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WIREs Clim Change
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Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles

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Abstract In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distinguishes three general sources of uncertainty: (1) uncertainty that arises during the generation of the dataset; (2) uncertainty due to biased samples; and (3) uncertainty that arises due to the choice of abstract properties, such as resolution and metric. Based on this framework, we identify four different types of dataset ensembles—parametric, structural, resampling, and property ensembles—as tools to understand and assess uncertainties arising from the use of datasets for a specific purpose. We advocate for a more systematic generation of dataset ensembles by using these sorts of tools. Finally, we discuss the use of dataset ensembles in climate model evaluation. We argue that a more systematic understanding and assessment of dataset uncertainty is needed to allow for a more reliable uncertainty assessment in the context of model evaluation. The more systematic use of such a framework would be beneficial for both scientific reasoning and scientific policy advice based on climate datasets. This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change
Framework of observational dataset uncertainty depicting three general sources of dataset uncertainty: Uncertainty concerning the accuracy of measurement (1a) and processing procedures (1b); uncertainty concerning the representativeness of a measurement sample (2); and uncertainty concerning the adequacy of abstract properties for a certain purpose (3). The depicted separation and linearity of three sources is a simplification. In practice, these sources are much more complex and mutually dependent. For example, learnings from past applications of a dataset feedback into new processing procedures. Furthermore, this adequacy‐for‐purpose evaluation needs to be done each time a dataset is used for a specific purpose by the user
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Variations at different locations of the framework lead to a dataset ensemble that helps to assess different sources of uncertainty. These variations concern (i) the parameter values, (ii) the model structures, (iii) the measurement sample, and (iv) the dataset properties. The choices made need to be evaluated in terms of their representational accuracy if they concern the first three points and overall adequacy‐for‐purpose, which concerns all four points
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