Land-Atmosphere Coupling

1. Land-Atmosphere Coupling • Land-atmosphere coupling strength: the degree to which the atmosphere responds to anomalies in land surface state • GLACE (Global Land-Atmosphere Coupling Experiment): An intercomparison of land-atmosphere coupling strength across a range of atmospheric general circulation models • A pilot study (Koster et al., 2002) shows a wide disparity in the land-atmosphere coupling strength between 4 models

2. time step n time step n 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 Experiment Design W Simulations: Establish a time series of surface conditions time step n+1 (Repeat without writing to obtain simulations W2 – W16) R(S) Simulations: Run a 16-member ensemble, with each member forced to maintain the same time series of surface(deeper) prognostic variables 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 file W1_STATES Throw out updated values of land surface prognostic variables; replace with values for time step n+1 from file W1_STATES All simulations are run from June through August Courtesy of Zhizhang Guo

3. Diagnostic Analysis Define diagnostic variables 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 Courtesy of Zhizhang Guo

4. In principle, imposing land surface boundary states should decrease the intra-ensemble variance of the atmospheric fields. corresponding pdf when land boundary is specified pdf of precipitation at a given point, across ensemble members s2P (S) We are examining this in GLACE by looking at the variance ratio: s2P (W) Courtesy of Zhizhang Guo

5. OSU:Experiment Design using GFS/OSU • AGCM used: GFS T62 L64 • LSM: OSU • Initial Conditions: CPC AMIP (provided by Jha) • Three ensemble experiments: W, R, S • 16 members each ensemble • Each member covers 1994/06/01 to 1994/08/31 • The land variables prescribed in ensemble • R-exp • Soil moisture at 2 layers • Soil temp at 2 layers • Canopy water content • Snow depth • S-exp • Soil moisture at 2nd layer

6. Noah: Experiment Design using GFS/Noah • AGCM used: GFS T62 L64 • LSM: Noah • Initial Conditions: CPC AMIP (provided by Jha) • Three ensemble experiments: W, R, S • 16 members each ensemble • Each member covers 1994/06/01 to 1994/08/31 • The land variables prescribed in ensemble • R-exp • Total soil moisture at 4 layers changed from 2 to 4 layers • Liquid soil moisture at 4 layers new field • Soil temp at 4 layers changed from 2 to 4 layers • Canopy water content • Water equivalent snow depth • Actual snow depth new field • S-exp • Total soil moisture at 2nd-4th layers increased from 1 to 3 • Liquid soil moisture at 2nd-4th layers new field

7. NoahX: Experiment Design using GFS/Noah • AGCM used: GFS T62 L64 • LSM: Noah • Initial Conditions: • Atm: CPC AMIP (provided by Jha) • Land: Noah cycled GDAS

8. Courtesy of Zhizhang Guo

9. Courtesy of Zhizhang Guo

10. Summary • The impact of land surface conditions on atmospheric processes is examined a. Results show a broad disparity in the inherent coupling strengths of the different models. b. Coupling strength is controlled by the model’s atmospheric formulation (boundary layer processes and moist convection).