Biogeosciences Discuss., 6, 2785-2835, 2009
www.biogeosciences-discuss.net/6/2785/2009/
doi:10.5194/bgd-6-2785-2009
© Author(s) 2009. This work is distributed
under the Creative Commons Attribution 3.0 License.
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This discussion paper has been under review for the journal Biogeosciences (BG). Please refer to the corresponding final paper in BG.
Improving land surface models with FLUXNET data
M. Williams1, A. D. Richardson2, M. Reichstein3, P. C. Stoy4, P. Peylin5, H. Verbeeck6, N. Carvalhais7, M. Jung3, D. Y. Hollinger8, J. Kattge3, R. Leuning9, Y. Luo10, E. Tomelleri3, C. Trudinger11, and Y.-P. Wang11
1School of GeoSciences and NERC Centre for Terrestrial Carbon Dynamics, University of Edinburgh, Edinburgh, UK
2University of New Hampshire, Complex Systems Research Center, Durham, USA
3Max Planck Institute for Biogeochemistry, Jena, Germany
4School of GeoSciences, University of Edinburgh, Edinburgh, UK
5LSCE, Gif Sur Yvette, France
6Laboratory of Plant Ecology, Ghent University, Belgium
7Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
8Northern Research Station, USDA Forest Service, Durham, NH, USA
9CSIRO Marine and Atmospheric Research, Canberra ACT 2601, Australia
10Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019, USA
11CSIRO Marine and Atmospheric Research, Centre for Australian Weather and Climate Research, Aspendale, Victoria, Australia

Abstract. There is a growing consensus that land surface models (LSMs) that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for fusing LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data availability, and its random and systematic biases. We then introduce methods for assessing LSM model runs against FLUXNET observations in temporal and spatial domains. These assessments are a prelude to more formal model-data fusion (MDF). MDF links model to data, based on error weightings. In theory, MDF produces optimal analyses of the modelled system, but there are practical problems. We first discuss how to set model errors and initial conditions. In both cases incorrect assumptions will affect the outcome of the MDF. We then review the problem of equifinality, whereby multiple combinations of parameters can produce similar model output. Fusing multiple independent data provides a means to limit equifinality. We then show how parameter probability density functions (PDFs) from MDF can be used to interpret model process validity, and to propagate errors into model outputs. Posterior parameter distributions are a useful way to assess the success of MDF, combined with a determination of whether model residuals are Gaussian. If the MDF scheme provides evidence for temporal variation in parameters, then that is indicative of a critical missing dynamic process. A comparison of parameter PDFs generated with the same model from multiple FLUXNET sites can provide insights into the concept and validity of plant functional types (PFT) – we would expect similar parameter estimates among sites sharing a single PFT. We conclude by identifying five major model-data fusion challenges for the FLUXNET and LSM communities: 1) to determine appropriate use of current data and to explore the information gained in using longer time series; 2) to avoid confounding effects of missing process representation on parameter estimation; 3) to assimilate more data types, including those from earth observation; 4) to fully quantify uncertainties arising from data bias, model structure, and initial conditions problems; and 5) to carefully test current model concepts (e.g. PFTs) and guide development of new concepts.

Citation: Williams, M., Richardson, A. D., Reichstein, M., Stoy, P. C., Peylin, P., Verbeeck, H., Carvalhais, N., Jung, M., Hollinger, D. Y., Kattge, J., Leuning, R., Luo, Y., Tomelleri, E., Trudinger, C., and Wang, Y.-P.: Improving land surface models with FLUXNET data, Biogeosciences Discuss., 6, 2785-2835, doi:10.5194/bgd-6-2785-2009, 2009.
 
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