Increasing water flowing into the Arctic Ocean affects oceanic freshwater balance, which may lead to the thermohaline circulation collapse and unpredictable climatic conditions if freshwater inputs continue to increase. Despite the crucial role of ocean inflow in the climate system, less is known about its predictability, variability, and connectivity to cryospheric and climatic patterns on different time scales. In this study, multi-scale variation modes were decomposed from observed daily and monthly snowcover and river flows to improve the predictability of Arctic Ocean inflows from the Mackenzie River Basin in Canada. Two multi-linear regression and Bayesian neural network models were used with different combinations of remotely sensed snowcover, in-situ inflow observations, and climatic teleconnection patterns as predictors. The results showed that daily and monthly ocean inflows are associated positively with decadal snowcover fluctuations and negatively with interannual snowcover fluctuations. Interannual snowcover and antecedent flow oscillations have a more important role in describing the variability of ocean inflows than seasonal snowmelt and large-scale climatic teleconnection. Both models forecasted inflows seven months in advance with a Nash–Sutcliffe efficiency score of ≈0.8. The proposed methodology can be used to assess the variability of the freshwater input to northern oceans, affecting thermohaline and atmospheric circulations.

流入北冰洋的水量的增加会影响海洋的淡水平衡，如果淡水的输入量继续增加，这可能导致热盐环流崩溃和不可预测的气候条件。尽管海洋流入在气候系统中起着至关重要的作用，但人们对其可预测性，可变性以及在不同时间尺度上与冰冻圈和气候模式的连通性知之甚少。在这项研究中，从观测到的每日和每月的积雪和河流流量分解了多尺度变化模式，以提高加拿大麦克肯齐河流域北冰洋流入量的可预测性。使用两个多线性回归模型和贝叶斯神经网络模型，将遥感雪覆盖物进行了不同的原位组合入流观测和气候遥相关模式作为预测因子。结果表明，每日和每月的海洋流入量与年代际积雪量波动呈正相关，而与年际积雪量波动呈负相关。与季节性融雪和大规模气候遥相关相比，年际积雪和先前的流量振荡在描述海洋流入的变化性方面具有更重要的作用。两种模型均预测提前七个月流入，纳什-萨特克利夫效率得分约为0.8。所提出的方法可用于评估输入北部海洋的淡水的变化，从而影响热盐和大气环流。