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

“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

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

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

Inventory Reporting Zones

Canada(n=15)

U.S.(n=49)

Mexico(n=32)

no data

Inventory-data Analysis

Conceptual diagram of the continental-scale carbon budget (including NEE) from the inventory-based approaches.

Inventory-based NEE estimates

FOREST LANDS CROPLAND “OTHER” LANDS TOTAL

Inventory-based estimatesFate of Harvested (Forest & Crop) Carbon

FOREST HARVEST CROP HARVEST TOTAL HARVEST

n = 17

n = 7

Model (Forward and Inverse) Data & Methods

* Distributing model output data variables across sector (Forestland, Cropland, and “Other”) within each reporting zone

Model-data Processing

Model-data Processing

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

Mean average annual NEE (gC m-2 yr-1) for each reporting zone.

Compare NEE spatial patterns

Compare Component Flux Estimates

Compare Component Flux Estimates

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?