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.
We examine the time-frequency dynamics of spillovers between oil price shocks and economic performance globally. We use both time and frequency domains simultaneously to find the response of macroeconomic performance to changes in oil prices during the global financial and pandemic crises. Using Wavelet analysis, this seminal study explores the connectedness between oil price shocks and economic activities during COVID-19 and the financial crises of 2008. This study finds that both economic activities and oil prices have shown high power during the period of global financial crises. The recently COVID-19 outbreak indicates significant volatility in economic activities and oil prices during the period of crisis. Moreover, we observe a strong interconnectedness between oil prices and economic activities during global financial crises and COVID-19 crises. We argue that a shock to oil prices in global financial crises and the COVID-19 outbreak has serious repercussions for economic activities. The highest total connectedness between oil prices and economic activities is observed during the COVID-19 outbreak, which advocates that the speed of information transmission amid oil prices and economic activities is greater in the era of the COVID-19 outbreak as compared to other global financial crises. The results of this study have significant implications for policymakers.
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