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Global predictability of temperature extremes

Extreme temperatures are one of the leading causes of death and disease in both developed and developing countries, and heat extremes are projected to rise in many regions. To reduce risk, heatwave plans and cold weather plans have been effectively implemented around the world. However, much of the world's population is not yet protected by such systems, including many data-scarce but also highly vulnerable regions. In this study, we assess at a global level where such systems have the potential to be effective at reducing risk from temperature extremes, characterizing (1) long-term average occurrence of heatwaves and coldwaves, (2) seasonality of these extremes, and (3) short-term predictability of these extreme events three to ten days in advance. Using both the NOAA and ECMWF weather forecast models, we develop global maps indicating a first approximation of the locations that are likely to benefit from the development of seasonal preparedness plans and/or short-term early warning systems for extreme temperature. The extratropics generally show both short-term skill as well as strong seasonality; in the tropics, most locations do also demonstrate one or both. In fact, almost 5 billion people live in regions that have seasonality and predictability of heatwaves and/or coldwaves. Climate adaptation investments in these regions can take advantage of seasonality and predictability to reduce risks to vulnerable populations.
- University of Chicago United States
- University of Amsterdam Netherlands
- Vrije Universiteit Amsterdam Netherlands
- University of Reading United Kingdom
- European Centre for Medium-Range Weather Forecasts United Kingdom
550, 330, Science, QC1-999, 910, extremes, Environmental technology. Sanitary engineering, Weather forecasting, Environmental Science(all), Heat waves (Meteorology), SDG 13 - Climate Action, Climatic changes--Health aspects, GE1-350, Renewable Energy, climate, TD1-1066, Sustainability and the Environment, Physics, Q, Environmental and Occupational Health, forecast verification, cold, preparedness, Environmental sciences, climate risk management, Cold waves (Meteorology), Public Health, heat
550, 330, Science, QC1-999, 910, extremes, Environmental technology. Sanitary engineering, Weather forecasting, Environmental Science(all), Heat waves (Meteorology), SDG 13 - Climate Action, Climatic changes--Health aspects, GE1-350, Renewable Energy, climate, TD1-1066, Sustainability and the Environment, Physics, Q, Environmental and Occupational Health, forecast verification, cold, preparedness, Environmental sciences, climate risk management, Cold waves (Meteorology), Public Health, heat
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).43 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
