A Multivariate Conditional Probability Ratio Framework for the Detection and Attribution of Compound Climate Extremes

Author(s):  
Felicia Chiang ◽  
Peter Greve ◽  
Omid Mazdiyasni ◽  
Yoshihide Wada ◽  
Amir AghaKouchak
2006 ◽  
Vol 19 (20) ◽  
pp. 5058-5077 ◽  
Author(s):  
Gabriele C. Hegerl ◽  
Thomas R. Karl ◽  
Myles Allen ◽  
Nathaniel L. Bindoff ◽  
Nathan Gillett ◽  
...  

Abstract A significant influence of anthropogenic forcing has been detected in global- and continental-scale surface temperature, temperature of the free atmosphere, and global ocean heat uptake. This paper reviews outstanding issues in the detection of climate change and attribution to causes. The detection of changes in variables other than temperature, on regional scales and in climate extremes, is important for evaluating model simulations of changes in societally relevant scales and variables. For example, sea level pressure changes are detectable but are significantly stronger in observations than the changes simulated in climate models, raising questions about simulated changes in climate dynamics. Application of detection and attribution methods to ocean data focusing not only on heat storage but also on the penetration of the anthropogenic signal into the ocean interior, and its effect on global water masses, helps to increase confidence in simulated large-scale changes in the ocean. To evaluate climate change signals with smaller spatial and temporal scales, improved and more densely sampled data are needed in both the atmosphere and ocean. Also, the problem of how model-simulated climate extremes can be compared to station-based observations needs to be addressed.


2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Karin van der Wiel ◽  
Richard Bintanja

AbstractThe frequency of climate extremes will change in response to shifts in both mean climate and climate variability. These individual contributions, and thus the fundamental mechanisms behind changes in climate extremes, remain largely unknown. Here we apply the probability ratio concept in large-ensemble climate simulations to attribute changes in extreme events to either changes in mean climate or climate variability. We show that increased occurrence of monthly high-temperature events is governed by a warming mean climate. In contrast, future changes in monthly heavy-precipitation events depend to a considerable degree on trends in climate variability. Spatial variations are substantial however, highlighting the relevance of regional processes. The contributions of mean and variability to the probability ratio are largely independent of event threshold, magnitude of warming and climate model. Hence projections of temperature extremes are more robust than those of precipitation extremes, since the mean climate is better understood than climate variability.


2016 ◽  
Vol 11 ◽  
pp. 17-27 ◽  
Author(s):  
David R. Easterling ◽  
Kenneth E. Kunkel ◽  
Michael F. Wehner ◽  
Liqiang Sun

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