Calculation of the global land surface energy, water and CO2 fluxes with an off‐line version of SiB2

Global land surface fluxes of energy and CO 2 have been simulated using an off-line version of a biosphere-atmosphere model, SiB2, forced with analyzed or observed atmospheric boundary layer mean potential temperature, water vapor mixing ratio, and wind, surface downward solar and thermal radiation, and precipitation. The off-line model is called SiBDRV. Soil and vegetation boundary conditions were specified from satellite data and other sources. The European Centre for Medium-Range Weather Forecasts (ECMWF) data assimilation system products were used to derive the atmospheric and radiative forcings. Precipitation was based on station observations. The SiBDRV results were compared with corresponding simulation results produced by the Colorado State University general circulation model (CSU GCM), with the ECMWF assimilation system output and with observations. Differences between the surface energy budget components and the surface climatology produced by SiBDRV and the ECMWF assimilation system are due to differences in the land surface parameterizations between the two models. SiBDRV produced lower surface latent heat fluxes and larger sensible heat fluxes than the ECMWF data assimilation, partly due to large canopy resistent term explicitly formulated by SiB2 and possible precipitation differences between the SiBDRV precipitation forcing and the ECMWF data. Differences between the SiBDRV and the CSU GCM results are due to the different climates associated with the ECMWF assimilation system output, which is strongly constrained by assimilated observations, and by the CSU GCM, which is run in pure simulation mode. More specifically, the major reasons for the surface energy and CO2 budget differences between the SiBDRV and the GCM are greater incoming solar radiation in the GCM and differences in the precipitation patterns. The simulated global annual carbon uptake by the terrestrial biosphere is similar in SiBDRV and the GCM. The annual gross primary productions of SiBDRV (116 Gt) and the GCM (113 Gt) agree well with other studies, using either ecological process models or empirical regression models. SiBDRV takes up 10 and 5% more CO2 than the GCM in January and July, respectively. The seasonally varying land surface CO2 fluxes estimated by the SiBDRV and the GCM both compare reasonably well with the results of other calculations.

[1]  Steven W. Leavit Biogeochemistry, An Analysis of Global Change , 1998 .

[2]  C. Justice,et al.  A revised land surface parameterization (SiB2) for GCMs. Part III: The greening of the Colorado State University general circulation model , 1996 .

[3]  D. Legates,et al.  Mean seasonal and spatial variability in gauge‐corrected, global precipitation , 1990 .

[4]  Minghua Zhang,et al.  Intercomparison and interpretation of surface energy fluxes in atmospheric general circulation models , 1992 .

[5]  J. Louis A parametric model of vertical eddy fluxes in the atmosphere , 1979 .

[6]  Piers J. Sellers,et al.  Effects of implementing the simple biosphere model in a general circulation model , 1989 .

[7]  W. Schlesinger 5 – The Terrestrial Biosphere , 1991 .

[8]  A. Dalcher,et al.  A Simple Biosphere Model (SIB) for Use within General Circulation Models , 1986 .

[9]  James W. DEARDORFF-National Parameterization of the Planetary Boundary layer for Use in Ceneral Circulation Models , 1972 .

[10]  J. Deardorff Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation , 1978 .

[11]  Inez Y. Fung,et al.  Application of Advanced Very High Resolution Radiometer vegetation index to study atmosphere‐biosphere exchange of CO2 , 1987 .

[12]  C. Field,et al.  A reanalysis using improved leaf models and a new canopy integration scheme , 1992 .

[13]  H. Lieth Modeling the Primary Productivity of the World , 1975 .

[14]  D. Randall,et al.  Liquid and Ice Cloud Microphysics in the CSU General Circulation Model. Part 1: Model Description and Simulated Microphysical Processes , 1996 .

[15]  J. Garratt Review of Drag Coefficients over Oceans and Continents , 1977 .

[16]  Zhanqing Li,et al.  Estimation of surface albedo from space: A parameterization for global application , 1994 .

[17]  D. Randall,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation , 1996 .

[18]  D. Legates,et al.  Mean seasonal and spatial variability in global surface air temperature , 1990 .

[19]  E. F. Bradley,et al.  Flux-Profile Relationships in the Atmospheric Surface Layer , 1971 .

[20]  Andrew J. Pitman,et al.  Assessing the Sensitivity of a Land-Surface Scheme to the Parameter Values Using a Single Column Model , 1994 .

[21]  J. Worley,et al.  An introduction to atmospheric and oceanographic datasets , 1994 .

[22]  Dorothy K. Hall,et al.  Nimbus-7 SMMR derived global snow cover parameters , 1987 .

[23]  Bruce R. Barkstrom,et al.  The Earth Radiation Budget Experiment (ERBE). , 1984 .

[24]  A. McGuire,et al.  Global climate change and terrestrial net primary production , 1993, Nature.

[25]  G. Collatz,et al.  Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants , 1992 .

[26]  J. Deardorff,et al.  Parameterization of the Planetary Boundary layer for Use in General Circulation Models1 , 1972 .

[27]  G. Collatz,et al.  The relationship between the Rubisco reaction mechanism and models of photosynthesis , 1990 .

[28]  Piers J. Sellers,et al.  A Global Climatology of Albedo, Roughness Length and Stomatal Resistance for Atmospheric General Circulation Models as Represented by the Simple Biosphere Model (SiB) , 1989 .

[29]  Harshvardhan,et al.  Diurnal Variability of the Hydrologic Cycle in a General Circulation Model , 1991 .

[30]  J. Randerson,et al.  Terrestrial ecosystem production: A process model based on global satellite and surface data , 1993 .

[31]  Christian Blondin,et al.  Parameterization of Land-Surface Processes in Numerical Weather Prediction , 1991 .

[32]  C. Justice,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data , 1996 .

[33]  G. Collatz,et al.  Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer , 1991 .

[34]  Zhanqing Li,et al.  Radiative Characteristics of the Canadian Climate Centre Second-Generation General Circulation Model , 1994 .

[35]  C. Paulson The Mathematical Representation of Wind Speed and Temperature Profiles in the Unstable Atmospheric Surface Layer , 1970 .