Process Drivers, Inter-Model Spread, and the Path Forward: A Review of Amplified Arctic Warming
Process Drivers, Inter-Model Spread, and the Path Forward: A Review of Amplified Arctic Warming
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DOI：
10.3389/feart.2021.758361
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发表时间：
2021-09
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通讯作者：
P. Taylor;R. Boeke;L. Boisvert;N. Feldl;M. Henry;Yiyi Huang;P. Langen;Wei Liu;F. Pithan;S. Sejas;I. Tan
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作者：
P. Taylor;R. Boeke;L. Boisvert;N. Feldl;M. Henry;Yiyi Huang;P. Langen;Wei Liu;F. Pithan;S. Sejas;I. Tan
北极放大（AA）是一种大气-海冰-海洋耦合过程。这种理解是从早期的AA概念演变而来的，作为冰雪线进展的结果，经过世纪多的研究，澄清了AA的相关过程和驱动机制。早期的模拟研究所作的预测，即秋季/冬季最大值，底部重结构，突出的表面反作用反馈，稳定层结的重要性经受住了多年的观测和更复杂的模式的审查。然而，北极气候预测的不确定性比地球上任何其他区域都大，因此难以或不可能评估影响大的近期区域变化。减少北极气候预测中的这种巨大差异需要对定量过程的理解。这份手稿旨在通过综合目前对AA的了解来建立这样一种理解，并提出一系列建议来指导未来的研究。它简要回顾了AA科学的历史，总结了观测到的北极变化，讨论了建模方法和反馈诊断，并评估了目前对AA最相关反馈的理解。这些部分最终导致AA的基本物理机制的概念模型和一系列建议，以加快减少北极气候预测的不确定性。我们的概念模型强调，需要考虑本地反馈和远程过程的相互作用，在年度周期的范围内，以限制预计AA。我们建议提高北极气候敏感性研究的优先级，提高北极地表能量收支观测的准确性，重新思考气候反馈定义，协调新的模式实验和相互比较，并进一步调查情景变率在AA中的作用。
Arctic amplification (AA) is a coupled atmosphere-sea ice-ocean process. This understanding has evolved from the early concept of AA, as a consequence of snow-ice line progressions, through more than a century of research that has clarified the relevant processes and driving mechanisms of AA. The predictions made by early modeling studies, namely the fall/winter maximum, bottom-heavy structure, the prominence of surface albedo feedback, and the importance of stable stratification have withstood the scrutiny of multi-decadal observations and more complex models. Yet, the uncertainty in Arctic climate projections is larger than in any other region of the planet, making the assessment of high-impact, near-term regional changes difficult or impossible. Reducing this large spread in Arctic climate projections requires a quantitative process understanding. This manuscript aims to build such an understanding by synthesizing current knowledge of AA and to produce a set of recommendations to guide future research. It briefly reviews the history of AA science, summarizes observed Arctic changes, discusses modeling approaches and feedback diagnostics, and assesses the current understanding of the most relevant feedbacks to AA. These sections culminate in a conceptual model of the fundamental physical mechanisms causing AA and a collection of recommendations to accelerate progress towards reduced uncertainty in Arctic climate projections. Our conceptual model highlights the need to account for local feedback and remote process interactions within the context of the annual cycle to constrain projected AA. We recommend raising the priority of Arctic climate sensitivity research, improving the accuracy of Arctic surface energy budget observations, rethinking climate feedback definitions, coordinating new model experiments and intercomparisons, and further investigating the role of episodic variability in AA.