The Next Generation of the Simple Biosphere Model (SiB3):

1. The Next Generation of the Simple Biosphere Model (SiB3): Model Formulation and Preliminary Results Ian T. Baker(1), A.S. Denning(1), N. Hanan(2), J.A. Berry(3), G.J. Collatz(4), K.M. Schaefer(5), A.W. Philpott(1), L. Prihodko(1), N.S. Suits(1) Colorado State University, Department of Atmospheric Science, USA Colorado State University, Natural Resources Ecology Laboratory, USA Carnegie Institute of Washington, USA Goddard Space Flight Center, USA NOAA Global Monitoring Division (formerly CMDL), USA Corresponding Author: baker@atmos.colostate.edu ABSTRACT • Introduced by P.J. Sellers and coauthors in 1986, the Simple Biosphere Model (SiB) was proposed as a physically and biologically realistic model of the terrestrial biosphere that could provide fluxes of energy and moisture for atmospheric General Circulation Models (GCMs)[1] while providing a level of biophysical realism useful to both meteorologists and biologists. The SiB code was revised in 1996[2], and the ability to generate biophysical parameters from satellite data was added [3][4]. We present a new version of SiB and describe the primary modifications, including: • Prognostic Canopy Air Space (CAS) equations for temperature, water vapor and CO2 following Vidale and Stockli[5] • A high-resolution 1-D soil and snow formulation based upon that used in the Community Land Model (CLM)[6] • Multiple-physiology capability, allowing different (generally, but not restricted to C3/C4) to share a common Canopy Air Space and soil column • Explicit calculation of radiative transfer in sunlit and shaded portions of the canopy[7][8] for calculation of both photosynthetic and energetic flux • Modification of the respiration formulation to increase heterotrophic respiration fraction; this allows for more realistic simulation of annual cycle of NEE while maintaining carbon balance [9] • Discrimination of Carbon and Oxygen isotopes • Adjustment of the interpolation of satellite-derived Normalized-Difference Vegetation Index (NDVI) used to prescribe vegetation phenology • SiB solves for phosotynthesis/transpiration simultaneously with other prognostic variables, providing and additional constraint on energy and moisture fluxes, as well as enhancing internal self-consistency. The prognostic CAS supplies a buffer for energy, moisture and trace gases; this buffer provides inertia that prevents rapid, unrealistic flux changes when forcing changes sign, such as at sunrise/sunset. • These modifications provide a higher level of biophysical realism and improve the quality of fluxes of energy, moisture and trace gases when SiB is coupled to GCMs, mesoscale atmosphteric models, chemical transport models or when compared to the ever-growing network of flux towers. • Results and Conclusions: • WLEF Tall Tower Site, Wisconsin USA • Mixed Forest • Net Ecosystem Exchange (NEE), Model vs. Observations: • SiB2: Model response ‘capped’ at both low and high NEE; model does not allow adequate photosynthesis at high light levels (see below) • SiB3: Model more closely replicates both high- and low-NEE regimes • Both Versions: Model is more responsive or sensitive to changes in environment at values of NEE near 0. SiB2 SiB3 • NEE vs. Radiation • OBSERVATIONS: Almost linear response <500W • SiB2: Saturates quickly (near 200W) where maximum canopy-scale NEE is reached • SiB3: More realistic response at lower light, but still saturates sooner than observed. • SiB3: Much better amplitude of response at mid- to high-light levels. Leaf-to-canopy Scaling: SiB2 calculates photosynthesis for a single square meter of sun-leaf. Canopy-scale photosyn-thesis is calculated by multiplying sun-leaf photosynthesis by satellite-derived quantity Π, where Π = fPAR/k. This represents the canopy as a single, continuous distribution of vegetation properties. SiB3 calculates photosynthesis explicitly for sunlit and shaded fractions of the canopy. Sunlit fraction does not mean sun-leaves, but rather the fraction