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Both full-fledged Earth system models (ESMs) and simple climate models (SCMs) have been used to investigate climate change for future representative CO2 concentration pathways under the sixth phase of the Coupled Model Intercomparison Project. Here, we explore to what extent complex and simple models are consistent in their carbon cycle response in concentration-driven simulations. Although ESMs and SCMs exhibit similar compatible fossil fuel CO2 emissions, ESMs systematically estimate a lower ocean carbon uptake than SCMs in the historical period and future scenarios. The ESM and SCM differences are especially large under low-concentration and overshoot scenarios. Furthermore, ESMs and SCMs deviate in their land carbon uptake estimates, but the differences are scenario-dependent. These differences are partly driven by a few model outliers (ESMs and SCMs) and the procedure of observational constraining that is present in the majority of SCMs but not applied in ESMs. The differences in land uptake arise from the difference in the way land-use change (LUC) emissions are calculated and different assumptions on how the carbon cycle feedbacks are defined, possibly reflecting the treatment of nitrogen limitation of biomass growth and historical calibration of SCMs. The differences in ocean uptake, which are especially large in overshoot scenarios, may arise from the faster mixing of carbon from the surface to the deep ocean in SCMs than in ESMs. We also discuss the inconsistencies that arise when converting CO2 emissions from integrated assessment models (IAMs) to CO2 concentrations inputs for ESMs, which typically rely on a single SCM. We further highlight the discrepancies in LUC emission estimates between models of different complexity, particularly ESMs and IAMs, and encourage climate modeling groups to address these potential areas for model improvement.

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 modeling 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 specialized 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°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°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°C above preindustrial 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.