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

====== Marine domain ======

For SST analyses, three products – HadSST4 (1850–present, [[#Kennedy--2019|Kennedy et al., 2019]] ), ERSSTv5 (1850–present, [[#Huang--2017|Huang et al., 2017]] ) and COBE SST2 (1880–present, ( [[#Hirahara--2014|Hirahara et al., 2014]] ) – now have bias adjustments applied throughout the record. The new SST datasets account for two major issues previously identified in AR5: that globally averaged buoy SSTs are about 0.12°C cooler than ship-based SSTs ( [[#Kennedy--2011|Kennedy et al., 2011]] ; [[#Huang--2015|Huang et al., 2015]] ), and that SSTs from ship engine room intakes may have biases for individual ships depending upon the sensor set-up ( [[#Kent--2006|Kent and Kaplan, 2006]] ) but have an overall warm bias when globally aggregated ( [[#Kennedy--2019|Kennedy et al., 2019]] ). The first issue primarily affects data since 1990, when buoys began to increasingly contribute to the observation network ( [[#Woodruff--2011|Woodruff et al., 2011]] ), and the second issue has its largest effect from the 1940s to the 1970s. From the standpoint of uncertainty, ERSSTv4 (W. [[#Liu--2015|]] [[#Liu--2015|Liu et al., 2015]] ; [[#Huang--2016|Huang et al., 2016]] ) and subsequent versions ( [[#Huang--2017|Huang et al., 2017]] ), and HadSST4 have estimates presented as ensembles that sample parametric uncertainty. Comparisons between these independently-derived analyses and the assessed uncertainties ( [[#Kennedy--2014|Kennedy, 2014]] ; [[#Kent--2017|Kent et al., 2017]] ) show unambiguously that global mean SST increased since the start of the 20th century, a conclusion that is insensitive to the method used to treat gaps in data coverage ( [[#Kennedy--2014|Kennedy, 2014]] ).

A number of recent studies also corroborate important components of the SST record ( [[#Hausfather--2017|Hausfather et al., 2017]] ; [[#Kent--2017|Kent et al., 2017]] ; [[#Cowtan--2018|Cowtan et al., 2018]] ; [[#Kennedy--2019|Kennedy et al., 2019]] ). In particular, ATSR SST satellite retrievals ( [[#Merchant--2012|Merchant et al., 2012]] ; [[#Berry--2018|Berry et al., 2018]] ), the near-surface records from hydrographical profiles ( [[#Gouretski--2012|Gouretski et al., 2012]] ; [[#Huang--2018|Huang et al., 2018]] ), and coastal observations ( [[#Cowtan--2018|Cowtan et al., 2018]] ) have all been shown to be broadly consistent with the homogenized SST analyses. [[#Hausfather--2017|Hausfather et al. (2017)]] also confirmed the new estimate of the rate of warming seen in ERSSTv4 since the late 1990s through comparison with independent SST data sources such as Argo floats and satellite retrievals. Nevertheless, dataset differences remain in the mid-20th century when there were major, poorly-documented, changes in instrumentation and observational practices ( [[#Kent--2017|Kent et al., 2017]] ), particularly during World War II, when ship observations were limited and disproportionately originated from US naval sources ( [[#Thompson--2008|Thompson et al., 2008]] ). [[#Kennedy--2019|Kennedy et al. (2019)]] also identify differences between the new HadSST4 dataset and other SST datasets in the 1980s and 1990s, indicating that some level of structural uncertainty remains during this period, whilst [[#Chan--2019|Chan et al. (2019)]] and [[#Davis--2019|Davis et al. (2019)]] document residual uncertainties in the early and later 20th century records respectively.

Historically, SST has been used as a basis for global temperature assessment on the premise that the less variable SST data provides a better estimate of marine temperature changes than marine air temperature (MAT) ( [[#Kent--2021|Kent and Kennedy, 2021]] ). However, MAT products are used to adjust SST biases in the NOAA SST product because they are assessed to be more homogeneous ( [[#Huang--2017|Huang et al., 2017]] ). Observational datasets exist for night-marine air temperature (NMAT) (e.g., [[#Cornes--2020|Cornes et al., 2020]] ; [[#Junod--2020|Junod and Christy, 2020]] ; [[#Rayner--2020|Rayner et al., 2020]] ) and there are methods to adjust daytime MATs ( [[#Berry--2004|Berry et al., 2004]] ), but there is to date no regularly updated dataset which combines MAT with temperatures over land. MAT datasets are more sparse in recent decades than SST datasets as marine datasets have become increasingly dependent on drifting buoys ( [[#Centurioni--2019|Centurioni et al., 2019]] ) which generally measure SST but not MAT, and there are almost no recent winter MAT data south of 40°S ( [[#Swart--2019|Swart et al., 2019]] ). However, the situation reverses in the 19th century with a greater prevalence of MAT than SST measurements available in the ICOADS data repository ( [[#Freeman--2017|Freeman et al., 2017]] , 2019; [[#Kent--2021|Kent and Kennedy, 2021]] ).

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