of the canopy that is sunlit at a given time. Therefore, we now have two continuous distributions of leaf properties, which gives us the latitude to more accurately represent canopy scale behavior such as acclimation. Model Structure SiB2 Sample Governing Equation: CAS Temperature • Prognostic Variables • Ta - Canopy Air Space Temperature • ea – CAS Water Vapor Mixing Ratio • Tcsun – Sunlit Leaf Temperature • Tcshade – Shaded Leaf Temperature • Tground – Soil/Snow Surface Temperature • Tsoil – Deep Soil Temperature • Θsoil – Soil Moisture • Interception Stores (Puddles, water on leaves) • CO2a – CAS CO2 concentration Where the component fluxes have the following form: SiB3 This is an ‘implicit temperature, explicit coefficient’ scheme, as described by Kalnay and Kanamitsu [9]. Terms and partial derivatives are gathered for each prognostic variable and solved simultaneously. • Bowen Ratio • Monthly mean diurnal composite, July 1997 • SiB2 (red dashed line): BR much too high; model has excess sensible heat due to lack of transpirational cooling. • SiB3: In general, BR is much closer to observed. We can modify canopy response by adjusting canopy parameters such as light and CO2 co-limitation and maximum Rubisco velocity (Vmax). • SiB2: no co-limitation, as a single sun-leaf is simulated Sunlit/Shaded Radiation Scheme Shaded LAI Sunlit LAI Radiation absorbed by sunlit leaves: Radiation absorbed by Shaded leaves: References • Monthly NDVI values are used to obtain time-varying • vegetation parameters such as: • Leaf Area Index (LAI) • Fraction of absorbed PAR (fPAR) • Green fraction • Roughness length • OLD SCHEME: Monthly maximum NDVI is assigned to the • midpoint of each month • NEW SCHEME: Slope are curvature of annual NDVI cycle is examined, • and assignment of observation date follows these rules: • Slope>0, curvature>0; end of month • Slope<0, curvature>0; beginning of month • Otherwise; midmonth [1] Sellers, P.J., Mintz, Y., Y.C. Sud, A. Dalcher, 1986: A Simple Biosphere Model (SiB) for Use within General Circulation Models. Journal of the Atmospheric Sciences, 43(6), 505-531. [2] Sellers, P.J. D.A. Randall, G.J. Collatz, J.A. Berry, C.B. Field, D.A. Dazlich, C.Zhang, G.D. Collelo, L. Bounoua, 1996: A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMs: Part I: Model Formulation. Journal of Climate, 9(4), 676-705. [3] Sellers, P.J., S.O. Los, C.J. Tucker, C.O. Justice, D.A. Dazlich, G.J. Collatz, D.A. Randall, 1996: A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMs. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data. Journal of Climate, 9(4), 706-737. [4] Randall, D.A., D.A. Dazlich, C. Zhang, A.S. Denning, P.J. Sellers, C.J. Tucker, L. Bounoua, J.A. Berry, G.J. Collatz, C.B. Field, S.O. Los, C.O. Justice, I. Fung, 1996: A Revised Land Surface for GCMs. Part III: The Greening of the Colorado State University General Circulation Model. Journal of Climate, 9(4), 738-762. [5] Vidale, P.L. and R. Stockli, 2005: Prognostic Canopy Air Space Solutions for Land Surface Exchanges. Theoretical and Applied Climatology, 80, 245-257. [6] Dai, Y., X. Zeng, R.E. Dickinson, I. Baker, G.B. Bonan, M.G. Bosilovich, A.S. Denning, P.A. Dirmeyer, P.R. Houser, G. Niu, K.W. Oleson, C.A. Schlosser, Z-L. Yang, 2003: The Common (Community) Land Model. Bulletin of the American Meteorological Society, August 2003, 1013-1023. [7] de Pury, D.G.G. and G.D. Farquhar, 1997: Simple Scaling of Photosynthesis from Leaves to Canopies Without the Errors of Big-Leaf Models. Plant, Cell and Environment, 20, 537-557. [8] Dai, Y., R.E. Dickinson, Y-P. Wang, 2004: A Two-Big-Leaf Model for Canopy Temperature, Photosynthesis, and Stomatal Conductance.Journal of Climate, 15 June 2004, 2281-2299. [9] Kalnay, E. and Kanamitsu, M., 1988: Time Schemes for Strongly Nonlinear Damping Equations. Monthly Weather Review, 116, 1945-1958. NDVI Interpolation This research was supported by the Office of Science (BER), U.S. Department of Energy, Grant No. DE-FG02-02ER63474 as well as by NASA Grant NCC5-621