Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill
Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill
复制标题
用于北极海冰运动每日预测的机器学习：模型预测技能的归因评估
DOI：
10.1175/aies-d-23-0004.1
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Matsuyoshi, Kayli
中科院分区：
文献类型：
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作者：
Hoffman, Lauren;Mazloff, Matthew R.;Gille, Sarah T.;Giglio, Donata;Bitz, Cecilia M.;Heimbach, Patrick;Matsuyoshi, Kayli
基于物理的北极海冰模拟是非常复杂的，涉及不同阶段，长度尺度和时间尺度之间的传输。结果表明，海冰动力学数值模拟具有较高的计算成本和模式不确定性。我们采用数据驱动的机器学习（ML）来预测海冰运动。在给定当前风速和前一天海冰浓度和速度的情况下，建立了预测当前海冰速度的ML模型。使用再分析风和卫星得出的海冰属性训练模型。我们比较了三种不同模型的预测：持久性（PS），线性回归（LR）和卷积神经网络（CNN）。我们量化的观测和统计模型预测之间的相关性的时空变异性。此外，我们分析模型的性能相比，与冰运动（风速，冰速度，冰浓度，距离海岸，水深）的属性的变化，以了解模型性能下降相关的过程。结果表明，CNN使熟练的每日海冰速度预测与预测和观测海冰速度之间的相关性高达0.81，而LR和PS实现表现出的相关性分别为0.78和0.69。相关性在空间和季节上各不相同：较低的值出现在浅水沿海地区和海冰范围最小的时候。LR参数分析表明，风速起着最大的作用，在1天的时间尺度上预测海冰速度，特别是在北极中部。风速具有最大LR参数的区域是CNN具有比LR更高预测技能的区域。显著性声明我们构建和评估不同的机器学习（ML）模型，这些模型使用当前风速和前一天的冰浓度和冰速度对北极海冰速度进行1天预测。我们发现，在输入（神经网络）之间引入非线性关系的模型捕获了重要信息（即，与线性和持久性模型相比，观测和预测之间的相关性更高）。这种性能增强主要发生在北极中部的较深区域，那里的风速是冰运动的主要预测因素。了解这些模型从增加的复杂性中受益的地方很重要，因为未来的工作将使用ML来阐明数据中有物理意义的关系，研究风和冰速度之间的关系如何随着冰融化而变化。
Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.Significance StatementWe build and evaluate different machine learning (ML) models that make 1-day predictions of Arctic sea ice velocity using present-day wind velocity and previous-day ice concentration and ice velocity. We find that models that incorporate nonlinear relationships between inputs (a neural network) capture important information (i.e., have a higher correlation between observations and predictions than do linear and persistence models). This performance enhancement occurs primarily in deeper regions of the central Arctic where wind speed is the dominant predictor of ice motion. Understanding where these models benefit from increased complexity is important because future work will use ML to elucidate physically meaningful relationships within the data, looking at how the relationship between wind and ice velocity is changing as the ice melts.