Abstract

Abstract. The annual variation of δD in the tropical lower stratosphere is a critical indicator for the relative importance of different processes contributing to the transport of water vapour through the cold tropical tropopause region into the stratosphere. Distinct observational discrepancies of the δD annual variation were visible in the works of Steinwagner et al. (2010) and Randel et al. (2012). Steinwagner et al. (2010) analysed MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) observations retrieved with the IMK/IAA (Institut für Meteorologie und Klimaforschung in Karlsruhe, Germany, in collaboration with the Instituto de Astrofísica de Andalucía in Granada, Spain) processor, while Randel et al. (2012) focused on ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. Here we reassess the discrepancies based on newer MIPAS (IMK/IAA) and ACE-FTS data sets, also showing for completeness results from SMR (Sub-Millimetre Radiometer) observations and a ECHAM/MESSy (European Centre for Medium-Range Weather Forecasts Hamburg and Modular Earth Submodel System) Atmospheric Chemistry (EMAC) simulation (Eichinger et al., 2015b). Similar to the old analyses, the MIPAS data set yields a pronounced annual variation (maximum about 75 ‰), while that derived from the ACE-FTS data set is rather weak (maximum about 25 ‰). While all data sets exhibit the phase progression typical for the tape recorder, the annual maximum in the ACE-FTS data set precedes that in the MIPAS data set by 2 to 3 months. We critically consider several possible reasons for the observed discrepancies, focusing primarily on the MIPAS data set. We show that the δD annual variation in the MIPAS data up to an altitude of 40 hPa is substantially impacted by a “start altitude effect”, i.e. dependency between the lowermost altitude where MIPAS retrievals are possible and retrieved data at higher altitudes. In itself this effect does not explain the differences with the ACE-FTS data. In addition, there is a mismatch in the vertical resolution of the MIPAS HDO and H2O data (being consistently better for HDO), which actually results in an artificial tape-recorder-like signal in δD. Considering these MIPAS characteristics largely removes any discrepancies between the MIPAS and ACE-FTS data sets and shows that the MIPAS data are consistent with a δD tape recorder signal with an amplitude of about 25 ‰ in the lowermost stratosphere.

Highlights

  • The transport of water vapour from the troposphere to the stratosphere through the cold tropical tropopause layer (TTL) is a critical atmospheric process

  • In the following we quantify the impact of this vertical resolution mismatch and show what a correction means for the comparison of the annual variation in δD between the MIPAS and Atmospheric Chemistry Experiment (ACE)-FTS data sets

  • Time series of tropical lower stratospheric δD exhibit distinct discrepancies. This concerns in particular the characteristics of the annual variation derived from the MIPAS and ACEFTS data sets, making it difficult to draw robust conclusions relevant for water vapour transport into the stratosphere

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Summary

Introduction

The transport of water vapour from the troposphere to the stratosphere through the cold tropical tropopause layer (TTL) is a critical atmospheric process. This is because it is sensitive to physical, chemical and radiative processes, which can be used to infer the history of air parcels As such it can help to discern the relative importance of slow ascent and convective ice lofting for the transport of water vapour into the stratosphere (Moyer et al, 1996). Högberg et al (2019) compared multiple δD data sets from MIPAS, ACE-FTS and the SMR instrument aboard the Odin satellite and concluded that all showed characteristics that typically are associated with the tape recorder signal They iterated that pronounced quantitative differences exist, which would yield different interpretations of the relative importance of convection for lower stratospheric water vapour.

Data sets and handling
Brief data set description
Data handling
Reassessment
Discussion
Temporal sampling and vertical resolution
The start altitude effect and its impact
Does the start altitude itself have an annual variation?
Vertical resolution mismatch of the HDO and H2O data
Impact
Correction
Further considerations
Summary and conclusions
Isotopic ratio
Data screening prior the time series calculation
Calculation of the annual variation characteristics
Findings
Calculation of the start altitude effect
Convolution of higher vertically resolved data
Full Text
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