Attribution of Extreme Weather Events in the Context of Climate Change
Author | : National Academies of Sciences, Engineering, and Medicine |
Publisher | : National Academies Press |
Total Pages | : 187 |
Release | : 2016-07-28 |
ISBN-13 | : 9780309380973 |
ISBN-10 | : 0309380979 |
Rating | : 4/5 (79 Downloads) |
Download or read book Attribution of Extreme Weather Events in the Context of Climate Change written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2016-07-28 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: As climate has warmed over recent years, a new pattern of more frequent and more intense weather events has unfolded across the globe. Climate models simulate such changes in extreme events, and some of the reasons for the changes are well understood. Warming increases the likelihood of extremely hot days and nights, favors increased atmospheric moisture that may result in more frequent heavy rainfall and snowfall, and leads to evaporation that can exacerbate droughts. Even with evidence of these broad trends, scientists cautioned in the past that individual weather events couldn't be attributed to climate change. Now, with advances in understanding the climate science behind extreme events and the science of extreme event attribution, such blanket statements may not be accurate. The relatively young science of extreme event attribution seeks to tease out the influence of human-cause climate change from other factors, such as natural sources of variability like El Niño, as contributors to individual extreme events. Event attribution can answer questions about how much climate change influenced the probability or intensity of a specific type of weather event. As event attribution capabilities improve, they could help inform choices about assessing and managing risk, and in guiding climate adaptation strategies. This report examines the current state of science of extreme weather attribution, and identifies ways to move the science forward to improve attribution capabilities.