Editing IPCC:Wg1:Chapter:Chapter-1-comments (section)

==== 1.5.4.1 Model ‘Fitness-for-Purpose’ ====

<div id="h3-33-siblings" class="h3-siblings"></div>

A key issue addressed in this Report is whether climate models are adequate or ‘fit’ for purposes of interest, that is, whether they can be used to successfully answer particular research questions, especially about the causes of recent climate change and the future evolution of climate (e.g., [[#Parker--2009|Parker, 2009]] ; [[#Notz--2015|Notz, 2015]] ; [[#Knutti--2018|Knutti, 2018]] ; [[#Winsberg--2018|Winsberg, 2018]] ). Assessment of a model’s fitness-for-purpose can be informed both by how the model represents relevant physical processes and by relevant performance metrics ( [[#Baumberger--2017|Baumberger et al., 2017]] ; [[#Parker--2020|Parker, 2020]] ). The processes and metrics that are most relevant can vary with the question of interest. For example, a question about changes in deep-ocean circulation compared with a question about changes in regional precipitation ( [[#Notz--2015|Notz, 2015]] ; [[#Gramelsberger--2020|Gramelsberger et al., 2020]] ). New model-evaluation tools ( [[#1.5.4.5|Section 1.5.4.5]] ) and emergent constraint methodologies ( [[#1.5.4.7|Section 1.5.4.7]] ) can also aid the assessment of fitness-for-purpose, especially in conjunction with process understanding ( [[#Klein--2015|Klein and Hall, 2015]] ; [[#Knutti--2018|Knutti, 2018]] ). The broader availability of large model ensembles may allow for novel tests of fitness that better account for natural climate variability ( [[#1.5.4.2|Section 1.5.4.2]] ). Fitness-for-purpose of models used in this Report is discussed in [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] ( [[IPCC:Wg1:Chapter:Chapter-3#3.8.4|Section 3.8.4]] ) for the global scale, in [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-10 Chapter 10] (Section 10.3) for regional climate, and in the other chapters for the process level.

Typical strategies for enhancing the fitness-for-purpose of a model include increasing resolution in order to explicitly simulate key processes, improving relevant parameterizations, and careful tuning. Changes to a model that enhance its fitness for one purpose can sometimes decrease its fitness for others, by upsetting a pre-existing balance of approximations. When it is unclear whether a model is fit for a purpose of interest, there is often a closely related purpose for which the evidence of fitness is clearer. For example, it might be unclear whether a model is fit for providing highly accurate projections of precipitation changes in a region, but reasonable to think that the model is fit for providing projections of precipitation changes that cannot yet be ruled out ( [[#Parker--2009|Parker, 2009]] ). Such information about plausible or credible changes can be useful to inform adaptation. Note that challenges associated with assessing models’ fitness-for-purpose need not prevent reaching conclusions with high confidence if there are multiple other lines of evidence supporting those same conclusions.

<div id="1.5.4.2" class="h3-container"></div>

<span id="ensemble-modelling-techniques"></span>