Continental, Landscape, and Ecosystem Scale Fluxes of CO 2, CO, and other Greenhouse Gases: Constraining Ecosystem Processes from Leaf to Continent (a.k.a. CO 2 Budget Regional Aircraft experiment-Maine [COBRA-ME]). PIs: Steven C. Wofsy (Division of Engineering and Applied Science and Department of Earth and Planetary Science) Paul R. Moorcroft (Department of Organismic and Evolutionary Biology), Harvard University CO-Is: David Hollinger (USFS); John C. Lin (Harvard; currently at Colorado State); Christoph Gerbig (Harvard; currently at MPI Jena); Arlyn E. Andrews (NOAA CMDL) Collaborators: Prof. Maria Assuncaõ de Silva Dias, Saulo Freitas,and Marcos Longo (Universidade de Saõ Paulo and Centro de Previsão de Tempo e Estudos Climáticos ( CPTEC), Brazil) TOP DOWN: VPRM (Satellite) model of CO 2 exchange with the Biosphere Respiration = α r + (β r × (EVI-EVI min )/(EVI max -EVI min )) Climate data GPP = (α × T scalar × W scalar × P scalar ) × FAPAR PAV × PAR Surface Reflectance: MODIS LSWIEVI Tower Data Validation Literature Citations: Gerbig, C., J. C. Lin, S. C. Wofsy, B. C. Daube, A. E. Andrews, B. B. Stephens, P. S. Bakwin, and C. A. Grainger, Towards constraining regional scale fluxes of CO 2 with atmospheric observations over a continent: 1. Observed Spatial Variability, J. Geophys. Res. 108,. D24, 4756 (14 pp), 2003; 2. Analysis of COBRA-2000 data using a receptor oriented framework, J. Geophys. Res. 108,. D24, 4757 (27 pp), Lin, J. C., C. Gerbig, S.C. Wofsy, A.E. Andrews, B.C. Daube, K.J. Davis, A. Grainger, The Stochastic Time-Inverted Lagrangian Transport Model (STILT): Quantitative analysis of surface sources from atmospheric concentration data using particle ensembles in a turbulent atmosphere, J. Geophys. Res. 108, No. D16, 4493, /2002JD003161, Lin, J. C., C. Gerbig, S.C. Wofsy, B.C. Daube, An Empirical Analysis of the Spatial Variability of Atmospheric CO2: Implications for Space-borne Sensors and Inverse Analyses, Geophys. Res. Lett. 31 (23): Art. No. L23104 DEC D. Medvigy, P.R. Moorcroft, R. Avissar & R.L. Walko (2005]. Mass conservation and atmospheric dynamics in the Regional Atmospheric Modeling System (RAMS). Environmental Fluid Mechanics 4: (in press). P.R. Moorcroft (2003). Recent advances in ecosystem-atmosphere interactions: an ecological perspective. Proceedings of the Royal Society Series B, 270: Xiao, XM; Zhang, QY; Braswell, B; Urbanski, S; Boles, S; Wofsy, S; Berrien, M; Ojima, D Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. REMOTE SENSING OF ENVIRONMENT 91 (2): Adaptation of the Vegetation Photosynthesis Model (VPM [Xiao et al., 2004]) and Respiration models. ( above ) Model equations; ,  r,  r are fit parameters, adjusted initially using tower flux data for each vegetation type. These values are used as priors for the atmospheric inverse (Bayesian) problem. The same tower flux data are are used to constrain the parameters of the ED model. ( below ) VPM compared to Howland flux data for summer, The left panel shows the fit using parameters developed from 2003 data, indicating that parameters are reproducible and conservative. The right panel shows daily data; variability is driven by solar input. COBRA CO 2 data (tower, aircraft) Fossil fuel CO 2 and CO inventories B-RAMS, GOES assimilated fields STILT “measured” vegetation  CO 2 AMERIFLUX, FCRN flux data Biosphere flux model: ED +/or VPM Optimally-constrained regional fluxes + functional response + ecosystem structure + predictive capability IGBP vegetation grid Lateral CO 2 and CO boundary condition from Pacific, remote observations (CMDL) fossil fuel CO 2 advected CO 2 modeled vegetation  CO 2 Optimization ( i ) INPUT DATA PRODUCT Influence (Footprints) -NEE gC m -2 d -1 Howland d Forest +x -NEE gC m -2 d -1 Harvard Forest x W scalar = (1 + LSWI)/(1 + LSWI max ) ; P scalar = (1 + LSWI)/2 Constrained regional carbon & ecosystem model: the concept in pictures stand structure & dynamics simulation of observables optimization of parameters CO 2, H 2 O fluxes phenolog y Synopsis : Future concentrations of atmospheric greenhouse gases will be strongly affected by the rates at which terrestrial ecosystems add or remove CO 2,CH 4, and CO from the atmosphere. Our BE project is developing a framework to link process-level biological knowledge that describes individual plants and ecosystems for short time scales, with observations and models that characterize atmosphere-biosphere exchange for large spatial domains and long time scales. Our case study attempts to determine the sources and sinks for CO 2 and CO in Northern New England and Quebec (4 million sq. km), using data that we acquired from satellites, forest inventories, an extensive campaign of aircraft observations of trace gas concentrations in summer of 2004, and tall and short flux towers. In summer, 2004, we flew an instrumented aircraft (Wyoming King Air) for 200 hours, spanning the growing season. To synthesize these data across space and time scales, we developed an integrated ecosystem-atmosphere model by coupling the Ecosystem Demography model and the BRAMS mesoscale atmospheric simulation system [Moorcroft, 2003; Medvigy et al., 2005], to capture slow and fast ecosystem processes with accurate environmental forcing. Our goal is to assimilate biological knowledge and diverse atmospheric and ecological data generating an integrate model satisfying complementary