Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method

• Published in: Scientific Data
• Main Authors: Ziqi Ma, Jianbin Huang, Xiangdong Zhang, Yong Luo, Minghu Ding, Jun Wen, Weixin Jin, Chen Qiao, Yifu Yin
• Format: Article in Journal/Newspaper
• Language: English
• Published: Nature Portfolio 2023
• Subjects: Science; Q; Arctic; North Pole
• Online Access: https://doi.org/10.1038/s41597-023-02059-5; https://doaj.org/article/29f2da45e14947aca5aac05bc5ffad9b

Abstract A precise Arctic surface air temperature (SAT) dataset, that is regularly updated, has more complete spatial and temporal coverage, and is based on instrumental observations, is critically important for timely monitoring and improving understanding of the rapid change in the Arctic climate. In this study, a new monthly gridded Arctic SAT dataset dated back to 1979 was reconstructed with a deep learning method by combining surface air temperatures from multiple data sources. The source data include the observations from land station of GHCN (Global Historical Climatology Network), ICOADS (International Comprehensive Ocean-Atmosphere Data Set) over the oceans, drifting ice station of Russian NP (North Pole), and buoys of IABP (International Arctic Buoy Programme). The last two are crucial for improving the representation of the in-situ observed temperatures within the Arctic. The newly reconstructed dataset includes monthly Arctic SAT beginning in 1979 and daily Arctic SAT beginning in 2011. This dataset would represent a new improvement in developing observational temperature datasets and can be used for a variety of applications.