Sharon M. Gourdji, K.L. Mueller, V. Yadav, A.E. Andrews, M. Trudeau, D.N. Huntzinger, A.Schuh, A.R. Jacobson, M. Butler, A.M. Michalak North American Carbon.

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Anna M. Michalak Department of Civil and Environmental Engineering

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Presentation transcript:

Sharon M. Gourdji, K.L. Mueller, V. Yadav, A.E. Andrews, M. Trudeau, D.N. Huntzinger, A.Schuh, A.R. Jacobson, M. Butler, A.M. Michalak North American Carbon Program Meeting New Orleans, LA February 4, 2011

 Variability in atmospheric CO 2 concentrations provides information about surface CO 2 exchange  Inversions potentially useful for validating bottom-up models and verifying emission reductions Measurement locations

 (Relatively) recent availability of continuous, continental measurement data necessitates improvements in inversions and transport models to appropriately use this data (Source:

 Use 2004 for comparison because large availability of (top-down and bottom-up) model results  Inversion inter-comparisons help to highlight impact of setup choices & assumptions on estimated fluxes  Compare estimates at multiple scales Grid-scale spatial patterns Biome-scale seasonal cycle Annual aggregated budgets

 Can identify areas well-constrained by atmospheric measurements using footprint analysis High sensitivity area shown here, where minimum level of sensitivity to measurements throughout year 2004 yearly-average sensitivity of measurements to fluxes from WRF-STILT

 Compare inversions to 16 forward models estimating North American biospheric fluxes in 2004 Collected for the North American Carbon Program Regional Interim Synthesis Biospheric flux, June to August, 2004  mol/(m 2 *s) Click here to play movie

Orchidee CASA-GFED

 Can see influence of explicit priors  Sources around LEF visible in 5 of 6 inversions; spatial extent of impact varies  NARR inversion similar to forward model mean

 Inversions look similar during height of growing season, and most correspond closely with forward model mean

 Strong sources in center of continent from all inversions relative to forward model mean; most visible in UMich “no prior” inversion

 Stronger sources in UMich than other inversions Fossil fuel inventory? Data choices? Boundary conditions?

 At annual scale, anomalies near measurement towers become more apparent (representation/ aggregation/ transport errors?)  Net sinks in agricultural Midwest, boreal forests, eastern temperate forests?

 Some convergence in UMich inversions & CarbonTracker  Differences in timing & magnitude of peak uptake; spread driven as much by inversion setup as prior assumptions?  Inversion spread narrower in well-constrained agricultural regions

 Can inversions give insight into forward model spread? EC-MODDLEM

 Boundary conditions needed to account for influence of fluxes outside North America on measurement data  For geostatistical inversions, test two different sets of boundary conditions  CarbonTracker  GlobalView

 Empirically-based dataset Single vertical curtain representing average of Atlantic & Pacific oceans Interpolated using both surface & aircraft measurements Intended to correct seasonal biases in CarbonTracker  Uncertainty +/- 1ppm

 Boundary conditions have strong impact on annual budgets from inversions, regardless of prior assumptions

 Annual budgets most reliable in high sensitivity areas  With GlobalView boundary conditions, inversions show weak sinks similar to majority of forward models

 Large spread in inversion results for 2004; need for: Community consensus on optimal setup (grid-scale vs. big regions, covariance assumptions, priors, etc.) and data choices More research into correct boundary conditions  Will more data increase or decrease model spread? Results less sensitive to inversion setup? Or more difficult to use new kinds of data (e.g. very short towers, urban sites, complex terrain, satellite column-averages?) Improvements in transport models needed to reduce risks in using new datastreams  Important to understand “simple” inversions using in situ data before incorporating satellite measurements into sophisticated data assimilation systems

 WRF-STILT: AER, Inc. (Janusz Eluszkiewicz, Thomas Nehrkorn, John Henderson), John Lin, Deyong Wen  Atmospheric data providers: NOAA, Doug Worthy, Bill Munger, Marc Fischer  NACP Regional Interim Synthesis team and modelers  Funders: NASA (ROSES NACP and NESSF fellowship)

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