Influence of the Arctic on the Predictability of Eurasian Winter Extreme Weather Events

et al. 2022

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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.

Section: Introductionmentioning

confidence: 99%

Influence of Arctic Sea Ice Concentration on Extended‐Range Prediction of Strong and Long‐Lasting Ural Blocking Events in Winter

et al. 2022

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“…Moreover, the frequency of extreme cold days during boreal winter is skillfully predicted by the ECMWF model 2–4 weeks in advance (Xiang et al., 2020). In addition to the frequency, it has also been suggested that skillful forecast of extreme cold events can be made with a lead time of more than 2 weeks (Dai & Mu, 2020). Ferranti et al.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Forecast Performance for Extreme Cold Events in East Asia With Subseasonal‐to‐Seasonal Data Sets From ECMWF

et al. 2021

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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.

“…Previous studies have shown that atmospheric reanalysis data have large uncertainties in the Arctic due to the limited number of observations in the region (Inoue et al, 2011; Jung et al, 2014; Screen & Simmonds, 2011). Thus, we believe that the weather forecasts for the mid‐latitude region will improve with more accurate Arctic initial conditions, especially for extreme weather events (Dai & Mu, 2020). Ensemble atmospheric forecasts with different initial data sets revealed that additional Arctic radiosonde observations are beneficial for the forecast of persistent strong winds north of 70°N (Inoue et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Arctic Influence on the Eastern Asian Cold Surge Forecast: A Case Study of January 2016

2020

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An extreme cold surge during 21-25 January 2016 resulted in extensive damage to eastern Asian countries. Synoptic diagnoses show that the southward movement of the north Siberian anomalous anticyclone to the midlatitudes was the key process for the cold surge. Operational forecasts from the European Centre for Medium-Range Weather Forecasts show that this event could be captured in the control forecast started on 12 January. Further diagnosis shows that the control forecast started on 11 January missed it, possibly due to uncertainties in the Arctic because the north Siberian anomalous anticyclone is not strong enough due to the erroneous eddy vorticity forcing (EVF) induced by the Arctic initial conditions uncertainty. Therefore, the north Siberian anomalous anticyclone cannot move southward to midlatitude when another anomalous anticyclone comes from the Caspian Sea. Finally, numerical experiments are conducted with the Open Integrated Forecasting System model to verify the role of the Arctic initial conditions. Numerical results show that integration with the ERA5 initial conditions could capture the cold surge in the forecast starting on 12 January, while integration with the ERA-Interim initial conditions could not. Sensitivity experiments with modified Arctic initial conditions show an improved cold surge forecast skill. This improvement may be due to the north Siberian anomalous anticyclone, which is sufficiently strong to move southward when the anomalous anticyclone comes from the Caspian Sea, with accurate EVF induced by the improved initial conditions in the Arctic. This result highlights the importance of the Arctic initial conditions in this eastern Asian cold surge forecast.