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

=== 10.3.2 Types of Model Experiments ===

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The most commonly used model experiments to generate regional climate information are transient simulations. Alternative experiment types serve specific purposes. The role of these experiment types for generating regional climate information is assessed in this subsection.

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==== 10.3.2.1 Transient Simulations and Time-slice Experiments ====

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Transient simulations intend to represent the evolving climate state of the Earth system (Chapter 4). They are typically based on coupled global model simulations, such as those in the Diagnostic, Evaluation and Characterization of Klima (DECK) and ScenarioMIP part of CMIP6 covering the period 1850–2100 ( [[#Eyring--2016a|Eyring et al., 2016a]] ), and HighResMIP (1950–2050; [[#Haarsma--2016|Haarsma et al., 2016]] ). Global transient climate simulations may be further downscaled by either dynamical or statistical downscaling. Currently available CORDEX RCM simulations (1950–2100) are based on CMIP5 ( [[#Gutowski%20Jr.--2016|Gutowski Jr. et al., 2016]] ).

In contrast, time-slice experiments are designed to represent only a specific period of time (typically 30 years). They are often run using global and regional models in atmosphere-only mode, forced by SSTs derived either from observations, as AMIP experiments, or from historical simulations and future projections of coupled global models. Compared to transient simulations, they offer advantages in being computationally cheaper (due to the lack of coupled ocean and short duration), which allows for the number of ensemble members(T. [[#Zhang--2016|]] [[#Zhang--2016|]] [[#Zhang--2016|Zhang et al., 2016]] ), and/or the resolution ( [[#Haarsma--2013b|Haarsma et al., 2013b]] ; [[#Davini--2017|Davini et al., 2017]] ) to be increased. Convection-permitting simulations, both covering the globe or particular regions, are currently conducted for short time slices only ( [[#Kendon--2017|Kendon et al., 2017]] ; [[#Hewitt--2018|Hewitt and Lowe, 2018]] ; [[#Coppola--2020|Coppola et al., 2020]] ; [[#Pichelli--2021|Pichelli et al., 2021]] ). Another high-resolution time-slice data base is d4PDF ( [[#Mizuta--2017|Mizuta et al., 2017]] ; [[#Ishii--2020|Ishii and Mori, 2020]] ). Experiments covering a limited integration period have been carried out for coupled ocean–atmosphere RCMs ( [[#Sein--2015|Sein et al., 2015]] ; [[#Zou--2016b|Zou and Zhou, 2016b]] , 2017). However, long spin-up periods are required to reach a stable stationary state in the deep ocean that otherwise might lead to invalid projections ( [[#Planton--2012|Planton et al., 2012]] ; [[#Soto-Navarro--2020|Soto-Navarro et al., 2020]] ).

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==== 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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==== 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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==== 10.3.2.4 Control Simulations ====

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In recent years, the role of internal variability in the interpretation of climate projections has become clearer, particularly at the regional scale ( [[#10.3.4.3|Section 10.3.4.3]] ). A considerable fraction of CMIP5 and CMIP6 resources has been invested in generating an ensemble of centennial or multi-centennial control simulations with constant external forcings ( [[#Pedro--2016|Pedro et al., 2016]] ; [[#Rackow--2018|Rackow et al., 2018]] ). As part ofthe CMIP6 DECK ( [[#Eyring--2016a|Eyring et al., 2016a]] ) pre-industrial control (piControl) simulations have been conducted ( [[#Menary--2018|Menary et al., 2018]] ). Similarly, control simulations with present-day conditions (pdControl) have been performed to represent internal variability under more recent forcing conditions ( [[#Pedro--2016|Pedro et al., 2016]] ; [[#Williams--2018|Williams et al., 2018]] ). Control simulations have been used to study the role of internal variability, teleconnections and many other fundamental aspects of climate models (Z. [[#Wang--2015|]] [[#Wang--2015|Wang et al., 2015]] ; [[#Krishnamurthy--2016|Krishnamurthy and Krishnamurthy, 2016]] ). Control simulations are also used along with large ensembles of historical or scenario simulations to assess the characteristics of the regional internal climate variability ( [[#Olonscheck--2017|Olonscheck and Notz, 2017]] ).

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==== 10.3.2.5 Simulations for Evaluating Downscaling Methods ====

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Experiments driven by quasi-perfect boundary conditions or predictors (observations or reanalysis) can be useful to evaluate downscaling performance ( [[#Frei--2003|Frei et al., 2003]] ; [[#Laprise--2013|Laprise et al., 2013]] ), including the simulation of observed past trends ( [[#Lorenz--2010|Lorenz and Jacob, 2010]] ; [[#Zubler--2011|Zubler et al., 2011]] ; [[#Nabat--2014|Nabat et al., 2014]] ; [[#Gutiérrez--2018|Gutiérrez et al., 2018]] ; [[#Drugé--2019|Drugé et al., 2019]] ; [[#Bozkurt--2020|Bozkurt et al., 2020]] ) and the added value of downscaling compared to the reanalysis fields ( [[#10.3.3.2|Section 10.3.3.2]] ). Although the reanalysis model itself can introduce biases especially for non-assimilated variables (such as precipitation) it is assumed that in such a setting, discrepancies between the modelled and observed climate arise mostly from errors in the downscaling method ( [[#Laprise--2013|Laprise et al., 2013]] ) or internal climate variability generated by the downscaling method ( [[#Böhnisch--2020|Böhnisch et al., 2020]] ; [[#Ehmele--2020|Ehmele et al., 2020]] ). Since AR5, reanalysis-driven RCMs have been extensively evaluated for many regions, especially in the CORDEX framework (see region specific examples in the Atlas).

Over Europe, the VALUE initiative assessed statistical downscaling for marginal, temporal, and spatial aspects of temperature and precipitation including extremes, and performed a process-based evaluation of specific climatic phenomena (Gutiérrezet al., 2019; [[#Maraun--2019a|Maraun et al., 2019a]] ). Alternatively, statistical downscaling can be evaluated in so-called perfect model or pseudo-reality simulations ( [[#Charles--1999|Charles et al., 1999]] ), where a high-resolution climate model simulation is used as a proxy for a hypothetical present and future realities. A statistical downscaling model is first calibrated with this pseudo present-day climate and, subsequently, assessed whether it correctly reproduces the pseudo-future conditions ( [[#Dixon--2016|Dixon et al., 2016]] ).

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