Title

FLUXNET and modelling the global carbon cycle

Document Type

Article

Publication Title

Global Change Biology

Publication Date

3-2007

Volume

13

Issue

3

First Page

610

Last Page

633

Abstract

Measurements of the net CO2 flux between terrestrial ecosystems and the atmosphere using the eddy covariance technique have the potential to underpin our interpretation of regional CO2 source–sink patterns, CO2 flux responses to forcings, and predictions of the future terrestrial C balance. Information contained in FLUXNET eddy covariance data has multiple uses for the development and application of global carbon models, including evaluation/validation, calibration, process parameterization, and data assimilation. This paper reviews examples of these uses, compares global estimates of the dynamics of the global carbon cycle, and suggests ways of improving the utility of such data for global carbon modelling.

Net ecosystem exchange of CO2 (NEE) predicted by different terrestrial biosphere models compares favourably with FLUXNET observations at diurnal and seasonal timescales. However, complete model validation, particularly over the full annual cycle, requires information on the balance between assimilation and decomposition processes, information not readily available for most FLUXNET sites. Site history, when known, can greatly help constrain the model-data comparison.

Flux measurements made over four vegetation types were used to calibrate the land-surface scheme of the Goddard Institute for Space Studies global climate model, significantly improving simulated climate and demonstrating the utility of diurnal FLUXNET data for climate modelling. Land-surface temperatures in many regions cool due to higher canopy conductances and latent heat fluxes, and the spatial distribution of CO2 uptake provides a significant additional constraint on the realism of simulated surface fluxes.

FLUXNET data are used to calibrate a global production efficiency model (PEM). This model is forced by satellite-measured absorbed radiation and suggests that global net primary production (NPP) increased 6.2% over 1982–1999. Good agreement is found between global trends in NPP estimated by the PEM and a dynamic global vegetation model (DGVM), and between the DGVM and estimates of global NEE derived from a global inversion of atmospheric CO2 measurements. Combining the PEM, DGVM, and inversion results suggests that CO2 fertilization is playing a major role in current increases in NPP, with lesser impacts from increasing N deposition and growing season length. Both the PEM and the inversion identify the Amazon basin as a key region for the current net terrestrial CO2 uptake (i.e. 33% of global NEE), as well as its interannual variability. The inversion's global NEE estimate of −1.2 Pg [C] yr−1 for 1982–1995 is compatible with the PEM- and DGVM-predicted trends in NPP. There is, thus, a convergence in understanding derived from process-based models, remote-sensing-based observations, and inversion of atmospheric data.

Future advances in field measurement techniques, including eddy covariance (particularly concerning the problem of night-time fluxes in dense canopies and of advection or flow distortion over complex terrain), will result in improved constraints on land-atmosphere CO2 fluxes and the rigorous attribution of mechanisms to the current terrestrial net CO2 uptake and its spatial and temporal heterogeneity. Global ecosystem models play a fundamental role in linking information derived from FLUXNET measurements to atmospheric CO2 variability.

A number of recommendations concerning FLUXNET data are made, including a request for more comprehensive site data (particularly historical information), more measurements in undisturbed ecosystems, and the systematic provision of error estimates. The greatest value of current FLUXNET data for global carbon cycle modelling is in evaluating process representations, rather than in providing an unbiased estimate of net CO2 exchange.

Keywords

CO2 exchange, eddy covariance, FLUXNET, global carbon cycle, inversion, model

DOI

http://dx.doi.org/10.1111/j.1365-2486.2006.01223.x

Rights

© 2007 The Authors

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