Anthropogenic Greenhouse Gas and Aerosol Contributions to Extreme Temperature Changes during 1951–2015

2021 ◽  
Vol 34 (3) ◽  
pp. 857-870
Author(s):  
Min-Gyu Seong ◽  
Seung-Ki Min ◽  
Yeon-Hee Kim ◽  
Xuebin Zhang ◽  
Ying Sun

AbstractThis study conducted a detection and attribution analysis of the observed global and regional changes in extreme temperatures during 1951–2015. HadEX3 observations were compared with multimodel simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6) using an optimal fingerprinting technique. Annual maximum daily maximum and minimum temperatures (TXx and TNx; warm extremes) and annual minimum daily maximum and minimum temperatures (TXn and TNn; cold extremes) over land were analyzed considering global, continental, and subcontinental scales. Response patterns (fingerprints) of extreme temperatures to anthropogenic (ANT), greenhouse gases (GHG), aerosols (AA), and natural (NAT) forcings were obtained from CMIP6 forced simulations. The internal variability ranges were estimated from preindustrial control simulations. A two-signal detection analysis where the observations are regressed onto ANT and NAT fingerprints simultaneously reveals that ANT signals are robustly detected in separation from NAT over global and all continental domains (North and South America, Europe, Asia, and Oceania) for most of the extreme indices. ANT signals are also detected over many subcontinental regions, particularly for warm extremes (more than 60% of 33 subregions). A three-signal detection analysis that considers GHG, AA, and NAT fingerprints simultaneously demonstrates that GHG signals are detected in isolation from other external forcings over global, continental, and several subcontinental domains especially for warm extremes, explaining most of the observed warming. Moreover, AA influences are detected for warm extremes over Europe and Asia, indicating significant offsetting cooling contributions. Overall, human influences are detected more frequently, compared to previous studies, particularly for cold extremes, due to the extended period and the improved spatial coverage of observations.

2021 ◽  
Author(s):  
Min-Gyu Seong ◽  
Seung-Ki Min ◽  
Yeon-Hee Kim ◽  
Xuebin Zhang ◽  
Ying Sun

<p>This study carried out an updated detection and attribution analysis of extreme temperature changes for 1951-2015. Four extreme temperature indices (warm extremes: annual maximum daily maximum/minimum temperatures; cold extremes: annual minimum daily maximum/minimum temperatures) were used considering global, continental (6 domains), and subcontinental (33 domains) scales. HadEX3 observations were compared with CMIP6 multi-model simulations using an optimal fingerprinting technique. Response patterns of extreme indices (fingerprints) to anthropogenic (ANT), greenhouse gas (GHG), anthropogenic aerosol (AA), and natural (NAT) forcings were estimated from corresponding CMIP6 forced simulations. Pre-industrial control simulations (CTL) were also used to estimate the internal variability. Results from two-signal detection analysis where the observations are simultaneously regressed onto ANT and NAT fingerprints reveal that ANT signals are robustly detected in separation from NAT in global and most continental regions for all extreme indices. At subcontinental scale, ANT detection occurs especially in warm extremes (more than 60% of regions). Results from three-signal detection analysis where observations are simultaneously regressed onto GHG, AA, and NAT fingerprints show that GHG signals are detected and separated from other external forcings over global, most continental, and several subcontinental (more than 60%) domains in warm extremes. In addition, AA influences are jointly detected in warm extremes over global, Europe and Asia. The detected GHG forcings are found to explain most of the observed warming while AA forcings contribute to the observed cooling for the early decades over globe, Europe, and Asia with a slight warming over Europe during recent decades. Overall, improved detection occurs compared to previous studies, especially in cold extremes, which is due to the use of extended period which increases signal-to-noise ratios.</p>


2017 ◽  
Vol 32 (3) ◽  
pp. 243-258 ◽  
Author(s):  
Melissa F. Colloff ◽  
Kimberley A. Wade ◽  
John T. Wixted ◽  
Elizabeth A. Maylor

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