Abstract

Abstract. To advance our understanding of the stratosphere, high-quality observational datasets of the stratosphere are needed. It is commonplace that reanalysis datasets are used to conduct stratospheric studies. However, the accuracy of these reanalyses 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 middle and 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 ratio 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 temperature datasets are useful for validating reanalysis datasets in the stratosphere, as they are not assimilated into reanalyses. Here, stratospheric temperature data from lidars in the Northern Hemisphere between 1990–2017 were compared with the European Centre for Medium-Range Weather Forecasts ERA-Interim and ERA5 reanalyses. To give confidence to any bias found, temperature data from NASA's EOS Microwave Limb Sounder were also compared to ERA-Interim and ERA5 at points over the lidar sites. In ERA-Interim a cold bias of −3 to −4 K between 10 and 1 hPa was found when compared to both measurement systems. Comparisons with ERA5 found a small bias of magnitude 1 K which varies between cold and warm bias with height between 10 and 1 hPa, indicating a good thermal representation of the middle atmosphere up to 1 hPa. A further comparison was undertaken looking at the temperature bias by year to see the effects of the assimilation of the Advanced Microwave Sounding Unit-A (AMSU-A) satellite data and the Constellation Observing System for Meteorology, Ionosphere, and Climate GPS Radio Occultation (COSMIC GPSRO) data on stratospheric temperatures within the aforementioned ERA analyses. It was found that ERA5 was sensitive to the introduction of COSMIC GPSRO in 2007 with the reduction of the cold bias above 1 hPa. In addition to this, the introduction of AMSU-A data caused variations in the temperature bias between 1–10 hPa between 1997–2008.

Highlights

  • The stratosphere influences the weather and climate in the troposphere (Domeisen, 2019; Domeisen et al, 2019)

  • For 7 hPa and above, Simmons et al (2020) found there was a warm bias of 1–2 K, whereas the results here for 7 to 1 hPa using the lidar have shown a cold bias at HOH, Mauna Loa Observatory (MLO) and Table Mountain Observatory Facility (TMO)

  • The bias was calculated using radiosonde data which are not independent as they are assimilated into ERA-Interim. This could explain the difference in sign in the results shown in this study

Read more

Summary

Introduction

The stratosphere influences the weather and climate in the troposphere (Domeisen, 2019; Domeisen et al, 2019). In the middle and upper stratosphere, the number of temperature observations are somewhat limited This makes diagnosing bias in a reanalysis dataset more difficult. Radiosondes, small balloon-borne instrument packages which provide in situ temperature profiles up to heights of about 30 km, are launched from thousands of locations daily, giving a wealth of information that is assimilated in the lower and middle stratosphere. The Aqua satellite combines data from Atmospheric Infra-red Sounder (AIRS) instruments with data from the Advanced Microwave Sounding Unit (AMSU) to provide temperature profiles in the troposphere and stratosphere (Susskind et al, 2006). Many of the above observations are assimilated into reanalyses, making it hard to make an unbiased comparison Another source of stratospheric temperature measurements is from Rayleigh temperature lidars. The lidar temperature profiles are not assimilated into reanalyses, making them independent for numerical dataset comparisons. Further analysis was undertaken to ascertain how ERA-Interim and ERA5’s stratospheric temperature bias evolved over the period 1990–2017 with the introduction of both COSMIC GPSRO and AMSU-A data

Stratospheric temperature lidar
Microwave Limb Sounder
European Centre for Medium-Range Weather Forecasts data
ERA-Interim comparisons
ERA5 comparisons
ERA performance due to assimilation of COSMIC GPSRO and AMSU-A
Conclusions
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call