Editing IPCC:AR6/WGI/Chapter-2 (section)

====== Land domain ======

The GHCNMv4 dataset ( [[#Menne--2018|Menne et al., 2018]] ) includes many more land stations than GHCNMv3, arising from the databank efforts of [[#Rennie--2014|Rennie et al. (2014)]] , and calculates a 100-member parametric uncertainty ensemble drawing upon the benchmarking analysis of [[#Williams--2012|Williams et al. (2012)]] , as well as accounting for sampling effects. A new version of the CRUTEM dataset (CRUTEMv5, [[#Osborn--2021|Osborn et al., 2021]] ) has increased data completeness and additional quality control measures. A new global land dataset, the China Land Surface Air Temperature (CLSAT) dataset ( [[#Xu--2018|Xu et al., 2018]] ) has higher network density in some regions (particularly Asia) than previously existing datasets. Global trends derived from CLSAT are generally consistent with those derived from other land datasets through 2014 ( [[#Xu--2018|Xu et al., 2018]] ).

The AR5 identified diurnal temperature range (DTR) as a substantial knowledge gap. The most recent analysis of Thorne et al. (2016a, b) compared a broad range of gridded estimates of change in DTR, including a new estimate derived from the ISTI databank release using the pairwise homogenization algorithm used to create GHCNMv4, and estimates derived from [[#Vose--2005|Vose et al. (2005)]] , HadEX2 ( [[#Donat--2013a|Donat et al., 2013a]] ), HadGHCND ( [[#Donat--2013b|Donat et al., 2013b]] ), GHCNDEX ( [[#Donat--2013b|Donat et al., 2013b]] ), Berkeley Earth ( [[#Rohde--2013|Rohde et al., 2013]] ), and CRU TS ( [[#Harris--2014|Harris et al., 2014]] ). The analysis highlighted substantial ambiguity in pre-1950 estimates arising from sparse data availability. After 1950 estimates agreed that DTR had decreased globally with most of that decrease occurring over the period 1960–1980. A subsequent DTR analysis using CLSAT further confirmed this behaviour (X. [[#Sun--2018|]] [[#Sun--2018|Sun et al., 2018]] ).

No recent literature has emerged to alter the AR5 finding that it is ''unlikely'' that any uncorrected effects from urbanization (Box 10.3), or from changes in land use or land cover ( [[#2.2.7|Section 2.2.7]] ), have raised global Land Surface Air Temperature (LSAT) trends by more than 10%, although larger signals have been identified in some specific regions, especially rapidly urbanizing areas such as eastern China (Y. [[#Li--2013|]] [[#Li--2013|Li et al., 2013]] ; [[#Liao--2017|Liao et al., 2017]] ; Z. [[#Shi--2019|]] [[#Shi--2019|Shi et al., 2019]] ). There is also no clear indication that site-specific data homogeneity issues have had any significant impact on global trends since the early 20th century; there is more uncertainty in the 19th century, mainly arising from a lack of standardization of instrument shelters, which has been largely accounted for in data from central Europe ( [[#Jones--2012|Jones et al., 2012]] ), but less so elsewhere.

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