Articles | Volume 9, issue 8
https://doi.org/10.5194/gmd-9-2833-2016
https://doi.org/10.5194/gmd-9-2833-2016
Development and technical paper
 | 
25 Aug 2016
Development and technical paper |  | 25 Aug 2016

Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0

Nina M. Raoult, Tim E. Jupp, Peter M. Cox, and Catherine M. Luke

Related authors

Towards the Assimilation of Atmospheric CO2 Concentration Data in a Land Surface Model using Adjoint-free Variational Methods
Simon Beylat, Nina Raoult, Cédric Bacour, Natalie Douglas, Tristan Quaife, Vladislav Bastrikov, Peter Julien Rayner, and Philippe Peylin
EGUsphere, https://doi.org/10.5194/egusphere-2025-109,https://doi.org/10.5194/egusphere-2025-109, 2025
Short summary
Assimilating ESA CCI land surface temperature into the ORCHIDEE land surface model: insights from a multi-site study across Europe
Luis-Enrique Olivera-Guerra, Catherine Ottlé, Nina Raoult, and Philippe Peylin
Hydrol. Earth Syst. Sci., 29, 261–290, https://doi.org/10.5194/hess-29-261-2025,https://doi.org/10.5194/hess-29-261-2025, 2025
Short summary
Modelling snowpack on ice surfaces with the ORCHIDEE land surface model: application to the Greenland ice sheet
Sylvie Charbit, Christophe Dumas, Fabienne Maignan, Catherine Ottlé, Nina Raoult, Xavier Fettweis, and Philippe Conesa
The Cryosphere, 18, 5067–5099, https://doi.org/10.5194/tc-18-5067-2024,https://doi.org/10.5194/tc-18-5067-2024, 2024
Short summary
Exploring the potential of history matching for land surface model calibration
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024,https://doi.org/10.5194/gmd-17-5779-2024, 2024
Short summary
Using Free Air CO2 Enrichment data to constrain land surface model projections of the terrestrial carbon cycle
Nina Raoult, Louis-Axel Edouard-Rambaut, Nicolas Vuichard, Vladislav Bastrikov, Anne Sofie Lansø, Bertrand Guenet, and Philippe Peylin
Biogeosciences, 21, 1017–1036, https://doi.org/10.5194/bg-21-1017-2024,https://doi.org/10.5194/bg-21-1017-2024, 2024
Short summary

Related subject area

Climate and Earth system modeling
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025,https://doi.org/10.5194/gmd-18-4009-2025, 2025
Short summary
ICON-HAM-lite 1.0: simulating the Earth system with interactive aerosols at kilometer scales
Philipp Weiss, Ross Herbert, and Philip Stier
Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025,https://doi.org/10.5194/gmd-18-3877-2025, 2025
Short summary
Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev., 18, 3799–3817, https://doi.org/10.5194/gmd-18-3799-2025,https://doi.org/10.5194/gmd-18-3799-2025, 2025
Short summary
Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
Carolina A. Bieri, Francina Dominguez, Gonzalo Miguez-Macho, and Ying Fan
Geosci. Model Dev., 18, 3755–3779, https://doi.org/10.5194/gmd-18-3755-2025,https://doi.org/10.5194/gmd-18-3755-2025, 2025
Short summary
Reducing time and computing costs in EC-Earth: an automatic load-balancing approach for coupled Earth system models
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev., 18, 3661–3679, https://doi.org/10.5194/gmd-18-3661-2025,https://doi.org/10.5194/gmd-18-3661-2025, 2025
Short summary

Cited articles

Ajami, N. K., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, w01403, https://doi.org/10.1029/2005WR004745, 2007.
Arora, V. and Boer, G.: A parameterization of leaf phenology for the terrestrial ecosystem component of climate models, Glob. Change Biol., 11, 39–59, 2005.
Bartholomew-Biggs, M., Brown, S., Christianson, B., and Dixon, L.: Automatic differentiation of algorithms, J. Comput. Appl. Math., 124, 171–190, https://doi.org/10.1016/S0377-0427(00)00422-2, 2000.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Download
Short summary
We present a set of "optimal" parameter values used to describe the influence of vegetation in a numerical climate model, and the software suite that we developed to find it. Observational data from ~ 100 locations were used, and the optimal parameters improve the fit in 90 % of the locations. The new parameter values will allow the climate model to give better predictions, and our software should prove useful in future calibrations.
Share