Convergence and synthesis of regional top-down and bottom-up estimates of CO2 flux estimates:

Results from the North American Carbon Program Midcontinent Intensive (MCI) regional studyKenneth Davis1, Arlyn Andrews2, Varaprasad Bandaru3, F. Jay Breidt4, Dan Cooley4, Scott Denning4, Liza Diaz1, Kevin Gurney5, Ram Gurung4, Linda Heath6, R. Cesar Izaurralde3, Thomas Lauvaux1, Zhengpeng Li7,

Shuguang Liu7, Natasha Miles1, Stephen Ogle4, Scott Richardson1, Andrew Schuh4, James Smith6, Colm Sweeney2, Tristram West3

1The Pennsylvania State University, 2NOAA ESRL, 3Pacific Northwest National Laboratories, 4Colorado State University, 5Arizona State

University, 6USDA Forest Service, 7EROS Data Center

RECCAP meeting, 26 May, 2011, Shepherdstown, West Virginia, USA

outline• Objectives• Results

– Inventory– Inversion– Comparison and synthesis

• Work to come

NACP Midcontinent Intensive (MCI)• To what degree can we demonstrate convergence in

regional flux estimates using top-down and bottom-up methods?

C

CO2 CO2

CO2

CO2

CO2

CO2

C

Atmospheric Inversions

Inventories

Evolution of the MCI• 1999 US Carbon Cycle Science Plan (Sarmiento and Wofsy)

proposed regional atmospheric inversions.• 2002 white paper by Pieter Tans proposed the U.S.

midcontinent as a good experimental site – agricultural fluxes are known because of harvest/inventory data.

• 2006 Midcontinent Intensive (MCI) science plan (Ogle et al) spelled out the objectives of this contribution to the North American Carbon Program (NACP).

• Field work, 2005-2009. Analyses are at hand!• The primary objective of the NACP MCI is to test of our ability

to achieve convergence of “top-down” and “bottom-up” estimates of the terrestrial carbon balance of a large, sub-continental region.

Experimental design• Dense, tower-based greenhouse gas measurement

network• Relatively simple terrain and dense meteorological

data These yield our best chances to derive robust flux

estimates using atmospheric inversions.

• Excellent “bottom-up” flux estimates from inventory methods This provides the test for the atmospheric inversion

methodology.

MCI Study Domain

“Inventory”

Δ SOC

Yield Total

Eroded C

Δ Live Aboveground C

(0)

Δ Dead AbovegroundLitter C

Δ Dead BelowgroundLitter C

Δ Live Belowground C

(0)

Cropland Carbon:Estimation based on stock change on cropland fields and

includes key lateral flows in harvested grain and eroded C.

West et al., 2011; Ogle et al., 2010

Forestland Carbon:Estimation based on stock change on forest land stands and

includes key lateral flow of harvested woody products.

Forest CForest C

Δ Live Aboveground C

Δ Live Belowground C

Δ SOC

Δ Dead AbovegroundCWD & Litter C

Δ Dead BelowgroundCWD & Litter C

Eroded C

Timber Harvest

Smith et al., 2003; EPA, 2009

Inventory Uncertainty Assessment

SimulationModel

PDF

Scaling Uncertainty

Results

Structural UncertaintyPDF

95%ConfidenceInterval

Input Uncertainies*

Ogle et al., Global Change Biology, 2010

*For example:-Crop yield data.-Yield to carbon conversion coefficient.-Fertilizer application rate.

Cropland Carbon Budget for 2008

11

Seedproduction

3

United States Cropland Carbon Budget for 2008

Crop carbon

Net soil C change

Beginning C stock (15)

Carryover Carbon Stock

NPP595

Imported C

10

7

18 147

3

2

39

47

carryover to followingyear (2009)

255

Harvested C stock (255)

Available C Stock for 2008

(258)

Non-grain C stock (3)

Food(Human)

Feed(Livestock)

Exported C

carryover fromprevious year (2007)

18

15

Decomposition329

Fuel(Ethanol & Biodiesel)

Fiber(Cotton)

Processing waste

1

“Where does carbon in crops ultimately end up?”“Can we account for all carbon and balance the budget?”

