Sufficient computing power to enable running our most comprehensive, physically complete climate models for every application of interest is not available. Thus, for many applications, less computationally demanding approaches are used. One common approach is the use of reduced-complexity climate models (RCMs), also 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. Typically, they describe highly parameterised macro-properties of the climate system. Usually this means that they simulate the climate system on a global-mean, annual-mean scale, although 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).

The computational efficiency of RCMs means that they can be used where computational constraints would otherwise be limiting. For example, in the hierarchy of climate models – RCMs, the Earth system models of intermediate complexity (EMICs) and Earth system models (ESMs) – it is only RCMs that are sufficiently efficient for large probabilistic ensembles for hundreds of scenarios. In addition, some integrated assessment models (IAMs) require iterative climate simulations. In such cases, only RCMs are computationally feasible because hundreds to thousands of climate realisations must be integrated by the IAM for a single scenario to be produced. RCMs also enable the exploration of interacting uncertainties from multiple parts of the climate system or the constraining of unknown parameters by combining multiple lines of evidence in an internally consistent setup. In the context of the assessment reports of the Intergovernmental Panel on Climate Change (IPCC), a prominent example is the climate assessment of emission scenarios by IPCC Working Group 3 (WGIII). Hundreds of emission scenarios were assessed in the IPCC's Fifth Assessment Report (AR5; see ) as well as its more recent Special Report on Global Warming of 1.5 ∘C (SR1.5; see ). (Scenario data are available at https://secure.iiasa.ac.at/web-apps/ene/AR5DB (last access: 22 October 2020) and https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/ (last access: 22 October 2020) for AR5 and SR1.5 respectively; both databases are hosted by the IIASA Energy Program.) For the IPCC's forthcoming Sixth Assessment Report (AR6), it is anticipated that the number of scenarios will be in the several hundreds to a thousand (for example, see the full set of scenarios based on the Shared Socioeconomic Pathway (SSPs) at https://tntcat.iiasa.ac.at/SspDb, last access: 22 October 2020). Both the number of scenarios and the tight timelines of the IPCC assessments render it infeasible to use the world's most comprehensive models to estimate the climate implications of these IAM scenarios.

The key point of this paper is to introduce RCMIP, its goals and its setup. As a proof of concept, we also include key initial research questions, the implemented experimental setup and associated results from RCMIP's first phase.

Fifteen models have participated in RCMIP Phase 1 (see Table  for an overview and links to key description papers; note that GIR has been renamed FaIR-v2 since the preparation of this paper). We encourage any other interested groups to join further phases of the project.

Even within the reduced-complexity category, there is considerable variation in both model complexity and the number of climate components (Table ). At the simplest end, we have the radiative-forcing-driven (see Sect. ) impulse response models, represented by the AR5IR model variants. These models project global-mean temperature only and, in the setup submitted here, provide only annual-mean values (although they can be run at higher temporal resolution if desired). At the other end of the spectrum, we have MAGICC, which includes representations of 43 greenhouse gas cycles, includes parameterisations of the relationship between aerosol emissions and aerosol effective radiative forcing, distinguishes between different hemispheres and land/ocean regions of the globe, has 50 ocean layers in each hemisphere, and runs on a monthly time step internally (although all output is annual mean only). Some models take a more hybrid approach, increasing complexity in only a single component whilst retaining simplicity elsewhere. Examples of increased complexity in specific domains include OSCAR's regionalised land carbon cycle and EMGC's representation of natural variability.

An in-depth description of these models and their differences is beyond the scope of this paper (but is planned for future research). For readers interested in the details of all the participating models, we refer to the references provided in Table .

RCMs generally model multiple steps in the emissions–climate change cause–effect chain, including gas cycles (emissions-to-concentration step), radiative forcing parameterisations (concentrations-to-radiative-forcing step) and temperature response (radiative-forcing-to-warming step). Here, effective radiative forcing and radiative forcing are defined following . In contrast to radiative forcing, effective radiative forcing includes rapid adjustments beyond stratospheric temperature adjustments and thus is a better indicator of long-term climate change.

Each point in the chain can be used as the starting point for simulations; i.e. the simulation might be defined in terms of prescribed concentrations, emissions or radiative forcing. In Phase 1 of RCMIP, we focus on experiments which are defined in terms of concentrations to facilitate a direct comparison with CMIP experiments, most of which are also defined in terms of concentrations.

RCMIP Phase 1 focuses on 19 experiments, which can be broken down into two categories: scenario-based and idealised. We provided all inputs following, and requested that all outputs follow, a standard format to facilitate ease of data analysis and re-use (Sect. S1 in the Supplement). This common data format was developed for RCMIP and combines elements of the integrated assessment community standard and the CMIP6 definitions of variables and scenarios.

