%0 Journal Article %1 dePerez2018Global %A de Perez, Erin Coughlan %A van Aalst, Maarten %A Bischiniotis, Konstantinos %A Mason, Simon %A Nissan, Hannah %A Pappenberger, Florian %A Stephens, Elisabeth %A Zsoter, Ervin %A van den Hurk, Bart %D 2018 %J Environmental Research Letters %K ensembles, extremes, forecasting, heat, %N 5 %P 054017 %T Global predictability of temperature extremes %U http://stacks.iop.org/1748-9326/13/i=5/a=054017 %V 13 %X 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.

@article{dePerez2018Global, abstract = {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.}, added-at = {2018-12-28T11:45:16.000+0100}, author = {de Perez, Erin Coughlan and van Aalst, Maarten and Bischiniotis, Konstantinos and Mason, Simon and Nissan, Hannah and Pappenberger, Florian and Stephens, Elisabeth and Zsoter, Ervin and van den Hurk, Bart}, biburl = {https://www.bibsonomy.org/bibtex/208ad4b192de8bd65f359cc77ff0cbc52/rutgerdankers}, interhash = {ad3f35f3333b086a3436307a36299dc1}, intrahash = {08ad4b192de8bd65f359cc77ff0cbc52}, journal = {Environmental Research Letters}, keywords = {ensembles, extremes, forecasting, heat,}, number = 5, pages = 054017, timestamp = {2018-12-28T11:45:16.000+0100}, title = {Global predictability of temperature extremes}, url = {http://stacks.iop.org/1748-9326/13/i=5/a=054017}, volume = 13, year = 2018 }