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SAC Meeting - 12 April 2010 Land-Climate Interaction Paul Dirmeyer Zhichang Guo, Dan Paolino, Jiangfeng Wei.

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Presentation on theme: "SAC Meeting - 12 April 2010 Land-Climate Interaction Paul Dirmeyer Zhichang Guo, Dan Paolino, Jiangfeng Wei."— Presentation transcript:

1 SAC Meeting - 12 April 2010 Land-Climate Interaction Paul Dirmeyer Zhichang Guo, Dan Paolino, Jiangfeng Wei

2 SAC Meeting - 12 April 2010 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

3 SAC Meeting - 12 April 2010 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

4 SAC Meeting - 12 April 2010 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

5 SAC Meeting - 12 April 2010 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.

6 SAC Meeting - 12 April 2010 Coupling AGCMs to Multiple LSMs SSiB NoahCLM GFS AGCM SSiB NoahCLM COLA AGCM

7 SAC Meeting - 12 April 2010 Land-Atmosphere – Many Models 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 Coupling Strength – Soil Moisture to Precipitation

8 SAC Meeting - 12 April 2010 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.

9 SAC Meeting - 12 April 2010 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).

10 SAC Meeting - 12 April 2010 Seasonal Reforecasts – Role of Land ICs Atmosphere – ICs from CAM states within AMIP-style run Land – ICs from CLM states within AMIP-style run Ocean – observed 3D ocean initialization Ocean Only (Ensembles of 6) Atmosphere – ICs from late Dec/June ERA40 Land – ICs from GSWP MMA (86-95) or ERA40 (other) scaled to match CLM’s mean, variance Ocean – observed 3D ocean initialization Full (Ensembles of 10) CCSM3.0 (JFM, JAS; ), T85, Eulerian Dynamics Lead: Dan Paolino

11 SAC Meeting - 12 April 2010 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. Correlation to CAMS: r 2 =0.155 r 2 =0.101r 2 =0.094 r 2 =0.131

12 SAC Meeting - 12 April 2010 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:

13 SAC Meeting - 12 April 2010 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. Correlation to CMAP: r 2 =0.080 r 2 =0.081 r 2 =0.089 r 2 =0.076

14 SAC Meeting - 12 April 2010


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