Articles | Volume 11, issue 1
https://doi.org/10.5194/gmd-11-195-2018
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, Gab Abramowitz, and Andy J. Pitman

Related authors

Does predictability of fluxes vary between FLUXNET sites?
Ned Haughton, Gab Abramowitz, Martin G. De Kauwe, and Andy J. Pitman
Biogeosciences, 15, 4495–4513, https://doi.org/10.5194/bg-15-4495-2018,https://doi.org/10.5194/bg-15-4495-2018, 2018
Short summary
FluxnetLSM R package (v1.0): a community tool for processing FLUXNET data for use in land surface modelling
Anna M. Ukkola, Ned Haughton, Martin G. De Kauwe, Gab Abramowitz, and Andy J. Pitman
Geosci. Model Dev., 10, 3379–3390, https://doi.org/10.5194/gmd-10-3379-2017,https://doi.org/10.5194/gmd-10-3379-2017, 2017
Short summary

Related subject area

Climate and Earth system modeling
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025,https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025,https://doi.org/10.5194/gmd-18-2193-2025, 2025
Short summary
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025,https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025,https://doi.org/10.5194/gmd-18-2111-2025, 2025
Short summary
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025,https://doi.org/10.5194/gmd-18-2005-2025, 2025
Short summary

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
Download
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.
Share