IMPACT OF THE EXPANDING MEASUREMENT NETWORK ON TOP-

DOWN BUDGETING OF CO2 SURFACE FLUXES IN NORTH

AMERICA

Kim Mueller, Sharon Gourdji, Vineet Yadav, Michael Trudeau, Abhishek Chatterjee, Deborah Huntzinger, Arlyn

Andrews, Andrew Schuh, Yoichi Shiga, Kenneth Davis, Britton Stephens, Beverly E. Law, Colm Sweeney, Marc

Fischer, Danilo Dragoni, Doug Worthy, Matt Parker, Mathias Goeckede, Scott Richardson, Natasha Miles, Anna M.

MichalakNACP Meeting, New OrleansFebruary 2, 2011

CO2 observations – fl ask measurements

CO2 observations –continuous measurements

2008log10(ppm/μmolCO2/m2s) log10(ppm/μmolCO2/m2s)

2oo4 2oo5 2oo6 2oo7 2oo8

Inversion

Sources and Sinks,Uncertainti

es

TransportCO2 observations How well can

you match your data

(R)?

How much you trust prior guess (Q)?

Approach to quantify

Q

Approach to quantify

R

Regional Atmospheric Inverse Modeling

Synthesis Bayesian Inverse Modeling (Bayes)

How is the underlying flux field spatially and temporally

correlated (Q)?

Geostatistical Inverse Modeling (GIM)

Coefficients (β)^

R

Q

0 50 100 150 200 250 300 350375

376

377

378

379

380

381

382

383

384Boundary Conditions, June 1 to July 8

"Globalview"CarbonTracker-newCarbonTracker-oldBoundary

Conditions

Subtract

? ?

@ what temporal

scale

?

?

?

Regularization Method

BETTER

COVERAGE

IN SPACE

& TIME

35 towers

10 towers

SPACE TIME

shortaft -3000 obs

1pm - 1050 obs

Inversion

Sources and Sinks

GV-BC

CT-BC

Boundary

Conditions

3hrly

4Ddiurnal

Estimation

Scale

(I) Synthetic data

experiment

(II) Real data (GIM)

experiment

all – 8400 obs

Shortaft ~ afternoon data at short towers, all data at tall towers

Explicit

prior

(III) Real data (Bayes)

experiment

Synthetic Data Experiment Results Using GIMNo explicit prior so experiment test how much information is within atmospheric content in measurements w/out transport error

“Truth”

3hrly Estimation Scale4Ddiurnal Estimation Scale

Adding more measurement in time and space improves both the spatial pattern and grid scale flux estimates.

TIME

SPA

CE

Biggest “bang for the buck” when adding in more data throughout the day with expanded network.

Could draw opposite conclusion if estimating fluxes at a coarser scale.

JUNE (all fluxes post aggregated to monthly scale)

Courtesy of J. RandersonSynthetic Data (GIM)

Real Data (GIM)Experiment Results

No explicit prior - fluxes are based almost solely on atmospheric content in measurements.

WRF-STILT Transport Model (Nehkorn et al., 2010)

Boundary conditions account for the influence of fluxes that occurred outside of the North American domain

Difference of GV-BCs and CT-BCs is approximately 0.5-1ppm with GV-BCs always being lower and therefore are associate with less sinks (more sinks occurred outside of domain of interest)

Boundary Conditions

3hrly Estimation Scale

TIME

SPA

CE

BC

The choice of boundary conditions doesn’t have much impact on monthly grid-scale fluxes except in boreal north

More constraint provided by increasing the number of measurements per day

JUNE,JULY,AUGUST (all fluxes post aggregated to monthly scale)

Real Data (GIM)

Courtesy of S. Ogle, MCI Campaign

InventoryMore constraint provided by the expanded network

Boundary conditional have a large impact on annual totals from MCI

Inversion using CT-BC results in very strong uptake that is not present in inventory estimates

Annual grid-scaleGV-BC (10TN) GV-BC (35TN) CT-BC (35TN)

Real Data (GIM)

Real Data (Bayes) Experiment Results

Used explicit prior (CASA) to see how much atmospheric measurements correct our first guess of grid-scale fluxes

WRF-STILT Transport Model (Nehkorn et al., 2010)

CASA - prior UMich-Bayes (10twrs) UMich-Bayes (35twrs)

Seasonal grid-scale

Start to pull away from explicit prior in South with the use of more towersMore corrections in the SouthWest and stronger sources in agricultural beltNot many deviations from prior in growing season with 10TN compared to 35TN. Corrections across the contiguous US.

