Arctic Dipole as primary driver of summer LKF variability in a high-resolution ice-ocean model
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摘要: 观测手段的局限性长期以来制约着对北极线性运动学特征（LKF）的深入研究。本研究基于一套为期28 a、水平分辨率约为2 km的全北极冰海耦合模式数据，并结合冬季RGPS观测资料（1996–2008年）进行验证，系统分析了北极LKF的长期变化及其气候驱动因子。结果表明，尽管北极海冰持续减薄可能促进冰内断裂，但全北极范围的LKF数量和密度并未呈现显著趋势，仅表现出极其微弱的增长（0.003 a−1）。模型模拟的冬季LKF密度与RGPS观测具有较好的一致性（r = 0.57），证实了模型在刻画LKF空间分布与季节变化方面的可靠性。此外，夏季LKF密度与北极偶极子（AD指数）存在强烈的负相关关系（r = −0.66），其影响力明显超过北极涛动（AO）。在空间分布上，AO与AD指数均与中央北极区夏季LKF密度呈负相关，而在喀拉海和巴伦支海，LKF密度与AO指数呈正相关，与AD指数呈负相关。这种区域响应的非均匀性，最终导致全北极尺度上夏季LKF密度与AD指数的负相关关系更为突出。
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关键词:
- 北极线性运动特征 /
- 北极偶极子 /
- 海冰形变 /
- 高分辨率冰海耦合模式
Abstract: Observational limitations constrain long-term studies of Arctic linear kinematic features (LKFs). This study systematically analyzes the long-term changes of Arctic LKFs and their climate drivers using a 28-year, ~2 km resolution pan-Arctic sea ice-ocean simulation, validated against winter RGPS data (1996–2008). We find no pronounced trend in pan-Arctic LKF number or density, though a very weak increase (0.003 a−1) aligns with thinning-enhanced fracturing. Modeled winter LKF density correlates well with RGPS observations (r = 0.57), confirming the model’s reliability in capturing the spatial distribution and seasonal variations of LKFs. Summer LKF density shows a strong negative correlation with the Arctic Dipole (AD) (r = −0.66), exceeding the Arctic Oscillation (AO) influence. While both AO and AD correlate negatively with LKF density in the central Arctic, LKF density correlates positively with AO but negatively with AD in the Kara Sea and Barents Sea, resulting in an overall stronger negative correlation with AD. -
图 1 海冰形变场与LKF的时空分布
a−d. 分别展示了根据模型输出得出的1995年3月1日、6月1日、9月1日和12月1日的海冰形变场；e−h. 为使用检测与追踪算法识别出的相应LKF，并使用形态学细化算法缩减为单像素宽度。形变率超过 0.5 d−1 的值以深红色显示，以突出强烈形变区域；i−an. 为2000、2005、2010和2015年对应的形变场与检测到的LKF。LKF时空分布图中，红色虚线与绿色实线分别表示海冰密集度为0.15与0.7
Fig. 1 Spatio-temporal distribution of sea ice deformation fields and LKFs
a−d. Sea ice deformation fields derived from model outputs on March 1, June 1, September 1, and December 1, 1995; e−h. corresponding LKFs identified using a detection and tracking algorithm and subsequently reduced to single-pixel width via a morphological thinning algorithm. Deformation rates exceeding 0.5 d−1 are highlighted in dark red to denote regions of intense deformation; i−an. deformation fields and detected LKFs for the years 2000, 2005, 2010 and 2015. In LKFs’ spatio-temporal distribution figures, the red dashed line and the green solid line represent sea ice concentrations of 0.15 and 0.7, respectively
图 2 LKF数量（a）与LKF密度（b）的时间序列与长期趋势
灰色实线表示模拟的LKF数量/密度时间序列，蓝色圆点代表RGPS观测数据。两条时间序列均经过其众数值归一化。黑色与红色虚线分别表示模拟数据与观测数据的线性趋势，绿色虚线则对应RGPS观测期内模拟数据的趋势
Fig. 2 Time series and long-term trends of LKF number (a) and LKF density (b)
Solid gray lines represent simulated LKF number/density time series, and blue dots represent RGPS observations. Both time series are normalized by their respective modal values. Black and red dashed lines indicate the linear trends of the simulated and observed data, respectively. Green dashed line indicates the trend of the simulated data over the RGPS observational period
图 3 LKF数量（a）与LKF密度（b）的季节变化
蓝色与红色实线分别代表模拟结果与 RGPS 观测的平均季节变化，其周围阴影带表示基于两周移动窗口计算的标准差。时间序列数据均经其众数值归一化处理。阴影反映了所有可用年份（模拟数据：1992–2019 年，RGPS 数据：1996–2008 年）内的年际变异性
Fig. 3 Seasonal variations in LKF number (a) and LKF density (b)
Solid blue and red lines denote the mean seasonal cycles from the simulation and RGPS observations, respectively, with surrounding shaded bands representing the standard deviation calculated with a two-week moving window. All time series are normalized by their respective modal values. Shadings reflect interannual variability across all available years (simulation: 1992–2019, RGPS: 1996–2008)
