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_at_atmos.colosta
  te.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 19962, and the ability to
  generate biophysical parameters from satellite
  data was added 34. 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 Stockli5
• 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 canopy78
  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 lt500W
• 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 ?, where ?
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
• Tsoil 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
• Slopegt0, curvaturegt0 end of month
• Slopelt0, curvaturegt0 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