Numerical experiments are carried out to explore the impacts of local and remote forcing on the interannual variability of tropical cyclone (TC) frequency. The first two groups of experiments focus on the regional simulations of Atlantic TCs, and the lateral boundary conditions and sea surface temperature (SST) are specified to investigate the relative importance of remote and local forcing. The results suggest that remote processes outside the North Atlantic, particularly extratropical processes, play an important role in modulating Atlantic TC frequency and that the remote impacts may exceed the impacts of local SST in some years. The total TC frequency in the northern tropics is explored in the third group of experiments. In contrast to the North Atlantic, tropical SST plays a dominant role in modulating the total TC frequency in the northern tropics. The difference may help to explain the uncertainties in the projections of future Atlantic TC frequency.

Tornadoes are one of the high-impact weather phenomena that can induce life loss and property damage. Here, we investigate the relationship between large-scale weather regimes and tornado occurrence in boreal spring. Results show that weather regimes strongly modulate the probability of tornado occurrence in the United States due to changes in shear and convective available potential energy and that persisting weather regimes (lasting ≥3 days) contribute to greater than 70% of outbreak days (days with ≥10 tornadoes). A hybrid model based on the weather regime frequency predicted by a numerical model is developed to predict above/below normal weekly tornado activity and has skill better than climatology out to Week 3. The hybrid model can be applied to real-time forecasting and aide in mitigation of severe weather events.Plain Language Summary Severe storms that are capable of producing tornadoes, strong winds, and hail may lead to a high number of fatalities and property damage. The relationship between tornadoes and the large-scale, recurrent weather patterns over the United States is investigated. It is shown that specific weather patterns may alter tornado activity and that persisting weather patterns contribute to greater than 70% of tornado outbreak days. Finally, a hybrid model is created to predict tornado activity, and the skill of the model is better than using a long-term average out to Week 3, which is an improvement of the current forecasting state.

Tropical cyclones (TC) are one of the most severe storm systems on Earth and cause significant loss of life and property upon landfall in coastal areas. A better understanding of their variability mechanisms will help improve the TC seasonal prediction skill and mitigate the destructive impacts of the storms. Early studies focused primarily on tropical processes in regulating the variability of TC activity, while recent studies suggest also some long-range impacts of extratropical processes, such as lateral transport of dry air and potential vorticity by large-scale waves. Here we show that stationary waves in the Northern Hemisphere integrate tropical and extratropical impacts on TC activity in July through October. In particular, tropical upper-tropospheric troughs (TUTTs), as part of the summertime stationary waves, are associated with the variability of large-scale environmental conditions in the tropical North Atlantic and North Pacific and significantly correlated to the variability of TC activity in these basins. TUTTs are subject to the modulation of diabatic heating in various regions and are the preferred locations for extratropical Rossby wave breaking (RWB). A strong TUTT in a basin is associated with enhanced RWB and tropical−extratropical stirring in that basin, and the resultant changes in the tropical atmospheric conditions modulate TC activity. In addition, the anticorrelation of TUTTs between the North Atlantic and North Pacific makes the TC activity indices over the two basins compensate each other, rendering the global TC activity less variable than otherwise would be the case if TUTTs were independent.

Polar lows (PLs) are intense mesoscale cyclones that form over high-latitude oceans (Rasmussen and Turner, 2003). They are characterized by intense surface winds and can produce heavy precipitation, strong icing, low visibility, and large ocean waves (Harrold & Browning, 1969;Orimolade et al., 2016;Samuelsen et al., 2015), posing hazards to high-latitude coastal communities and marine operations. However, it is challenging for global models to realistically represent or skillfully predict PLs due to their small scale (with a typical diameter of 200-500 km), short lifetime (typically <48 hr), and convective nature (e.g.

Atmospheric blocking is a major producer of extreme weather events in midlatitudes that have profound socioeconomic impacts. However, few strides toward seasonal prediction of atmospheric blocking have been made. Here, we developed a new statistical model for prediction of the winter seasonal blocking frequency over Eurasia 1 month in advance using sea surface temperature, geopotential height at 70-hPa, and sea ice concentration as predictors, and the model captures more than 65% of the interannual variance. Furthermore, we applied the same predictors used for blocking prediction to predict the seasonal occurrence of winter extreme hot and cold days, and skillful prediction was achieved over Greenland and large portions of Eurasia. The predictive models provide insight into the seasonal predictability of atmospheric blocking and extreme temperature and also aide in valuable decisions across a variety of sectors.Plain Language Summary Atmospheric blocking events refers to a "block" of the normal movement of weather systems. A prolonged block during the winter season can produce above normal temperatures within the blocking high, while cold air outbreaks can occur downstream and have an impact on energy demand. Therefore, it is highly desirable that we extend the prediction skill of these extreme weather producers. We use various predictors that contain "memory" in the Earth system to predict the number of blocking events that occurs each winter season over the Eurasian continent. In addition, we are able to use the same predictors to skillfully predict extreme temperature frequency over Greenland and Eurasia. Since research into long-range prediction of atmospheric blocking is sparse, this research advances seasonal prediction of atmospheric blocking events and could aide in decision making across several sectors. Key Points:• A statistical prediction model captures more than 65% of the interannual variance of winter seasonal blocking frequency over Eurasia • The connection to blocking allows extreme temperature frequency to be skillfully predicted • Atlantic SST, the stratosphere, and sea ice are skillful predictors for atmospheric blocking frequency over Eurasia Supporting Information:• Supporting Information S1