图 4 对SLP进行EOF分解所选取的区域（红色区域）（a）和不同海区的划分（b）
a. 该区域排除了在1992–2019年研究期间未被海冰持续覆盖的陆地和开阔海域；b. 依据美国国家冰雪数据中心（NSDIC）定义的区域划分方案绘制
Fig. 4 Region (the red area) selected for EOF decomposition of SLP (a) , and division of different sea regions (b)
a. The region excludes land and open ocean areas which were not continuously covered by sea ice during the study period 1992–2019; b. mapped according to the regional division scheme defined by the National Snow and Ice Data Center (NSIDC)
图 5 模拟 LKF密度与AO、AD指数在冬季（11月至次年5月）（a） 和夏季（6–10月）（b）期间的归一化时间序列；冬季 AO+（c）、冬季 AD+（d）、夏季 AO+（e）、夏季 AD+（f） 的空间分布；LKF 发生概率的空间分布：年平均值（1992–2019年）（g） ，夏季（6–10月）（h），冬季（11月至次年5月）（i）
a, b.相关系数在图例中标注为 cor。灰色与蓝色实线分别代表 AO指数（PC1） 和 AD 指数（PC2），红色与绿色实线分别表示模拟的与观测的（RGPS）LKF 密度。LKF 密度与 AO、AD 指数均经过方差标准化处理；c–f. 各图中标注了其解释方差（Var）；g–i. 由于 LKF 宽度在检测中被简化表示，图中LKF出现概率值低于实际值
Fig. 5 Simulated LKF density and normalized time series of AO and AD indices during winter (November–May) (a) and summer (June–October) (b); spatial patterns of winter AO+ (c), winter AD+ (d), summer AO+ (e), and summer AD+ (f); spatial distribution of LKF occurrence probability: annual mean (1992–2019) (g) , summer (June–October) (h) , winter (November–May) (i)
a, b. Correlation coefficients denoted as “cor” in the legend. Solid gray and blue lines represent the AO (PC1) and AD (PC2) indices respectively, while solid red and green lines represent the simulated and observed (RGPS) LKF densities respectively. Both LKF densities and the AO and AD indices are normalized by their variances; c–f. explained variance (Var) is indicated in each panel; g–i. the LKF occurrence probability values shown in the panel are lower than the actual values due to the simplified representation of LKF width in the detection process
图 6 夏季北极8个海区LKF密度与大气环流的关系
每个海区展示3个散点图：LKF密度与AO指数的关系（a, d, g, j, m, p, s, v）、LKF密度与AD指数的关系（b, e, h, k, n, q, t, w）和LKF密度与SLP指数的关系（c, f, i, l, o, r, u, x）。LKF密度数据已进行去均值及标准差归一化处理。黑色圆点表示月平均数据，蓝色线条表示线性回归拟合线。各子图右上方显示统计信息，包括回归斜率（α）、标准误差（SE）和p值（若p > 0.05则标为红色）。通过学生t检验（p < 0.05）的回归系数以黑色显示，未通过以红色显示
Fig. 6 Relationships between LKF density and atmospheric circulation in eight Arctic sea regions during summer
There are three scatter plots for each region: LKF density versus AO indices (a, d, g, j, m, p, s, v), LKF density versus AD indices (b, e, h, k, n, q, t, w) and LKF density versus SLP indices (c, f, i, l, o, r, u, x). LKF density data have been normalized by standard deviation. Black dots represent monthly mean data, and blue lines indicate linear regression fits. Statistical information is shown in the upper right corner of each subplot, including regression slope (α), standard error (SE), and p-value (p > 0.05 shown in red). Regression coefficients that pass the Student’s t-test (p < 0.05) are shown in black, while those do not are shown in red
图 7 夏季AO正位相与AD正位相期间各物理量异常场的合成分析
a–h. AO+位相期间的异常场：海平面气压梯度（a），10 m风速（b），海冰漂移速度（c），海冰密集度（d），海冰厚度（e），海冰形变率（f），海冰覆盖天数（g）和归一化LKF 密度（h）；i–p. AD+位相期间对应的异常场，排列顺序同（a–h）
Fig. 7 Composite analysis of anomalous fields of physical variables during summer AO positive phase and AD positive phase
a–h. Anomalous fields during the AO+ phase: sea level pressure gradient (a), 10 m wind speed (b), sea ice drift velocity (c), sea ice concentration (d), sea ice thickness (e), sea ice deformation rate (f), sea ice cover days (g), and normalized LKF density (h); i–p. corresponding anomalous fields during the AD+ phase, arranged in the same order as (a–h)
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