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

===== 2.3.1.2.1 Dataset developments =====

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There have been updated radiosonde estimates from the University of Vienna (RAOBCORE and RICH; [[#Haimberger--2012|Haimberger et al., 2012]] ) and a new dataset from the State University of New York (UAHRD, [[#Zhou--2020|Zhou et al., 2020]] ). There are new versions of AMSU products from the University of Alabama in Huntsville (UAHv6.0; [[#Spencer--2017|Spencer et al., 2017]] ) and Remote Sensing Systems (RSSv4.0; [[#Mears--2017|Mears and Wentz, 2017]] ). These updates have led to convergence in the lower stratosphere layer ( [[#Maycock--2018|Maycock et al., 2018]] ); in particular, the move to UAHv6.0 has addressed homogeneity issues identified by [[#Seidel--2016|Seidel et al. (2016)]] , although residual differences remain ( [[#Christy--2018|Christy et al., 2018]] ). Reanalyses products had identified limitations near the 300 hPa level where the contribution of aircraft observations has increased rapidly in recent years ( [[#Dee--2011|Dee et al., 2011]] ; [[#Gelaro--2017|Gelaro et al., 2017]] ), leading to identified biases ( [[#Dee--2009|Dee and Uppala, 2009]] ), that have been addressed in ERA5 ( [[#Hersbach--2020|Hersbach et al., 2020]] ). Modern reanalyses are generally well aligned with radiosonde and satellite observations in the middle and lower troposphere and lower stratosphere. A new operational mid- and upper-stratospheric dataset (STAR) has been developed by [[#Zou--2016|Zou and Qian (2016)]] , merging the previous 1979–2006 SSU dataset ( [[#Zou--2014|Zou et al., 2014]] ) with a dataset from 1998 onwards drawn from relevant AMSU channels ( [[#Wang--2014|Wang and Zou, 2014]] ). Further stratospheric satellite-based datasets from various combinations of satellites have been developed by [[#McLandress--2015|McLandress et al. (2015)]] and [[#Randel--2016|Randel et al. (2016)]] .

New assessments of free-atmosphere temperature are available through radio occultation (RO) and Atmospheric Infrared Sounder (AIRS) products which begin in the early 2000s ( [[IPCC:Wg1:Chapter:Chapter-1#1.5.1.1|Section 1.5.1.1]] ). Global Navigation Satellite System (GNSS)-RO datasets have been compared against AMSU data records, finding almost identical trends ( [[#Khaykin--2017|Khaykin et al., 2017]] ). Comparison of RO with collocated radiosondes, Vaisala RS90/92 and GCOS Reference Upper Air Network data (RS92-GDP; [[#Dirksen--2014|Dirksen et al., 2014]] ), show very good correspondence with global annual mean differences of less than 0.2°C in the upper troposphere and lower stratosphere. Radiosonde daytime radiation biases were identified at higher altitudes ( [[#Ladstädter--2015|Ladstädter et al., 2015]] ; [[#Ho--2017|Ho et al., 2017]] ). The stability of RO makes this data a useful comparator for AMSU ( [[#Chen--2014|Chen and Zou, 2014]] ) and radiosondes ( [[#Ho--2017|Ho et al., 2017]] ; [[#Tradowsky--2017|Tradowsky et al., 2017]] ), as well as anchoring post-2006 reanalyses datasets and improving their consistency in the lower and middle stratosphere ( [[#Long--2017|Long et al., 2017]] ; [[#Ho--2020|Ho et al., 2020]] ). The effective vertical resolution of RO measurements in the upper troposphere and lower stratosphere was found to be up to 100 m at the tropical tropopause ( [[#Zeng--2019a|Zeng et al., 2019a]] ), which is favourable for resolving atmospheric variability ( [[#Scherllin-Pirscher--2012|Scherllin-Pirscher et al., 2012]] ; [[#Wilhelmsen--2018|Wilhelmsen et al., 2018]] ; [[#Stocker--2019|Stocker et al., 2019]] ). Temperature trends in RO products are most consistent with each other and with other observations between 8 km and 25 km ( [[#Ho--2012|Ho et al., 2012]] ; [[#Steiner--2013|Steiner et al., 2013]] , 2020a). The uncertainty increases above 25 km for the early RO period, for which data are based on the single-satellite CHAMP mission, but data at higher altitudes become more reliable for later missions based on advanced receivers ( [[#Steiner--2020a|Steiner et al., 2020a]] ), along with the application of corrections for ionospheric effects ( [[#Danzer--2020|Danzer et al., 2020]] ). The uncertainty due to the changing number of observations is reduced by correcting for the sampling uncertainty in RO climatological fields (e.g., [[#Scherllin-Pirscher--2011|Scherllin-Pirscher et al., 2011]] ). For AIRS, thus far, stability of the instrument has been constrained to less than 0.03°C per decade for selected window channels in a comparison to SSTs measured by ocean buoys ( [[#Aumann--2019|Aumann et al., 2019]] ). Trends were inter-compared with trends in RO data and reanalysis data to assess systematic uncertainties ( [[#Leroy--2018|Leroy et al., 2018]] ).

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