Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state-of-the-art knowledge in an internally self-consistent modelling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialised research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7{degree sign}C (relative to 1850-1900, using an observationally-based historical warming estimate of 0.8{degree sign}C between 1850-1900 and 1995-2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community’s goal to contain warming to below 1.5{degree sign}C above pre-industrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.

We use the Empirical Model of Global Climate (EM-GC) to show that human activity has been responsible for ~0.14 °C/decade (range: 0.08 to 0.20) of warming from 1979 to 2010. This EM-GC based quantification of Attributable Anthropogenic Warming Rate (AAWR) is constrained by the observed global mean surface temperature and ocean heat content records; the largest contribution to the uncertainty in our estimate of AAWR is imprecise knowledge of the radiative forcing due to tropospheric aerosols (AER RF). Our value of AAWR is noticeably lower than the mean value from the IPCC 2013 models, 0.22 °C/decade (range: 0.08 to 0.32) with no overlap of interquartile ranges. We also compute probabilistic forecasts of the rise in GMST where again the largest source of uncertainty is AER RF, and cast results in terms of the likelihood of achieving either 1.5 °C or 2.0 °C warmings relative to pre-industrial. We show that the likelihoods of limiting global warming to 2°C are 92%, 50%, and 20% if greenhouse gases follow the RCP 2.6, 4.5, and 6.0 scenarios; the likelihoods of limiting warming to 1.5°C drop to 67%, 10%, and 0.1% for these same three RCPs. Warming forecasts based upon our EM-GC are more optimistic than found by CMIP5 GCMs, following how many GCMs exhibit faster warming than inferred from the recent climate record. Our EM-GC forecasts show that aggressive controls on emissions of both CO2 and CH4 starting this decade are needed to limit global warming to 1.5°C with high probability.