Biogeosciences www.biogeosciences.net 1726-4170 1726-4189 6 7 2009 10.5194/bg-6-1341-2009 http://www.biogeosciences.net/6/1341/2009/ http://www.biogeosciences.net/6/1341/2009/bg-6-1341-2009.html http://www.biogeosciences.net/6/1341/2009/bg-6-1341-2009.pdf 1341 1359 2009-07-30 Improving land surface models with FLUXNET data M. Williams mat.williams@ed.ac.uk A. D. Richardson M. Reichstein P. C. Stoy P. Peylin H. Verbeeck N. Carvalhais M. Jung D. Y. Hollinger J. Kattge R. Leuning Y. Luo E. Tomelleri C. M. Trudinger Y. -P. Wang School of GeoSciences and NERC Centre for Terrestrial Carbon Dynamics, University of Edinburgh, Edinburgh, UK University of New Hampshire, Complex Systems Research Center, Durham, USA Max Planck Institute for Biogeochemistry, Jena, Germany School of GeoSciences, University of Edinburgh, Edinburgh, UK Laboratoire des sciences du climat et de l'environnement (LSCE), Gif Sur Yvette, France Laboratory of Plant Ecology, Ghent University, Belgium Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal Northern Research Station, USDA Forest Service, Durham, NH, USA CSIRO Marine and Atmospheric Research Canberra ACT 2601, Australia Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019, USA CSIRO Marine and Atmospheric Research, Centre for Australian Weather and Climate Research, Aspendale, Victoria, Australia 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" (i.e. linking) 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 and orthogonal data provides a means to limit equifinality. We then show how parameter probability density functions (PDFs) from MDF can be used to interpret model 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. 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