An open workflow to gain insights about low‐likelihood high‐impact weather events from initialized predictions

Low-likelihood weather events can cause dramatic impacts, especially when they are unprecedented. In 2020, amongst other high-impact weather events, UK floods caused more than £300 million damage, prolonged heat over Siberia led to infrastructure failure and permafrost thawing, while wildfires ravaged California. Such rare phenomena cannot be studied well from historical records or reanalysis data. One way to improve our awareness is to exploit ensemble prediction systems, which represent large samples of simulated weather events. This ‘UNSEEN’ method has been successfully applied in several scientific studies, but uptake is hindered by large data and processing requirements, and by uncertainty regarding the credibility of the simulations. Here, we provide a protocol to apply and ensure credibility of UNSEEN for studying low-likelihood high-impact weather events globally, including an open workflow based on Copernicus Climate Change Services (C3S) seasonal predictions. Demonstrating the workflow using European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5, we find that the 2020 March–May Siberian heatwave was predicted by one of the ensemble members; and that the record-shattering August 2020 California-Mexico temperatures were part of a strong increasing trend. However, each of the case studies exposes challenges with respect to the credibility of UNSEEN and the sensitivity of the outcomes to user decisions. We conclude that UNSEEN can provide new insights about low-likelihood weather events when the decisions are transparent, and the challenges and sensitivities are acknowledged. Anticipating plausible low-likelihood extreme events and uncovering unforeseen hazards under a changing climate warrants further research at the science-policy interface to manage high impacts.</p

Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times

Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting in Ireland, we benchmark the skill of Ensemble Streamflow Prediction (ESP) for a diverse sample of 46 catchments using the GR4J hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 day to 12 months for each of 12 initialisation months, January to December. Our results show that ESP is skilful against a probabilistic climatology benchmark in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. Mean ESP skill was found to decay rapidly as a function of lead time, with continuous ranked probability skill scores (CRPSS) of 0.8 (1 day), 0.32 (2-week), 0.18 (1-month), 0.05 (3-month), and 0.01 (12-month). Forecasts were generally more skilful when initialised in summer than other seasons. A strong correlation (ρ = 0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and East, being those where slowly responding, high storage catchments are located. Forecast reliability and discrimination were also assessed with respect to low and high flow events. In addition to our benchmarking experiment, we conditioned ESP with the winter North Atlantic Oscillation (NAO) using adjusted hindcasts from the Met Office’s Global Seasonal Forecasting System version 5. We found gains in winter forecast skill (CRPSS) of 7–18% were possible over lead times of 1 to 3 months, and that improved reliability and discrimination make NAO-conditioned ESP particularly effective at forecasting dry winters, a critical season for water resources management. We conclude that ESP is skilful in a number of different contexts and thus should be operationalised in Ireland given its potential benefits for water managers and other stakeholders.</div

Disruption of emergency response to vulnerable populations during floods

Emergency responders must reach urgent cases within mandatory timeframes, regardless of weather conditions. However, flooding of transport networks can add critical minutes to travel times between dispatch and arrival. Here, we explicitly model the spatial coverage of all Ambulance Service and Fire and Rescue Service stations in England during flooding of varying severity under compliant response times. We show that even low-magnitude floods can lead to a reduction in national-level compliance with mandatory response times and this reduction can be even more dramatic in some urban agglomerations, making the effectiveness of the emergency response particularly sensitive to the expected impacts of future increases in extreme rainfall and flood risk. Underpinning this sensitivity are policies leading to the centralization of the Ambulance Service and the decentralization of the Fire and Rescue Service. The results provide opportunities to identify hotspots of vulnerability (such as care homes, sheltered accommodation, nurseries and schools) for optimizing the distribution of response stations and developing contingency plans for stranded sites.<br