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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Oct 2020

07 Oct 2020

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This preprint is currently under review for the journal ACP.

Using a global network of temperature lidars to identify temperature biases in the upper stratosphere in ECMWF reanalyses

Graeme Marlton1, Andrew Charlton-Perez1, Giles Harrison1, Inna Polichtchouk2, Alain Hauchecorne3, Phillipe Keckhut3, Robin Wing3, Thierry Leblanc4, and Wolfgang Steinbrecht5 Graeme Marlton et al.
  • 1Department of Meteorology, University of Reading, Reading, RG6 6LA
  • 2European Centre for Medium Range Weather Forecasts, Shinfield Road, Reading, United Kingdom
  • 3LATMOS/IPSL, UVSQ Université Paris-Saclay, Sorbonne Univerités, CNRS, Guyancourt, France
  • 4JPL-Table Mountain Facility, 24490 Table Mountain Road, Wrightwood, CA., USA
  • 5Deutscher Wetterdienst, Albin-Schwaiger-Weg 10, 82383, Hohenpeissenberg, Germany

Abstract. To advance our understanding of the stratosphere, high quality observational datasets of the upper atmosphere are needed. It is commonplace that reanalysis is used to conduct stratospheric studies. However the accuracy of the standard reanalysis at these heights is hard to infer due to a lack of in-situ measurements. Satellite measurements provide one source of temperature information. As some satellite information is already assimilated into reanalyses, the direct comparison of satellite temperatures to the reanalysis is not truly independent. Stratospheric lidars use Rayleigh scattering to measure density in the upper atmosphere, allowing temperature profiles to be derived for altitudes from 30 km (where Mie scattering due to stratospheric aerosols becomes negligible) to 80–90 km (where the signal-to-noise begins to drop rapidly). The Network for the Detection of Atmospheric Composition Change (NDACC) contains several lidars at different latitudes that have measured atmospheric temperatures since the 1970s, resulting in a long running upper-stratospheric temperature dataset. These temper1ature datasets are useful for validating reanalysis datasets in the stratosphere, as they are not assimilated into reanalyses. Here we take stratospheric temperature data from lidars in the northern hemisphere for winter months between 1990–2017 and compare them with the European Centre for ECMWF's ERA-interim and ERA-5 reanalyses. To give confidence in any bias found, temperature data from NASA's EOS Microwave Limb Sounder is also compared to ERA-interim and ERA-5 at points over the lidar sites. In ERA-interim a cold bias of −3 to −4 K between 10 hPa and 1 hPa is found when compared to both measurement 15 systems. Comparisons with ERA-5 found a small bias of magnitude 1 K which varies between cold and warm bias with height between 10 hPa and 3 hPa, indicating a good thermal representation of the upper atmosphere to 3 hPa. At heights above this, comparisons with EOS MLS yield a slight warm bias and the temperature lidar yield a cold bias. A further comparison is undertaken to see the effects of the assimilation of the Advanced Microwave Sounding Unit-A satellite data and the Constellation Observing System for Meteorology, Ionosphere, and Climate GPS Radio Occulation (COSMIC GPSRO) data on stratospheric temperatures. By comparing periods before and after the introduction of each data source it is clear that COSMIC GPSRO improves the cold bias in the 3 hPa to 0.5 hPa altitude range.

Graeme Marlton et al.

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Graeme Marlton et al.

Graeme Marlton et al.


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Publications Copernicus
Short summary
A network of Rayleigh lidars have been used to infer the middle atmosphere temperature bias in ECMWF ERA-5 and ERA-interim reanalyses during 1990-2017. Results show that ERA-interim exhibits a cold bias of -3 to -4 K between 10 and 1 hPa. Comparisons with ERA-5 found a smaller bias of 1 K which varies between cold and warm between 10 and 3 hPa, indicating a good thermal representation of the atmosphere to 3 hPa. These biases must be accounted for in stratospheric studies using these reanalyses.
A network of Rayleigh lidars have been used to infer the middle atmosphere temperature bias in...