Sharon M. Gourdji, K.L. Mueller, V. Yadav, A.E. Andrews, M. Trudeau, D.N. Huntzinger, A.Schuh, A.R....
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Transcript of Sharon M. Gourdji, K.L. Mueller, V. Yadav, A.E. Andrews, M. Trudeau, D.N. Huntzinger, A.Schuh, A.R....
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
Atmospheric inversions Variability in
atmospheric CO2 concentrations provides information about surface CO2 exchange
Inversions 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-comparison 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
Specific inversionsDomain
Temporal resolution
Spatial resolution
Covariance assumptions Priors Transport
Data??
Butler et al.Global monthly
10 sub-regions in North America None SiB3 PCTM
CarbonTracker Global weekly
25 eco-regions in North America Limited CASA-GFEDv2 TM5
Schuh et al.North Americaweekly 1°x1°
Spatial covariance (fixed length scales) SiB3 PCTM
UMich - geostats
North America
4-day average diurnal cycle 1°x1°
Spatial covariance (estimated with atmospheric data)
Simple mean flux WRF-STILT
UMich- geostats w/ NARR
North America3-hourly 1°x1°
Spatial covariance (estimated with atmospheric data)
Linear trend with NARR variables, calibrated with atmospheric data WRF-STILT
UMich – Bayesian
North America3-hourly 1°x1°
Spatial covariance (estimated with atmospheric data) CASA-GFEDv2 WRF-STILT
Atmospheric data constraint over North America in 2004
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
Forward models Compare inversions to 16 forward models
estimating North American biospheric fluxes in 2004• Collected for the North American Carbon
Program Regional Interim SynthesisCan-IBISCLM-CASA'
CLM-CNDLEMISAMLPJmLMC1
ORCHIDEESiB3.1TEM6
VEGAS2BEPS
CASA-GFEDv2EC-MODMOD17+
NASA-CASA
Biospheric flux,June to
August, 2004
mmol/(m2*s)
Click hereto playmovie
Grid-scale (March to May)
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
Grid-scale (June to August)
Inversions look similar during height of growing season, and most correspond closely with forward model mean
Grid-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
Grid-scale (December to February)
Stronger sources in UMich than other inversions• Fossil fuel inventory? Data choices? Boundary
conditions?
Comparison to other inversions
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
Boundary conditions for regional inversions
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
2004 continental budget
Boundary conditions have strong impact on annual budgets from inversions, regardless of prior assumptions
2004 budget in high sensitivity areas
Annual budgets most reliable in high sensitivity areas
With 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 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
Acknowledgements 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)