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

==== 10.3.2.3 Sensitivity Studies With Selected Drivers ====

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Sensitivity studies are used to identify the impact of a specific forcing, driver or process on regional climate phenomena and changes and improve the process understanding. The influence of a single external forcing can be assessed with transient historical simulations within two different frameworks ( [[#Bindoff--2013|Bindoff et al., 2013]] ; [[#Gillett--2016|Gillett et al., 2016]] ). The first entails simulations taking prescribed (often observed) changes only in the external forcing of interest, the others being fixed at a constant value (often pre-industrial). The second framework is based on simulations in which all external forcings are applied other than the one of interest. Both approaches may not give the same results since the climate response to a range of forcings is not necessarily equal to the sum of climate responses to individual forcings ( [[#Ming--2011|Ming and Ramaswamy, 2011]] ; [[#Jones--2013|Jones et al., 2013]] ; [[#Schaller--2013|Schaller et al., 2013]] ; [[#Shiogama--2013|Shiogama et al., 2013]] ; [[#Marvel--2015|Marvel et al., 2015]] ; [[#Deng--2020|Deng et al., 2020]] ).

To study the influence of internal variability, new approaches such as partial coupling simulations are now routinely used since AR5. These are coupled ocean–atmosphere simulations in which the interaction between atmosphere and ocean is only one-way over a specified ocean basin or sub-basin and two-way everywhere else. Different implementations have been used such as SST anomaly Newtonian relaxation at the air–sea interface or prescription of wind-stress anomalies from reanalysis ( [[#Kosaka--2013|Kosaka and Xie, 2013]] , 2016; [[#England--2014|England et al., 2014]] ; [[#McGregor--2014|McGregor et al., 2014]] ; [[#Douville--2015|Douville et al., 2015]] ; [[#Deser--2017a|Deser et al., 2017a]] ). Such simulations have been applied to identify the regional impacts of the Pacific Decadal Variability (PDV) and Atlantic Multi-decadal Variability (AMV) ( [[#Kosaka--2013|Kosaka and Xie, 2013]] ; [[#Watanabe--2014|Watanabe et al., 2014]] ; [[#Delworth--2015|Delworth et al., 2015]] ; [[#Boer--2016|Boer et al., 2016]] ; [[#Ruprich-Robert--2017|Ruprich-Robert et al., 2017]] , 2018).

Nudging experiments have been used to identify the relative roles of dynamic and thermodynamic processes in climate model biases and specific extreme events ( [[#Wehrli--2018|Wehrli et al., 2018]] , 2019). Another related framework is used to evaluate the impact land conditions have on a climate phenomenon in a pair of experiments with one simulation serving as control run, and a perturbed simulation with prescribed land conditions (i.e., soil moisture, leaf area index, or surface albedo) characterizing a specific state of the land surface (i.e., afforestation or deforestation). The difference between the perturbed and control simulations enables a robust assessment of the possible impact of land conditions on events like droughts and heatwaves ( [[#Seneviratne--2013|Seneviratne et al., 2013]] ; [[#Stegehuis--2015|Stegehuis et al., 2015]] ; [[#Hauser--2016|Hauser et al., 2016]] , 2017; [[#van%20den%20Hurk--2016|van den Hurk et al., 2016]] ; [[#Vogel--2017|Vogel et al., 2017]] ; [[#Rasmijn--2018|Rasmijn et al., 2018]] ; [[#Strandberg--2019|Strandberg and Kjellström, 2019]] ).

RCM sensitivity simulations have been used in a similar way to assess the contribution of external forcings and large-scale drivers to projected regional climate change ( [[#Nabat--2014|Nabat et al., 2014]] ; [[#Brogli--2019a|Brogli et al., 2019a]] , b) and the influence of selected drivers on observed extreme events ( [[#Meredith--2015b|Meredith et al., 2015b]] ; J. [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] ; [[#Ardilouze--2019|Ardilouze et al., 2019]] ).

In summary, there is ''robust evidence'' that sensitivity experiments are key to assessing the influence of different forcings and drivers on regional climate change.

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