Estimating Terrestrial Wood Biomass from Observed Concentrations of Atmospheric CO2

Kevin Schaefer1, Wouter Peters2, Nuno Carvalhais3, Guido van der Werf4, John Miller56

Acknowledgements: This research was funded by National Oceanic and Atmospheric Administration (NOAA) grant NA07OAR4310115. We thank investigators in Europe and North America for providing CO2 observations.

HypothesisCan we improve wood biomass estimates using observed atmospheric CO2 concentrations with a transport inversion and a biogeochemical model?

3Universidade Nova de Lisboa1National Snow and Ice Data Center 5NOAA Earth System Research Lab

Contact: Kevin Schaefer: 303-492-8869; kevin.schaefer@nsidc.org

Figure 8: Estimated above ground wood biomass (a) and observed

values from Forest Inventory Analysis (b) (short ton acre-1).

Pixels in the Pacific Northwest are categorized as agriculture land, so SiBCASA does not capture highly

localized bands of extremely dense wood. In the east, the

estimated wood is much higher than observed because the

SiBCASA GPP is too high, the wood pool includes seeds, or both.

(b) FIA Observed wood

Background and MethodsNet Ecosystem Exchange (NEE) is heterotrophic respiration (Rh) minus Net Primary Productivity (NPP). Rh depends on soil organic matter and surface litter. Many models assume steady-state initial pools where Rh ~ NPP and NEE ~ 0. However, forests are typically not in steady state and relaxing this assumption improves model performance [Carvalhais et al., 2008]. In forests, wood is the primary source of organic matter, controlling litter and soil pools and modulating the mean Rh. Thus, one could estimate the wood required to produce a mean NEE averaged over several years from a transport inversion of observed atmospheric CO2 concentrations.

Arm

CWD

Leaf

Root

Slow

Soilmet

Soilmic

Soilstr

Surfmet

Surfmic

Surfstr

Wood

Store

Pool

out

inin

out

Live Biomass

Surface Litter

Soil Carbon

GPP Figure 1: The SiBCASA carbon pool configuration highlighting the dominant wood pathway. Quasi-steady state assumes the slower soil carbon and litter pools are in equilibrium with the wood pool, although the wood pool itself may not be in equilibrium

Figure 6: Estimated initial wood biomass (short ton acre-1). Non-forest regions with little or no wood are omitted.

Figure 5: Estimated wood scaling factor (est). Zero means no wood and one means steady state wood. Non-forest biome types with little or no wood are omitted.Figure 2: The experiment

setup showing how we estimate wood biomass.

Calculating from the 2000-6 mean NEEest

assumes SiBCASA NPP is correct. Estimating wood

biomass assumes quasi-steady state conditions.

SiBCASA combines the Simple Biosphere (SiB2.5) biophysical model with the Carnegie-Ames-Stanford Approach (CASA) biogeochemical model [Schaefer et al., 2008]. SiBCASA has prognostic biomass in 13 pools (Figure 1), a 15-m deep soil model, and a prognostic snowpack. As input weather, SiBCASA currently uses the NCEP reanalysis regridded from 2x2 to 1x1 resolution. SiBCASA uses the GIMMSg NDVI dataset to define leaf biomass and absorbed fraction of PAR.

Conclusions1) Adjusting wood to non-steady state conditions improves

simulated NEE and atmospheric CO2 concentrations.

2) values measure the ‘distance’ from steady state

3) values are more robust than estimated wood biomass.

Analysis

Figure 3: The components of CarbonTracker. In these

experiments, we used SiBCASA as the biosphere module and did

not include the fire module.

SiBCASA

CarbonTracker uses the TM5 nested atmospheric transport model using ECMWF winds with a 1x1 degree zoom over Europe [Peters et al., 2007]. CarbonTracker uses a square root ensemble Kalman filter with 150 members, adjusting a priori NEE from SiBCASA once per week. Observed atmospheric CO2 comes from NOAA and CSIRO flask sampling networks, and continuous observations in North America and Europe.

Figure 4: 2001-6 average of NEE estimated (NEEest) by CarbonTracker (mole C m-2 s-1). Negative values indicate non-steady state conditions with net carbon sinks from 2000-6. The large sink in North America results from agriculture.

Results

CarbonTrackersteadyh

esth

R

R

_

_

Mean NEEest 2000-6

Adjusted litter/soil pools Adjusted NEE

SiBCASA

Rh_steady

Rh

Rh_est

est0.0

Rh_no_wood

1.0 steady

est

Wood

Wood

Estimated Wood Biomass

CarbonTracker

Adjusted CO2

SiBCASA

Steady State NEE

Figure 7: 2000-6 average of -adjusted NEE from SiBCASA (mole C m-2 s-1). Adjusting the wood pool improves the match with NEEest from CarbonTracker in forest regions, but does not capture the agricultural uptake in North America.

(a) Barrow Concentrations

(a) Cape Grim Concentrations

(a) Estimated wood

(b) Boreal Eurasia NEE

(b) South America NEE

Estimated by CarbonTracker SiBCASA adjusted woodObserved

Figure 9: Atmospheric CO2 concentrations at Barrow, Alaska (a) and total NEE for Boreal Eurasia (b). Adjusting the wood pool improves the simulated CO2 concentrations. However, simulated spring is too early in Boreal Eurasia and the growing season too long, resulting in too much winter respiration and higher than observed concentrations at Barrow and other Northern Hemisphere stations.

Figure 10: Atmospheric CO2 concentrations at Cape Grim, Tasmania (a) and total NEE for South America (b). SiBCASA does not account for biomass burning, producing a long-term sink in South America, resulting in lower than observed concentrations at Cape Grim and other Southern Hemisphere stations.

Estimated by CarbonTracker SiBCASA adjusted woodObserved

2Wageningen Research University 4Vrije Universiteit 6Cooperative Institute for Research in Environmental Sciences