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A comparison of recent model- and inventory- based estimates of the
continental-scale carbon balance of North America
A. David McGuireUSGS / University of Alaska Fairbanks
North American Carbon Program 3rd All-Investigators MeetingNew Orleans – 3 February 2011
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“Fast-Track” Analysis:Model– vs. Inventory– based Data Comparisons
A component of the North American Carbon Program’s Regional / Continental Interim Synthesis Activities
• Organizing– Dave McGuire– Mac Post– Dan Hayes
• Inventory-based data– Graham Stinson, Werner Kurz
(Canada Forest Inventory)– Brian McConkey (Canada
Cropland Inventory)– Linda Heath (U.S. Forest
Inventory)
– Tris West (U.S. Cropland Inventory)
– Ben deJong (Mexico)• Model-data processing
– Yaxing Wei– NACP Regional/Continental
Interim Synthesis Participants• Data analysis
– Dan Hayes– Dave Turner
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Introduction
• Current understanding of state of the North American Carbon Cycle (e.g., SOCCR; King et al., 2007):–Magnitude of sink, trends, driving forces, uncertainty
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Introduction
• Methodologies for assessing continental-scale carbon balance:– Inventory-based methods (forest stock changes, crop
productivity, harvest and soil stocks, land use change)– Forward modeling: terrestrial biosphere process-based
models– “top-down” observations with an Inverse approach via
atmospheric transport modeling
* Data and model results contributed to the NACP Regional / Continental Interim Synthesis activity
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Inventory Reporting ZonesInventory Reporting Zones
Canada(n=15)Canada(n=15)
U.S.(n=49)
U.S.(n=49)
Mexico(n=32)Mexico(n=32)
no datano
data
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Inventory-data Analysis
Conceptual diagram of the continental-scale carbon budget (including NEE) from the inventory-based approaches.
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Inventory-based NEE estimates
FOREST LANDS CROPLAND “OTHER” LANDS TOTAL
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Inventory-based estimatesFate of Harvested (Forest & Crop) Carbon
FOREST HARVEST CROP HARVEST TOTAL HARVEST
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n = 17
n = 7
Model (Forward and Inverse) Data & Methods
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* Distributing model output data variables across sector (Forestland, Cropland, and “Other”) within each reporting zone
Model-data Processing
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Model-data Processing
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Compare NEE by country / sectorAverage Annual Total NEE (Pg C yr-1), 2000 to 2006
Inventory-based Inverse Forwardestimate model mean model mean
Canada n=1 n=7 n=15Forestland -0.046 -0.151 -0.073Cropland -0.033 -0.035 -0.022Other 0.057 -0.051 -0.029Total -0.022 -0.238 -0.125
United States n=1 n=7 n=17Forestland -0.311 -0.282 -0.158Cropland -0.264 -0.137 -0.095Other 0.296 -0.266 -0.105Total -0.279 -0.685 -0.357
Mexico n=1 n=7 n=12Forestland 0.036 0.001 -0.015Cropland n/a 0.006 -0.018Other -0.017 -0.015 0.004Total 0.018 -0.009 -0.029
North AmericaForestland -0.321 -0.432 -0.246Cropland -0.295 -0.167 -0.134Other 0.358 -0.332 -0.130Total -0.282 -0.931 -0.511
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Mean average annual NEE (gC m-2 yr-1) for each reporting zone.
Compare NEE spatial patterns
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Compare Component Flux Estimates
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Compare Component Flux Estimates
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Discussion
• What does it all mean?– Lack of convergence between approaches
• Different Approaches:– Strengths and weaknesses of each– Is our inventory approach (e.g. harvested product transfers and
emissions) realistic? How can we reconcile with modeling approaches?– Inventory data gaps and uncertainties?– Uncertainties in the inverse approach: observation network, flux
priors, transport models– Variability in the forward modeling approach: driver data, model
formulations, processes / mechanisms simulated• Formal model inter-comparisons?