Within 3 months of beginning RCMIP and publishing the protocols, 15 different RCMs submitted data. Given that this is the first phase of RCMIP, we expect even shorter turnarounds in future. The submitted results demonstrate that the RCM community, via RCMIP, now has the capacity to run multi-model studies, and to run them comparatively quickly. In addition, the number of participating modelling groups demonstrates that the RCMIP infrastructure is accessible to a wide range of modelling teams.

All the RCMs are able to capture the approximately 1 ∘C of warming seen in the historical observations (Fig. ), compared to a pre-industrial reference period . However, the RCMs vary in the detail which they represent. Most of the RCMs include some representation of the impact of volcanic eruptions, most notably the drop in global-mean temperatures after the eruption of Mount Agung in 1963. In addition, most of the RCMs do not capture natural variability driven by processes such as the El Niño–Southern Oscillation , the Pacific Decadal Oscillation and the Indian Ocean Dipole . The exception to this is the EMGC model, which includes representations of the impact of all of these processes. At the other end of the complexity spectrum, we have the CO2-only model, GREB. Unlike the other RCMs, GREB lacks the volcanic and aerosol-induced cooling signals of the 19th and 20th centuries.

RCMIP also facilitates a comparison of model calibrations and CMIP output (Fig. ). Examining multiple emulation setups, we see that RCMs can reproduce the temperature response of CMIP models to forcing changes to within a root-mean-square error of 0.2 ∘C (Table ). A detailed comparison of RCMs with 24 CMIP6 ESM ensemble members is available in the Supplement (Table S1 and Figs. S1–S24). In scenario-based experiments, it appears to be harder for RCMs to emulate CMIP output than in idealised experiments. We suggest two key explanations. The first is that effective radiative forcing cannot be easily diagnosed in SSP-based scenarios and hence it is hard to know how best to force the RCM during calibration. The second is that the forcing in these scenarios includes periods of increase, sudden decrease due to volcanoes and longer-term stabilisation rather than the simpler changes seen in the idealised experiments. Fitting all three of these regimes is a more difficult challenge than fitting the idealised experiments alone.

Only 6 models (Table ) have been able to submit emulation configurations. Furthermore, each RCM is calibrated to a different number of CMIP models, with some modelling teams unable to provide any calibrations at all. The reason is that there is to date no common resource of calibration data from the CMIP6 repositories. The technical challenge of diagnosing, stitching together, creating area-weighted averages and de-drifting a large amount of CMIP6 output data within a short time period has turned out to be a hurdle for many modelling teams. As an offspring from RCMIP, we attempt to address this challenge for the future by providing a unifying data portal see https://cmip6.science.unimelb.edu.au/, last access: 22 October 2020,.

The ensemble of RCMs also provides insights into the differences between CMIP5 and CMIP6 generation scenarios (“RCP” and “SSP-based” scenarios respectively) when these scenarios are run with identical models (Fig. ). In the selection of models which have submitted all RCP and SSP-based scenario pairs, the SSP-based scenarios are 0.20 ∘C (standard deviation 0.10 ∘C across the available models) warmer than their corresponding RCPs (Fig. b). This difference is driven by the 0.39 ±0.24 Wm-2 larger effective radiative forcing in the SSP-based scenarios (Fig. d), which itself is driven by the 0.53±0.44 Wm-2 larger CO2 effective radiative forcing in the SSP-based scenarios (Fig. f). These results add to the work of , which suggests that, even when run with the same model (in a concentration-driven setup), the SSP-based scenarios result in warmer projections than the RCPs. When we run one of the RCMs (MAGICC) with an AR5-consistent stratospheric-adjusted radiative forcing definition , the SSP-based and RCP scenarios are within 6 % of each other in 2100 (although their AR5-consistent stratospheric-adjusted radiative forcing trajectories can differ by up to 15 % at different times over the 21st century). Thus, we find that the update to effective radiative forcing , mainly using the formulations presented in plus any rapid adjustment terms , increases the total forcing in the SSP-based scenarios, because their generally higher CO2 concentrations are partially, but not fully, offset by lower CH4 concentrations see e.g. Fig. 11 in. There is a clear need for further, more comprehensive exploration of the differences between the RCP and SSP-based scenarios.

