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Global Land-Atmosphere Coupling Experiment ---- An intercomparison of land-atmosphere

WGSIP. Global Land-Atmosphere Coupling Experiment ---- An intercomparison of land-atmosphere coupling strength across a range of atmospheric general circulation models Zhichang Guo Paul Dirmeyer Randal Koster. __________________________________

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Global Land-Atmosphere Coupling Experiment ---- An intercomparison of land-atmosphere

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

  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. Participating Groups Model Contact Status 1. BMRC with CHASM McAvaney/Pitman submitted 2. COLA with SSiB Dirmeyer submitted 3. CSIRO w/ 2 land schemes Kowalczyk submitted submitted Verseghy 4. Env. Canada with CLASS submitted 5. GFDL with LM2p5 Gordon submitted 6. GSFC(GLA) with SSiB Sud submitted 7. Hadley Centre w/ MOSES2 Taylor 8. NCEP/EMC with NOAH Lu/Mitchell submitted 9. NSIPP with Mosaic Koster submitted 10. UCLA with SSiB Xue submitted submitted 11. U. Tokyo w/ MATSIRO Kanae/Oki

  5. Experiment Design All simulations are run from June through August W Simulations: Establish a time series of surface conditions time step n time step n+1 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 (Repeat without writing to obtain simulations W2 –16) R Simulations

  6. Experiment Design 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 time step n+1 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 Throw out updated values of land surface prognostic variables; replace with values for time step n+1 from files W1_STATES S Simulations

  7. Experiment Design 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 time step n+1 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 Throw out updated values of subsurface soil moisture prognostic variables; replace with values for time step n+1 from file W1_STATES

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

  9. Wide disparity in coupling strength

  10. Ωpis limited by ΩE --- the coherence of the response of evaporation to soil moisture

  11. Highest ΩE tends to occur for midrange soil moisture

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

  13. 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.

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