RESUMO
The IPCC's scientific assessment of the timing of net-zero emissions and 2030 emission reduction targets consistent with limiting warming to 1.5°C or 2°C rests on large scenario databases. Updates to this assessment, such as between the IPCC's Special Report on Global Warming of 1.5°C (SR1.5) of warming and the Sixth Assessment Report (AR6), are the result of intertwined, sometimes opaque, factors. Here we isolate one factor: the Earth System Model emulators used to estimate the global warming implications of scenarios. We show that warming projections using AR6-calibrated emulators are consistent, to within around 0.1°C, with projections made by the emulators used in SR1.5. The consistency is due to two almost compensating changes: the increase in assessed historical warming between SR1.5 (based on AR5) and AR6, and a reduction in projected warming due to improved agreement between the emulators' response to emissions and the assessment to which it is calibrated.
RESUMO
Estimates of 21st Century global-mean surface temperature increase have generally been based on scenarios that do not include climate policies. Newly developed multigas mitigation scenarios, based on a wide range of modeling approaches and socioeconomic assumptions, now allow the assessment of possible impacts of climate policies on projected warming ranges. This article assesses the atmospheric CO(2) concentrations, radiative forcing, and temperature increase for these new scenarios using two reduced-complexity climate models. These scenarios result in temperature increase of 0.5-4.4 degrees C over 1990 levels or 0.3-3.4 degrees C less than the no-policy cases. The range results from differences in the assumed stringency of climate policy and uncertainty in our understanding of the climate system. Notably, an average minimum warming of approximately 1.4 degrees C (with a full range of 0.5-2.8 degrees C) remains for even the most stringent stabilization scenarios analyzed here. This value is substantially above previously estimated committed warming based on climate system inertia alone. The results show that, although ambitious mitigation efforts can significantly reduce global warming, adaptation measures will be needed in addition to mitigation to reduce the impact of the residual warming.
Assuntos
Efeito Estufa , Temperatura , Dióxido de Carbono/metabolismo , Clima , Ecossistema , Modelos TeóricosRESUMO
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.