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
FINAM is not a model (v1.0): a new Python-based model coupling framework
Sebastian Müller, Martin Lange, Thomas Fischer, Sara König, Matthias Kelbling, Jeisson Javier Leal Rojas, and Stephan Thober
Geosci. Model Dev., 18, 4483–4498, https://doi.org/10.5194/gmd-18-4483-2025,https://doi.org/10.5194/gmd-18-4483-2025, 2025
Short summary
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
Geosci. Model Dev., 18, 4399–4416, https://doi.org/10.5194/gmd-18-4399-2025,https://doi.org/10.5194/gmd-18-4399-2025, 2025
Short summary
Enhancing winter climate simulations of the Great Lakes: insights from a new coupled lake–ice–atmosphere (CLIAv1) system on the importance of integrating 3D hydrodynamics with a regional climate model
Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj B. Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, and Eric Kemp
Geosci. Model Dev., 18, 4293–4316, https://doi.org/10.5194/gmd-18-4293-2025,https://doi.org/10.5194/gmd-18-4293-2025, 2025
Short summary
Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol–climate model ECHAM6.3–HAM2.3
Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
Geosci. Model Dev., 18, 4183–4213, https://doi.org/10.5194/gmd-18-4183-2025,https://doi.org/10.5194/gmd-18-4183-2025, 2025
Short summary
Assessing the climate impact of an improved volcanic sulfate aerosol representation in E3SM
Ziming Ke, Qi Tang, Jean-Christophe Golaz, Xiaohong Liu, and Hailong Wang
Geosci. Model Dev., 18, 4137–4153, https://doi.org/10.5194/gmd-18-4137-2025,https://doi.org/10.5194/gmd-18-4137-2025, 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