Preprints
https://doi.org/10.5194/egusphere-2023-2932
https://doi.org/10.5194/egusphere-2023-2932
26 Feb 2024
 | 26 Feb 2024
Status: this preprint is open for discussion.

A rapid application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)

Camilla Therese Mathison, Eleanor Burke, Eszter Kovacs, Gregory Munday, Chris Huntingford, Chris Jones, Chris Smith, Norman Steinert, Andy Wiltshire, Laila Gohar, and Rebecca Varney

Abstract. Climate policies evolve quickly, and new scenarios designed around these policies are used to illustrate how they impact global mean temperatures using simple climate models (or climate emulators). Simple climate models are extremely efficient although limited to showing only the global picture. Within the Intergovernmental Panel on Climate Change (IPCC) framework, there is a need to understand the regional impacts of scenarios that include the most recent science and policy decisions quickly to support government in negotiations. To address this, we present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), a new flexible probabilistic framework which aims to provide an efficient means to run new scenarios without the significant overheads of larger more complex Earth system models (ESMs). PRIME provides the capability to include the most recent models, science and scenarios to run ensemble simulations on multi-centennial timescales and include analysis of many variables that are relevant and important for impacts assessments. We use a simple climate model to provide the global temperatures to scale the patterns from a large number of the CMIP6 ESMs. These provide the inputs to a weather generator and a land-surface model, which generates an estimate of the land-surface impacts from the emissions scenarios. Here we test PRIME using known scenarios in the form of the Shared Socioeconomic Pathways (SSPs) to demonstrate that PRIME reproduces the climate response to a range of emissions scenarios, as shown in the IPCC reports. We show results for a range of scenarios including the SSP5-8.5 high emissions scenario, which was used to define the patterns; SSP1-2.6, a mitigation scenario with low emissions and SSP5-3.4-OS, an overshoot scenario. PRIME correctly represents the climate response for these known scenarios, which gives us confidence that PRIME will be useful for rapidly providing probabilistic spatially resolved information for novel climate scenarios; substantially reducing the time between the scenarios being released and being used in impacts assessments.

Camilla Therese Mathison, Eleanor Burke, Eszter Kovacs, Gregory Munday, Chris Huntingford, Chris Jones, Chris Smith, Norman Steinert, Andy Wiltshire, Laila Gohar, and Rebecca Varney

Status: open (until 06 May 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2932', Anonymous Referee #1, 22 Mar 2024 reply
Camilla Therese Mathison, Eleanor Burke, Eszter Kovacs, Gregory Munday, Chris Huntingford, Chris Jones, Chris Smith, Norman Steinert, Andy Wiltshire, Laila Gohar, and Rebecca Varney

Data sets

FaIR: Calibration data for FaIR v1.6.2 is available from zenodo Chris Smith https://doi.org/10.5281/zenodo.6601980

ESMValTool Climate patterns code Greg Munday, Eleanor Burke, and Chris Huntingford https://zenodo.org/records/10635588

Temps and CO2 concentrations for running PRIME from FaIRv1.6.4 Camilla Mathison and Chris Smith https://zenodo.org/records/10524337

JULES output from PRIME version 1 Eleanor Burke and Camilla Mathison https://doi.org/10.5281/zenodo.10634291

Model code and software

FaIR v1.6.2 Chris Smith https://doi.org/10.5281/zenodo.4465032

Camilla Therese Mathison, Eleanor Burke, Eszter Kovacs, Gregory Munday, Chris Huntingford, Chris Jones, Chris Smith, Norman Steinert, Andy Wiltshire, Laila Gohar, and Rebecca Varney

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Short summary
We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emission scenarios and rapidly provide regional impacts information. PRIME allows large ensembles to be run on multi-centennial timescales including analysis of many important variables for impacts assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios giving confidence in using PRIME for novel scenarios.