Articles | Volume 11, issue 8
https://doi.org/10.5194/gmd-11-3131-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-3131-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
03 Aug 2018
Methods for assessment of models |
| 03 Aug 2018
| 03 Aug 2018
Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output
Lancaster Environment Centre, Lancaster University, Lancaster, UK
Oliver Wild
Lancaster Environment Centre, Lancaster University, Lancaster, UK
Apostolos Voulgarakis
Department of Physics, Imperial College London, London, UK
Lindsay Lee
School of Earth and Environment, University of Leeds, Leeds, UK
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38 citations as recorded by crossref.
- Sensitivity analysis using Morris: Just screening or an effective ranking method? L. Paleari et al. https://doi.org/10.1016/j.ecolmodel.2021.109648
- Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry–climate model L. Revell et al. https://doi.org/10.5194/acp-18-16155-2018
- Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator D. Watson-Parris et al. https://doi.org/10.5194/gmd-14-7659-2021
- Global sensitivity analysis of chemistry–climate model budgets of tropospheric ozone and OH: exploring model diversity O. Wild et al. https://doi.org/10.5194/acp-20-4047-2020
- ANN-Based Inverse Modeling and Global Sensitivity Analysis of a CAR1 Damper M. Titirla & W. Larbi https://doi.org/10.3390/app16062925
- Parameter Sensitivity Analysis for Computationally Intensive Spatially Distributed Dynamical Environmental Systems Models X. Huo et al. https://doi.org/10.1029/2018MS001573
- Integrating ground-based and satellite rainfall data for hydrological modeling: A SWAT+ application with sensitivity analysis in the Saguling Watershed, Citarum River Basin H. Kardhana et al. https://doi.org/10.1016/j.rineng.2025.106370
- Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data J. Verrelst et al. https://doi.org/10.3390/rs11161923
- Comparison of machine learning methods emulating process driven crop models D. Johnston et al. https://doi.org/10.1016/j.envsoft.2023.105634
- Decoding parameter sensitivities across scales: a Wavelet–Sobol framework for hydrological modelling V. Sai Suswara et al. https://doi.org/10.1016/j.jhydrol.2026.135482
- Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation L. Conibear et al. https://doi.org/10.1029/2021GH000570
- A Framework for the Bayesian Calibration of Complex and Data-Scarce Models in Applied Sciences C. Schenk & I. Romero https://doi.org/10.1007/s11831-026-10665-w
- Predicting global patterns of long-term climate change from short-term simulations using machine learning L. Mansfield et al. https://doi.org/10.1038/s41612-020-00148-5
- Evaluation of Sobol’ sensitivity indices conditioned on model performance R. Bikkumalla & R. Govindaraju https://doi.org/10.1016/j.jhydrol.2026.136245
- Mitigation of PM2.5 and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period Y. Chen et al. https://doi.org/10.5194/acp-20-499-2020
- Identification and verification of worst-case radiological transport scenarios for Ireland: A simulation-based approach to nuclear emergency preparedness (2011–2024) M. Sturrock et al. https://doi.org/10.1016/j.scitotenv.2026.181817
- Variance‐Based Global Sensitivity Analysis: A Methodological Framework and Case Study for Microkinetic Modeling B. van den Boorn et al. https://doi.org/10.1002/adts.202200615
- Comparison of Machine Learning Methods Emulating Process Driven Crop Models D. Johnston et al. https://doi.org/10.2139/ssrn.4111406
- Uncertainty and Sensitivity Analysis of Significant Parameters for Superlarge Diameter Shield Excavation E. Chen et al. https://doi.org/10.1155/2021/8819393
- Optimal control of uncertain batch processes via Koopman expectation-assisted gradient methods D. Zadravec & N. Ferdelji https://doi.org/10.1016/j.compchemeng.2025.109353
- Bayesian neural network-driven accelerometer-based type intelligent tire force measurement system X. Sun et al. https://doi.org/10.1016/j.measurement.2025.117699
- Comprehensive multicriteria optimization of energy systems in tropical islands: A thermoenvironmental and life cycle assessment framework applied to Ylang-Ylang distillation in Anjouan, Comoros M. Ahamadi et al. https://doi.org/10.1016/j.egyr.2026.109811
- Coupling Morgan–Morgan–Finney model with machine learning for soil erosion assessment in the Shivalik foothills of Punjab, India S. Bhattacharjee et al. https://doi.org/10.1016/j.ecofro.2026.07.016
- Advancing Scientific Understanding of the Global Methane Budget in Support of the Paris Agreement A. Ganesan et al. https://doi.org/10.1029/2018GB006065
- Quantifying and addressing the uncertainties in tropospheric ozone and OH in a global chemistry transport model O. Wild & E. Ryan https://doi.org/10.5194/acp-26-2255-2026
- Analysis of Hepatic Lipid Metabolism Model: Simulation and Non-Stationary Global Sensitivity Analysis M. Kosić et al. https://doi.org/10.3390/nu14234992
- Sensitivity Analysis of a Transmission Interruption Model for the Soil-Transmitted Helminth Infections in Kenya C. Okoyo et al. https://doi.org/10.3389/fpubh.2022.841883
- Temporally resolved sectoral and regional contributions to air pollution in Beijing: informing short-term emission controls T. Ansari et al. https://doi.org/10.5194/acp-21-4471-2021
- Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis A. Feinberg et al. https://doi.org/10.5194/acp-20-1363-2020
- Multi-method global sensitivity analysis of mathematical models A. Dela et al. https://doi.org/10.1016/j.jtbi.2022.111159
- Probabilistic financial modeling and EFAST based sensitivity analysis of industrial rooftop solar photovoltaic systems F. Khan et al. https://doi.org/10.1016/j.rineng.2026.109269
- Position paper: Sensitivity analysis of spatially distributed environmental models- a pragmatic framework for the exploration of uncertainty sources H. Koo et al. https://doi.org/10.1016/j.envsoft.2020.104857
- Application of the Observation‐Oriented CNOP‐P Sensitivity Analysis Method in Evapotranspiration Simulation and Prediction Over the Tibetan Plateau G. Sun et al. https://doi.org/10.1029/2022WR033216
- Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China L. Conibear et al. https://doi.org/10.1029/2021GH000391
- Improved metamodels for predicting high-dimensional outputs by accounting for the dependence structure of the latent variables: application to marine flooding J. Rohmer et al. https://doi.org/10.1007/s00477-023-02426-z
- Calibrating a global atmospheric chemistry transport model using Gaussian process emulation and ground-level concentrations of ozone and carbon monoxide E. Ryan & O. Wild https://doi.org/10.5194/gmd-14-5373-2021
- Assessing the potential for simplification in global climate model cloud microphysics U. Proske et al. https://doi.org/10.5194/acp-22-4737-2022
- Sensitivity analysis of effective parameters on bone drilling force by using E-fast method a. sousanabadi farahani et al. https://doi.org/10.61186/masm.3.1.83
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Latest update: 04 Nov 2026
Short summary
Global sensitivity analysis (GSA) identifies which parameters of a model most affect its output. We performed GSA using statistical emulators as surrogates of two slow-running atmospheric chemistry transport models. Due to the high dimension of the model outputs, we considered two alternative methods: one that reduced the output dimension and one that did not require an emulator. The alternative methods accurately performed the GSA but were significantly faster than the emulator-only method.
Global sensitivity analysis (GSA) identifies which parameters of a model most affect its output....
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