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Monitoring and quantifying high‐latitude atmospheric circulation variability and trends are important towards understanding the anatomy of extreme events and constraining their probabilities under continued Arctic change. The greater Alaska region stands out as one region of enhanced warming and environmental changes over the Arctic amplification era, which has shown proclivity to extreme events and speaks to the need to consistently monitor overlying atmospheric variability. In this paper, we describe the creation and analysis of an updated, daily mid‐tropospheric Alaska Blocking Index (ABI) time series from 1948 to 2020. Sensitivity testing and index modifications from the initial dataset are discussed, and the new ABI is evaluated over the full period and subperiods. Additionally, we assess the role of the ABI as a regional climate indicator by analysing its variability against surface air temperature and precipitation from 13 climate divisions across Alaska as well as broader‐scale hemispheric temperatures. Months of the highest ABI means mainly transpired in the most recent climatological period, 1991–2020, and most notably during the spring and autumn transition seasons, reflecting the nonstationary nature of the jet stream magnitude through time atop Alaska. ABI trends are positive across all months, seasons, and annually only in the 1991–2020 period. Contemporaneous ABI values are strongly and positively correlated with air temperature across all Alaskan climate divisions during the warm season, but negatively correlated with winter temperatures over southeastern Alaska. Meanwhile, the ABI is significantly anticorrelated with summer precipitation over Interior and southern Alaska, but only over southeastern Alaska in other seasons. The ABI is statistically differentiated from the primary modes of atmospheric variability as shown by its generally weak correlations with the Arctic Dipole and the Arctic Oscillation (r ≤ 0.40). [ABSTRACT FROM AUTHOR]

Permafrost is very sensitive to climate factors, with warm permafrost particularly vulnerable to climate change. This paper aims to provide a detailed case study of climate trends of a high-latitude Alaskan site underlain by warm permafrost based on historical data and predictions from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The climate data, including air temperature, precipitation, wind speed, and their annual and seasonal trends from 1950 to 2099, are presented for Bethel, Alaska. The downscaled data are compared with the historical data in the overlap period. Both air and design freezing and thawing indices are calculated using various methods. In comparison, the downscaled model predictions align well with air temperature and wind speed records but not for precipitation, indicating the need for further downscaling at a finer resolution. The historical annual temperature records show a warming rate of 0.40 °C per decade, which is predicted to almost double to 0.78 °C per decade in the 21st century, leading to shorter winters and longer summers. The historical annual precipitation increased by a rate of 14.8 mm per decade from 1950 to 2020, and this trend is predicted to continue but at a slightly lower rate. The wind speed decreased consistently from 1984 to 2020 by 0.9 km/h per decade. However, the CMIP5 predicts that such a trend will diminish, with the mean annual wind speed remaining almost unchanged in this century. The air freezing index exhibits a consistent downtrend from 1950 to 2020, whereas the air thawing index remained flat until 1980 and rose afterward; the CMIP5 predicts that both trends will continue in the 21st century. The design air freezing and thawing indices are sensitive to the assessment periods for both methods. An assessment period shorter than 30 years, e.g., ten years, is recommended to find design indices. It is conservative to use the monthly mean air temperature method for evaluating the design air freezing index and apply the extreme event method for the design thawing index. • Compare downscaled model data at a finer resolution with other models in CMIP5. • Assess the design freezing and thawing indices by two methods with varying periods. • Recommend methods and periods for evaluating design freezing/thawing indices. • Discuss climate change effects on infrastructure, design strategies, future studies. [ABSTRACT FROM AUTHOR]