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

=== 10.2.4 Outlook for Improving Observational Data for Regional Climates ===

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An encouraging development for understanding climate variations over the past 250 years or so at the global and regional scale lies in the field of data rescue, in which hitherto hidden archives of meteorological data are brought to the forefront (Sections 1.5.1.1 and 2.5). Surface observations from data rescue projects may then be assimilated to derive long-term high-resolution gridded surface regional reanalysis ( [[#Devers--2020|Devers et al., 2020]] ). Global extended reanalyses such as 20CR ( [[#Compo--2011|Compo et al., 2011]] ), ERA-20C ( [[#Poli--2016a|Poli et al., 2016a]] , b) or CERA-20C ( [[#Laloyaux--2018|Laloyaux et al., 2018]] ) may be further downscaled to quantify the variability of past climate at the regional scale ( [[#Caillouet--2016|Caillouet et al., 2016]] , 2019).

One of the main scientific challenges related to high-resolution regional climate modelling is dealing with the representation of fine-scale processes (e.g., [[#Yano--2018|Yano et al., 2018]] ) in observational datasets. Additionally, reliable observation networks following WMO standards have a very sparse geographical representation. Hence, regional climate models have started to use high-resolution data combined with crowdsourced observations ( [[#Zheng--2018|Zheng et al., 2018]] ). Recent efforts have led to the production of homogeneously processed long-term datasets for regional climate model evaluation ( [[#Goudenhoofdt--2016|Goudenhoofdt and Delobbe, 2016]] ; [[#Humphrey--2017|Humphrey et al., 2017]] ; [[#Yang--2019|Yang and Ng, 2019]] ). While they are far less reliable and accurate than professional observations, crowdsourced data are abundantly available and can give spatial representations at very high resolution. This technological trend could prove very useful ( ''high confidence'' ), and the regional climate community is making efforts to understand the extent to which these data sources can be exploited, at least as a complement to traditional datasets ( [[#Overeem--2013|Overeem et al., 2013]] ; [[#Meier--2017|Meier et al., 2017]] ; [[#Uijlenhoet--2018|Uijlenhoet et al., 2018]] ; [[#de%20Vos--2019|de Vos et al., 2019]] ; [[#Langendijk--2019b|Langendijk et al., 2019b]] ).

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