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Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model

机译:冰厚度分布离散化对NemO3.6-LIM3全球海洋海冰模型海冰浓度变异性的影响

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This study assesses the impact of different sea ice thickness distribution (ITD) discretizations on the sea ice concentration (SIC) variability in ocean stand-alone NEMO3.6–LIM3 simulations. Three ITD discretizations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by K-means clustering both in the Arctic and Antarctica between 1979 and 2014. We focus on two seasons, winter (January–March) and summer (August–October), in which correlation coefficients across clusters in individual months are largest. In the Arctic, clusters are computed before and after detrending the series with a second-degree polynomial to separate interannual from longer-term variability. The analysis shows that, before detrending, winter clusters reflect the SIC response to large-scale atmospheric variability at both poles, while summer clusters capture the negative and positive trends in Arctic and Antarctic SIC, respectively. After detrending, Arctic clusters reflect the SIC response to interannual atmospheric variability predominantly. The cluster analysis is complemented with a model–data comparison of the sea ice extent and SIC anomaly patterns. The single-category discretization shows the worst model–data agreement in the Arctic summer before detrending, related to a misrepresentation of the long-term melting trend. Similarly, increasing the number of thin categories reduces model–data agreement in the Arctic, due to a poor representation of the summer melting trend and an overly large winter sea ice volume associated with a net increase in basal ice growth. In contrast, more thin categories improve model realism in Antarctica, and more thick ones improve it in central Arctic regions with very thick ice. In all the analyses we nonetheless identify no optimal discretization. Our results thus suggest that no clear benefit in the representation of SIC variability is obtained from increasing the number of sea ice thickness categories beyond the current standard with five categories in NEMO3.6–LIM3.
机译:本研究评估不同的海冰厚度分布(ITD)离散化对海冰密集度(SIC)的变化在海洋独立NEMO3.6-LIM3模拟的影响。三ITD离散化具有不同数量的海冰厚度类别和边界是针对三个不同的卫星的产品(以下称作“数据”)进行评价。际SIC变化的典型模型和数据的特点是K-均值在1979年和2014年我们重点关注两个赛季,冬季(1月至3月)和夏季(八月至十月)之间的北极和南极洲都聚类在其相关系数跨越在个别月份簇是最大的。在北极,簇之前和消解趋势串联后计算的二次多项式来分离从长期变化际。分析表明,消解趋势之前,冬季集群反映两极的SIC应对大规模的大气变化,而夏天集群捕捉北极和南极SIC消极和积极趋势,分别。去趋势后,北极集群反映主要际大气变化的响应SIC。聚类分析补充有海冰程度和SIC异常模式的模型的数据相比较。单类离散显示在北极夏季最差的模型数据协议消解趋势之前,相关的长期熔化趋势不实陈述。类似地,增加细分类的数量减少了模型的数据协议在北极地区,由于夏季融化的趋势不佳表现,并在基础冰的增长,净增加相关的过大的冬季海冰的体积。相比之下,更细的类别提高南极洲模型的现实主义,更厚的人非常厚的冰层提高到中央北极地区。在所有的分析,我们仍然没有确定最优的离散化。因此,我们的研究结果表明,在SIC变异的表现没有明显的好处是从与五类NEMO3.6-LIM3增大到超过目前的标准海冰厚度类别的数量而获得。

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