Articles | Volume 11, issue 1
Geosci. Model Dev., 11, 195–212, 2018
https://doi.org/10.5194/gmd-11-195-2018
Geosci. Model Dev., 11, 195–212, 2018
https://doi.org/10.5194/gmd-11-195-2018

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. a
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. a
Batty, M. and Torrens, P. M.: Modeling complexity: the limits to prediction, Cybergeo Eur. J. Geogr., https://doi.org/10.4000/cybergeo.1035, 2001. a, b
Best, M. J., Abramowitz, G., Johnson, H. R., Pitman, A. J., Balsamo, G., Boone, A., Cuntz, M., Decharme, B., Dirmeyer, P. A., Dong, J., Ek, M. B., Guo, Z., Haverd, V., van den Hurk, B. J. J., Nearing, G. S., Pak, B., Peters-Lidard, C. D., Santan, J. S., Stevens, L. E., and Vuichard, N.: The plumbing of land surface models: benchmarking model performance, J. Hydrometeorol., 16, 1425–1442, https://doi.org/10.1175/JHM-D-14-0158.1, 2015. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac, ad, ae
Boone, A., Decharme, B., Guichard, F., de Rosnay, P., Balsamo, G., Beljaars, A., Chopin, F., Orgeval, T., Polcher, J., Delire, C., Ducharne, A., Gascoin, S., Grippa, M., Jarlan, L., Kergoat, L., Mougin, E., Gusev, Y., Nasonova, O., Harris, P., Taylor, C., Norgaard, A., Sandholt, I., Ottlé, C., Poccard-Leclercq, I., Saux-Picart, S., and Xue, Y.: The AMMA Land Surface Model Intercomparison Project (ALMIP), B. Am. Meteorol. Soc., 90, 1865–1880, https://doi.org/10.1175/2009BAMS2786.1, 2009. a
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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.