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.
Atmospheric rivers (ARs) play a vital role in shaping the hydroclimate of many regions globally, and can substantially impact water resource management, emergency response planning, and other socioeconomic entities. The second International Atmospheric Rivers Conference took place at the
Between 2013 and 2015, the northeast Pacific Ocean experienced the warmest surface temperature anomalies in the modern observational record. This "marine heatwave" marked a shift of Pacific decadal variability to its warm phase and was linked to significant impacts on marine species as well as exceptionally arid conditions in western North America. Here we show that the subtropical signature of this warming, off Baja California, was associated with a record deficit in the spatial coverage of co-located marine boundary layer clouds. This deficit coincided with a large increase in downwelling solar radiation that dominated the anomalous energy budget of the upper ocean, resulting in record-breaking warm sea surface temperature anomalies. Our observation-based analysis suggests that a positive cloud-surface temperature feedback was key to the extreme intensity of the heatwave. The results demonstrate the extent to which boundary layer clouds can contribute to regional variations in climate. Plain Language SummaryThe northeast Pacific Ocean experienced a "marine heatwave" between 2013 and 2015. This was characterized by the highest surface temperatures ever recorded in a vast swath of the ocean from near the Gulf of Alaska to off the coast of Baja California. The unprecedented warming event was linked to significant impacts on marine life and a severe drought in western North America. We analyze satellite data to show that the heatwave was associated with a record decrease in the typically high cloudiness over an area of the Pacific off Baja California that is roughly half the size of the contiguous United States. Such a deficit in cloud cover coincided with a large increase in the amount of sunlight absorbed by the ocean surface, resulting in extremely warm temperatures. Our findings suggest that a reinforcing interaction (or positive feedback) between clouds and ocean surface temperature can strongly contribute to significant and difficult-to-predict changes in marine climate.
Atmospheric rivers (ARs) are global phenomena that transport water vapor horizontally and are associated with hydrological extremes. In this study, the Atmospheric River Skill (ATRISK) algorithm is introduced, which quantifies AR prediction skill in an object-based framework using Subseasonal to Seasonal (S2S) Project global hindcast data from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The dependence of AR forecast skill is globally characterized by season, lead time, and distance between observed and forecasted ARs. Mean values of daily AR prediction skill saturate around 7–10 days, and seasonal variations are highest over the Northern Hemispheric ocean basins, where AR prediction skill increases by 15%–20% at a 7-day lead during boreal winter relative to boreal summer. AR hit and false alarm rates are explicitly considered using relative operating characteristic (ROC) curves. This analysis reveals that AR forecast utility increases at 10-day lead over the North Pacific/western U.S. region during positive El Niño–Southern Oscillation (ENSO) conditions and at 7- and 10-day leads over the North Atlantic/U.K. region during negative Arctic Oscillation (AO) conditions and decreases at a 10-day lead over the North Pacific/western U.S. region during negative Pacific–North America (PNA) teleconnection conditions. Exceptionally large increases in AR forecast utility are found over the North Pacific/western United States at a 10-day lead during El Niño + positive PNA conditions and over the North Atlantic/United Kingdom at a 7-day lead during La Niña + negative PNA conditions. These results represent the first global assessment of AR prediction skill and highlight climate variability conditions that modulate regional AR forecast skill.
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