{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T08:10:21Z","timestamp":1773821421126,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T00:00:00Z","timestamp":1626480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["311021008"],"award-info":[{"award-number":["311021008"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41976214"],"award-info":[{"award-number":["41976214"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In data-sparse regions such as the Arctic, atmospheric reanalysis is one of the key tools for understanding rapid climate change at the regional and global scales. The utility of reanalysis datasets based on data assimilation is affected by their accuracy and biases. Therefore, it is important to evaluate their performance. Here, we conduct inter-comparisons of two temperature variables, namely, the 2-m air temperature (Ta) and the surface temperature (Ts), from the widely used ERA-I and ERA5 reanalysis datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) against in situ observations from three international buoy programs (i.e., the International Arctic Buoy Programme (IABP), the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), and the Cold Regions Research and Engineering Laboratory (CRREL)) during 2010\u20132020 in the Arctic. Overall, the results show that both the ERA-I and ERA5 were well correlated with the buoy observations, with the highest correlation coefficient reaching 0.98. There were generally warm Ta biases for both ERA-I (2.27 \u00b1 3.33 \u00b0C) and ERA5 (2.34 \u00b1 3.22 \u00b0C) when compared with more than 3000 matching pairs of daily buoy observations. The warm Ta biases of both reanalysis datasets exhibited seasonal variations, reaching the maximum of 3.73 \u00b1 2.84 \u00b0C in April and the minimum of 1.36 \u00b1 2.51 \u00b0C in September. For Ts, both ERA-I and ERA5 exhibited good consistencies with the buoy observations, but have higher amplitude biases compared with those for Ta, with generally negative biases of \u22124.79 \u00b1 4.86 \u00b0C for ERA-I and \u22124.11 \u00b1 3.92 \u00b0C for ERA5. For both reanalysis datasets, the largest bias of Ts (\u221211.18 \u00b1 3.08 \u00b0C) occurred in December, while the biases were rather small (less than \u22123 \u00b0C) in the warmer months (April to October). The cold Ts biases for ERA-I and ERA5 were probably overestimated due to the location of the surface temperature sensors on the buoys, which may have been affected by snow cover. Both the Ta and Ts biases varied for different buoy programs and different sea ice concentration conditions, yet they exhibited similar trends.<\/jats:p>","DOI":"10.3390\/rs13142813","type":"journal-article","created":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T21:18:52Z","timestamp":1626643132000},"page":"2813","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Evaluation of 2-m Air Temperature and Surface Temperature from ERA5 and ERA-I Using Buoy Observations in the Arctic during 2010\u20132020"],"prefix":"10.3390","volume":"13","author":[{"given":"Yining","family":"Yu","sequence":"first","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"},{"name":"University Corporation for Polar Research, Beijing 100875, China"}]},{"given":"Wanxin","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"},{"name":"University Corporation for Polar Research, Beijing 100875, China"}]},{"given":"Zhilun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"},{"name":"University Corporation for Polar Research, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6910-6565","authenticated-orcid":false,"given":"Xiao","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"},{"name":"University Corporation for Polar Research, Beijing 100875, China"}]},{"given":"Fengming","family":"Hui","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"},{"name":"University Corporation for Polar Research, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7134-057X","authenticated-orcid":false,"given":"Jiechen","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.gloplacha.2011.03.004","article-title":"Processes and impacts of Arctic amplification: A research synthesis","volume":"77","author":"Serreze","year":"2011","journal-title":"Glob. 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