Retrieving Sea Ice Drag Coefficients and Turning Angles From In Situ and Satellite Observations Using an Inverse Modeling Framework
Retrieving Sea Ice Drag Coefficients and Turning Angles From In Situ and Satellite Observations Using an Inverse Modeling Framework
复制标题
DOI：
10.1029/2018jc014881
复制
发表时间：
2019-08-01
影响因子：
3.6
通讯作者：
Armitage, T. W. K.
中科院分区：
文献类型：
--
作者：
Heorton, H. D. B. S.;Tsamados, M.;Armitage, T. W. K.
当海冰浓度低于85%时，海冰内部的应力较小，海冰处于自由漂移状态。因此，海冰漂移是科里奥利加速度和来自海洋和大气的压力之间平衡的结果。我们使用来自单个漂流浮标的数据以及北极范围内的风、海冰和海洋速度网格场的数据来研究海冰漂移。我们对自由漂移的海冰的动量平衡进行了概率逆模拟，实现了Nansen数、比例Rossby数和应力转角的反演。由于该问题涉及一个非线性、欠约束系统，我们使用蒙特卡罗引导搜索方案--邻域算法--来为多个观测点寻找最优参数值。我们从2014年7月北极地区10天平均资料中提取了C-A=1.2×10(-3)和C-O=2.4×10(-3)的最佳阻力系数，符合AIDJEX标准，具有明显的时空变化。反演日平均浮标数据给出的参数，虽然得到了更准确的解析，但表明正向模型在这些空间和时间尺度上过度简化了物理系统。我们的结果表明了正确表示地转流的重要性。大气拖曳系数和海洋拖曳系数均随时间平均周期的缩短而减小，这为短时间尺度气候模式拖曳系数的选择提供了依据。
For ice concentrations less than 85%, internal ice stresses in the sea ice pack are small and sea ice is said to be in free drift. The sea ice drift is then the result of a balance between Coriolis acceleration and stresses from the ocean and atmosphere. We investigate sea ice drift using data from individual drifting buoys as well as Arctic-wide gridded fields of wind, sea ice, and ocean velocity. We perform probabilistic inverse modeling of the momentum balance of free-drifting sea ice, implemented to retrieve the Nansen number, scaled Rossby number, and stress turning angles. Since this problem involves a nonlinear, underconstrained system, we used a Monte Carlo guided search scheme-the Neighborhood Algorithm-to seek optimal parameter values for multiple observation points. We retrieve optimal drag coefficients of C-A = 1.2 x 10(-3) and C-O = 2.4 x 10(-3) from 10-day averaged Arctic-wide data from July 2014 that agree with the AIDJEX standard, with clear temporal and spatial variations. Inverting daily averaged buoy data give parameters that, while more accurately resolved, suggest that the forward model oversimplifies the physical system at these spatial and temporal scales. Our results show the importance of the correct representation of geostrophic currents. Both atmospheric and oceanic drag coefficients are found to decrease with shorter temporal averaging period, informing the selection of drag coefficient for short timescale climate models.