Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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
On the predictability of land surface fluxes from meteorological variables
Ned Haughton, Gab Abramowitz, and Andy J. Pitman Climate Change Research Centre, UNSW Australia, Sydney, Australia
Abstract. Previous research has shown that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

Citation: Haughton, N., Abramowitz, G., and Pitman, A. J.: On the predictability of land surface fluxes from meteorological variables, Geosci. Model Dev., 11, 195-212, https://doi.org/10.5194/gmd-11-195-2018, 2018.
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Short summary
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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