Abstract
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 2010–2020 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 ± 3.33 °C) and ERA5 (2.34 ± 3.22 °C) 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 ± 2.84 °C in April and the minimum of 1.36 ± 2.51 °C 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 −4.79 ± 4.86 °C for ERA-I and −4.11 ± 3.92 °C for ERA5. For both reanalysis datasets, the largest bias of Ts (−11.18 ± 3.08 °C) occurred in December, while the biases were rather small (less than −3 °C) 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.
Highlights
This article is an open access articleThe climate in the Arctic has undergone profound changes in recent decades, mainly caused by the Arctic amplification effect; that is, the Arctic is warming more than twice as fast as the global average temperature [1,2,3]
We focused on two temperature variables from both ERA-I and ERA5, namely, the 2-m air temperature (Ta) and the surface temperature (Ts)
Since the ERA-I and ERA5 reanalysis datasets were gridded as 0.25◦ and 0.125◦, respectively, and the observations from the buoys were collected based on single coordinates, we assigned the temperature record of the buoy observations to its corresponding grid point in the reanalysis data based on the principle of the nearest distance to generate a set of matching pairs between the buoy observations and the reanalysis data
Summary
This article is an open access articleThe climate in the Arctic has undergone profound changes in recent decades, mainly caused by the Arctic amplification effect; that is, the Arctic is warming more than twice as fast as the global average temperature [1,2,3]. The recent decline in sea ice has significantly increased during the freezing season (from November to February) [9,10]. This was partly due to the abnormal breakup of ice arches in the Nares Strait, which allowed more ice to migrate from the central Arctic to southern latitudes [11]. There is a trend of earlier melt onset and later freeze-up of sea ice, leading to an extension in the duration of the melt season in the Arctic [15,16,17]
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