An Internal Atmospheric Process Determining Summertime Arctic Sea Ice Melting in the Next Three Decades: Lessons Learned from Five Large Ensembles and Multiple CMIP5 Climate Simulations
An Internal Atmospheric Process Determining Summertime Arctic Sea Ice Melting in the Next Three Decades: Lessons Learned from Five Large Ensembles and Multiple CMIP5 Climate Simulations
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DOI：
10.1175/jcli-d-19-0803.1
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发表时间：
2020-09
影响因子：
4.9
通讯作者：
Dániel Topál;Q. Ding;Jonathan L. Mitchell;Ian Baxter;M. Herein;T. Haszpra;Rui Luo;Qingquan Li
中科院分区：
文献类型：
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作者：
Dániel Topál;Q. Ding;Jonathan L. Mitchell;Ian Baxter;M. Herein;T. Haszpra;Rui Luo;Qingquan Li
由于大气内部变化引起的夏季北极海冰融化过程最近受到了相当大的关注。格陵兰岛和北冰洋夏季（6月至8月）的区域正压大气过程，具有逐年变化或低频率的趋势向位势高度上升，已被确定为9月海冰损失的重要贡献者，在观测和CESM 1大型Ensemble（CESM-LE）的模拟。进一步发现，这种局部融化是敏感的远程海表温度（SST）的变化，在中东部的热带太平洋。在这里，我们利用五个可用的大型“初始条件”地球系统模型合奏和31 CMIP 5模型的工业化前控制模拟表明，相同的大气过程，类似于观察到的和CESM-LE中发现的，也主导了夏季的内部海冰变化的年际到年代际的时间尺度在工业化前，历史和未来的情况下，无论建模环境。然而，所有模式在复制观测到的当地大气-海冰耦合的大小及其对过去四十年热带SST变率的敏感性方面都存在局限性。这些偏见要求在解释现有模型的模拟时保持谨慎，并对模型在模拟海冰变化与北极和全球气候系统相互作用方面的可信度进行新的思考。进一步努力确定这些模式的局限性的原因可能会提供影响，以减轻偏见，提高年际和十年时间尺度的海冰预测和未来的海冰预测。
Arctic sea ice melting processes in summer due to internal atmospheric variability have recently received considerable attention. A regional barotropic atmospheric process over Greenland and the Arctic Ocean in summer (June–August), featuring either a year-to-year change or a low-frequency trend toward geopotential height rise, has been identified as an essential contributor to September sea ice loss, in both observations and the CESM1 Large Ensemble (CESM-LE) of simulations. This local melting is further found to be sensitive to remote sea surface temperature (SST) variability in the east-central tropical Pacific Ocean. Here, we utilize five available large “initial condition” Earth system model ensembles and 31 CMIP5 models’ preindustrial control simulations to show that the same atmospheric process, resembling the observed one and the one found in the CESM-LE, also dominates internal sea ice variability in summer on interannual to interdecadal time scales in preindustrial, historical, and future scenarios, regardless of the modeling environment. However, all models exhibit limitations in replicating the magnitude of the observed local atmosphere–sea ice coupling and its sensitivity to remote tropical SST variability in the past four decades. These biases call for caution in the interpretation of existing models’ simulations and fresh thinking about models’ credibility in simulating interactions of sea ice variability with the Arctic and global climate systems. Further efforts toward identifying the causes of these model limitations may provide implications for alleviating the biases and improving interannual- and decadal-time-scale sea ice prediction and future sea ice projection.