Abstract

The stratospheric role in the European winter surface climate response to El Niño–Southern Oscillation sea surface temperature forcing is investigated using an intermediate general circulation model with a well-resolved stratosphere. Under El Niño conditions, both the modeled tropospheric and stratospheric mean-state circulation changes correspond well to the observed “canonical” responses of a late winter negative North Atlantic Oscillation and a strongly weakened polar vortex, respectively. The variability of the polar vortex is modulated by an increase in frequency of stratospheric sudden warming events throughout all winter months. The potential role of this stratospheric response in the tropical Pacific–European teleconnection is investigated by sensitivity experiments in which the mean state and variability of the stratosphere are degraded. As a result, the observed stratospheric response to El Niño is suppressed and the mean sea level pressure response fails to resemble the temporal and spatial evolution of the observations. The results suggest that the stratosphere plays an active role in the European response to El Niño. A saturation mechanism whereby for the strongest El Niño events tropospheric forcing dominates the European response is suggested. This is examined by means of a sensitivity test and it is shown that under large El Niño forcing the European response is insensitive to stratospheric representation.

Abstract

The evolution of the Arctic polar vortex during observed major midwinter stratospheric sudden warmings (SSWs) is investigated for the period 1957–2002, using 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) Ertel’s potential vorticity (PV) and temperature fields. Time-lag composites of vertically weighted PV, calculated relative to the SSW onset time, are derived for both vortex-displacement SSWs and vortex-splitting SSWs, by averaging over the 15 recorded displacement and 13 splitting events. The evolving vertical structure of the polar vortex during a typical SSW of each type is clearly illustrated by plotting an isosurface of the composite PV field, and is shown to be very close to that observed during representative individual events. Results are verified by comparison with an elliptical diagnostic vortex moment technique.

For both types of SSW, little variation is found between individual events in the orientation of the developing vortex relative to the underlying topography; that is, the location of the vortex during SSWs of each type is largely fixed in relation to the earth’s surface. During each type of SSW, the vortex is found to have a distinctive vertical structure. Vortex-splitting events are typically barotropic, with the vortex split occurring near simultaneously over a large altitude range (20–40 km). In the majority of cases, of the two daughter vortices formed, it is the “Siberian” vortex that dominates over its “Canadian” counterpart. In contrast, displacement events are characterized by a very clear baroclinic structure; the vortex tilts significantly westward with height, so that the top and bottom of the vortex are separated by nearly 180° longitude before the upper vortex is sheared away and destroyed.

Abstract

The 2016/17 wildfire season in Chile was the worst on record, burning more than 600,000 ha. While wildfires are an important natural process in some areas of Chile, supporting its diverse ecosystems, wildfires are also one of the biggest threats to Chile’s unique biodiversity and its timber and wine industries. They also pose a danger to human life and property because of the sharp wildland–urban interface that exists in many Chilean towns and cities. Wildfires are, however, difficult to predict because of the combination of physical (meteorology, vegetation, and fuel condition) and human (population density and awareness level) factors. Most Chilean wildfires are started because of accidental ignition by humans. This accidental ignition could be minimized if an effective wildfire warning system alerted the population to the heightened danger of wildfires in certain locations and meteorological conditions. Here, we demonstrate the design of a novel probabilistic wildfire prediction system. The system uses ensemble forecast meteorological data together with a long time series of fire products derived from Earth observation to predict not only fire occurrence but also how intense wildfires could be. The system provides wildfire risk estimation and associated uncertainty for up to six days in advance and communicates it to a variety of end users. The advantage of this probabilistic wildfire warning system over deterministic systems is that it allows users to assess the confidence of a forecast and thus make more informed decisions regarding resource allocation and forest management. The approach used in this study could easily be adapted to communicate other probabilistic forecasts of natural hazards.

Abstract

Extreme weather events have devastating impacts on human health, economic activities, ecosystems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on time scales of several weeks for many extreme events. Here we provide an overview of subseasonal predictability for case studies of some of the most prominent extreme events across the globe using the ECMWF S2S prediction system: heatwaves, cold spells, heavy precipitation events, and tropical and extratropical cyclones. The considered heatwaves exhibit predictability on time scales of 3–4 weeks, while this time scale is 2–3 weeks for cold spells. Precipitation extremes are the least predictable among the considered case studies. ­Tropical cyclones, on the other hand, can exhibit probabilistic predictability on time scales of up to 3 weeks, which in the presented cases was aided by remote precursors such as the Madden–Julian oscillation. For extratropical cyclones, lead times are found to be shorter. These case studies clearly illustrate the potential for event-dependent advance warnings for a wide range of extreme events. The subseasonal predictability of extreme events demonstrated here allows for an extension of warning horizons, provides advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events.

Abstract

The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.