The interaction between atmosphere and land states simulated in the NCEP Global Model

1. The interaction between atmosphere and land states simulated in the NCEP Global Model Cheng-Hsuan Lu1,2, Zhichang Guo3, Kenneth Mitchell2 1 RS Information Systems Inc. 2 NOAA/NWS/NCEP EMC 3 Center for Ocean-Land-AtmosphereStudies 2005 AMS Annual Meeting, Jan 9-13, San Diego

2. GLACEGlobal Land-Atmosphere Coupling Experiment • An inter-comparison study across a range of atmospheric general circulation models • Land-atmosphere coupling strength: the degree to which the atmosphere responds to anomalies in land surface state • A pilot study (Koster et al., 2002) shows a wide disparity in the land-atmosphere coupling strength between 4 models • This study presents the land-atmosphere coupling strength in NCEP Global Forecast System (GFS) in the context of multi-model GLACE approach

3. Experiment Design

4. Experiment Design time step n time step n W Simulations: 16-member ensemble; W1: establish a time series of surface conditions; W2-W16: repeat without writing out land variables 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 R(S) Simulations: 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

5. 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 Wis high intra-ensemble variance is small Simulations in ensemble have no coherent response to the land surface boundary condition Wis low intra-ensemble variance is large

6. OSU:Experiment Design using GFS/OSU • AGCM: GFS T62 L64 • LSM: OSU • Initial Conditions: CPC AMIP, using OSU • 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 Three GFS runs OSU: OSU LSM Noah: Noah LSM NoahX: Noah LSM, initialized from Noah cycled GDAS

7. Multi-Model Average

8. Global map of variance ratio  averaged across the participating models in GALCE is established:  = 2 P,prescribed-soil/2 P,variable-soil Courtesy of Randy Koster

9. Hot Spots of Land-Atmosphere Coupling • Three hot spots are identified: the Great Plains of North America, the Sahel in Africa, and a zonal band spanning eastern Europe and western Asia. • These hot spots indicate where the monitoring of soil moisture could yield the greatest return in seasonal forecasting.

10. Model Diversity

11. 12 AGCMs 15 model runs GFS runs

13. Evap T2m

14. Daily avg column soil moisture (fraction) Daily total evaporation (mm) W R S W R S Zn1 US Zn2 CA Zn3 EU Zn4 RUS Zones: 1:Eastern US, 2:Southeast China, 3:Central Europe, 4: Southeast Russia

15. Daily avg air temperature (K) Daily total precipitation (mm) W R S Zn1 US Zn2 CA Zn3 EU Zn4 RUS W R S Zones: 1:Eastern US, 2:Southeast China, 3:Central Europe, 4: Southeast Russia

16. Summary • Land-atmosphere coupling strength (the impact of land surface conditions on atmospheric process) is examined • Regions with significant land-atmosphere coupling are identified from multi-model average

17. Summary (-continued) • Results show a broad disparity in the inherent precipitation responses of the different models • The NCEP GFS has weak land-atmosphere coupling strength (rainfall is insensitive to prescribed land states) • The differences among three GFS runs are small compared to the inter-AGCM differences

18. Thank You

20. Noah: Experiment Design using GFS/Noah • AGCM: GFS T62 L64 • LSM: Noah • Initial Conditions: CPC AMIP, using OSU • 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

21. NoahX: Experiment Design using GFS/Noah • AGCM: GFS T62 L64 • LSM: Noah • Initial Conditions: • Atm: CPC AMIP • Land: Noah cycled GDAS (for 2003 June period)

22. LSM AGCM LndIC Noah Noah OSU OSU OSU OSU NoahX Noah Noah Zn1 US Zn2 CA Zn3 EU The impact of soil moisture on rainfall is inconclusive but the opposite direction (a first order impact) is very robust. Zones: 1:Eastern US, 2:Southeast China, 3:Central Europe