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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 1
Geosci. Model Dev., 11, 195-212, 2018
https://doi.org/10.5194/gmd-11-195-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 11, 195-212, 2018
https://doi.org/10.5194/gmd-11-195-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 17 Jan 2018

Methods for assessment of models | 17 Jan 2018

On the predictability of land surface fluxes from meteorological variables

Ned Haughton et al.
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Cited articles  
Abramowitz, G.: Calibration, compensating errors and data-based realism in LSMs, Presentation, 2013.
Abramowitz, G., Leuning, R., Clark, M., and Pitman, A. J.: Evaluating the performance of land surface models, 21, 5468–5481, https://doi.org/10.1175/2008JCLI2378.1, 2010.
Batty, M. and Torrens, P. M.: Modeling complexity: the limits to prediction, Cybergeo Eur. J. Geogr., https://doi.org/10.4000/cybergeo.1035, 2001.
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Previous studies indicate that fluxes of heat, water, and carbon between the land surface and atmosphere are substantially more predictable than the performance of the current crop of land surface models would indicate. This study uses simple empirical models to estimate the amount of useful information in meteorological forcings that is available for predicting land surface fluxes. These models can be used as benchmarks for land surface models and may help identify areas ripe for improvement.
Previous studies indicate that fluxes of heat, water, and carbon between the land surface and...
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