The combined Simple Biosphere/Carnegie-Ames-Stanford Approach (SiBCASA) Model
Kevin
Schaefer, CIRES, University of Colorado, kevin@atmos.colostate.edu
James
Collatz, NASA GSFC, jcollatz@biome.gsfc.nasa.gov
Pieter
P
Tans, NOAA ESRL, pieter.tans@noaa.gov
Scott
Denning, Atmospheric Science, Colorado State University, denning@atmos.colostate.edu
(Presenting)
Ian
Baker, Atmospheric Science, Colorado State University, baker@atmos.colostate.edu
Joseph
A
Berry, Carnegie Institution of Washington, joberry@globalecology.stanford.edu
Lara
Prihodko, Atmospheric Science, Colorado State University, lara@atmos.colostate.edu
Neil
Suits, Atmospheric Science, Colorado State University, nsuits@atmos.colostate.edu
Andrew
Philpott, Atmospheric Science, Colorado State University, andrew@atmos.colostate.edu
We combined the sophisticated photosynthesis and biophysical calculations in the Simple Biosphere model, Version 2.5 (SiB2.5) with the robust biogeochemistry from the Carnegie-Ames-Stanford Approach (CASA) model to create SiBCASA, a hybrid capable of estimating terrestrial carbon fluxes from diurnal to decadal time scales. SiBCASA includes dynamic allocation of Gross Primary Productivity to grow leaves, roots, and wood, explicit autotrophic respiration, and leaf biomass prescribed from Normalized Difference Vegetation Index. Simulated carbon fluxes and biomass compared well with observations at AmeriFlux eddy covariance flux towers and a paper fully describing SiBCASA is in peer review.
The detailed biophysical and biogeochemical processes, high time resolution, and short spinup time make SiBCASA well suited for use in a number of NASA funded research projects. SiBCASA will provide initial flux estimates for global and regional transport inversions of observed atmospheric CO2 concentration (Scott Denning PI, Randy Kawa PI). SiBCASA will be used in a NACP process study to link winter conditions to summer carbon fluxes for North America (Tingjun Zhang PI). We plan to eventually use SiBCASA in estimates of biomass burning carbon fluxes (Jon Ranson PI) and in the GMAO carbon cycle data assimilation project (Steve Pawson PI).