In 1991, the Canadian documentaryStarting Fire with Gunpowderwas produced, focusing in part on the history of early media productions by the Inuit Broadcasting Corporation (IBC). It examined how the creation of Inuktitut media content could be an effective means of creative improvisation, linguistic and cultural preservation, and transmission of traditional knowledge. Almost 20 years later, the Internet serves as one of the primary communicative methods for young Inuit in the Canadian Arctic. However, it remains to be seen whether the quality of Inuktitut media online can compare with the example of linguistic and cultural preservation set by the visionaries of the early IBC. This article challenges prevailing critical approaches to the Inuit as linguistically and culturally vulnerable. It views Inuktitut New Media content as an embodiment of the wordairaq(‘edible roots’), used here as a model of strength, resilience, and adaptability. It concludes that the creativity and prolificacy of the early IBC productions should set the standard for a new generation of Inuktitut content creators online.

The Arctic environment is experiencing profound and rapid changes that will have far-reaching implications for resilient and sustainable development at the local and global levels. To achieve sustainable Arctic futures, it is critical to equip policymakers and global and regional stake- and rights-holders with knowledge and data regarding the ongoing changes in the Arctic environment. Community monitoring is an important source of environmental data in the Arctic but this research argues that community-generated data are under-utilized in the literature. A key challenge to leveraging community-based Arctic environmental monitoring is that it often takes the form of large, unstructured data consisting of field documents, media reports, and transcripts of oral histories. In this study, we integrated two computational approaches—topic modeling and network analysis—to identify environmental changes and their implications for resilience and sustainability in the Arctic. Using data from community monitoring reports of unusual environmental events in the Arctic that span a decade, we identified clusters of environmental challenges: permafrost thawing, infrastructure degradation, animal populations, and fluctuations in energy supply, among others. Leveraging visualization and analytical techniques from network science, we further identified the evolution of environmental challenges over time and contributing factors to the interconnections between these challenges. The study concludes by discussing practical and methodological contributions to Arctic resiliency and sustainability.

Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weather conditions, while in spring, these forecasts may be used to determine flood risk during annual snow melt. To this end, a hybrid VMD-WT-InceptionTime model is proposed for multi-horizon multivariate forecasting of remote-region temperatures in Alaska over short-term horizons (the next seven days). First, the Spearman correlation coefficient is employed to analyze the relationship between each input variable and the forecast target temperature. The most output-correlated input sequences are decomposed using variational mode decomposition (VMD) and, ultimately, wavelet transform (WT) to extract time-frequency patterns intrinsic in the raw inputs. The resulting sequences are fed into a deep InceptionTime model for short-term forecasting. This hybrid technique has been developed and evaluated using 35+ years of data from three locations in Alaska. Different experiments and performance benchmarks are conducted using deep learning models (e.g., Time Series Transformers, LSTM, MiniRocket), and statistical and conventional machine learning baselines (e.g., GBDT, SVR, ARIMA). All forecasting performances are assessed using four metrics: the root mean squared error, the mean absolute percentage error, the coefficient of determination, and the mean directional accuracy. Superior forecasting performance is achieved consistently using the proposed hybrid technique.

The well-documented decrease in the annual minimum Arctic sea ice extent over the past few decades is an alarming indicator of current climate change. However, much less is known about the thickness of the Arctic sea ice. Developing accurate forecasting models is critical to better predict its changes and monitor the impacts of global warming on the total Arctic sea ice volume (SIV). Significant improvements in forecasting performance are possible with the advances in signal processing and deep learning. Accordingly, here, we set out to utilize the recent advances in machine learning to develop non-physics-based techniques for forecasting the sea ice volume with low computational costs. In particular, this paper aims to provide a step-wise decision process required to develop a more accurate forecasting model over short- and mid-term horizons. This work integrates variational mode decomposition (VMD) and bidirectional long short-term memory (BiLSTM) for multi-input multi-output pan-Arctic SIV forecasting. Different experiments are conducted to identify the impact of several aspects, including multivariate inputs, signal decomposition, and deep learning, on forecasting performance. The empirical results indicate that (i) the proposed hybrid model is consistently effective in time-series processing and forecasting, with average improvements of up to 60% compared with the case of no decomposition and over 40% compared with other deep learning models in both forecasting horizons and seasons; (ii) the optimization of the VMD level is essential for optimal performance; and (iii) the use of the proposed technique with a divide-and-conquer strategy demonstrates superior forecasting performance.