Editing IPCC:AR6/WGI/Chapter-10 (section)

==== 10.3.2.2 Pseudo-global Warming Experiments ====

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Results from downscaling experiments often suffer from large-scale circulation biases in the driving global models such as misplaced storm tracks ( [[#10.3.3.4|Section 10.3.3.4]] ), while changes in atmospheric circulation are often uncertain owing to both climate response uncertainty ( [[#10.3.4.2|Section 10.3.4.2]] ) and internal variability ( [[#10.3.4.3|Section 10.3.4.3]] ). In a given application, if one can assume that changes in the regional climate are dominated by thermodynamic rather than by circulation changes, so-called pseudo-global warming (PGW) experiments ( [[#Schär--1996|Schär et al., 1996]] ) may be helpful in mitigating the effects of circulation biases, and to fix the large-scale circulation to present climate. In classical PGW experiments, boundary conditions for the downscaling are taken from reanalysis data, but modified according to the thermodynamic signals of climate change. The boundary conditions thus represent the sequence of observed weather, but with adjusted temperatures, humidity and atmospheric stability. Recent applications of PGW experiments include assessments of climate change in Japan ( [[#Adachi--2012|Adachi et al., 2012]] ; [[#Kawase--2012|Kawase et al., 2012]] , 2013), the Los Angeles area ( [[#Walton--2015|Walton et al., 2015]] ), Hawaii ( [[#Zhang--2016|]] [[#Zhang--2016|]] [[#Zhang--2016|C. Zhang et al., 2016]] ), and the Alps ( [[#Keller--2018|Keller et al., 2018]] ). Recently, PGW studies have been generalized to modify global model simulations with the objective of separating the drivers of regional climate change, such as the Mediterranean amplification (e.g., [[#Brogli--2019b|Brogli et al., 2019b]] ; [[#10.3.2.3|Section 10.3.2.3]] ).

Equivalent simulations can be conducted for individual events, thereby allowing for very high resolution. With counterfactual past climate conditions, such simulations can be used for conditional event attribution ( [[#Trenberth--2015|Trenberth et al., 2015]] ; Chapter 11), using hypothetical future conditions to generate physical climate storylines of how specific events may manifest in a warmer climate. The approach has been employed to study extreme events that require very high resolution simulations such as tropical cyclones ( [[#Lackmann--2015|Lackmann, 2015]] ; [[#Takayabu--2015|Takayabu et al., 2015]] ; [[#Lau--2016|Lau et al., 2016]] ; [[#Kanada--2017a|Kanada et al., 2017a]] ; [[#Gutmann--2018|Gutmann et al., 2018]] ; [[#Patricola--2018|Patricola and Wehner, 2018]] ; J. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] ) or convective precipitation events ( [[#Pall--2017|Pall et al., 2017]] ; [[#Hibino--2018|Hibino et al., 2018]] ). The range of possible events is broader and has included Korean heatwaves ( [[#Kim--2018|Kim et al., 2018]] ) and monsoon onset in West Africa ( [[#Lawal--2016|Lawal et al., 2016]] ). However, if only individual events are simulated, no immediate conclusions can be derived for changes to the occurrence probability of these events (F.E.L. [[#Otto--2016|]] [[#Otto--2016|Otto et al., 2016]] ; [[#Shepherd--2016a|Shepherd, 2016a]] ).

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