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Here we propose a novel model\u2010data comparison strategy considering that CO<jats:sub>2<\/jats:sub> and H<jats:sub>2<\/jats:sub>O exchanges fluctuate on a wide range of timescales. Decomposing simulated and observed time series into subsignals allows to quantify model performance as a function of frequency, and to localize model\u2010data disagreement in time. This approach is illustrated using site level predictions from two models of different complexity, Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and Lund\u2010Potsdam\u2010Jena (LPJ), at four eddy covariance towers in different climates. Frequency\u2010dependent errors reveal substantial model\u2010data disagreement in seasonal\u2010annual and high\u2010frequency net CO<jats:sub>2<\/jats:sub> fluxes. By localizing these errors in time we can trace these back, for example, to overestimations of seasonal\u2010annual periodicities of ecosystem respiration during spring greenup and autumn in both models. In the same frequencies, systematic misrepresentations of CO<jats:sub>2<\/jats:sub> uptake severely affect the performance of LPJ, which is a consequence of the parsimonious representation of phenology. ORCHIDEE shows pronounced model\u2010data disagreements in the high\u2010frequency fluctuations of evapotranspiration across the four sites. We highlight the advantages that our novel methodology offers for a rigorous model evaluation compared to classical model evaluation approaches. We propose that ongoing model development will benefit from considering model\u2010data (dis)agreements in the time\u2010frequency domain.<\/jats:p>","DOI":"10.1029\/2009jg001016","type":"journal-article","created":{"date-parts":[[2010,4,8]],"date-time":"2010-04-08T20:00:22Z","timestamp":1270756822000},"source":"Crossref","is-referenced-by-count":70,"title":["Comparing observations and process\u2010based simulations of biosphere\u2010atmosphere exchanges on multiple timescales"],"prefix":"10.1029","volume":"115","author":[{"given":"M. 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