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Wildfire is a dominant disturbance agent in pan‐Arctic tundra and can significantly impact terrestrial carbon balance and ecosystem functioning. Interactions between fire and climate change can enhance their impacts on the Arctic. However, the driving mechanisms of tundra fire occurrence remain poorly understood. This study focuses on identifying key environmental factors controlling fire occurrence in Arctic tundra of Alaska. Our random forest models, considering ignition source, fuel, fire weather, and topography, have shown a strong predictive capability with an overall accuracy above 91%. We found cloud‐to‐ground (CG) lightning to be the dominant driver controlling tundra fire occurrence. Near‐surface weather warmer and drier than normal was required to support burning, while fuel composition and topography have modest correlations with fire occurrence. Our results highlight the critical role of CG lightning in driving tundra fires and that incorporating lightning in modeling is essential for fire monitoring, forecasting, and management in the Arctic. Plain Language Summary: Tundra fires can exert a considerable influence on the local ecosystem functioning and contribute to climate change. However, the drivers and mechanisms of tundra fires are still poorly understood. Research on modeling contemporary fire occurrence in the tundra is also lacking. Here we examined the key environmental factors driving tundra fire occurrence with numeric weather prediction and statistical models. We found that tundra fire occurrence is primarily controlled by cloud‐to‐ground lightning. Warmer and drier fire weather conditions also support burning in the tundra. We recommend the integration of lightning modeling for fire monitoring and forecasting in the data‐scarce regions like the Arctic. Key Points: Cloud‐to‐ground lightning probability is the key driver of fire occurrence in Arctic tundraWarmer and drier near‐surface fire weather conditions also promote tundra firesAn empirical‐dynamic framework combining Weather Research and Forecast (WRF) and statistical learning methods shows promise for modeling tundra fire occurrence [ABSTRACT FROM AUTHOR]

Recent rapid Arctic sea-ice reduction has been well documented in observations, reconstructions and model simulations. However, the rate of sea ice loss is highly variable in both time and space. The western Arctic has seen the fastest sea-ice decline, with substantial interannual and decadal variability, but the underlying mechanism remains unclear. Here we demonstrate, through both observations and model simulations, that the Pacific North American (PNA) pattern is an important driver of western Arctic sea-ice variability, accounting for more than 25% of the interannual variance. Our results suggest that the recent persistent positive PNA pattern has led to increased heat and moisture fluxes from local processes and from advection of North Pacific airmasses into the western Arctic. These changes have increased lower-tropospheric temperature, humidity and downwelling longwave radiation in the western Arctic, accelerating sea-ice decline. Our results indicate that the PNA pattern is important for projections of Arctic climate changes, and that greenhouse warming and the resultant persistent positive PNA trend is likely to increase Arctic sea-ice loss. The fastest sea-ice decline has been observed in the western Arctic, but the underlying mechanisms are still unclear. Here, the authors show that the Pacific North American pattern plays an important role in western Arctic sea-ice variability. [ABSTRACT FROM AUTHOR]

Satellite remote sensing has been widely used for the evaluation of wildfire burn severity in various ecosystems. While a variety of remote sensing-based burn severity indices have been developed, the Landsat-based differenced Normalized Burn Ratio (dNBR) presents the most widely-used approach to burn severity assessment for fire research and management. Although dNBR-based approaches have been continuously updated, including the development of the relative dNBR (RdNBR) and the Relativized Burn Ratio (RBR), one key obstacle for the reliable applications of the burn severity indices in the high northern latitudes has not been adequately addressed. Specifically, optimal Landsat image pairs are very rarely available for the calculation of the burn severity indices in certain regions of the high northern latitudes (HNL), resulting in the burn severity index outputs calculated for a large number of wildfires being considerably affected by non-fire-related factors. The suboptimal selection of image pairs may have partially contributed to the lack of consistency in the performance of the burn severity indices in the HNL regions. In this paper, we systematically evaluated the impacts of suboptimal image pairs on signal stability through two sets of analyses conducted at different spatial scales. First, at the regional scale, we examined the burn severity indices calculated for a selection of wildfires in Alaska. Here, we demonstrate the inconsistent performance of the same index calculated based on Landsat data that are typically considered as "optimal". In addition, we show that the calculated indices may be more prone to the negative impacts of these inconsistencies, thus leading to reduced reliability, when there is limited Landsat data availability. Second, at the continental scale, we show that certain areas in the HNL, especially Alaska during the pre-2000 era, are subject to the potentially strong negative impact associated with the limited data availability. Through systematically analyzing this issue, we hope to not only divert more attention to it but also to provide potential solutions, based on which further improvements that may be of particular importance for research on Arctic wildfires could be made. • Examines a limitation of burn severity indices that is specific to the Arctic region • Takes advantage both local and cloud-based platforms for more robust analyses • Utilizes full Landsat archive over North America in the spatio-temporal analysis • Offers potential solutions to address the revealed limitation [ABSTRACT FROM AUTHOR]

The last two decades have seen a dramatic decline and strong year-to-year variability in Arctic winter sea ice, especially in the Barents-Kara Sea (BKS), changes that have been linked to extreme midlatitude weather and climate. It has been suggested that these changes in winter sea ice arise largely from a combined effect of oceanic and atmospheric processes, but the relative importance of these processes is not well established. Here, we explore the role of atmospheric circulation patterns on BKS winter sea ice variability and trends using observations and climate model simulations. We find that BKS winter sea ice variability is primarily driven by a strong anticyclonic anomaly over the region, which explains more than 50% of the interannual variability in BKS sea-ice concentration (SIC). Recent intensification of the anticyclonic anomaly has warmed and moistened the lower atmosphere in the BKS by poleward transport of moist-static energy and local processes, resulting in an increase in downwelling longwave radiation. Our results demonstrate that the observed BKS winter sea-ice variability is primarily driven by atmospheric, rather than oceanic, processes and suggest a persistent role of atmospheric forcing in future Arctic winter sea ice loss.