Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1785-2025
© Author(s) 2025. 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-18-1785-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
14 Mar 2025
Model description paper |
| 14 Mar 2025
| 14 Mar 2025
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
Camilla Mathison
CORRESPONDING AUTHOR
Met Office Hadley Centre, Exeter, UK
School of Geography, University of Leeds, Leeds, UK
Eleanor J. Burke
Met Office Hadley Centre, Exeter, UK
Gregory Munday
Met Office Hadley Centre, Exeter, UK
Chris D. Jones
Met Office Hadley Centre, Exeter, UK
School of Geographical Sciences, University of Bristol, Bristol, UK
Chris J. Smith
Met Office Hadley Centre, Exeter, UK
Department of Water and Climate, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Energy, Climate and Environment Program, International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Norman J. Steinert
Norwegian Research Centre AS (NORCE), Bjerknes Centre for Climate Research, Bergen, Norway
CICERO Center for International Climate Research, Oslo, Norway
Andy J. Wiltshire
Met Office Hadley Centre, Exeter, UK
Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK
Chris Huntingford
UK Centre for Ecology and Hydrology, Wallingford, UK
Eszter Kovacs
School of Earth and Environment, University of Leeds, Leeds, UK
Laila K. Gohar
Met Office Hadley Centre, Exeter, UK
Rebecca M. Varney
Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK
Douglas McNeall
Met Office Hadley Centre, Exeter, UK
Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK
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Cited
12 citations as recorded by crossref.
- METEORv1.0.1: a novel framework for emulating multi-timescale regional climate responses M. Sandstad et al. https://doi.org/10.5194/gmd-18-8269-2025
- Research progress of integrated assessment models in food-water-energy-environment analysis: A bibliometric analysis S. Huan & L. Liu https://doi.org/10.1016/j.egycc.2025.100215
- A theoretical framework to understand sources of error in Earth System Model emulation C. Womack et al. https://doi.org/10.5194/esd-17-107-2026
- Building Interpretable Climate Emulators for Economics A. Eftekhari et al. https://doi.org/10.1093/ej/ueaf131
- The representation of climate impacts in the FRIDAv2.1 Integrated Assessment Model C. Wells et al. https://doi.org/10.5194/gmd-19-1229-2026
- Framework for global emulation of extreme precipitation along global warming trajectories L. Pierini et al. https://doi.org/10.1088/1748-9326/ae5fad
- The implications of overshooting 1.5 °C on Earth system tipping elements—a review P. Ritchie et al. https://doi.org/10.1088/1748-9326/ae3cad
- Rewiring climate modeling with machine learning emulators P. Van Katwyk et al. https://doi.org/10.1038/s43247-026-03238-z
- Projecting the FIA’s GHG Emissions: A Forecast for the 2030 Sustainability Target A. Al-rubaye & S. Naimi https://doi.org/10.3390/su172310633
- Review of climate simulation by Simple Climate Models A. Romero-Prieto et al. https://doi.org/10.5194/gmd-19-115-2026
- Risks of unavoidable impacts on forests at 1.5 °C with and without overshoot G. Munday et al. https://doi.org/10.1038/s41558-025-02327-9
- Using reduced-complexity volcanic aerosol and climate models to produce large ensemble simulations of Holocene temperature M. Verkerk et al. https://doi.org/10.5194/cp-21-1755-2025
12 citations as recorded by crossref.
- METEORv1.0.1: a novel framework for emulating multi-timescale regional climate responses M. Sandstad et al. https://doi.org/10.5194/gmd-18-8269-2025
- Research progress of integrated assessment models in food-water-energy-environment analysis: A bibliometric analysis S. Huan & L. Liu https://doi.org/10.1016/j.egycc.2025.100215
- A theoretical framework to understand sources of error in Earth System Model emulation C. Womack et al. https://doi.org/10.5194/esd-17-107-2026
- Building Interpretable Climate Emulators for Economics A. Eftekhari et al. https://doi.org/10.1093/ej/ueaf131
- The representation of climate impacts in the FRIDAv2.1 Integrated Assessment Model C. Wells et al. https://doi.org/10.5194/gmd-19-1229-2026
- Framework for global emulation of extreme precipitation along global warming trajectories L. Pierini et al. https://doi.org/10.1088/1748-9326/ae5fad
- The implications of overshooting 1.5 °C on Earth system tipping elements—a review P. Ritchie et al. https://doi.org/10.1088/1748-9326/ae3cad
- Rewiring climate modeling with machine learning emulators P. Van Katwyk et al. https://doi.org/10.1038/s43247-026-03238-z
- Projecting the FIA’s GHG Emissions: A Forecast for the 2030 Sustainability Target A. Al-rubaye & S. Naimi https://doi.org/10.3390/su172310633
- Review of climate simulation by Simple Climate Models A. Romero-Prieto et al. https://doi.org/10.5194/gmd-19-115-2026
- Risks of unavoidable impacts on forests at 1.5 °C with and without overshoot G. Munday et al. https://doi.org/10.1038/s41558-025-02327-9
- Using reduced-complexity volcanic aerosol and climate models to produce large ensemble simulations of Holocene temperature M. Verkerk et al. https://doi.org/10.5194/cp-21-1755-2025
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is...
