Estimating regional-scale CO2 fluxes over North America within a geostatistical atmospheric inversion framework

The Top-Down Constraint on North American CO2 Fluxes: an Inter-comparison of Regional Inversion Results for 2004 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 MeetingNew Orleans, LAFebruary 4, 2011

1Atmospheric inversionsVariability in atmospheric CO2 concentrations provides information about surface CO2 exchangeInversions potentially useful for validating bottom-up models and verifying emission reductions

Measurement locations

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

(Source: http://www.esrl.noaa.gov/gmd/ccgg/)Model inter-comparisonUse 2004 for comparison because large availability of (top-down and bottom-up) model resultsInversion inter-comparisons help to highlight impact of setup choices & assumptions on estimated fluxesCompare estimates at multiple scalesGrid-scale spatial patternsBiome-scale seasonal cycleAnnual aggregated budgets4Specific inversionsDomainTemporal resolutionSpatial resolutionCovariance assumptionsPriorsTransportData??Butler et al.Globalmonthly10 sub-regions in North AmericaNoneSiB3PCTMCarbonTrackerGlobalweekly25 eco-regions in North AmericaLimitedCASA-GFEDv2TM5Schuh et al.North Americaweekly1x1Spatial covariance (fixed length scales)SiB3PCTMUMich - geostatsNorth America4-day average diurnal cycle1x1Spatial covariance (estimated with atmospheric data)Simple mean fluxWRF-STILTUMich- geostats w/ NARRNorth America3-hourly1x1Spatial covariance (estimated with atmospheric data)Linear trend with NARR variables, calibrated with atmospheric dataWRF-STILTUMich BayesianNorth America3-hourly1x1Spatial covariance (estimated with atmospheric data)CASA-GFEDv2WRF-STILT5Atmospheric data constraint over North America in 2004Can identify areas well-constrained by atmospheric measurements using footprint analysisHigh sensitivity area shown here, where minimum level of sensitivity to measurements throughout year2004 yearly-average sensitivity of measurements to fluxes from WRF-STILT

6Forward modelsCompare inversions to 16 forward models estimating North American biospheric fluxes in 2004Collected for the North American Carbon Program Regional Interim SynthesisCan-IBISCLM-CASA'CLM-CNDLEMISAMLPJmLMC1ORCHIDEESiB3.1TEM6VEGAS2BEPSCASA-GFEDv2EC-MODMOD17+NASA-CASABiospheric flux,June to August, 2004

mmol/(m2*s)Click hereto playmovie7Grid-scale spatial patternsPseudo-data spatial patterns

OrchideeCASA-GFEDGrid-scale (March to May)Can see influence of explicit priorsSources around LEF visible in 5 of 6 inversions; spatial extent of impact variesNARR inversion similar to forward model mean

10Grid-scale (June to August)Inversions look similar during height of growing season, and most correspond closely with forward model mean

11Grid-scale (September to November)Strong sources in center of continent from all inversions relative to forward model mean; most visible in UMich no prior inversion

12Grid-scale (December to February)Stronger sources in UMich than other inversionsFossil fuel inventory? Data choices? Boundary conditions?

13

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

14Biome-scale seasonal cycle

Comparison to other inversions

Some convergence in UMich inversions & CarbonTrackerDifferences in timing & magnitude of peak uptake; spread driven as much by inversion setup as prior assumptions?Inversion spread narrower in well-constrained agricultural regions16Biome-scale with priors

Comparison to forward modelsCan inversions give insight into forward model spread?

EC-MOD

DLEM18Pseudo-data results at biome-scale

Annual aggregated budgetsBoundary conditions for regional inversionsBoundary conditions needed to account for influence of fluxes outside North America on measurement data

For geostatistical inversions, test two different sets of boundary conditionsCarbonTrackerGlobalView

21Globalview boundary conditionsEmpirically-based datasetSingle vertical curtain representing average of Atlantic & Pacific oceansInterpolated using both surface & aircraft measurementsIntended to correct seasonal biases in CarbonTrackerUncertainty +/- 1ppm2004 continental budgetBoundary conditions have strong impact on annual budgets from inversions, regardless of prior assumptions

2004 budget in high sensitivity areasAnnual budgets most reliable in high sensitivity areasWith GlobalView boundary conditions, inversions show weak sinks similar to majority of forward models

Conclusions 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 choicesMore research into correct boundary conditionsWill 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 datastreamsImportant to understand simple inversions using in situ data before incorporating satellite measurements into sophisticated data assimilation systemsAcknowledgementsWRF-STILT: AER, Inc. (Janusz Eluszkiewicz, Thomas Nehrkorn, John Henderson), John Lin, Deyong WenAtmospheric data providers: NOAA, Doug Worthy, Bill Munger, Marc FischerNACP Regional Interim Synthesis team and modelersFunders: NASA (ROSES NACP and NESSF fellowship)

Contact: sgourdji@umich.eduQUESTIONS?