Land-Climate Interaction

1. Land-Climate Interaction Paul Dirmeyer Zhichang Guo, Dan Paolino, Jiangfeng Wei

2. 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. 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. 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. 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. Coupling AGCMs to Multiple LSMs COLA AGCM GFS AGCM

7. 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

8. 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. 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. Seasonal Reforecasts – Role of Land ICs CCSM3.0 (JFM, JAS; 1982-1998), T85, Eulerian Dynamics Lead: Dan Paolino

11. 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:

12. 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. 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: