Flux-Biomass Integration Scott Denning, Colorado State University Nancy French, Michigan Technological University Eric Kasischke, Univ of Maryland Don.

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Flux-Biomass Integration Scott Denning, Colorado State University Nancy French, Michigan Technological University Eric Kasischke, Univ of Maryland Don McKenzie, University of Washington Tristam West, Pacific Northwest National Laboratory Kevin Bowman, NASA Jet Propulsion Laboratory Skee Houghton, Woods Hole Research Center George Hurtt, University of Maryland Jim Collatz, NASA GSFC

Strategy Define domains in space and time for which various projects overlap Cross-compare flux and biomass products where appropriate Subtract biomass at two different times and compare to integrated fluxes

French, McKenzie, Kasischke, Collatz Objective: Fire emissions. Inputs: Fuels and biomass; weather, fire occurrence? Algorithm: WFEIS (wfeis.mtri.org); uses FCCS fuels maps (type and biomass) and weather-defined daily mapped fuel moisture as inputs to Consume emissions model Output: Spatial fuel consumption and fire emissions of CO2, CO, CH4, NMHC, PM2.5, PM10, total carbon Spatial Domain & Resolution: USA, 1-km Time Period: 1983 to 2011 Evaluation: Comparisons to GFED fire emissions are planned under Phase 2 CMS; publication on intercomparison available (French et al 2011) Reference: French, N. H. F., W. J. de Groot, L. K. Jenkins, B. M. Rogers, E. C. Alvarado, B. Amiro, B. de Jong, S. Goetz, E. Hoy, E. Hyer, R. Keane, D. McKenzie, S. G. McNulty, B. E. Law, R. Ottmar, D. R. Perez- Salicrup, J. Randerson, K. M. Robertson and M. Turetsky (2011). Model comparisons for estimating carbon emissions from North American wildland fire. Journal of Geophysical Research 116: G00K05 DOI: /2010JG001469

GFED/WFEIS Comparison - Outputs Comparison of WFEIS CONUS to published GFED outputs for TENA Next Steps: Comparisons of model outputs – Annual and monthly emissions – By ecoregion and with gridded output

Houghton Objective: Net and gross fluxes of carbon due to changes in land use in tropical regions Inputs: 12-year transitions (deforestation, reforestation) And 500m resolution aboveground biomass density (MgC/ha) Algorithm: Carbon bookkeeping model Output: Annual net carbon balance ( ) for tropical lands at 500m resolution Spatial Domain: tropics, 500m Time Period: Evaluation: compare with other estimates of land-use carbon flux (at coarser resolution)

Pantropical Forest Carbon Mapped with Satellite and Field Observations Amazon Basin detail from the map DRC detail from the map PNG detail from the map Error 19 Mg C ha -1 Error 24 Mg C ha -1 Error 25 Mg C ha -1 Baccini et al. 2012

West, PNNL Objectives: Estimate uptake and release of cropland carbon globally Resolution: useful for global analyses, but with accuracy needed for regional analyses. Algorithm: Combine multiple national inventories with remote sensing and other spatial data. Distribute summed NPP, harvested amount, above- and below-ground biomass to reconciled land areas for Bottom-up methods used to estimate human and livestock consumption Output: global gridded cropland cover and fluxes Spatial Domain & Resolution: Global, 0.05 degree Time Period: 2005-present Evaluation: inventory data from FAO/FAS

Denning, Haynes, Baker (CSU) Objective: Develop a self-consistent suite of hourly GPP and Ecosystem Respiration and monthly biomass using SiB4, for use as a prior flux field in the CMS Flux Pilot Product. Inputs: MERRA hourly reanalysis of surface weather, MODIS distribution of plant-functional types. Algorithm: radiative transfer, gas exchange, and enzyme kinetic calculation of GPP. Allocation of photosynthate to cascading pools of respiring and decomposing biomass. Equilibrium spinup followed by disturbance from land-use and fires. Output: Global hourly GPP and Resp on a 0.51-degree grid. Global monthly above-ground biomass for each of 15 plant-functional types on the same 1-degree grid. Spatial Domain & Resolution: Global, 1-degree Time Period: 2000-present Evaluation: NEE vs flux towers; simulated CO2 using GEOS-Chem vs in-situ, TCCON, and GOSAT, above-ground biomass vs CMS Biomass product, GPP vs GOSAT Fluorescence

Joint Prediction of GPP, RESP, Fluorescence, LAI, and Biomass with SiB4 Fractional coverage by 22 PFTs in every 0.5° x 0.5° grid D t = 15 min SiB4 Beer 2010 GPP Seasonal Amplitude GPP SiB4 Jung 2011 Self-consistent prediction of fluxes and biomass with prediction of multiple satellite products SiB4 LAI GPP RESP Biomass Crop production Fluorescence MERRA weather MODIS veg map GEOS- Chem CMS Flux Product Eval vs MODIS Eval vs USDA Eval vs GOSAT CMS Biomass GOSAT CO2 etc

SiB4 LAI, Biomass, & Fluorescence Biomass in tropics overestimated a bit Subtropics underestimated a bit Fluorescence is a bit too weak in dry places SiB4 MODIS LAI Chlorophyll Fluorescence 1:1 SiB4 GOSAT