Upon landfall, atmospheric rivers (ARs)-plumes of intense water vapor transport-often trigger weather and hydrologic extremes. Presently, no guidance is available to alert decision makers to anomalous AR activity within the subseasonal time scale (~2-5 weeks). Here, we construct and evaluate an empirical prediction scheme for anomalous AR activity based solely on the initial state of two prominent modes of tropical variability: the Madden-Julian oscillation (MJO) and the quasi-biennial oscillation (QBO). The MJO-the dominant mode of intraseasonal variability in the tropical troposphere-modulates landfalling AR activity along the west coast of North America by exciting large-scale circulation anomalies over the North Pacific. In light of emerging science regarding the modulation of the MJO by the QBO-the dominant mode of interannual variability in the tropical stratosphere-we demonstrate that the MJO-AR relationship is further influenced by the QBO. Evaluating the prediction scheme over 36 boreal winter seasons, we find skillful subseasonal "forecasts of opportunity" when knowledge of the MJO and the QBO can be leveraged to predict periods of increased or decreased AR activity. Certain MJO and QBO phase combinations provide empirical subseasonal predictive skill for anomalous AR activity that exceeds that of a state-of-the-art numerical weather prediction model. Given the wide-ranging impacts associated with landfalling ARs, even modest gains in the subseasonal prediction of anomalous AR activity may support decision making and benefit numerous sectors of society.

There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer time scales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden–Julian oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, reevaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.

Atmospheric rivers are elongated plumes of intense moisture transport that are capable of producing extreme and impactful weather. Along the West Coast of North America, they occasionally cause considerable mayhem—delivering flooding rains during periods of heightened activity and desiccating droughts during periods of reduced activity. The intrinsic chaos of the atmosphere makes the prediction of atmospheric rivers at subseasonal‐to‐seasonal time scales (3 to 5 weeks) an inherently difficult task. We demonstrate here that the potential exists to advance forecast lead times of atmospheric rivers into subseasonal‐to‐seasonal time scales through knowledge of two of the atmosphere's most prominent oscillations, the Madden‐Julian oscillation (MJO) and the quasi‐biennial oscillation (QBO). Strong MJO and QBO activity modulates the frequency at which atmospheric rivers strike—offering an opportunity to improve subseasonal‐to‐seasonal forecast models and thereby skillfully predict atmospheric river activity up to 5 weeks in advance.

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

Heretofore, the tropically excited Arctic warming (TEAM) mechanism put forward that localized tropical convection amplifies planetary-scale waves, which transport sensible and latent heat into the Arctic, leading to an enhancement of downward infrared radiation and Arctic surface warming. In this study, an investigation is made into the previously unexplored contribution of the synoptic-scale waves and their attendant atmospheric rivers to the TEAM mechanism. Reanalysis data are used to conduct a suite of observational analyses, trajectory calculations, and idealized model simulations. It is shown that localized tropical convection over the Maritime Continent precedes the peak of the planetary-scale wave life cycle by ~10–14 days. The Rossby wave source induced by the tropical convection excites a Rossby wave train over the North Pacific that amplifies the climatological December–March stationary waves. These amplified planetary-scale waves are baroclinic and transport sensible and latent heat poleward. During the planetary-scale wave life cycle, synoptic-scale waves are diverted northward over the central North Pacific. The warm conveyor belts associated with the synoptic-scale waves channel moisture from the subtropics into atmospheric rivers that ascend as they move poleward and penetrate into the Arctic near the Bering Strait. At this time, the synoptic-scale waves undergo cyclonic Rossby wave breaking, which further amplifies the planetary-scale waves. The planetary-scale wave life cycle ceases as ridging over Alaska retrogrades westward. The ridging blocks additional moisture transport into the Arctic. However, sensible and latent heat amounts remain elevated over the Arctic, which enhances downward infrared radiation and maintains warm surface temperatures.

In the United States, severe weather poses a threat to society, producing tornadoes and hail that can result in hundreds of casualties and billions of dollars in damages. Fortunately, skillful predictions of severe weather for short lead times of 0–8 days and longer lead times exceeding 1 month have been realized. However, this leaves a forecast gap at subseasonal to seasonal lead times of 2–5 weeks, when early‐action decision making by stakeholders is typically made. Here we develop an empirical prediction model that fills this gap during March–June when severe weather is most prevalent across the United States. We demonstrate skillful weekly forecasts of opportunity with lead times of 2–5 weeks of environmental parameters favorable to severe weather, as well as actual tornado and hail activity. To attain this skill, we use as a predictor the current state of active phases of the Madden‐Julian Oscillation, known to have physical teleconnections with future weather over the United States. The model has significant skill in regions such as the Plains and the Southeast, providing stakeholders with valuable extended forewarning.

One of the challenging tasks in climate science is to understand the equator-to-pole temperature gradient. The poleward heat flux generated by baroclinic waves is known to be central in reducing the equator-to-pole temperature gradient from a state of radiative-convective equilibrium. However, invoking this relationship to explain the wide range of equator-to-pole temperature gradients observed in past climates is challenging because baroclinic waves tend to follow the flux-gradient relationship such that their poleward heat flux is proportional to the equator-to-pole temperature gradient and zonal available potential energy (ZAPE). With reanalysis data, the authors show the existence of poleward heat transport by planetary-scale waves that are independent of the fluxgradient relationship and baroclinic instability. This process arises from a forced tapping of atmospheric ZAPE by planetary-scale waves that are triggered by enhanced tropical convection over the Pacific warm pool region. The Rossby waves excited by this tropical convection propagate northeastward over the Pacific Ocean and constructively interfere with the climatological stationary waves at higher latitudes. During polar night, when the current warming is most rapid, the forced tapping of ZAPE by planetary-scale waves produces a substantially greater warming than that by the synoptic-scale eddy fluxes that presumably arise from baroclinic instability.

Correlations between springtime stratospheric ozone extremes and subsequent surface temperatures have been previously reported for both models and observations at particular locations in the Northern Hemisphere. Here we quantify for the first time the potential use of ozone information for Northern Hemisphere seasonal forecasts, using observations and a nine‐member chemistry climate model ensemble. The ensemble composite correlations between March total column ozone (TCO) and April surface temperatures display a similar structure to observations, but with slightly lower correlation magnitudes. This is likely due to the larger number of cases smoothing out sampling error in the pattern, which is visible in the difference between correlations calculated from individual ensemble members. Using a linear regression model with March TCO as the predictor, predictions of the following April surface temperatures in regions that show large correlations are possible up to 4 years following the regression model end date in individual ensemble members, and up to 6 years in observations. We create an empirical forecast model to predict the sign of the observed as well as the modeled surface temperature anomalies using March TCO. Through a leave‐three‐years‐out cross‐validation method, we show that March TCO can forecast the sign of the April surface temperature anomalies well in parts of Eurasia that show the lowest model internal variability.