@article{a5b4fd2c18f64a5ea2baa6d38e5e5ba7,
title = "Projected timing of perceivable changes in climate extremes for terrestrial and marine ecosystems",
abstract = "Human and natural systems have adapted to and evolved within historical climatic conditions. Anthropogenic climate change has the potential to alter these conditions such that onset of unprecedented climatic extremes will outpace evolutionary and adaptive capabilities. To assess whether and when future climate extremes exceed their historical windows of variability within impact-relevant socioeconomic, geopolitical, and ecological domains, we investigate the timing of perceivable changes (time of emergence; TOE) for 18 magnitude-, frequency-, and severity-based extreme temperature (10) and precipitation (8) indices using both multimodel and single-model multirealization ensembles. Under a high-emission scenario, we find that the signal of frequency- and severity-based temperature extremes is projected to rise above historical noise earliest in midlatitudes, whereas magnitude-based temperature extremes emerge first in low and high latitudes. Precipitation extremes demonstrate different emergence patterns, with severity-based indices first emerging over midlatitudes, and magnitude- and frequency-based indices emerging earliest in low and high latitudes. Applied to impact-relevant domains, simulated TOE patterns suggest (a) unprecedented consecutive dry day occurrence in >50% of 14 terrestrial biomes and 12 marine realms prior to 2100, (b) earlier perceivable changes in climate extremes in countries with lower per capita GDP, and (c) emergence of severe and frequent heat extremes well-before 2030 for the 590 most populous urban centers. Elucidating extreme-metric and domain-type TOE heterogeneities highlights the challenges adaptation planners face in confronting the consequences of elevated twenty-first century radiative forcing.",
keywords = "climate change, climate extremes, marine realms, terrestrial biomes, time of emergence",
author = "Xuezhi Tan and Gan, {Thian Yew} and Horton, {Daniel E.}",
note = "Funding Information: We thank the World Climate Research Programme and the National Center for Atmospheric Research for making their data public available. The first author was partly funded by the China Scholarship Council (CSC) of P.R. China and the University of Alberta. This research is supported by Natural Science and Engineering Research Council of Canada (155440/2012). Extreme climate data derived from the multimodel ensemble (CMIP5) was downloaded from the ETCCDI indices archive (http://www.cccma.ec.gc.ca/data/climdex/). The climate data for calculating ETCCDI indices from the single-model ensemble (CESM-LE) was downloaded from the Large Ensemble Community Project (http://www.cesm.ucar.edu/projects/community-projects/LENS/). Maps of World Wildlife Fund (WWF) terrestrial ecoregions and marine ecoregions were downloaded from http://www.worldwildlife.org/pages/conservation-science-data-and-tools. Maps for urban populations of the largest 590 cities of the world were obtained from the archive of Demographia World Urban Areas (http://www.demographia.com/), and maps of GDP per person for all countries of the world in 2010 were obtained from the World Bank (http://data.worldbank.org/). All codes for producing the analysis results and all data supporting the findings of this study are available from the corresponding author upon reasonable request. Funding Information: We thank the World Climate Research Programme and the National Center for Atmospheric Research for making their data public available. The first author was partly funded by the China Scholarship Council (CSC) of P.R. China and the University of Alberta. This research is supported by Natural Science and Engineering Research Council of Canada (155440/2012). Extreme climate data derived from the multimodel ensemble (CMIP5) was downloaded from the ETCCDI indices archive (http://www.cccma.ec.gc.ca/data/climdex/). The climate data for calculating ETCCDI indices from the single-model ensemble (CESM-LE) was downloaded from the Large Ensemble Community Project (http://www.cesm.ucar.edu/projects/community-projects/LENS/). Maps of World Wildlife Fund (WWF) terrestrial ecoregions and marine ecoregions were downloaded from http://www.worldwildlife.org/pages/conservation-science-data-and-tools. Maps for urban populations of the largest 590 cities of the world were obtained from the archive of Demographia World Urban Areas (http://www.demographia.com/), and maps of GDP per person for all countries of the world in 2010 were obtained from the World Bank (http://data.worldbank.org/). All codes for producing the analysis results and all data supporting the findings of this study Publisher Copyright: {\textcopyright} 2018 John Wiley & Sons Ltd",
year = "2018",
month = oct,
doi = "10.1111/gcb.14329",
language = "English (US)",
volume = "24",
pages = "4696--4708",
journal = "Global Change Biology",
issn = "1354-1013",
publisher = "Wiley-Blackwell",
number = "10",
}