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

==== 10.3.3.2 Model Improvement and Added Value ====

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Obtaining regional information from global simulations may involve a range of different methods ( [[#10.3.1|Section 10.3.1]] ). An approach with higher complexity or resolution is useful if it adds further, useful information to that of a reference model. [[#10.5|Section 10.5]] discusses the set of considerations that determine if the information is useful. This further useful information is often referred to as added value and is a function of variables, processes, and the temporal and spatial scales targeted taking into account the needs of specific users ( [[#Di%20Luca--2012|Di Luca et al., 2012]] ; [[#Ekström--2015|Ekström et al., 2015]] ; [[#Giorgi--2015|Giorgi and Gutowski, 2015]] ; [[#Torma--2015|Torma et al., 2015]] ; [[#Rummukainen--2016|Rummukainen, 2016]] ; [[#Falco--2019|Falco et al., 2019]] ). There is no common definition of added value, but here it is considered a characteristic that arises when one methodology gives further value to what another methodology yields.

Downscaling is expected to improve the representation of a region’s climate compared to the driving global model ( [[#Di%20Luca--2015|Di Luca et al., 2015]] ). Arguably, there should be a clear physical reason for the improvement, which is applicable to the evaluation of added value in downscaled projections ( [[#Giorgi--2016|Giorgi et al., 2016]] ). The added value depends on the region, season, and governing physical processes ( [[#Lenz--2017|Lenz et al., 2017]] ; [[#Schaaf--2018|Schaaf and Feser, 2018]] ). Thus, added value of downscaling global model simulations is most likely where regional- and local-scale processes play an important role in a region’s climate, for example in complex or heterogeneous terrain such as mountains ( [[#Lee--2014|Lee and Hong, 2014]] ; [[#Prein--2016b|Prein et al., 2016b]] ), urban areas ( [[#Argüeso--2014|Argüeso et al., 2014]] ), along coastlines ( [[#Feser--2011|Feser et al., 2011]] ; [[#Herrmann--2011|Herrmann et al., 2011]] ; [[#Bozkurt--2019|Bozkurt et al., 2019]] ), or where convective processes are important ( [[#Prein--2015|Prein et al., 2015]] ). Examples of model improvements and added value are given in the following subsections and the Atlas.

A first step in determining added value in downscaling is to analyse whether the downscaling procedure gives detail on spatial or temporal scales not well-resolved by a global model, thus potentially representing climatic features missing in the GCM. This added detail, referred to as potential added value (PAV; [[#Di%20Luca--2012|Di Luca et al., 2012]] ), is insufficient for demonstrating added value in downscaling ( [[#Takayabu--2016|Takayabu et al., 2016]] ), but lack of PAV indicates that the downscaling method lacks usefulness. Added value is not guaranteed simply by producing model output at finer resolution. It depends on several factors, such as the simulation setup and the specific climatic variables analysed ( [[#Di%20Luca--2012|Di Luca et al., 2012]] ; [[#Hong--2014|Hong and Kanamitsu, 2014]] ; [[#Xue--2014|Xue et al., 2014]] ). A variety of performance measures are needed to assess added value ( [[#10.3.3.1|Section 10.3.3.1]] ; [[#Di%20Luca--2016|Di Luca et al., 2016]] ; [[#Wilks--2016|Wilks, 2016]] ; [[#Ivanov--2017|Ivanov et al., 2017]] , 2018; [[#Soares--2018|Soares and Cardoso, 2018]] ).

A further challenge, especially at increasingly higher resolutions, is that adequate observational data may not be available to assess added value ( [[#10.2|Section 10.2]] , e.g., [[#Di%20Luca--2016|Di Luca et al., 2016]] ; [[#Zittis--2017|Zittis et al., 2017]] ; [[#Bozkurt--2019|Bozkurt et al., 2019]] ). This implies a need for additional efforts to obtain, catalogue and quality-control higher resolution observational (or observation-based) datasets ( [[#Thorne--2017|Thorne et al., 2017]] ; [[#10.2|Section 10.2]] ). Univariate demonstration of added value is necessary, but may be insufficient, as better agreement with observations in the downscaled variable may be a consequence of compensating errors that are not guaranteed to compensate similarly as climate changes. Multivariate analysis of added value is better able to demonstrate physical consistency between observed and simulated behaviour ( [[#Prein--2013a|Prein et al., 2013a]] ; [[#Meredith--2015a|Meredith et al., 2015a]] ; [[#Reboita--2018|Reboita et al., 2018]] ).

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