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.

中文翻译：

---

降低复杂性模型比对项目第二阶段：综合地球系统知识以进行概率气候预测


在过去的几十年里，气候科学在多个专家领域迅速发展。我们在内部自洽的建模框架中捕获最先进知识的最佳工具是日益复杂的全耦合地球系统模型（ESM）。然而，计算限制和 ESM 的结构刚性意味着仅用 ESM 很难捕获跨多个领域的全部不确定性。相反，选择的工具是计算效率更高的降低复杂度模型 (RCM)，其结构灵活，可以跨越一系列特定领域模型和 ESM 实验的响应动态。在这里，我们介绍了降低复杂性模型比对项目（RCMIP 第 2 阶段）的第 2 阶段，这是 RCM 的第一个全面比对，这些 RCM 与专业研究社区的关键基准范围进行了概率校准。毫不奇怪，但至关重要的是，我们发现被限制为反映关键基准的模型更好地反映了关键基准。在低排放 SSP1-1.9 情景下，整个 RCM 的中位峰值变暖预测范围为 1.3 至 1.7°C（相对于 1850-1900 年，使用 1850-1900 年和 1995 年之间基于观测的历史变暖估计为 0.8°C） 2014）。鉴于国际社会的目标是长期将升温控制在比工业化前高 1.5°C 以下，进一步开发方法来限制这些预测的不确定性似乎至关重要。我们的研究结果表明，RCM 的用户应仔细评估其 RCM，特别是其针对关键基准的技能，并考虑是否需要纳入来自 ESM 结果或其他评估的预测基准，以减少未来预测中的分歧。