Finally, we present variations in the relationship between surface air temperature change and cumulative CO2 emissions from the 1pctCO2 and 1pctCO2-4xext experiments (Fig. ). To date, only three models (GIR (since renamed FaIR-v2), MCE and OSCAR) have been able to provide the required outputs (in particular deriving inverse emissions from these concentration-defined experiments). From the available results, it is clear that the relationship between these two key variables varies over MCE's parameter ensemble, from weakly sub-linear to weakly super-linear. Such variation can have notable implications for the remaining carbon budget . We also see that the MCE model's parameter ensemble covers a large range, dwarfing the differences between it and the GIR (since renamed FaIR-v2) and OSCAR models, which are shown here in their 3 ∘C climate sensitivity configurations. This suggests that, at least for RCMs, the response of individual components and their configuration is more important than model structure, although this conclusion is tempered by the paucity of available results.

RCMIP Phase 1 provides proof of concept of the RCMIP approach to RCM evaluation, comparison and examination. However, Phase 1 has been limited to a very specific set of questions, and there is wide scope to use RCMs to examine other scientific questions of interest. In this section we present a number of ways in which further research and phases of RCMIP could build on the work presented in this paper.

The first is an exploration of probabilistic outputs. Most RCMs can be calibrated, i.e. have their parameters adjusted, such that they reproduce our best-estimate (typically median) observations. However, RCMs are also used in a probabilistic mode. In this mode a parametric ensemble is run for a given RCM and set of climate forcers. The results are then used to capture the likelihood that different climate changes will unfold, particularly the likelihood of reaching different warming levels. Given the widespread use of probabilistic distributions, particularly for quantifying likely ranges of climate sensitivity and climate projections see e.g., examining the differences between existing probabilistic model setups is an obvious next step.

Secondly, there are a wide range of RCMs available in the literature. This variety can be confusing, especially to those who are not intimately involved in developing the models. An overview of the different models, their structure and relationship to one another (in the form of a genealogy) would help reduce the confusion and provide clarity about the implications of using one model over another.

Thirdly, emulation results have generally only been submitted for a limited set of experiments. Hence it is still not clear whether the emulation performance seen in idealised experiments also carries over to scenarios, particularly the SSP-based scenarios. As the number of available CMIP6 results continues to grow, this area is ripe for investigation and will lead to improved understanding of the limits of the reduced-complexity approach. The development of a common resource see https://cmip6.science.unimelb.edu.au/, last access: 22 October 2020; for RCM calibration will greatly aid this effort by ensuring that each group has access to the same set of calibration data.

Finally, whilst evaluating RCMs is a useful exercise, the root causes of these differences may not be clear. This can be addressed by performing experiments which specifically diagnose the reasons for differences between models, for example simple pulse emissions of different species or prescribed step changes in atmospheric greenhouse gas concentrations. Such experiments could build on existing research and would allow even more comprehensive examination and understanding of RCM behaviour. This would require custom experiments, particularly for the carbon cycle, which is strongly coupled to other parts of the climate system. However, unlike in the case of ESMs, adding extra RCM experiments adds relatively little technical or human burden, because RCMs are computationally cheap and because RCMIP's standardised formats facilitate highly automated experiment pipelines.

RCMs are used in many applications, particularly where computational constraints prevent other techniques from being used. Due to their importance in climate policy assessments and in carbon budget calculations, as well as their applicability to a wide range of scientific questions, understanding the behaviour and output from RCMs is highly relevant and requires continuous updating with the latest science. Here we have presented the Reduced Complexity Model Intercomparison Project (RCMIP), an effort to facilitate the evaluation and understanding of RCMs in a systematic, standardised and detailed way. We hope this can greatly improve ease of use of, and familiarity with, RCMs.

We have performed RCMIP Phase 1, which provides an initial database of experiments conducted with 15 participating models from the RCM community. RCMIP Phase 1 focused on basic comparisons of RCMs with observed global-mean temperature changes, comparisons of RCMs with the global-mean temperature response of more complex models, the difference between the SSP-based and RCP scenarios, and an exploration of the relationship between cumulative CO2 emissions and surface air temperature change in the RCMs. These initial comparisons demonstrate that RCMIP's infrastructure is a useful tool for such intercomparisons and that the RCM community is able to perform such intercomparisons on timescales of the order of months. Further work will examine the relationship between different RCMs, RCMs' probabilistic projections and the cause of differences between RCMs.

RCMIP fills a gap in our understanding of RCM behaviour, in particular, how different RCMs perform relative to each other as well as how they compare with observations. This gap is particularly important to fill given the widespread use of RCMs throughout the integrated assessment modelling community and in large-scale climate science assessments. We welcome requests, suggestions and further involvement from throughout the climate modelling research community. With our efforts, we aim to increase understanding of and confidence in RCMs, particularly for their many users at the science–policy interface.