The Beaufort Gyre is the largest freshwater reservoir in the Arctic Ocean. Long-term changes in freshwater reservoirs are critical for understanding the Arctic Ocean, and data from various sources, particularly observation or reanalysis data, must be used to the greatest extent possible. Over the past two decades, a large number of intensive field observations and ship surveys have been conducted in the western Arctic Ocean to obtain a large amount of CTD (Conductivity, Temperature, and Depth) data. Multi-machine learning methods were assessed and merged to reconstruct the annual salinity product in the Western Arctic Ocean over the period 2003-2022. Data mining-based machine learning methods reconstructed salinity product based on input variables determined by physical processes, such as sea level pressure, bathymetry, sea ice concentration, and sea ice drift. The root-mean-square error of sea surface salinity, in comparison to deep water, was effectively managed during machine learning, which exhibits higher sensitivity to variations in the atmosphere, sea ice, and ocean. The mean absolute errors in freshwater content and halocline depth within the Beaufort Gyre region for the salinity product from 2003 to 2022 are 0.98 m and 1.31 m, respectively, when compared to observational data. The salinity product provides reliable characterizations of freshwater content in the Beaufort Gyre and its variations at halocline depth. In polar regions where lacking observed data, we can build data mining-based machine learning methods to generate reliable data products to compensate for the inconvenience. Furthermore, the application potential of this multi-machine learning results approach for evaluating and integrating extends beyond the salinity field, encompassing hydrometeorology, sea ice thickness, polar biogeochemistry, and other related fields.

中文翻译：

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基于数据挖掘的机器学习方法改进水文数据——以西北冰洋盐度田为例


博福特环流是北冰洋最大的淡水水库。淡水储层的长期变化对于了解北冰洋至关重要，必须尽可能利用来自各种来源的数据，特别是观测或再分析数据。在过去的二十年里，在北冰洋西部进行了大量密集的野外观测和船舶调查，以获得大量的 CTD（电导率、温度和深度）数据。评估并合并了多机器学习方法，以重建 2003-2022 年期间西北冰洋的年盐度积。基于数据挖掘的机器学习方法根据物理过程确定的输入变量（例如海平面压力、测深、海冰浓度和海冰漂移）重建盐度积。与深水相比，海面盐度的均方根误差在机器学习过程中得到了有效管理，机器学习对大气、海冰和海洋的变化表现出更高的敏感性。与观测数据相比，2003 年至 2022 年博福特环流地区盐度产物的淡水含量和盐层深度的平均绝对误差分别为 0.98 m 和 1.31 m。盐度产品提供了博福特环流中淡水含量及其在盐跃层深度变化的可靠特征。在缺乏观测数据的极地地区，我们可以构建基于数据挖掘的机器学习方法，以生成可靠的数据产品来弥补不便。 此外，这种多机器学习结果方法在评估和整合方面的应用潜力超出了盐度领域，包括水文气象学、海冰厚度、极地生物地球化学和其他相关领域。