Abstract

Abstract. Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1 ∘C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2 ∘C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3 ∘C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23∘C(±0.12 ∘C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs.

Highlights

  • Sufficient computing power to enable running our most comprehensive, physically complete climate models for every application of interest is not available

  • In Phase 1 of Reduced Complexity Model Intercomparison Project (RCMIP), we focus on experiments which are defined in terms of concentrations to facilitate a direct comparison with Coupled Model Intercomparison Project (CMIP) experiments, most of which are defined in terms of concentrations

  • We focus on simulations which cover the range in forcing scenarios from the CMIP6 ScenarioMIP exercise (O’Neill et al, 2016; Riahi et al, 2017) and CMIP5 Representative Concentration Pathway (RCP) scenarios

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Summary

Introduction

Sufficient computing power to enable running our most comprehensive, physically complete climate models for every application of interest is not available. One common approach is the use of reduced-complexity climate models (RCMs), known as simple climate models (SCMs). RCMs are designed to be computationally efficient tools, allowing for exploratory research, and have smaller spatial, if any, and temporal resolution than complex models. They describe highly parameterised macro-properties of the climate system. This means that they simulate the climate system on a global-mean, annual-mean scale, some RCMs even use coarse-resolution spatial grids and monthly time steps. As a result of their highly parameterised approach, RCMs can be of the order of a million or more times faster than more complex models (in terms of simulated model years per unit CPU time)

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