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北极海冰在天气时间尺度上的可预测性。

Predictability of Arctic sea ice on weather time scales.

作者信息

Mohammadi-Aragh M, Goessling H F, Losch M, Hutter N, Jung T

机构信息

Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany.

Institute of Coastal Research, Helmholtz-Zentrum Geesthacht, Geesthacht, Germany.

出版信息

Sci Rep. 2018 Apr 25;8(1):6514. doi: 10.1038/s41598-018-24660-0.

Abstract

The field of Arctic sea ice prediction on "weather time scales" is still in its infancy with little existing understanding of the limits of predictability. This is especially true for sea ice deformation along so-called Linear Kinematic Features (LKFs) including leads that are relevant for marine operations. Here the potential predictability of the sea ice pack in the wintertime Arctic up to ten days ahead is determined, exploiting the fact that sea ice-ocean models start to show skill at representing sea ice deformation at high spatial resolutions. Results are based on ensemble simulations with a high-resolution sea ice-ocean model driven by atmospheric ensemble forecasts. The predictability of LKFs as measured by different metrics drops quickly, with predictability being almost completely lost after 4-8 days. In contrast, quantities such as sea ice concentration or the location of the ice edge retain high levels of predictability throughout the full 10-day forecast period. It is argued that the rapid error growth for LKFs is mainly due to the chaotic behaviour of the atmosphere associated with the low predictability of near surface wind divergence and vorticity; initial condition uncertainty for ice thickness is found to be of minor importance as long as LKFs are initialized at the right locations.

摘要

“天气时间尺度”下的北极海冰预测领域仍处于起步阶段,人们对其可预测性的极限了解甚少。对于沿所谓线性运动特征(LKFs)的海冰变形而言,情况尤其如此,这些特征包括与海上作业相关的冰间水道。在此,利用海冰 - 海洋模型在高空间分辨率下开始展现出表征海冰变形的技能这一事实,确定了冬季北极海冰在未来十天内的潜在可预测性。结果基于由大气集合预报驱动的高分辨率海冰 - 海洋模型的集合模拟。通过不同指标衡量的LKFs的可预测性迅速下降,在4 - 8天后可预测性几乎完全丧失。相比之下,海冰浓度或冰缘位置等变量在整个10天的预报期内都保持着较高的可预测性。有人认为,LKFs的误差快速增长主要是由于大气的混沌行为,这与近地表风散度和涡度的低可预测性有关;只要LKFs在正确的位置初始化,冰厚度的初始条件不确定性被发现是次要的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ba2/5916911/1013656db4f2/41598_2018_24660_Fig1_HTML.jpg

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