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. Atmospheric reanalyses are data-assimilating weather modelswhich are widely used as proxies for the true state of the atmosphere in therecent past. This is particularly the case for the stratosphere, wherehistorical observations are sparse. But how realistic are these stratosphericreanalyses? Here, we resample stratospheric temperature data from six modernreanalyses (CFSR, ERA-5, ERA-Interim, JRA-55, JRA-55C and MERRA-2) to producesynthetic satellite observations, which we directly compare to retrievedsatellite temperatures from COSMIC, HIRDLS and SABER and to brightnesstemperatures from AIRS for the 10-year period of 2003–2012. We explicitlysample standard public-release products in order to best assess theirsuitability for typical usage. We find that all-time all-latitudecorrelations between limb sounder observations and synthetic observationsfrom full-input reanalyses are 0.97–0.99 at 30 km in altitude, falling to0.84–0.94 at 50 km. The highest correlations are seen at high latitudes andthe lowest in the sub-tropics, but root-mean-square (RMS) differences arehighest (10 K or greater) in high-latitude winter. At all latitudes,differences increase with increasing height. High-altitude differences becomeespecially large during disrupted periods such as the post-suddenstratospheric warming recovery phase, in which zonal-mean differences can be ashigh as 18 K among different datasets. We further show that, for thecurrent generation of reanalysis products, a full-3-D sampling approach (i.e.one which takes full account of the instrument measuring volume) is alwaysrequired to produce realistic synthetic AIRS observations, but is almostnever required to produce realistic synthetic HIRDLS observations. Forsynthetic SABER and COSMIC observations full-3-D sampling is required inequatorial regions and regions of high gravity-wave activity but nototherwise. Finally, we use cluster analyses to show that full-inputreanalyses (those which assimilate the full suite of observations, i.e.excluding JRA-55C) are more tightly correlated with each other than withobservations, even observations which they assimilate. This may suggest thatthese reanalyses are over-tuned to match their comparators. If so, this couldhave significant implications for future reanalysis development.

How well do stratospheric reanalyses reproduce high-resolution satellite temperature measurements?