Improved representation of Arctic sea ice velocity field in ocean?sea ice models based on satellite observations
Improved representation of Arctic sea ice velocity field in ocean?sea ice models based on satellite observations
复制标题
基于卫星观测的海洋海冰模型中北极海冰速度场的改进表示
DOI：
10.1007/s00382-021-05843-4
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发表时间：
2021
期刊：
影响因子：
4.6
通讯作者：
Kawaguchi Yusuke
中科院分区：
文献类型：
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作者：
Toyoda Takahiro;Kimura Noriaki;Urakawa L. Shogo;Tsujino Hiroyuki;Nakano Hideyuki;Sakamoto Kei;Yamanaka Goro;Komatsu Kensuke K.;Matsumura Yoshimasa;Kawaguchi Yusuke
海冰运动速度的准确描述是提高高纬气候模拟和预测精度的必要条件。为了改善基于卫星逐日资料的海冰速度场模拟效果，采用绿色函数法对海洋-海冰模式的海冰动力学参数进行了优化。在有海冰密集度约束和无海冰密集度约束的情况下，得到了误差减小15-20%的改进海冰速度场。此外，海冰动力学参数的背景条件的依赖性进行了检查，并通过上述方法进行了优化的线性相关系数。海冰速度场在自由漂移区和高压区得到进一步改善，平均误差减小约25%。这导致边缘冰区的表面盐度降低，使其更接近观测结果。虽然作为常数的参数调整往往主要是为了补偿偏差的影响，在表面强迫，所获得的依赖性一般是一致的，与以前的观测和理论研究，从而改善参数化的气-冰-海洋动量交换和冰压力，广泛用于OGCM。增强的海洋-海冰初始化和参数化将有助于业务季节预测和气候预测研究。
Accurate description of sea ice velocity is necessary for improving the reproduction and prediction of high-latitude climate in modeling studies. In order to improve the simulated sea ice velocity field based on satellite-derived daily data, a set of sea ice dynamic parameters of the ocean–sea ice model was optimized with a Green’s function method, which uses linear combination of forward sensitivity operators for these parameters. An improved sea ice velocity field with 15–20% error reduction was obtained for both simulations with and without the data constraint of sea ice concentration. Moreover, dependencies of the sea ice dynamic parameters on background conditions were examined and linear dependency coefficients were also optimized by extending the above method. The sea ice velocity field was further improved for both free-drift and high-pressure regions with mean error reduction of about 25%. This resulted in decreases of surface salinities in the marginal ice zones, making them closer to observations. While the parameter adjustments as constants tended mainly to compensate influences of biases in surface forcing, the obtained dependencies were generally consistent with previous observational and theoretical studies and thus improved parameterizations of air–ice–ocean momentum exchange and ice pressure which are widely used in OGCMs. The enhanced ocean–sea ice initialization and parameterizations would be useful for operational seasonal prediction and climate prediction studies.