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Revisiting climatic features in the Alaskan Arctic using newly collected data

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Abstract

Climatic features in the Alaskan Arctic have typically been analyzed using data from the limited National Weather Service stations. However, the increasing availability of in situ data in this area allows a more comprehensive understanding of recent changes. This study used newly collected data from 41 stations to investigate climatic features and recent changes in the Alaskan Arctic from the mid-1940s to 2018. We found that the mean annual air temperature (MAAT) ranged from –11.0 to –6.4 ∘C, annual amplitude of air temperature (AAAT) ranged from 16 to 22 ∘C, annual precipitation ranged from 85 to 300 mm, and annual mean snow depth ranged from 13.5 to 34.5 cm during 2007–2012. Compared with data since the late 1980s, MAAT increased by \(\sim \)2 ∘C near the coastline whereas AAAT did not significantly change. Changes in annual precipitation were complex among stations but showed a considerable increase in precipitation, snowfall, and snow depth during the cold months. The number of snow cover days declined, whereas the number of snowfall days increased at both Barrow and Kuparuk. This increase in snowfall events may be attributed to the declining sea ice concentration, which may enhance hydrological cycles. The observed bulk density of fresh snow was around 40–80 kg/m3 and declined from the mid-1980s to the late-1990s, then increased until the end of the study period. This expanded in situ dataset provides a more comprehensive understanding of climatic conditions in the Alaskan Arctic and confirms rapid changes during recent decades. This study may also serve to validate and benchmark high-resolution climate models.

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Acknowledgments

We thank the data sources and research teams for producing and making their data available. We also appreciate the reviewers and editor for their insightful comments and suggestions that improved the manuscript. PermaModel is a package developed with Python, which is available at https://github.com/permamodel/permamodel. The nonlinear least squares method in Python was implemented by scipy.optimize.curve_fit.

Funding

This study was funded by the National Research and Development Program of China (2019YFC1509100 and 2019YFA0607003), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA2010030805), and the U.S. National Science Foundation (grant No. 1503559).

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Authors and Affiliations

  1. School of Geographic Sciences, East China Normal University, Shanghai, 200241, China

    Kang Wang

  2. Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China

    Tingjun Zhang

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  1. Kang Wang
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Kang Wang and Tingjun Zhang. The first draft of the manuscript was written by Tingjun Zhang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Tingjun Zhang.

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Wang, K., Zhang, T. Revisiting climatic features in the Alaskan Arctic using newly collected data. Theor Appl Climatol 143, 1251–1259 (2021). https://doi.org/10.1007/s00704-020-03495-8

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