The 2019 novel coronavirus (2019-nCoV) outbreak has been treated as a Public Health Emergency of International Concern by the World Health Organization. This work made an early prediction of the 2019-nCoV outbreak in China based on a simple mathematical model and limited epidemiological data. Combing characteristics of the historical epidemic, we found part of the released data is unreasonable. Through ruling out the unreasonable data, the model predictions exhibit that the number of the cumulative 2019-nCoV cases may reach 76,000 to 230,000, with a peak of the unrecovered infectives (22,000-74,000) occurring in late February to early March. After that, the infected cases will rapidly monotonically decrease until early May to late June, when the 2019-nCoV outbreak will fade out. Strong anti-epidemic may reduce the cumulative infected cases by 40%-49%. The improvement of medical care can also lead to about one-half transmission decrease and effectively shorten the duration of the 2019-nCoV.

Two intense heatwaves of July and early August 2018 are found to be associated with a European blocking (EB) event accompanied by a series of consecutive positive North Atlantic Oscillation (NAO⁺) events. Further analyses show that the collaborative role of an EB event and its upstream NAO⁺ pattern could increase the frequency, persistence, magnitude and scale of heatwaves over Europe. Compared with NAO⁺-unrelated EB events, NAO⁺-related EB events are less movable (quasi-stationary) and more persistent over Europe, which could contribute to an increase in the intensity and persistence of heatwaves. In addition, the blocking high of this type has a northeast–southwest orientation with stronger warm airflow and less precipitation in northern and western Europe, where large scopes of higher temperatures tend to occur. In contrast, NAO⁺-unrelated EB events without orientation correspond to a trough in the south, which results in increased precipitation and cold air in the southern part of Europe, and thus high temperatures contract to the northern part of Europe. Moreover, considering that the NAO⁺ pattern leads the formation of an EB event, the NAO⁺ pattern might serve as a potential predictor for European heatwaves. Our conclusions are strongly supported by the analysis of CMIP6 historical simulations which also capture the differences of high temperatures and atmospheric circulations between NAO⁺-related EB events and NAO⁺-unrelated EB events.

In this paper, the physical processes underlying recent winter cold anomalies over East Asia (EA) are examined via statistical analysis. It is found that the EA cold anomaly depends on the warming in the North Atlantic, sea ice loss in the Barents–Kara Sea (BKS), and atmospheric teleconnection patterns. Specifically, the sea ice loss in the BKS can anchor teleconnection patterns originating from different North Atlantic sea surface temperature (SST) patterns. Different patterns of North Atlantic warming can affect the position of the cold anomaly region through altering the atmospheric circulations. In addition, whether the relevant teleconnection pattern leads to enhanced cold anomaly over EA crucially depends on the sea ice loss in the BKS, because it can anchor the blocking anticyclone embedded in the teleconnection pattern over the Ural region and make it more persistent and quasi‐stationary. Furthermore, it is found that the role of SST modes in the EA cold anomaly depends on their time scales. Although the strong basin‐scale warming (north–south SST tripolar mode) in the North Atlantic mid‐ to high‐latitudes plays a major role in decadal (interannual) cold anomaly over EA, it appears that the Atlantic east–west SST dipole structure dominates winter temperature variations over EA in recent decades on both the interannual and decadal time scales.

Water vapor is critical to Arctic sea ice loss and surface air warming, particularly in winter. Whether the local process or poleward transport from lower latitudes can explain the Arctic warming is still a controversial issue. In this work, a hydrological tool, a dynamical recycling model (DRM) based on time-backward Lagrangian moisture tracking, is applied to quantitatively evaluate the relative contributions of local evaporation and external sources to Barents–Kara Seas (BKS) moisture in winter during 1979–2015. On average, the local and external moistures explain 35.4% and 57.3% of BKS moisture, respectively. The BKS, Norwegian Sea, and midlatitude North Atlantic are the three major sources and show significant increasing trends of moisture contribution. The local moisture contribution correlates weakly to downward infrared radiation (IR) but significantly to sea ice variation, which suggests that the recent-decade increase of local moisture contribution is only a manifestation of sea ice melting. In contrast, the external moisture contribution significantly correlates to both downward IR and sea ice variation, thus suggesting that meridional moisture transport mainly explains the recent BKS warming. The moisture contributions due to different sources are governed by distinct circulation patterns. The negative Arctic Oscillation–like pattern suppresses external moisture but favors local evaporation. In the case of dominant external moisture, a well-organized wave train spanning from across the midlatitude Atlantic to mid–high-latitude Eurasia has the mid–high-latitude components similar to a positive-phase North Atlantic Oscillation with a Ural blocking to the east. Moreover, the meridional shift of the wave train pathway and the spatial scale of the wave train anomalies determine the transport passage and strength of the major external moisture sources.

The northwestern corner of China (NWCC) experienced a decadal transition in summer precipitation during 1982–2010, with a significant upward trend in 1982–2000 (P1) but a downward one in 2001–2010 (P2). A spatially unbounded dynamic recycling model is developed to estimate the moisture sources and moisture transport variations based on ERA‐Interim data. The results suggest that more than 88% of NWCC precipitation has external moisture origins in the southwest and northwest terrestrial areas. The increasing precipitation trend during P1 can be explained by the increasing moisture contribution from the southwest and decreasing contribution from the northwest. However, the opposite trends cause the decreasing precipitation trend during P2. In general, the decadal precipitation transition is mainly determined by the variation of short‐distance moisture transport from central Asia, although opposite moisture transport variations exist in the Ural Mountains and Northeast Europe. The variation of the precipitation trend is closely associated with a well‐organized wave train propagation from the North Atlantic to central Asia. During P1, the wave train structure consists of a titled positive phase North Atlantic Oscillation (NAO), an anticyclonic circulation over Europe, and a cyclonic anomaly over central Asia, which promotes the southwest moisture flux to NWCC. But the opposite circulation pattern dominates P2. The energy dispersion due to the breakdown of the NAO determines the phase and strength of the downstream wave anomalies over Eurasia. This suggests that the summer NAO might influence the decadal variation of NWCC precipitation through the decadal modulation of the Eurasia wave train.