_applied sciences_  
_Article_  
Statistical Modeling of Arctic Sea Ice Concentrations for Northern Sea Route Shipping  
[PERSON] 1,2,3,6, [PERSON] 1,2,3,4,5,*, [PERSON] 6,*,  
in the prediction of the window period, [PERSON] [8] researched the impact of radio detection observation on reducing uncertainty in Arctic sea ice prediction and identified issues regarding how to make forecast information convenient for crew and shipping companies to use. [PERSON] et al. [9] developed and verified two ship performance models which have been implemented in a voyage planning tool designed for summer Arctic operations of a cargo ship on the Northern Sea Route. The SIC is typically recorded by satellite observations or by the observations of onboard master mariners. Researchers have utilized recorded data to formulate various models that attempt to discern the relationship between atmospheric anomalies and the sea ice regime. [PERSON] et al. [10] incorporated the rheology mechanics of sea ice and its thickness distribution, ultimately positing that SIC is primarily governed by thermodynamics. [PERSON] et al. [11] devised a scheme for reconstructing SIC using ice drift records, while [PERSON] et al. [12] demonstrated that changes in SIC can be attributed to anomalous surface air temperatures and low-level wind fields. In a subsequent study [13], [PERSON] et al. compared and appraised different methods for measuring sea ice type concentration. [PERSON] et al. [14] proposed an ensemble-based Arctic Ice Ocean Prediction System, designed to facilitate forecasts of Arctic sea ice. However, physical models tend to be computationally intensive and require substantial amounts of data, and the resolution of available data may sometimes be insufficient to accurately model the geography along the NSR. Furthermore, the statistical properties of sea ice concentration data are often neglected and under-investigated. As a statistical methodology, the autoregressive integrated moving average (ARIMA) model benefits from low computational requirements and high ease of use for time series analysis (TSA). Such techniques have been utilized in the context of oceanic environmental analysis, as demonstrated by [PERSON] [15], who compared an autoregressive (AR) model, a moving average (MA) model, and an autoregressive moving average (ARMA) model for simulating oceanic waves. [PERSON] et al. [16] modeled significant wave heights through the use of an AR process that accounted for seasonality. Additionally, [PERSON] and [PERSON] [17] conducted a comparison between the ARMA model and an artificial neural network. These techniques have also been extended to a wide range of applications beyond oceanic waves, as seen in the works of [PERSON] et al. [18], who utilized the AR model for real-time prediction of ship motion, and [PERSON] et al. [19], who applied the AR model for forecasting the speeds of container ships. The ARIMA model has been utilized in prior studies to examine the SIC. [PERSON] and [PERSON] [20] elucidated the autoregressive character of Arctic sea ice concentration. [PERSON] et al. [21] conducted a reanalysis of Arctic sea ice data through the ARIMA model, taking into account the changing temporal relationships between sea ice concentration and other climatic factors. Despite these studies, there remain limited instances where statistical models have been employed for the forecasting of Arctic sea ice concentration with a view toward the management of shipping along the Northern Sea Route. This study proposes an ARIMA model to analyze and forecast the SIC along the NSR. A statistical interpolation approach is introduced to contract the SIC data obtained from the ERA5 [22] gridded reanalysis values into values along the NSR. The ARIMA model is trained and tested using reanalysis data from January 1980 to September 2022. Forecasts are generated for the period from 2022 to 2025 and validated through a comparison with ensemble experiments from the CMIP5 [23].