1. Global Land-Atmosphere Coupling Experiment ---- An intercomparison of land-atmosphere coupling strength across a range of atmospheric general circulation models Zhichang Guo Paul DirmeyerRandal Koster __________________________________ The 84th AMS Annual Meeting, Seattle, WA, Jan. 13, 2004 WGSIP

2. Acknowledgements GLACE is jointly sponsored by the GEWEX GLASS (Global Land Atmosphere System Study) panel and CLIVAR WGSIP (Working Group on Seasonal-to- Interannual Prediction) Special thanks are given to the all GLACE participants: Tony Gordon and Sergey Malyshev (GFDL); Yongkang Xue and Ratko Vasic (UCLA); David Lawrence, Peter Cox, and Chris Taylor (HadAM3): Bryant McAvaney (BMRC); Sarah Lu and Ken Mitchell (NCEP/GFS); Diana Verseghy and Edmond Chan (CCCma); Ping Liu (NSIPP); and Eva Kowalczyk and Harvey Davies (CSIRO); Polcher Jan; Andy Pitman; Pedro Viterbo; Taikan Oki and Tomohito Yamada (University of Tokyo ); Yogesh Sud and David M. Mocko (GSFC).

3. Review Observations of real-world coupling strength at the global scale are not available. Nevertheless, the coupling strength is a key element of the climate system. Land-atmosphere coupling problem has been widely examined using AGCMs. (Shukla and Mintz, 1982; Henderson-Sellers and Gornitz, 1984, Dirmeyer, 2001) Computer-based experimental results are model-dependent. Koster, et al. (2002) show that the strength of the coupling varies significantly among four AGCMs. GLACE is a broad follow-on to this study. It is designed to examine the strength of land-atmosphere coupling across a range of AGCMs. Website: http://glace.gsfc.nasa.gov

4. Kanae/Oki11. U. Tokyo w/ MATSIRO Xue10. UCLA with SSiB Koster9. NSIPP with Mosaic Lu/Mitchell8. NCEP/EMC with NOAH Taylor7. Hadley Centre w/ MOSES2 Sud6. GSFC(GLA) with SSiB Gordon5. GFDL with LM2p5 Verseghy 4. Env. Canada with CLASS Kowalczyk3. CSIRO w/ 2 land schemes Dirmeyer2. COLA with SSiB McAvaney/Pitman1. BMRC with CHASM ContactModel Participating Groups Status submitted

5. W Simulations: Establish a time series of surface conditions time step n (Repeat without writing to obtain simulations W2 –16) Experiment Design All simulations are run from June through August Step forward the coupled AGCM-LSM Step forward the coupled AGCM-LSM Write the values of the land surface prognostic variables into file W1_STATES Write the values of the land surface prognostic variables into file W1_STATES time step n+1 R Simulations

6. R Simulations: Run a 16-member ensemble, with each member forced to maintain the same time series of land surface prognostic variables time step n Experiment Design Step forward the coupled AGCM-LSM Step forward the coupled AGCM-LSM Throw out updated values of land surface prognostic variables; replace with values for time step n from files W1_STATES time step n+1 S Simulations Throw out updated values of land surface prognostic variables; replace with values for time step n+1 from files W1_STATES

7. S Simulations: Run a 16-member ensemble, with each member forced to maintain the same time series of subsurface soil moisture prognostic variables time step n Experiment Design Step forward the coupled AGCM-LSM Step forward the coupled AGCM-LSM Throw out updated values of subsurface soil moisture prognostic variables; replace with values for time step n from file W1_STATES time step n+1 Throw out updated values of subsurface soil moisture prognostic variables; replace with values for time step n+1 from file W1_STATES

8. All simulations in ensemble respond to the land surface boundary condition in the same way  is high intra-ensemble variance is small Simulations in ensemble have no coherent response to the land surface boundary condition  is low intra-ensemble variance is large Define a diagnostic variable that describes the impact of the surface boundary on the generation of precipitation. Diagnostic Analysis Ω = _________________ 16σ(t) – σ(t, E ) 15σ ( t, E) 22 2

9. Wide disparity in coupling strength

10. Ω p is limited by Ω E --- the coherence of the response of evaporation to soil moisture

14. Highest Ω E tends to occur for midrange soil moisture

15. “Hot spots” of coupling, as determined from multi-model analysis

16. Summary Results show a broad disparity in the inherent coupling strengths of the different models In some models, strong coupling strength favors the transition zones between dry and wet areas. Some agreement is seen in the geographical patterns of the coupling strength; several models agree on certain “hot spots” of coupling.