Abstract

Abstract. Accurate measurement of water vapor in the climate-sensitive region near the tropopause is very challenging. Unexplained systematic discrepancies between measurements at low water vapor mixing ratios made by different instruments on airborne platforms have limited our ability to adequately address a number of relevant scientific questions on the humidity distribution, cloud formation and climate impact in that region. Therefore, during the past decade, the scientific community has undertaken substantial efforts to understand these discrepancies and improve the quality of water vapor measurements. This study presents a comprehensive intercomparison of airborne state-of-the-art in situ hygrometers deployed on board the DLR (German Aerospace Center) research aircraft HALO (High Altitude and LOng Range Research Aircraft) during the Midlatitude CIRRUS (ML-CIRRUS) campaign conducted in 2014 over central Europe. The instrument intercomparison shows that the hygrometer measurements agree within their combined accuracy (±10 % to 15 %, depending on the humidity regime); total mean values agree within 2.5 %. However, systematic differences on the order of 10 % and up to a maximum of 15 % are found for mixing ratios below 10 parts per million (ppm) H2O. A comparison of relative humidity within cirrus clouds does not indicate a systematic instrument bias in either water vapor or temperature measurements in the upper troposphere. Furthermore, in situ measurements are compared to model data from the European Centre for Medium-Range Weather Forecasts (ECMWF) which are interpolated along the ML-CIRRUS flight tracks. We find a mean agreement within ±10 % throughout the troposphere and a significant wet bias in the model on the order of 100 % to 150 % in the stratosphere close to the tropopause. Consistent with previous studies, this analysis indicates that the model deficit is mainly caused by too weak of a humidity gradient at the tropopause.

Highlights

  • Water vapor is one of the most important trace gases in Earth’s atmosphere due to its large influence on the radiation budget and atmospheric dynamics

  • The aim of this study is to provide another step towards a better understanding of the accuracy of airborne water vapor measurements

  • The extensive ML-CIRRUS in situ data set of uppertropospheric and lower-stratospheric humidity further enables an evaluation of the accuracy of upper troposphere and lower stratosphere (UTLS) humidity in the European Centre for MediumRange Weather Forecasts (ECMWF) (European Centre for Medium-Range Weather Forecasts) numerical weather prediction (NWP) model

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Summary

Introduction

Water vapor is one of the most important trace gases in Earth’s atmosphere due to its large influence on the radiation budget and atmospheric dynamics. During the past several decades, a number of H2O measurement intercomparisons during field campaigns including aircraft in situ, balloon-borne and satellite instruments revealed that the relative measurement uncertainty in water vapor mixing ratio was significantly higher than 10 %, even occasionally exceeding 100 % at the lowest mixing ratios in the lower stratosphere (e.g., Oltmans et al, 2000; Vömel et al, 2007; Weinstock et al, 2009) These large discrepancies motivated the comprehensive intercomparison campaign AquaVIT-1 at the AIDA (Aerosol Interaction and Dynamics in the Atmosphere) cloud chamber in Karlsruhe in 2007 (Fahey et al, 2014) and the follow-up but as-yet undocumented campaigns AquaVIT-2 and -3 in 2013 and 2015, respectively. This section includes a comparison of relative humidity inside of cirrus clouds as well as an intercomparison of in situ measurements with ECMWF IFS model data

ML-CIRRUS campaign
AIMS
Instruments
AIMS-H2O
Additional instrumentation
Methodology and conditions for intercomparison
Flight strategy
Data processing and filtering
Reference value
Intercomparison
Correlation of single instruments
Deviation with respect to reference value
Comparison of relative humidity in clouds
AIMS FISH SHARC HAI WARAN
Comparison to the ECMWF numerical weather prediction model
Findings
Discussion and summary
Full Text
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