As with Dec-Feb, start to pull away from explicit prior in South with expanded network

Real Data (Bayes)

Annual Budgets (synthetic, GIM and Bayes)

WRF-STILT Transport Model (Nehkorn et al., 2010)

GIM

Annual biospheric budgets for NA

Synthetic data experiments indicates that with the expanded measurement network, we should be able to recover annual budget using GIM. No boundary conditions needed but did simulate real measurement gaps.

Spread of the budgets due to boundary conditions is wide (>1GtC/year). This spread may be exacerbated by the setup of GIM to recover 3hrly fluxes for the year.

The Bayesian results have less of a spread of the estimates due to choice of boundary conditions but still wider than the differences between estimates from the smaller and expanded network.The impact of the boundary conditions was also apparent in the 2004 results.

The ability of the expanded measurement network to budget continental sources and sinks is hampered by the influence of boundary conditions. The spread is likely the same if not wider when using more data is space.

Orchidee courtesy of D.N. Huntzinger and Interim-Synthesis TeamCarbonTracker courtesy of A. Jacobsen and NOAA

Bayes

35TN(08)10TN(08)

9TN(04)

2004 results courtesy of S. Gourdji

Conclusions

1. Estimate fluxes to account for underlying variability in transport or flux field (e.g. 3hrly)

2. Use more observations from more times of the day1. Need a method to verify simulated atmospheric

transport at these additional times3. Better means of validating our boundary conditions (A.E.

Andrews has new version available)

Can the expanded observational network help us to identify sources and sinks at regional scales?

Results look promising but more work to be done …

4. Improve atmospheric transport models5. Better ways to assess uncertainty

1. Assess at what spatial and temporal scales we can trust estimates

To help maximize the extent to which the inversion can extract information content of measurements need to:

ACKNOWLEDGEMENTS Other Contributors:

NOAA-ESRL: Adam Hirsch, Andy JacobsenAER: Thomas Nehrkorn, John Henderson, Janusz EluszkiewiczNACP-Interim Sythensis Team Members

NASA NAS: technical support staff (Johnny Chang and others)Funding: NASA (NNX06AE84G Constraining North American Fluxes

of Carbon Dioxide and Inferring their Spatiotemporal Covariances through Assimilation of Remote Sensing and Atmospheric Data in Geostatistical Framework)

QUESTIONS? Check out: The Top-Down Constraint on North American CO2 Fluxes: and Inter-comparison of Region Inversion Results for 2004, Gourdji et al., 2004Friday at 8:50am

Check out: Come to the data-assimilation side meeting (Yadav & Michalak) Form 5:15-6:15 in the Lafitte Room

Annual difference between fluxesusing GVBCs and CTBCs

Boundary conditions account for the influence of fluxes that occurred outside of the North American domain

Difference of GV-BCs and CT-BCs is approximately 0.5-1ppm with GV-BCs always being lower and therefore are associate with less sinks (more sinks occurred outside of domain of interest)

Even though we saw big differences in MCI region with choice of boundary conditions, differences are the greatest in the West Coast and under-constrained regions of the continent.

Post aggregated fluxesJune More spatial locations reduces

the spread of the monthly budgetand improves ability to recover the“truth”

More spatial data reduces spread but still a lot of variability in estimatesassociated with different setup choices

MidContinental Intensive9 measurement

locations

MCI9 measurement

locations

Average grid-scale diagnostics

Post aggregated scales

Boundary conditions only shift seasonal cycle up or down. Not shown.More difference in growing season with choice of observations to use throughout the day with more measurement locations.Temporal aggregation error has an influence at aggregated areas size of MCI but less so at continental scale.

Post aggregated scales

1. What is the information content of the expanding measurement network in terms of budgeting sources and sinks?

2. How does the inversion setup influence our ability to extract the information from the measurements?

SPACE & TIMEO

ther

Esti

mate

s

Courtesy of S. Gourdji

Courtesy of J. Randerson