Articles | Volume 14, issue 12
https://doi.org/10.5194/gmd-14-7659-2021
© Author(s) 2021. 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-14-7659-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
20 Dec 2021
Model description paper |
| 20 Dec 2021
| 20 Dec 2021
Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator
Duncan Watson-Parris
CORRESPONDING AUTHOR
Atmospheric, Oceanic and Planetary Physics, Department of Physics,
University of Oxford, Oxford, UK
Andrew Williams
Atmospheric, Oceanic and Planetary Physics, Department of Physics,
University of Oxford, Oxford, UK
Lucia Deaconu
Faculty of Environmental Science and Engineering, University of Babeș-Bolyai, Cluj-Napoca, Romania
Philip Stier
Atmospheric, Oceanic and Planetary Physics, Department of Physics,
University of Oxford, Oxford, UK
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Cited
30 citations as recorded by crossref.
- Calibrating a large-domain land/hydrology process model in the age of AI: the SUMMA CAMELS emulator experiments M. Farahani et al.
- Addressing Complexity in Global Aerosol Climate Model Cloud Microphysics U. Proske et al.
- A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0) L. Li et al.
- Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing L. Regayre et al.
- ClimateBench v1.0: A Benchmark for Data‐Driven Climate Projections D. Watson‐Parris et al.
- Review of climate simulation by Simple Climate Models A. Romero-Prieto et al.
- An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6 T. Eidhammer et al.
- METEORv1.0.1: a novel framework for emulating multi-timescale regional climate responses M. Sandstad et al.
- Machine Learning for Climate Physics and Simulations C. Lai et al.
- Assessing the potential for simplification in global climate model cloud microphysics U. Proske et al.
- Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching P. Bonnet et al.
- Statistical constraints on climate model parameters using a scalable cloud-based inference framework J. Carzon et al.
- Climate variability can outweigh the influence of climate mean changes for extreme precipitation under global warming K. Nordling et al.
- Calibration of climate model parameterizations using Bayesian experimental design T. Reichelt et al.
- Opportunities and challenges of quantum computing for climate modeling M. Schwabe et al.
- Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al.
- Toward Efficient Calibration of Higher‐Resolution Earth System Models C. Fletcher et al.
- Machine learning for the physics of climate A. Bracco et al.
- Emulators of Climate Model Output C. Tebaldi et al.
- Tree-ring based forest model calibrations with a deep learning algorithm X. Yu et al.
- RTM Surrogate Modeling in Optical Remote Sensing: A Review of Emulation for Vegetation and Atmosphere Applications J. Verrelst et al.
- Dust radiative forcing in CMIP6 Earth System models: insights from the AerChemMIP piClim-2xdust experiment O. Haugvaldstad et al.
- Surrogate-based model parameter optimization in simulations of the West African monsoon M. Fischer et al.
- Opinion: The role of AerChemMIP in advancing climate and air quality research P. Griffiths et al.
- ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool D. Di Santo et al.
- Towards the assimilation of atmospheric CO2 concentration data in a land surface model using adjoint-free variational methods S. Beylat et al.
- Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation L. Conibear et al.
- Uncertainty in aerosol effective radiative forcing from anthropogenic and natural aerosol parameters in ECHAM6.3-HAM2.3 Y. Bhatti et al.
- Aircraft in-situ measurements from SOCRATES constrain the anthropogenic perturbations of cloud droplet number C. Song et al.
- Constraining aerosol–cloud adjustments by uniting surface observations with a perturbed parameter ensemble A. Mikkelsen et al.
30 citations as recorded by crossref.
- Calibrating a large-domain land/hydrology process model in the age of AI: the SUMMA CAMELS emulator experiments M. Farahani et al.
- Addressing Complexity in Global Aerosol Climate Model Cloud Microphysics U. Proske et al.
- A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0) L. Li et al.
- Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing L. Regayre et al.
- ClimateBench v1.0: A Benchmark for Data‐Driven Climate Projections D. Watson‐Parris et al.
- Review of climate simulation by Simple Climate Models A. Romero-Prieto et al.
- An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6 T. Eidhammer et al.
- METEORv1.0.1: a novel framework for emulating multi-timescale regional climate responses M. Sandstad et al.
- Machine Learning for Climate Physics and Simulations C. Lai et al.
- Assessing the potential for simplification in global climate model cloud microphysics U. Proske et al.
- Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching P. Bonnet et al.
- Statistical constraints on climate model parameters using a scalable cloud-based inference framework J. Carzon et al.
- Climate variability can outweigh the influence of climate mean changes for extreme precipitation under global warming K. Nordling et al.
- Calibration of climate model parameterizations using Bayesian experimental design T. Reichelt et al.
- Opportunities and challenges of quantum computing for climate modeling M. Schwabe et al.
- Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al.
- Toward Efficient Calibration of Higher‐Resolution Earth System Models C. Fletcher et al.
- Machine learning for the physics of climate A. Bracco et al.
- Emulators of Climate Model Output C. Tebaldi et al.
- Tree-ring based forest model calibrations with a deep learning algorithm X. Yu et al.
- RTM Surrogate Modeling in Optical Remote Sensing: A Review of Emulation for Vegetation and Atmosphere Applications J. Verrelst et al.
- Dust radiative forcing in CMIP6 Earth System models: insights from the AerChemMIP piClim-2xdust experiment O. Haugvaldstad et al.
- Surrogate-based model parameter optimization in simulations of the West African monsoon M. Fischer et al.
- Opinion: The role of AerChemMIP in advancing climate and air quality research P. Griffiths et al.
- ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool D. Di Santo et al.
- Towards the assimilation of atmospheric CO2 concentration data in a land surface model using adjoint-free variational methods S. Beylat et al.
- Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation L. Conibear et al.
- Uncertainty in aerosol effective radiative forcing from anthropogenic and natural aerosol parameters in ECHAM6.3-HAM2.3 Y. Bhatti et al.
- Aircraft in-situ measurements from SOCRATES constrain the anthropogenic perturbations of cloud droplet number C. Song et al.
- Constraining aerosol–cloud adjustments by uniting surface observations with a perturbed parameter ensemble A. Mikkelsen et al.
Saved (final revised paper)
Latest update: 30 Apr 2026
Short summary
The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide...
