It is traditionally considered that the predictability of atmosphere reaches approximately 2 weeks due to its chaotic features. Considering boundary conditions, the lead prediction time can exceed 2 weeks in certain cases. We find that the Arctic sea ice concentration (SIC) is crucial for extended‐range prediction of strong and long‐lasting Ural blocking (UB) formation. By applying the rotated empirical orthogonal function‐based particle swarm optimization algorithm, the conditional nonlinear optimal perturbation is calculated with the Community Atmosphere Model, version 4, to identify the optimally growing boundary errors in extended‐range prediction of strong and long‐lasting UB formation. It is found that SIC perturbations in the Greenland Sea (GS), Barents Sea (BS), and Okhotsk Sea (OKS) are important for strong and long‐lasting UB formation prediction in four pentads. Further analysis reveals that the SIC perturbations in these areas first influence the local temperature field through the diabatic heating process and further affect the temperature field in the Ural sector mainly by advection and convection processes. Moreover, the zonal winds in the Ural sector are adjusted by the thermal wind balance, thus affecting UB formation. The local characteristics of the SIC perturbations indicate that the GS, BS, and OKS may be sensitive areas in regard to extended‐range prediction of strong and long‐lasting UB formation, which can provide scientific support for the SIC target observations in the future.

Three extreme cold events invaded China during the early winter period between December 2020 to mid-January 2021 and caused drastic temperature drops, setting new low-temperature records at many stations during 6−8 January 2021. These cold events occurred under background conditions of low Arctic sea ice extent and a La Niña event. This is somewhat expected since the coupled effect of large Arctic sea ice loss in autumn and sea surface temperature cooling in the tropical Pacific usually favors cold event occurrences in Eurasia. Further diagnosis reveals that the first cold event is related to the southward movement of the polar vortex and the second one is related to a continent-wide ridge, while both the southward polar vortex and the Asian blocking are crucial for the third event. Here, we evaluate the forecast skill for these three events utilizing the operational forecasts from the ECMWF model. We find that the third event had the highest predictability since it achieves the best skill in forecasting the East Asian cooling among the three events. Therefore, the predictability of these cold events, as well as their relationships with the atmospheric initial conditions, Arctic sea ice, and La Niña deserve further investigation.

The North Atlantic Oscillation (NAO) is the most significant mode of the atmosphere in the North Atlantic, and it plays an important role in regulating the local weather and climate and even those of the entire Northern Hemisphere. Therefore, it is vital to predict NAO events. Since the NAO event can be quantified by the NAO index, an effective neural network model EEMD-ConvLSTM, which is based on Convolutional Long Short-Term Memory (ConvLSTM) with Ensemble Empirical Mode Decomposition (EEMD), is proposed for NAO index prediction in this paper. EEMD is applied to preprocess NAO index data, which are issued by the National Oceanic and Atmospheric Administration (NOAA), and NAO index data are decomposed into several Intrinsic Mode Functions (IMFs). After being filtered by the energy threshold, the remaining IMFs are used to reconstruct new NAO index data as the input of ConvLSTM. In order to evaluate the performance of EEMD-ConvLSTM, six methods were selected as the benchmark, which included traditional models, machine learning algorithms, and other deep neural networks. Furthermore, we forecast the NAO index with EEMD-ConvLSTM and the Rolling Forecast (RF) and compared the results with those of Global Forecast System (GFS) and the averaging of 11 Medium Range Forecast (MRF) model ensemble members (ENSM) provided by the NOAA Climate Prediction Center. The experimental results show that EEMD-ConvLSTM not only has the highest reliability from evaluation metrics, but also can better capture the variation trend of the NAO index data.

is often subjected to the influence of extreme cold events during the boreal winter. Observations show that the frequency of winter extreme cold events has increased during the past two decades and East Asia has experienced several severe extreme cold events in the past few winters (Cohen et al., 2020; Johnson et al., 2018). For example, an unprecedented low-temperature and freezing disaster occurred in southern China in early January 2008, which caused hundreds of human deaths and great economic loss (Ding et al., 2008; Tao & Wei, 2008). Record-breaking blizzards and low temperatures frequently affected many areas in East Asia during the winter of 2010-2011 (Gong et al., 2014). In January 2016, a strong cold event occurred in East Asia and snowfall was observed for the first time during past 115 years in Amami-Oshima (Qian et al., 2018; Yamaguchi et al., 2019). Moreover, the winter of 2017-2018 was extremely cold in East Asia and many countries, including China, Japan, and Korea, experienced record-breaking low temperatures (Tachibana et al., 2019). All of these extreme cold events have caused great damage to human lives, agriculture, transportation, and power infrastructure in East Asia. Skillful forecasts of these extremes may have benefits for effective hazard preparedness and risk management. The lead time for skillful forecasts of atmospheric motion is limited owing to its chaotic features. The study of atmospheric predictability was first recognized by Lorenz (1963). After that, he investigated the behavior of error growth and found that motions in small scales have a shorter saturation time by utilizing a two-dimensional barotropic vorticity equation (Lorenz, 1969). His results suggest that the intrinsic predictability of the atmosphere is about 16.8 days. He also pointed out that it seems possible to forecast the instantaneous weather patterns with a lead time of 2 weeks by investigating the operational products from the European Centre for Medium-Range Weather Forecasts (ECMWF) (Lorenz, 1982). However, recent studies have shown that skillful forecast lead times may exceed 2 weeks (Buizza & Leutbecher, 2015; Xiang et al., 2018), which indicates that it may be possible to achieve long lead times for the skillful forecast of extreme events. The predictability of weather is mainly based on the atmospheric conditions, and weather forecasts on short timescales usually depend on the accuracy of the atmospheric initial conditions. But for timescales longer than one season, the boundary conditions play the principal role in atmospheric circulation prediction.