TY - JOUR
T1 - Attribution of observed changes in extreme temperatures to anthropogenic forcing using CMIP6 models
AU - Engdaw, Mastawesha Misganaw
AU - Steiner, Andrea K.
AU - Hegerl, Gabriele C.
AU - Ballinger, Andrew P.
N1 - Funding Information:
This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change – Uncertainties, Thresholds and Coping Strategies). Andrew Ballinger was funded by the EUCP project funded by the European Commission's Horizon 2020 programme, Grant Agreement number 776613, and Gabriele Hegerl by the NERC grant ’EMERGENCE’, NE/S004645/1. The authors acknowledge the Berkeley Earth Surface Temperature (BEST), the European Centre for Medium-Range Weather Forecasts (ECMWF), National Oceanic Atmospheric Administration (NOAA) for making accessible the BEST, ERA5 and 20C data sets. BEST data are available at http://berkeleyearth.org/. The 20C, ERA5, JRA55 and HadEX3 data sets are available at https://psl.noaa.gov/data/20thC_Rean/, https://cds.climate.copernicus.eu/cdsapp#!/home, https://jra.kishou.go.jp/JRA-55/index_en.html, and https://www.metoffice.gov.uk/hadobs/hadex3/, respectively. We also acknowledge the climate modelling groups for making their respective simulations publicly available through the Center for Environmental Data Analysis (CEDA) node at https://esgf-index1.ceda.ac.uk/search/cmip6-ceda/. We also acknowledge Rafael de Abreu for making the Python package freely available at https://github.com/rafaelcabreu/attribution.
Funding Information:
This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change – Uncertainties, Thresholds and Coping Strategies). Andrew Ballinger was funded by the EUCP project funded by the European Commission’s Horizon 2020 programme , Grant Agreement number 776613 , and Gabriele Hegerl by the NERC grant ’EMERGENCE’, NE/S004645/1 . The authors acknowledge the Berkeley Earth Surface Temperature (BEST), the European Centre for Medium-Range Weather Forecasts (ECMWF), National Oceanic Atmospheric Administration (NOAA) for making accessible the BEST, ERA5 and 20C data sets. BEST data are available at http://berkeleyearth.org/ . The 20C, ERA5, JRA55 and HadEX3 data sets are available at https://psl.noaa.gov/data/20thC_Rean/ , https://cds.climate.copernicus.eu/cdsapp#!/home , https://jra.kishou.go.jp/JRA-55/index_en.html , and https://www.metoffice.gov.uk/hadobs/hadex3/ , respectively. We also acknowledge the climate modelling groups for making their respective simulations publicly available through the Center for Environmental Data Analysis (CEDA) node at https://esgf-index1.ceda.ac.uk/search/cmip6-ceda/ . We also acknowledge Rafael de Abreu for making the Python package freely available at https://github.com/rafaelcabreu/attribution .
Publisher Copyright:
© 2023 The Authors
PY - 2023/3/1
Y1 - 2023/3/1
N2 - Global warming has clearly affected the occurrence of extreme events in recent years. Here, we assess changes in the frequency of temperature extremes and their causes, using percentile-based indices. Cold extremes are defined as temperatures below the 10th percentile of daily minimum (TN10) and maximum (TX10) temperatures while hot extremes exceed the 90th percentile of daily minimum (TN90) and maximum (TX90) temperatures. We analyze Berkeley Earth Surface Temperature (BEST) for observed changes in the last four decades 1981-2020, for two extended seasons, boreal summer April–September (AMJJAS) and boreal winter October–March (ONDJFM), and evaluate results using several reanalysis data sets. For the attribution of causes we use CMIP6 climate model simulations, analyzing natural-only and anthropogenic-only forcings. We use an attribution method that accounts for climate modeling uncertainty in both amplitude and pattern of responses.The observations show detectable changes in both cold and hot extreme temperatures. Hot extremes have increased in all regions and in both seasons while cold extremes have decreased over the past decades. Our attribution analysis revealed anthropogenic forcings are robustly detectable and the main drivers of observed changes in all indices for all regions, consistently in all data sets. Contributions from natural forcings are found small and detectable only in a few regions mainly for daytime cold extremes in ONDJFM. Anthropogenic forcing contributed to an increase of 3.4 days per decade in TN90 and of 2.7 days per decade in TX90, on average, at the global scale. Regionally, the anthropogenic contribution caused a range of decrease of 2–4.7 days per decade in TN10, 1.5–3.6 days per decade in TX10 while it caused an increase of 2.2–4.8 days per decade for TN90 and 2–3.3 days per decade in TX90. Anthropogenic-only warming in ONDJFM is slightly less than in AMJJAS.
AB - Global warming has clearly affected the occurrence of extreme events in recent years. Here, we assess changes in the frequency of temperature extremes and their causes, using percentile-based indices. Cold extremes are defined as temperatures below the 10th percentile of daily minimum (TN10) and maximum (TX10) temperatures while hot extremes exceed the 90th percentile of daily minimum (TN90) and maximum (TX90) temperatures. We analyze Berkeley Earth Surface Temperature (BEST) for observed changes in the last four decades 1981-2020, for two extended seasons, boreal summer April–September (AMJJAS) and boreal winter October–March (ONDJFM), and evaluate results using several reanalysis data sets. For the attribution of causes we use CMIP6 climate model simulations, analyzing natural-only and anthropogenic-only forcings. We use an attribution method that accounts for climate modeling uncertainty in both amplitude and pattern of responses.The observations show detectable changes in both cold and hot extreme temperatures. Hot extremes have increased in all regions and in both seasons while cold extremes have decreased over the past decades. Our attribution analysis revealed anthropogenic forcings are robustly detectable and the main drivers of observed changes in all indices for all regions, consistently in all data sets. Contributions from natural forcings are found small and detectable only in a few regions mainly for daytime cold extremes in ONDJFM. Anthropogenic forcing contributed to an increase of 3.4 days per decade in TN90 and of 2.7 days per decade in TX90, on average, at the global scale. Regionally, the anthropogenic contribution caused a range of decrease of 2–4.7 days per decade in TN10, 1.5–3.6 days per decade in TX10 while it caused an increase of 2.2–4.8 days per decade for TN90 and 2–3.3 days per decade in TX90. Anthropogenic-only warming in ONDJFM is slightly less than in AMJJAS.
U2 - 10.1016/j.wace.2023.100548
DO - 10.1016/j.wace.2023.100548
M3 - Article
SN - 2212-0947
VL - 39
JO - Weather and Climate Extremes
JF - Weather and Climate Extremes
M1 - 100548
ER -