Freeze-Up and Break-Up Dynamics in Northern Canada: Trends, Climate Teleconnections and Nonstationary Modelling

Freeze-up and break-up dynamics serve as a highly sensitive indicators of climate change and variability in northern regions. In northern Canada, changes in ice conditions across lakes, rivers, and coastal environments may affect aquatic ecosystems, seasonal transportation, and communities that depend on ice-covered routes. This study investigates freeze-up and break-up changes at six Arctic stations in northern Canada through a combined approach to trend analysis, frequency analysis, and predictive modeling. Trend analysis reveals a general shift toward later freeze-up and earlier break-up dates, in line with observed regional warming. Spearman correlation analysis identified statistical associations between freeze-up and break-up dates and selected large-scale climate indices. After applying the Benjamini–Hochberg false discovery rate (FDR) correction (q = 0.05) separately to freeze-up and break-up correlations, 35 of the 600 tested associations remained statistically significant, predominantly involving the North Pacific (NP) index. Both stationary and non-stationary frequency analyses were then carried out using several probability distributions to identify the most appropriate models for representing the observed variability. Among the models tested, non-stationary models incorporating time and/or selected climate indices generally achieved lower Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values than their stationary counterparts, suggesting that incorporating time-varying climate information can improve the statistical representation of freeze-up and break-up variability at the studied stations. On the other hand, Least Absolute Shrinkage and Selection Operator (LASSO) regression models incorporating lagged large-scale climate indices and local meteorological variables, including air temperature, atmospheric pressure, liquid and solid precipitation, and wind speed, showed variable predictive performance across stations, with relatively low coefficients of determination in several cases, highlighting the challenges of predicting freeze-up and break-up dates from limited observational records. Overall, the results highlight the importance of accounting for non-stationarity and large-scale climate variability in frequency analysis, while combining teleconnection indices with local meteorological information may provide additional predictive information, although model performance remains variable across stations and limited by the available observational records.