Assessment of Arctic sea ice dynamics and their impacts on precipitation moisture sources using deep learning approaches

Stable isotopes of water ( 18 O and 2 H) are essential for analyzing the Arctic water cycle and climate variations. However, the link between sea ice extent changes as an important factor influencing Arctic climate and the isotopic composition of Arctic precipitation remains unclear. This study examined how sea ice extent in different Arctic marine regions affects precipitation isotopes at stations belonging to the Global Network of Isotopes in Precipitation (GNIP) across the Arctic. The main objective of this study was to evaluate the influence of sea ice extent variability on moisture sources and the isotopic composition of precipitation, with a particular focus on d -excess. Advanced deep learning techniques, including Long Short-Term Memory (LSTM), Deep Neural Network (DNN), and Recurrent Neural Network (RNN), were employed to analyze how variations in sea ice coverage impact the isotopic content in Arctic precipitation. To enhance prediction accuracy, Entropy Model Averaging (EMA) was used to ensemble the outputs of the models. Interpolated maps of the simulated isotope values were generated using Inverse Distance Weighting (IDW) to visualize spatial patterns. This study demonstrated the influence of sea ice changes on the isotopic composition of Arctic precipitation and simulated d -excess values. The reduction in sea ice increased Arctic moisture proportion (AMP) in precipitation, altering its isotopic composition. Analysis of d -excess revealed lower values in locally sourced precipitation and higher values in precipitation from subtropical sources. These findings highlight the key role of sea ice extent changes in influencing moisture sources and the isotopic composition of Arctic precipitation. • The interplay between d -excess in Arctic precipitation and sea ice extent changes was investigated. • Deep learning models (DNN, LSTM, RNN) simulated d -excess in Arctic precipitation. • Ensemble model (EMA) improved accuracy of d -excess simulations in the Arctic. • Copula theory assessed relationships between d -excess and sea ice extent. • The study revealed spatial variability of d -excess related to Arctic moisture sources.