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.

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Presentation transcript:

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

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

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. 10-km 60-km

360-kmTopography60-kmTopography10-kmTopography ► Coarse Domain:  ~250 grid points ► Medium Domain:  ~9,000 grid points ► Fine Domain:  ~325,000 grid points

60-kmLanduse 10-kmLanduse 360-kmLanduse ► Coarse Domain:  ~250 grid points ► Medium Domain:  ~9,000 grid points ► Fine Domain:  ~325,000 grid points

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. 60-km Mean Landuse and Elevation

Methodology: Disaggregation 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 Temperature disaggregated according to the subgrid elevation difference:Temperature disaggregated according to the subgrid elevation difference:

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

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

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

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:

Numerical Experiments 10-km 15-km 60-km ► 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

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

Results: Temperature WINTER (DJF) OBS (CRU)CTL SUMMER (JJA) OBS (CRU)CTL EXP15EXP10 EXP15EXP10

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

Results: Precipitation OBS (CRU) WINTER (DJF) CTL SUMMER (JJA) OBS (CRU)CTL EXP15EXP10 EXP15EXP10 OBS (Frei & Schär)

Results: Snow WINTER (DJF) CTL SPRING (MAM) CTL EXP15EXP10 EXP15EXP10 Station OBS

Results: Water Budget

Results: Energy Budget

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.

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) 60-km Mean Landuse and Elevation 60-km

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

Rainfall Anomalies (mm/d) June & July 1993 May & June 1988 Rainfall Anomalies (mm/d) 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?

Domain & Topography Analysis Domain Full Model Domain

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

► 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 Boundary Layer Height Net Radiation Net Radiation 25MW-CTL 25MW-CTL 25MW-CTL Moist Static Energy 25MW-CTL Rainfall (U.S. only)

500mb Zonal Winds 500mb Zonal Winds 25MW-CTL25MW-CTL 500mb Winds & Heights 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

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

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

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

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

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

Precipitation (U.S. only) USHCN (Obs) CLMCTL 25%50%75%

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.

Initial Root Zone Soil Moisture (June 25) Climatology

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