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Land-Climate Interaction. Paul Dirmeyer Zhichang Guo, Dan Paolino, Jiangfeng Wei. Recent Activities. Hydrologic Cycle Feedbacks Synthesis of land-atmosphere interaction Precipitation spectrum and predictability Linking floods to remote moisture sources Land Impact on Prediction GLACE2

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land climate interaction

Land-Climate Interaction

Paul Dirmeyer

Zhichang Guo, Dan Paolino, Jiangfeng Wei

recent activities
Recent Activities
  • Hydrologic Cycle Feedbacks
    • Synthesis of land-atmosphere interaction
    • Precipitation spectrum and predictability
    • Linking floods to remote moisture sources
  • Land Impact on Prediction
    • GLACE2
    • Land feedbacks in coupled O-A models
    • Coupling AGCMs to multiple LSMs
  • Land Surface Modeling
    • Multi-model skill, impact of forcing data on simulations
    • Role of land model in climate change projections
recent activities1
Recent Activities
  • Hydrologic Cycle Feedbacks
    • Synthesis of land-atmosphere interaction
    • Precipitation spectrum and predictability
    • Linking floods to remote moisture sources
  • Land Impact on Prediction
    • GLACE2
    • Land feedbacks in coupled O-A models
    • Coupling AGCMs to multiple LSMs
  • Land Surface Modeling
    • Multi-model skill, impact of forcing data on simulations
    • Role of land model in climate change projections
recent activities2
Recent Activities
  • Hydrologic Cycle Feedbacks
    • Synthesis of land-atmosphere interaction
    • Precipitation spectrum and predictability
    • Linking floods to remote moisture sources
  • Land Impact on Prediction
    • GLACE2
    • Land feedbacks in coupled O-A models
    • Coupling AGCMs to multiple LSMs
  • Land Surface Modeling
    • Multi-model skill, impact of forcing data on simulations
    • Role of land model in climate change projections
land group collaborations
Land Group Collaborations

Atmospheric Research

Beijing Normal U.

BoM (Australia)

Catalan Institute of Science and Climate

Center for Euro-Mediterranean Climate Change

CNRM

CSIRO

Earth Water Global

ECMWF

Environment Canada

Florida State U.

GFDL

Hadley Center

Hokkaido U.

Institute of Hydrology, Wallingford

Russian Academy of Sciences, Institute of Water Problems

KNMI

Kyoto U.

LMD/CNRS

Météo-France

MIT

Nanjing U.

NASA/GSFC

National Institute for Environmental Studies (Japan)

National Oceanography Centre, Southampton

NCAR

NCEP (EMC and CPC)

NERC, Centre for Ecology & Hydrology

Princeton U.

Purdue U.

Research Institute for Humanity and Nature (Japan)

Swiss Federal Institute of Technology

Texas A&M U.

U. Colorado

U. Exeter

U. Gothenburg

U. Lisbon

U. Maryland

U. Maryland Baltimore County

U. Miami

U. Minnesota

U. New South Wales

U. Texas

U. Tokyo

UCLA

UK Met Office

Western Kentucky U.

land atmosphere many models
Land-Atmosphere – Many Models

Coupling Strength – Soil Moisture to Precipitation

  • We have coupled 3 LSMs to both GFS and COLA AGCMs.
  • The GFS AGCM does not translate even strong ET signals into precipitation. NOAA’s operational global forecast model is unresponsive to the choice of LSM or the strength of SM/ET coupling.

Lead: Jiangfeng Wei

maya express
Maya Express
  • Moisture that supplies MJJ rainfall over US Plains evaporates from terrestrial and oceanic (GOM, Caribbean, Pacific)
  • Floods have a much larger fraction of moisture from western Gulf and Caribbean, less recycling.
  • Droughts have stagnant circulation, more local (already desiccated) land surface sources.
twelve rainiest months
Twelve Rainiest Months

There is tremendous variation from case to case, but most show enhanced transport from the south. The fetch curves around, suggesting circulation about an extended or westward displaced subtropical ridge (Bermuda High).

seasonal reforecasts role of land ics
Seasonal Reforecasts – Role of Land ICs

CCSM3.0 (JFM, JAS; 1982-1998), T85, Eulerian Dynamics

Lead: Dan Paolino

cam seasonal skill
CAM Seasonal Skill
  • Realistic initialization improves surface temperature simulation (top) compared to SST only (bottom)
  • Some of the early skill (first two weeks) comes from the atmospheric initialization.

r2=0.155

r2=0.131

r2=0.094

r2=0.101

Correlation to CAMS:

soil moisture memory
Soil Moisture Memory
  • GSWP2 MMA shows a large amount of persistence in column soil moisture (top)
  • This behavior is well reflected in CLM3, implying a source for predictability beyond the atmospheric ICs.

Correlation:

precipitation skill is poorer
Precipitation Skill is Poorer
  • There are areas of improved skill with realistic ICs, especially in the extratropics.
  • Seasonal time scales may be too coarse to discern land surface impacts, which are largely confined to sub-seasonal periods.

r2=0.089

r2=0.080

r2=0.076

r2=0.081

Correlation to CMAP: