The combined Simple Biosphere/Carnegie-Ames-Stanford Approach (SiBCASA) Model

Kevin Schaefer1, G. James Collatz2, Pieter Tans3, A. Scott Denning4, Ian Baker4, Joe Berry5, Lara Prihodko3, Neil Suits4, Andrew Philpott4

1Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO2Goddard Space Flight Center, Greenbelt, Maryland

3Earth System Research laboratory, National Oceanic and Atmospheric Administration, Boulder, CO4Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

5Department of Global Ecology, Carnegie Institution of Washington, Stanford, CA

Acknowledgements: This research was funded by the National Academy of Sciences postdoctoral fellowship program through the Earth System Research Laboratory, Climate Monitoring Division. Current funding comes from the North American Carbon Program. We thank all the Ameriflux Principal Investigators for access to their data. This research was supported by the Office of Science (BER) Program, U.S. Department of Energy, and through its Southeast Regional Center (SERC) of the National Institute for Global Environmental Change (NIGEC) under Cooperative Agreement No. DE-FC02-03ER63613, and its Terrestrial Carbon Processes Program (TCP).

ReferencesPotter CS, Randerson JT, Field CB, Matson PA, Vitousek PM, Mooney HA, Klooster SA (1993), Terrestrial ecosystem production - A process model based on global satellite and surface data, Global Biogeochem. Cycles, 7(4), 811-841.

Schaefer, K., G. J. Collatz, P. Tans, A. S. Denning, I. Baker, J. Berry, L. Prihodko, N. Suits, A. Philpott (2006), The combined Simple Biosphere/Carnegie-Ames-Stanford Approach (SiBCASA) Model, Global Biogeochem. Cycles, in review.

Sellers, P. J., D. A. Randall, G. J. Collatz, J. A. Berry, C. B. Field, D. A. Dazlich, C. Zhang, G. D. Collelo, and L. Bounoua (1996), A Revised Land Surface Parameterization of GCMs, Part I: Model Formulation, J. Clim., 9(4), 676-705.

Sellers, P. J., S. O. Los, C. J. Tucker, C. O. Justice, D. A. Dazlich, G. J. Collatz, and D. A. Randall (1996), A Revised Land Surface Parameterization of GCMs, Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data, J. Clim., 9(4), 706-737

The SiBCASA Model

Figure 1: The combined Simple Biosphere-Carnegie-Ames-Stanford Approach (SibCasa) model. Yellow is the carbon cycle, blue the water cycle, and red the energy cycle. Primary inputs are weather data (observed, reanalysis, or circulation model) and the 8 km GIMMSg NDVI dataset to specify Leaf Area Index (LAI). Primary outputs are biomass, NEE, sensible heat flux, and latent heat flux.

Figure 2. The flow of carbon between SibCasa carbon pools. GPP is the primary input and the leaf pool is specified from NDVI. Pool turnover times vary from days (storage), to decades (wood), to centuries (armored).

SiBCASA Biomass Pools

Armored

CWD

Leaf

Root

Slow

metabolic

microbe

sturcture

Metabolic

Microbe

Structure

Wood

Storage

Pool

out

inin

out

Live Biomass

Surface Litter

Soil Carbon

GPP

NDVI

NEE = Respiration – GPP

IntroductionOur goal is to identify how various processes influence the carbon budget using regional and general atmospheric circulation models and data assimilation. We needed a highly mechanistic carbon model accurately simulating terrestrial Net Ecosystem Exchange (NEE) on time scales of minutes to decades and spatial scales of ~1 km to global. We combined the Simple Biosphere model (SiB) [Sellers et al., 1996a; Sellers et al., 1996b] and the Carnegie-Ames-Stanford Approach (CASA) model [Potter et al., 1993] to produce SiBCASA [Schaefer et al., 2006].

SiBCASA Performance

CO2Temperature

Humidity

NEE Latent Heat

Sensible Heat

Snow

R

Moi

stur

e

Tem

pera

ture

Car

bon

Canopy Air Space

Soil

GPP Canopy

NDVI (LAI)

WeatherBoundary Layer

Figure 3: Observed NEE (black) and simulated NEE (red) at deciduous and mixed deciduous/evergreen site near Park Falls, Wisconsin (WLEF)

Figure 9: Observed NEE (black), simulated NEE using NCEP weather (red), and simulated NEE using observed weather (green) at the Oklahoma tallgrass prairie site. NCEP is drier than observed in 1998.

SiBCASA BiomassWhat’s New in SiBCASA• Leaf biomass specified by LAI derived from NDVI

• Explicit calculation of autotrophic respiration

• New carbohydrate storage pool

• Dynamic allocation of leaf, root, and wood growth

• Steady-state pool estimates and short spinup time

Date (year)

NE

E (m

ole

m-2 s

-1)

2

1

0

-1

-2

-31996 1997 1998 1999 2000 2001 2002

The simulated NEE compared well with observed NEE at AMERIFLUX eddy covariance sites representative of several biome types [Schaefer et al., 2006]. As input, we used the NCEP Reanalysis and GIMMSg NDVI and assumed steady state conditions for initial biomass.

Date (year)1998 2000 2002 2004

NE

E (m

ole

m-2 s

-1)

2

1

0

-1

-2

-319961994

Figure 4: Observed NEE (black), simulated NEE (red), and simulated NEE with constant LAI (green) at BOREAS old black spruce. The constant LAI corrects for false seasonal variation in remotely sensed LAI due to snow burial.

Date (year)

NE

E (m

ole

m-2 s

-1)

3

10

-1-2-3

1997 1998 1999 2002 2001

2

-4

-5

45

Other sites include a tropical forest (Tapajos prime and logged), more mixed deciduous/evergreen forests (Walker Branch, Harvard Forest, and Howland Forest) and corn/soybean agriculture (Bondville).

Figure 3: Simulated steady state wood biomass (mole C m-2) using NCEP reanalysis and GIMMg NDVI. Deciduous forests, grasslands, and agriculture lands look ok, but the boreal forests are too low and the tropical forests too high.

More testing, more testing, and more testing…

1) Adjust LAI for snow burial. In boreal forests, burial of vegetation by snow is perceived as a ~95% seasonal drop in LAI. In SiBCASA, this diverts photosynthates from wood to leaf growth, resulting in low wood biomass in boreal forests.

2) Better initial biomass based on forest inventory and land-use history. We assumed steady state or old growth conditions, which is unrealistically high in most forests.

3) Adjust root growth in tropical forests. Simulated root biomass (not shown) is low, so too much photosynthate is used to grow wood, resulting in too much wood in tropical forests.

Global simulations using the NCEP reanalysis and GIMMg NDVI look encouraging, but we still need to adjust turnover times and other parameters to get the biomass correct.

What’s next for SiBCASA?