West et al, 2011

12

Cropland Carbon Budget for 2008

Seedproduction

3

United States Cropland Carbon Budget for 2008

Crop carbon

Net soil C change

Beginning C stock (15)

Carryover Carbon Stock

NPP595

Imported C

10

7

18 147

3

2

39

47

carryover to followingyear (2009)

255

Harvested C stock (255)

Available C Stock for 2008

(258)

Non-grain C stock (3)

Food(Human)

Feed(Livestock)

Exported C

carryover fromprevious year (2007)

18

15

Decomposition329

Fuel(Ethanol & Biodiesel)

Fiber(Cotton)

Processing waste

1

What part of this budget does the atmosphere see?

Outside budget boundaries

Included in Vulcan fossil fuel estimates

West et al, 2011

National-scale agricultural inventory

13

Harvested biomass is transported out of the MCI region.

Agriculture is a strong sink from the regional atmospheric perspective

West et al, 2011

MCI domain

MCI inventory estimates: Forest and crop yield dominate

Units are Gg C per ½ degree pixel.

Units are Gg C per ½ degree pixel.

(Gurney et al. 2009)

Units are Gg C per ½ degree pixel.

MCI inventory summary• Carbon sink associated with cropland due to carbon

fixation through photosynthesis and lateral transport of harvested grain (West et al. 2011) dominates.

• Uncertainties are dominated by crop yield data and crop yield to C coefficients. (Data are relatively precise, but contribute to large fluxes.)

• Agricultural uncertainties are highly coherent across space.

Inversion

COCO22 Concentration Network: 2008 Concentration Network: 2008

Midcontinent intensive, 2007-2009

INFLUX, 2010-2012

Gulf coast intensive, 2013-2014

Corn-dominated sites

MCI Tower-Based CO2 Observational Network

• Large differences in seasonal drawdown, despite nearness of stations.

• 2 groups: 33-39 ppm drawdown and 24 – 29 ppm drawdown. Tied to density of corn.

Mauna Loa

Miles et al, in review

MCI 31 day running mean daily daytime average CO2

Daily differences from day to day (or site to site – not shown) as large at 30 ppm.

Synoptic variability in boundary-layer CO2 mixing ratios: Daily daytime averages

Miles et al, in review

Inversion Toolbox: “Forwards” system

Air Parcel Air Parcel

Air Parcel

Sources Sinks

wind wind

SampleSample

Network of tower-based GHG sensors:(9 sites with CO2)

Atmospheric transport model:(WRF, 10km)

Prior flux estimate:(SiB-Crop and CASA)

Boundary conditions:CO2: NOAA aircraft

profiles and Carbon TrackerMet: NCEP meteorology

Lauvaux et al, in prep, A

Inversion Toolbox, continued• Lagrangian Particle Dispersion Model

(LPDM, Uliasz). – Determines “influence function” – the

areas that contribute to GHG concentrations at measurement points.

• Influence functions include the lateral boundaries. Inversion solves for both surface flux and boundary condition corrections.

• Bayesian matrix inversion. Weekly time step. 20km resolution. Experiment with coherence of solution in space: Default 100 km.

Lauvaux et al, in prep, A

Inversion method (graphic)

Estimated together

Enhance uncertainty assessment by experimenting with the prior and the uncertainty estimates (model-data, prior) and examining the impact on the derived fluxes.

CO2 boundary condition adjustmentCT vs. NOAA aircraft profiles

Lauvaux et al, in preparation, A

Lauvaux et al, in preparation, A

Spatial pattern of NEE is not overly sensitive to the prior.

Units are TgC/degree2, Jun-Dec07

Regionally and time integrated C flux uncertainty assessment

Experiments with the PSU inversion include varying the:- prior flux- prior flux uncertainty (magnitude and spatial correlation)- model-data error (magnitude and temporal correlation)- boundary condition temporal persistence.

Net flux estimate is fairly robust to the assumptions made in the inversion.

Lauvaux et al, in preparation, A

Impact of observational network: Tower removal experiments

Prior flux Posterior flux with all sites

Posterior with only “corn” sitesPosterior without “corn” sites

Regional integral is fairly robust to tower removal.

Spatial patterns are quite sensitive to tower removal.

Lauvaux et al, in prep, B

Spatial correlation in [CO2] residuals for two transport models: Signs of oversampling?Carbon Tracker Growing Season 2007

WRF-CASA Growing Season 2007

CT 2007 shows that sites close to each other and with the same vegetation are the only ones highly correlated (WBI-Kewanee).

Using the same fluxes but a different atmospheric transport model (WRF) to predict [CO2] produces substantially higher spatial correlations.

Diaz et al, in prepDaily daytime averages.

