Estimating Terrestrial Wood Biomass from Observed Concentrations of Atmospheric CO 2 Kevin Schaefer 1, Wouter Peters 2, Nuno Carvalhais 3, Guido van der Werf 4, John Miller 56 Acknowledgements: This research was funded by National Oceanic and Atmospheric Administration (NOAA) grant NA07OAR We thank investigators in Europe and North America for providing CO 2 observations. Hypothesis Can we improve wood biomass estimates using observed atmospheric CO 2 concentrations with a transport inversion and a biogeochemical model? 3 Universidade Nova de Lisboa 1 National Snow and Ice Data Center 5 NOAA Earth System Research Lab Contact: Kevin Schaefer: ; 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 Methods Net Ecosystem Exchange (NEE) is heterotrophic respiration (R h ) minus Net Primary Productivity (NPP). R h 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 R h. Thus, one could estimate the wood required to produce a mean NEE averaged over several years from a transport inversion of observed atmospheric CO 2 concentrations. Arm CWD Leaf Root Slow Soilmet Soilmic Soilstr Surfmet Surfmic Surfstr Wood Store Pool out in 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 mean NEE est 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. Conclusions 1) Adjusting wood to non-steady state conditions improves simulated NEE and atmospheric CO 2 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 CO 2 comes from NOAA and CSIRO flask sampling networks, and continuous observations in North America and Europe. Figure 4: average of NEE estimated (NEE est ) by CarbonTracker (  mole C m -2 s -1 ). Negative values indicate non- steady state conditions with net carbon sinks from The large sink in North America results from agriculture. Results CarbonTracker Mean NEE est Adjusted litter/soil pools Adjusted NEE SiBCASA R h_steady RhRh R h_est  est 0.0 R h_no_wood 1.0 Estimated Wood Biomass CarbonTracker Adjusted CO 2 SiBCASA Steady State NEE Figure 7: average of  -adjusted NEE from SiBCASA (  mole C m -2 s -1 ). Adjusting the wood pool improves the match with NEE est 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 wood Observed Figure 9: Atmospheric CO 2 concentrations at Barrow, Alaska (a) and total NEE for Boreal Eurasia (b). Adjusting the wood pool improves the simulated CO 2 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 CO 2 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 wood Observed 2 Wageningen Research University 4 Vrije Universiteit 6 Cooperative Institute for Research in Environmental Sciences