jeremy pal filippo giorgi raquel francisco elfatih eltahir
Download
Skip this Video
Download Presentation
Jeremy Pal Filippo Giorgi, Raquel Francisco, Elfatih Eltahir

Loading in 2 Seconds...

play fullscreen
1 / 36

Jeremy Pal Filippo Giorgi, Raquel Francisco, Elfatih Eltahir - PowerPoint PPT Presentation


  • 69 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Jeremy Pal Filippo Giorgi, Raquel Francisco, Elfatih Eltahir' - mari


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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 HydrologyPart II: The Effects of Soil Moisture on the Simulation of Surface Climate and Hydrology

Jeremy Pal

Filippo Giorgi, Raquel Francisco, Elfatih Eltahir

slide2
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
subgrid topography and landuse scheme
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

slide4
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

slide5
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
general methodology
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.
methodology disaggregation
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

methodology disaggregation1
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).
methodology disaggregation2
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).
methodology disaggregation3
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]
methodology reaggregation
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
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

results temperature
OBS (CRU)

CTL

OBS (CRU)

CTL

Results: Temperature

WINTER (DJF)

SUMMER (JJA)

results temperature1
OBS (CRU)

CTL

EXP15

EXP10

OBS (CRU)

CTL

EXP15

EXP10

Results: Temperature

WINTER (DJF)

SUMMER (JJA)

results precipitation
OBS (Frei & Schär)

OBS (CRU)

CTL

OBS (Frei & Schär)

Results: Precipitation

OBS (CRU)

CTL

WINTER (DJF)

SUMMER (JJA)

results precipitation1
EXP15

EXP10

OBS (Frei & Schär)

OBS (CRU)

CTL

EXP15

EXP10

OBS (Frei & Schär)

Results: Precipitation

OBS (CRU)

CTL

WINTER (DJF)

SUMMER (JJA)

results snow
CTL

EXP15

EXP10

Station OBS

WINTER (DJF)

CTL

EXP15

EXP10

Station OBS

SPRING (MAM)

Results: Snow
part i summary conclusions
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
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)
slide22
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
slide23
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

domain topography
Full Model

Domain

Analysis Domain

Domain & Topography
25mw fixed patch experiment initial root zone soil moisture
Storm Track

CappingInversion

LLJ

25MW Fixed Patch Experiment: Initial Root Zone Soil Moisture

Midwest: 25MW

Fixed Soil

Moisture (25%)

25%

Interactive Soil

Moisture (CTL)

slide26
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

slide27
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

75sw fixed patch experiment initial root zone soil moisture
Storm Track

CappingInversion

LLJ

75SW Fixed Patch Experiment: Initial Root Zone Soil Moisture

Southwest: 75SW

Interactive Soil

Moisture (CTL)

75%

Fixed Soil

Moisture (75%)

75sw experiments
75SW Experiments

500mb Zonal Winds

Rainfall (U.S. only)

75SW-CTL

75SW-CTL

slide30
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)

slide31
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

slide32
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

part ii summary conclusions
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.
ad