Comparison and Synthesis

Compare? Or Combine?

Cooley et al, in preparation

Cooley et al, in preparation

Cooley et al, in preparation

Total uncertainty is reduced.

Inventory and inversions bring independent samples of the same quantity.

Model for RECCAP best estimates.

Cooley et al., in preparationOgle et al., in preparation

Conclusions• Regional C flux inverse estimates for the MCI appear

to converge with inventory estimates.• Regional C flux inverse estimates for the MCI are

fairly robust to assumptions.• Regional sums of NEE do not require a very dense

observational network, but spatial patterns are highly sensitive to the network.

• Differences across inversion systems (transport, structure of inversion) have not yet been assessed.

What’s next?

MCI synthesis papers

MCI atmospheric transport uncertainty analyses

INFLUX and Gulf coast intensives

COCO22 Concentration Network: 2008 Concentration Network: 2008

Midcontinent intensive, 2007-2009

INFLUX, 2010-2012

Gulf coast intensive, 2013-2014

INFLUX (Indianapolis FLUX)

Project Goals:

• Compare top-down emission estimates from aircraft and tower-based measurements with bottom-up emission estimates from inventory methods

(include CO2 – fossil and biological, and CH4)

• Quantify uncertainties in the two approaches

Why Indianapolis?

• Medium-sized city, with fossil fuel CO2 emissions of ~3.4 MtC yr-1

• Located far from other metropolitan areas, so the signal from Indianapolis can be isolated with relative ease

• Flat terrain, making the meteorology relatively simple

View of Indianapolis from the White River (photo by Jean Williams)

1

Tower-based measurements: continuous

• Current: continuous measurements of CO2 at two sites

• Planned• Two sites measuring

CO2/CO/CH4• Three sites measuring CO2/CO• Three sites measuring CO2/CH4• Four sites measuring CO2

Tower locations

• Sites 1 and 2 are currently measuring CO2.

• Sites 3 through 12 are planned, with tentative locations shown.

• Mixture of continuous CO2, CH4 and CO sensors, and flask 14CO2 data.

1

21

Hestia Annual Fluxes for Indianapolis

Purdue airborne sampling(budget flux estimates, source ID, transport test)

Mays, K. L., P. B. Shepson, B. H. Stirm, A. Karion, C. Sweeney, and K. R. Gurney, 2009. Aircraft-Based Measurements of the Carbon Footprint of Indianapolis, Environ. Sci. Technol., 43, 7816-7823

Publications in press or publishedEPA (2009) Inventory of U.S. greenhouse gas emissions and sinks: 1990-20067

http://www.epa.gov/globalwarming/ publications/emissions, United States Environmental Protection Agency, Washington, D.C.

Gurney, K.R., D.L. Mendoza, et al. (2009). High resolution fossil fuel combustion CO2 emission fluxes for the United States. Environmental Science and Technology 43: 5535-5541.

Ogle, S.M., F.J. Breidt, M. Easter, S. Williams, K. Killian, and K. Paustian. 2010. Scale and uncertainty in modeled soil organic carbon stock changes for US croplands using a process-based model. Global Change Biology 16:810-822.

Smith, J.E., L. Heath, J.C. Jenkins 2003. Forest volume-to-biomass models and estimates of mass for live and standing dead trees of U.S. forests. Gen. Tech. Rep. NE-298. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station. 57 p.

West, T.O., N. Singh, G. Marland, B.L. Bhaduri, A. Roddy. 2009. The human carbon budget: An estimate of the spatial distribution of metabolic carbon consumption and release in the United States. Biogeochemistry 94: 29-41, DOI 10.1007/s10533-009-9306-z.

West, T.O., V. Bandaru, C.C. Brandt, A.E. Schuh, S.M. Ogle. 2011. Regional Uptake and Release of Crop Carbon in the United States. Biogeosciences, In review.

• Richardson et al, cavity ring down spectroscopic CO2 field measurements

• Stephens et al, LI-820 based, well-calibrated CO2 field measurements

• Miles et al, MCI atmospheric CO2 observations and the impact of the corn belt

• Lauvaux et al, A, Regional flux inversion methodology applied to the MCI, 2007.

• Lauvaux et al, B. Sensitivity of regional MCI inversion to tower sampling density

• Diaz et al, Analysis of atmospheric CO2 model-data residuals for the MCI

Publications in review or prep

These data are open!

Collaborators are welcome.