Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations
Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations
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DOI：
10.5194/tc-2021-387
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
W. Gregory;J. Stroeve;M. Tsamados
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文献类型：
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作者：
W. Gregory;J. Stroeve;M. Tsamados
抽象的。冬季北极涛动（AO）事件对夏季北极海冰范围的间接影响表明海冰可预测性存在固有的冬季到夏季机制。另一方面，大量耦合气候模型中的业务区域夏季海冰预报显示，在春季融化开始日期之前初始化的预报的预测能力大幅下降，这表明较长时间尺度上海冰变化的一些驱动因素可能无法在这些模型中得到很好的体现。为此，我们引入了一种基于聚类分析和复杂网络的无监督学习方法，以确定参与世界气候研究计划耦合模型比对项目（CMIP6）第六阶段的最新一代耦合气候模型能够在多大程度上反映1979-2020年期间北半球冬季海平面压力和北极夏季海冰浓度的时空变化模式，相对于 分别是 ERA5 大气再分析和卫星海冰观测。引入了两种特定的全球指标作为比较模型和观测/再分析之间变异模式的方法：调整兰德指数（一种用于比较变异空间模式的方法）和网络距离指标（一种用于比较两个地理区域之间连通程度的方法）。我们发现，CMIP6 模型总体上较好地反映了 AO 变化的空间模式，尽管高估了西北太平洋海平面压力变化的幅度，并低估了北非和南欧的变化程度。他们还低估了波弗特海、东西伯利亚海和拉普捷夫海等地区在解释泛北极夏季海冰面积变化方面的重要性，我们假设这是由于海冰厚度的区域偏差造成的。最后，观测结果表明，历史上，冬季 AO 事件（负向）与北极东太平洋地区夏季海冰浓度强烈协变，尽管现在在冰层变薄的情况下，东太平洋地区和西太平洋地区都表现出类似的行为。然而，CMIP6 模型平均并未显示这种转变，这可能会妨碍它们对夏季海冰进行熟练的季节性到年际预测的能力。
Abstract. The indirect effect of winter Arctic Oscillation (AO) events on the proceeding summer Arctic sea ice extent suggests an inherent winter-to-summer mechanism for sea ice predictability. On the other hand, operational regional summer sea ice forecasts in a large number of coupled climate models show a considerable drop in predictive skill for forecasts initialised prior to the date of melt onset in spring, suggesting that some drivers of sea ice variability on longer time scales may not be well represented in these models. To this end, we introduce an unsupervised learning approach based on cluster analysis and complex networks to establish how well the latest generation of coupled climate models participating in phase 6 of the World Climate Research Programme Coupled Model Intercomparison Project (CMIP6) are able to reflect the spatio-temporal patterns of variability in northern-hemisphere winter sea-level pressure and Arctic summer sea ice concentration over the period 1979–2020, relative to ERA5 atmospheric reanalysis and satellite-derived sea ice observations respectively. Two specific global metrics are introduced as ways to compare patterns of variability between models and observations/reanalysis: the Adjusted Rand Index – a method for comparing spatial patterns of variability, and a network distance metric – a method for comparing the degree of connectivity between two geographic regions. We find that CMIP6 models generally reflect the spatial pattern of variability of the AO relatively well, although over-estimate the magnitude of sea-level pressure variability over the north-western Pacific Ocean, and under-estimate the variability over the north Africa and southern Europe. They also under-estimate the importance of regions such as the Beaufort, East Siberian and Laptev seas in explaining pan-Arctic summer sea ice area variability, which we hypothesise is due to regional biases in sea ice thickness. Finally, observations show that historically, winter AO events (negatively) covary strongly with summer sea ice concentration in the eastern Pacific sector of the Arctic, although now under a thinning ice regime, both the eastern and western Pacific sectors exhibit similar behaviour. CMIP6 models however do not show this transition on average, which may hinder their ability to make skilful seasonal to inter-annual predictions of summer sea ice.