; i,j = subgrid cell; overbar coarse grid T = near surface air temperature; h = topographical elevation GT= average atmospheric lapse rate = 6.5 °C/km • Relative humidity is held constant (more or less). • Height, temperature, and moisture conserved. • For example: • Convective precipitation is randomly distributed over 30% of the gridcell [e.g. CCM; Kiehl et al 96]

  11. Methodology: Reaggregation • The surface heat fluxes, temperature and humidity are reaggregated to the coarse grid after BATS computations are performed • For example, for the latent heat flux LH:

  12. Simulation period: 1 Oct 1994 to 1 Sept 1995 Land Surface computations performed on subgrid. CTL 60-km; no subgrid cells EXP15 15-km; 16 subgrid cells EXP10 10-km; 36 subgrid cells 60-km Numerical Experiments 15-km 10-km

  13. OBS (CRU) CTL OBS (CRU) CTL Results: Temperature WINTER (DJF) SUMMER (JJA)

  14. OBS (CRU) CTL EXP15 EXP10 OBS (CRU) CTL EXP15 EXP10 Results: Temperature WINTER (DJF) SUMMER (JJA)

  15. OBS (Frei & Schär) OBS (CRU) CTL OBS (Frei & Schär) Results: Precipitation OBS (CRU) CTL WINTER (DJF) SUMMER (JJA)

  16. EXP15 EXP10 OBS (Frei & Schär) OBS (CRU) CTL EXP15 EXP10 OBS (Frei & Schär) Results: Precipitation OBS (CRU) CTL WINTER (DJF) SUMMER (JJA)

  17. CTL EXP15 EXP10 Station OBS WINTER (DJF) CTL EXP15 EXP10 Station OBS SPRING (MAM) Results: Snow

  18. Results: Water Budget

  19. Results: Energy Budget

  20. Part I: Summary & Conclusions • Fine scale topography and landuse variability can have a significant effect on surface climate. • Better agreement of temperature, precipitation (summer) and snow with observations. • implies improved simulation of the seasonal evolution of the surface hydrologic cycle. • Primary effects are likely to be due to topographic variability (not landuse). • Our mosaic-type approach can provide an effective tool of intermediate complexity to bridge the scaling gap between climate models (both global and regional) and surface hydrologic processes.

  21. Mean Landuse and Elevation 60-km 60-km In the works… • Implement parameterization of subgrid scale effects on the formation of precipitation (both large-scale and convective). • Apply disaggregation techniques for other variables (e.g. precipitation, radiation)

  22. Part I: Representation of the Effects of Sub-grid Scale Topography and Landuse on the Simulation of Surface Climate and HydrologyPart II: The Effects of Soil Moisture on the Simulation of Surface Climate and Hydrology

  23. ISWS Soil Saturation Time Series What role does soil moisture play in the prediction rainfall? What are the pathways and mechanisms responsible for the soil moisture-rainfall feedback? Rainfall Anomalies (mm/d) Rainfall Anomalies (mm/d) June & July 1993 May & June 1988

  24. Full Model Domain Analysis Domain Domain & Topography

  25. Storm Track CappingInversion LLJ 25MW Fixed Patch Experiment: Initial Root Zone Soil Moisture Midwest: 25MW Fixed Soil Moisture (25%) 25% Interactive Soil Moisture (CTL)

  26. Rainfall (U.S. only) 25MW-CTL Net Radiation Boundary Layer Height • Decrease in the energy per unit depth of boundary layer via radiative effects • Should decrease the likelihood and magnitude of rainfall of the region of the anomaly 25MW-CTL 25MW-CTL Moist Static Energy 25MW-CTL

  27. 500mb Winds & Heights CTL • Decrease in convection via local feedbacks • Anomalous high pressure • Anomalous anticyclonic flow • Increased descent and a northward stormtrack shift • Changes in rainfall distribution 500mb Zonal Winds 500mb Winds & Heights 25MW-CTL 25MW-CTL

  28. Storm Track CappingInversion LLJ 75SW Fixed Patch Experiment: Initial Root Zone Soil Moisture Southwest: 75SW Interactive Soil Moisture (CTL) 75% Fixed Soil Moisture (75%)

  29. 75SW Experiments 500mb Zonal Winds Rainfall (U.S. only) 75SW-CTL 75SW-CTL

  30. Local Soil Moisture-Rainfall Feedbacks A high pressure anomaly A dry soil moisture anomaly Less local rainfall (Pal& Eltahir,2001) A wet soil moisture anomaly A low pressure anomaly More local rainfall (Pal& Eltahir,2001)

  31. Remote Soil Moisture-Rainfall Feedbacks (3)Shift in Storm-track northward (1)Dry anomaly (2)High pressure anomaly A soil moisture anomaly leads to a shift in the storm-track Pal and Eltahir (2003), QJRMS

  32. Remote Soil Moisture-Rainfall Feedbacks (3)Shift in Storm-track southward (1)Wet anomaly (2)Low pressure anomaly A soil moisture anomaly leads to a shift in the storm-track Pal and Eltahir (2003), QJRMS

  33. USHCN (Obs) CTL CLM 75% 25% 50% Precipitation (U.S. only)

  34. Part II: Summary & Conclusions • The feedbacks of soil moisture to the local climate can induce positive feedbacks to the large-scale circulation patterns. • Local soil moisture anomalies can potentially lead to drought- and flood-like conditions not only in the local region, but also in remote regions. • An accurate representation of the distribution of soil moisture is crucial to accurately represent observed rainfall. • The spatial variability of soil moisture in North America appears to be an important in predicting rainfall.

  35. Initial Root Zone Soil Moisture (June 25) Climatology 1988 1993

  36. Dry Soil Storm Track Normal Storm Track Wet Soil Storm Track Additional Soil Moisture-Rainfall Mechanism

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