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

==== 10.2.2.1 Quality Control ====

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The usefulness of any observational dataset is conditioned by the availability and outcome of a quality control (QC) process. The objective of the QC is to verify that data are representative of the measured variable and to what degree the value could be contaminated by unrelated or conflicting factors ( [[#WMO--2017a|WMO, 2017a]] ). Data quality assessment is key for ensuring that the data are credible and to establish trusted relationships between the data provider and the users ( [[#Nightingale--2019|Nightingale et al., 2019]] ). QC is performed for all relevant global climate datasets (e.g., [[#Menne--2018|Menne et al., 2018]] ). For instance, QC informs users that old reanalysis datasets can be inconsistent in the long term because they assimilated inhomogeneous observations over the reanalyses period ( [[#Kobayashi--2015|Kobayashi et al., 2015]] ). As a consequence, the evaluation against independent observations suggests that reanalyses should not be automatically regarded as climate-quality products for monitoring long-term trends at the regional level ( [[#Manzanas--2014|Manzanas et al., 2014]] ; [[#Torralba--2017|Torralba et al., 2017]] ). QC needs to be systematically carried out by the institutions responsible for handling the data (e.g., [[#Cao--2016b|Cao et al., 2016b]] ).

The QC procedure depends strongly on the specific nature of the dataset. It focuses on aspects such as the correct identification of sensor, time and location, detection of unfeasible or inconsistent data, error estimation, assessment of the adequacy of the uncertainty information and the adequacy of the documentation (e.g., [[#Heaney--2016|Heaney et al., 2016]] ). QC principles also apply to model data ( [[#Tapiador--2017|Tapiador et al., 2017]] ). An important piece of information provided is the representativeness error ( [[#10.2.1.1|Section 10.2.1.1]] ; [[#Gervais--2014|Gervais et al., 2014]] ). When problems in the data representativeness are identified, observational datasets are provided with a quality mask ( [[#Contractor--2020|Contractor et al., 2020]] ), or the problematic dataare either removed or corrected ( [[#Ashcroft--2018|Ashcroft et al., 2018]] ). These are factors often taken into account in constructing regional climate information ( [[#Kotlarski--2019|Kotlarski et al., 2019]] ).

Quality-controlled data are now produced widely at the regional level, as in the case of sub-daily precipitation records in the United Kingdom ( [[#Blenkinsop--2017|Blenkinsop et al., 2017]] ) and the USA ( [[#Nelson--2016|Nelson et al., 2016]] ). However, many more datasets and variables lack the same level of scrutiny ( [[#Alexander--2016|Alexander, 2016]] ). Quality-controlled, high-resolution observational datasets are especially needed at regional and local scales to assess models as their resolution increases ( [[#Di%20Luca--2016|Di Luca et al., 2016]] ; [[#Zittis--2017|Zittis and Hadjinicolaou, 2017]] ), although the awareness and appropriate use of the QC information is challenging ( [[#Tapiador--2017|Tapiador et al., 2017]] ) when generating regional climate information ( ''high confidence'' ).

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