Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections.
Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections.
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降低复杂性模式相互比较项目第2阶段：为概率气候预测综合地球系统知识。
DOI：
10.1029/2020ef001900
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Woodard DL
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文献类型：
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作者：
Nicholls Z;Meinshausen M;Lewis J;Corradi MR;Dorheim K;Gasser T;Gieseke R;Hope AP;Leach NJ;McBride LA;Quilcaille Y;Rogelj J;Salawitch RJ;Samset BH;Sandstad M;Shiklomanov A;Skeie RB;Smith CJ;Smith SJ;Su X;Tsutsui J;Vega-Westhoff B;Woodard DL
在过去的几十年里，气候科学在多个专家领域迅速发展。我们在内部自洽建模框架中获取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，计算的限制和ESM的结构刚性意味着，整个范围内的不确定性跨多个域是难以捕捉与ESM单独。相反，选择的工具是计算效率更高的简化复杂性模型（RCMs），它在结构上灵活，可以跨越一系列特定领域的模型和ESM实验的响应动态。在这里，我们介绍了简化复杂性模型相互比较项目（RCMIP第2阶段）的第2阶段，这是第一次全面的RCM相互比较，这些RCM是用专业研究团体的关键基准范围进行概率校准的。不出所料，但至关重要的是，我们发现，模型已被限制，以反映关键基准更好地反映关键基准。在低排放SSP 1 - 1.9情景下，在所有区域气候模型中，峰值变暖预测的中位数范围为1.3至1.7°C（相对于1850-1900年，使用1850-1900年和1995-2014年之间基于观测的历史变暖估计值0.8°C）。考虑到国际社会的目标是在长期内将升温控制在比工业化前水平高1.5°C以下，进一步开发限制这些预测不确定性的方法似乎至关重要。我们的研究结果表明，区域协调机制的使用者应仔细评估其区域协调机制，特别是其针对关键基准的技能，并考虑是否需要包括来自无害环境管理结果或其他评估的预测基准，以减少未来预测中的分歧。概率全球平均温度预测通常使用复杂度较低的气候模式（RCM），因为它们的计算成本较低我们评估了RCM的概率设置如何综合多个研究领域的知识，以进行政策相关预测没有RCM能够捕获我们评估的所有强迫，变暖，热量吸收和碳循环指标，但有些指标在范围内接近
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. Probabilistic global‐mean temperature projections often use reduced complexity climate models (RCMs) because of their low computational cost We evaluate how well RCMs’ probabilistic setups can synthesize knowledge from multiple research domains for policy relevant projections No RCM is able to capture all forcing, warming, heat uptake and carbon cycle metrics we evaluate, however some come close across a range