Articles | Volume 9, issue 8
https://doi.org/10.5194/gmd-9-2833-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-2833-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
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
National Centre for Earth Observation, University of Exeter, Exeter EX4 4QF, UK
Tim E. Jupp
National Centre for Earth Observation, University of Exeter, Exeter EX4 4QF, UK
Peter M. Cox
National Centre for Earth Observation, University of Exeter, Exeter EX4 4QF, UK
Catherine M. Luke
National Centre for Earth Observation, University of Exeter, Exeter EX4 4QF, UK
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- A framework for improved predictions of the climate impacts on potential yields of UK winter wheat and its applicability to other UK crops G. Hayman et al. 10.1016/j.cliser.2024.100479
- Multi‐Objective Adaptive Surrogate Modeling‐Based Optimization for Distributed Environmental Models Based on Grid Sampling R. Sun et al. 10.1029/2020WR028740
- Combining local model calibration with the emergent constraint approach to reduce uncertainty in the tropical land carbon cycle feedback N. Raoult et al. 10.5194/esd-14-723-2023
- Consistent assimilation of multiple data streams in a carbon cycle data assimilation system N. MacBean et al. 10.5194/gmd-9-3569-2016
- Description and validation of an intermediate complexity model for ecosystem photosynthesis and evapotranspiration: ACM-GPP-ETv1 T. Smallman & M. Williams 10.5194/gmd-12-2227-2019
- Constraining a terrestrial biosphere model with remotely sensed atmospheric carbon dioxide T. Kaminski et al. 10.1016/j.rse.2017.08.017
- Uncertainty Quantification for the Noah‐MP Land Surface Model: A Case Study in a Grassland and Sandy Soil Region H. Wang et al. 10.1029/2023JD038556
- Ozone deposition impact assessments for forest canopies require accurate ozone flux partitioning on diurnal timescales A. Visser et al. 10.5194/acp-21-18393-2021
- Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems M. Scholze et al. 10.5194/bg-14-3401-2017
- Reducing the uncertainty of parameters controlling seasonal carbon and water fluxes in Chinese forests and its implication for simulated climate sensitivities Y. Li et al. 10.1002/2017GB005714
- Using Free Air CO2 Enrichment data to constrain land surface model projections of the terrestrial carbon cycle N. Raoult et al. 10.5194/bg-21-1017-2024
- Development of an ecophysiology module in the GEOS-Chem chemical transport model version 12.2.0 to represent biosphere–atmosphere fluxes relevant for ozone air quality J. Lam et al. 10.5194/gmd-16-2323-2023
- The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0 E. Pinnington et al. 10.5194/gmd-13-55-2020
- Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO2 Flux and its Inter‐Annual Variability K. Mahmud et al. 10.1029/2021JG006400
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- ICLASS 1.1, a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model: description, validation, and application P. Bosman & M. Krol 10.5194/gmd-16-47-2023
- Improving modelled albedo over the Greenland ice sheet through parameter optimisation and MODIS snow albedo retrievals N. Raoult et al. 10.5194/tc-17-2705-2023
- Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2) V. Bastrikov et al. 10.5194/gmd-11-4739-2018
- Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH J. Mäkelä et al. 10.5194/gmd-12-4075-2019
- Dry Deposition of Ozone Over Land: Processes, Measurement, and Modeling O. Clifton et al. 10.1029/2019RG000670
- Assimilation of multiple datasets results in large differences in regional- to global-scale NEE and GPP budgets simulated by a terrestrial biosphere model C. Bacour et al. 10.5194/bg-20-1089-2023
- Impact of remotely sensed soil moisture and precipitation on soil moisture prediction in a data assimilation system with the JULES land surface model E. Pinnington et al. 10.5194/hess-22-2575-2018
- A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle P. Peylin et al. 10.5194/gmd-9-3321-2016
- Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data R. Ma et al. 10.5194/gmd-15-6637-2022
- Assessing MODIS carbon and water fluxes in grasslands and shrublands in semiarid regions using eddy covariance tower data Y. Li et al. 10.1080/01431161.2020.1811915
- Confronting the Challenge of Modeling Cloud and Precipitation Microphysics H. Morrison et al. 10.1029/2019MS001689
- Calibration of cloud and aerosol related parameters for solar irradiance forecasts in WRF-solar Y. Liu et al. 10.1016/j.solener.2022.05.064
- Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data N. MacBean et al. 10.1038/s41598-018-20024-w
- Parameter optimization for carbon and water fluxes in two global land surface models based on surrogate modelling J. Li et al. 10.1002/joc.5428
- Impacts of Land Cover and Soil Texture Uncertainty on Land Model Simulations Over the Central Tibetan Plateau J. Li et al. 10.1029/2018MS001377
- Numerical Evaluation of JULES Surface Tiling Scheme with High-Resolution Atmospheric Forcing and Land Cover Data J. Park et al. 10.2151/sola.2018-004
Latest update: 29 Jun 2024
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.
We present a set of "optimal" parameter values used to describe the influence of vegetation in a...