constraints from atmospheric concentration and flux data, and structure and growth data form inventories, satellite data, and more. The product, a model with realistic structure and responses to environmental forcing, meeting all relevant biotic and atmospheric constraints, quantitatively links emergent properties of the terrestrial biosphere-atmosphere system with the underlying fundamental biological and physical processes, for length scales from individual trees to 2000 km and time scales from hours to centuries. This new type of model will provide new ways to estimate the carbon budget for areas as large as continents, using observations from remote sensing and atmospheric data, and, using the Bayesian framework, these estimates will be bracketed within defensible error bars. Moreover, the model will also tell us why the budget behaves as we infer, and the model can readily be incorporated into an analysis of future carbon budgets in a changing environment. Cumulative Distance [km] Altitude [km ASL] b) My “Lagrangian” expt 11 June 2004 Cumulative Distance [km] Altitude [km ASL] a) AM PM Aircraft and tower measurements of CO 2 concentrations and fluxes Ecosystem Demography Model ED-- “Ecological Statistical Mechanics” -- accurately captures the behavior, including competition, mortality, vegetation change, and carbon budget, of individual-based ecosystem models (e.g. patch models). Tracks the dynamic, sub-grid scale heterogeneity in canopy structure within grid cells statistically (computes the PDF, p,  pda=1). of plant type i BOTTOM UP: ED-LSM Size and Age-Structured Ecosystem Model (SEM) optimizedinitialobserved month NEP tC/ha/yr obsinitialoptimized Net Ecosystem Productivity MgC/ha/yr Initial 2-Year ( ) Model test using Harvard Forest data and the Ecosystem Demography-Land Surface model (ED-LSM). Harvard Forest flux tower data were used to optimize ED-LSM behavior in temperate mixed hardwood forests -- an important forest type with the region. The forest structure was initialized with FIA-style ecological plot data. Then 3 parameters were optimized: m (relates stomatal conductance to CO 2 flux per unit leaf area), a leaf respiration parameter, and a growth respiration parameter (partitions fixed C to growth), constrained against: hourly/ monthly/yearly NEE, hourly ET, and above-ground woody increment. ECMWF ERA-40 provided environmental forcing, and soil respiration was matched to night-time CO 2 -flux. The model provides excellent simulation of Harvard Forest data for time scales from hours to a decade. observedoptimizedinitial respiration combust. photosynthesis CO 2 CO log 10 > ppm/(  mol-m -2 s -1 ) STILT: Stochastic Time-Reversed Lagrangian Transport Model STILT uses the same assimilated meteorological fields as the biosphere models (ED-LSM, VPRM) to compute the “ influence function ” (response [ppm] at the aircraft or tower caused by unit flux at a given time upstream. STILT resembles a trajectory model, but accounts for mixing in the planetary boundary layer, convective storms, etc.. STILT used forecast winds to predict where to fly to best observe atmosphere-biosphere exchange. It uses analyzed winds to link atmosphere-biosphere exchange from ED-LSM or VPRM to our aircraft and tower data. The figures in this panel show reconstruction of CO and CO 2 data observed at Harvard Forest, based on constrained flux models from COBRA flights in Note the changing influence area during the synoptic progression from northerly to westerly flow. Atmospheric adjoint (receptor-oriented inverse) model: STILT GOES-E visible 11 June UT (1400 EDT)GOES-E visible 10 June UT (1400 EDT) Distribution of clouds in the study region(dashed box) on two flight days, 10 and 11 June A particularly favorable combination of fair weather and stable flow was present. Weather and Climate Drivers of the Biosphere Mass Flux in the study region(left, from our RAMS assimilation run), flight track [center], and surface temperatures [right] on 10 June The flow traversed the study areas along a NW track, the weather was cool and sunny over much of the study area, but there were dense clouds and rain over the southern boundary. The flights sampled along the core of the main regional flow on the first of the two flight days (center, 10 th of June), then examined cross sections moving with the mean flow on the 11 th of June. Brazilian Regional Atmospheric Modelling System (BRAMS)– High-resolution, nested, mesoscale meteorological model for data assimilation and forecasting ( above ) CO 2 data from aircraft on 11 June The King Air picked up air measured on June 10, executing two cross sections that moved with the mean flow (see below). Mixed layer heights ( left ) validated well, a key success for BRAMS. The data quantitatively determine CO 2 uptake by Maine forests in a Lagrangian framework. ( below ) Fluxes (upper panel) and concentrations (lower panel) of CO 2 on the 30m tower in Howland Forest and the 100m tower at Argyle, 10 miles away. Note the increasing uptake (negative flux) between mid- and late June ( left, right respectively ) seen in both fluxes and in daytime gradients, also note the close agreement in forest uptake of CO 2 at these sites. The tall tower data can be used to determine CO 2 fluxes over a large area (see STILT panel of this poster). [Lin et al., 2003, 2004; Gerbig et al., 2003, 2004] ED model equations: