Abstract

This work employs ground- and space-based observations, together with model data to study columnar abundances of atmospheric trace gases (XH2O, XCO2, XCH4, and XCO) in two high-latitude Russian cities, St. Petersburg and Yekaterinburg. Two portable COllaborative Column Carbon Observing Network (COCCON) spectrometers were used for continuous measurements at these locations during 2019 and 2020. Additionally, a subset of data of special interest (a strong gradient in XCH4 and XCO was detected) collected in the framework of a mobile city campaign performed in 2019 using both instruments is investigated. All studied satellite products (TROPOMI, OCO-2, GOSAT, MUSICA IASI) show generally good agreement with COCCON observations. Satellite and ground-based observations at high latitude are much sparser than at low or mid latitude, which makes direct coincident comparisons between remote-sensing observations more difficult. Therefore, a method of scaling continuous CAMS model data to the ground-based observations is developed and used for creating virtual COCCON observations. These adjusted CAMS data are then used for satellite validation, showing good agreement in both Peterhof and Yekaterinburg cities. The gradients between the two study sites (ΔXgas) are similar between CAMS and CAMS-COCCON data sets, indicating that the model gradients are in agreement with the gradients observed by COCCON. This is further supported by a few simultaneous COCCON and satellite ΔXgas measurements, which also agree with the model gradient. With respect to the city campaign observations recorded in St. Petersburg, the downwind COCCON station measured obvious enhancements for both XCH4 (10.6 ppb) and XCO (9.5 ppb), which is nicely reflected by TROPOMI observations, which detect city-scale gradients of the order 9.4 ppb for XCH4 and 12.5 ppb XCO, respectively.

Highlights

  • Since human beings exist on the Earth’s surface, their activities have deteriorated the environment in several manners

  • In contrast to the papers above, this paper focuses on the complete set of COllaborative Column Carbon Observing Network (COCCON) measurements collected in the framework of VERIFY to validate and inter-compare Tropospheric Monitoring Instrument (TROPOMI), Orbiting Carbon Observatory-2 (OCO-2), Gases Observing Satellite (GOSAT), MUSICA Infrared Atmospheric Sounding Interferometer (IASI) and Copernicus Atmosphere

  • 2.1.2 Side-by-side measurements 125 After the instruments were calibrated, solar side-by-side measurements between the instruments used in the campaign (FTS#80 and FTS#84), the COCCON reference and the Total Column Carbon Observing Network (TCCON) spectrometer operated at the same location were carried out at Karlsruhe Institute of Technology (KIT)

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Summary

Introduction

Since human beings exist on the Earth’s surface, their activities have deteriorated the environment in several manners. 80 The EU project VERIFY (https://verify.lsce.ipsl.fr/, last access 2 July, 2021) aims to quantify/estimate the anthropogenic and natural GHG emissions based on atmospheric measurements, emission inventories and ecosystem data. Within this project two cities in Russia (St. Petersburg and Yekaterinburg) were selected with the objective of improving our understanding of a key important region with anticipated huge biosphere fluxes and potentially extensive carbon sinks (Reuter et al, 2014). A scaling method is developed and its results are used to better inter-compare satellite products This method is based on COCCON measurements at both sites to scale CAMS XCO2, XCH4 and XCO. A city-scale transport event occurred during the city campaign and tracked by TROPOMI is presented in this study

Russian Campaign location and set-up
Stability of the COCCON spectrometers during the campaign period
Instrumental Line Shape (ILS) characterization
Side-by-side measurements 125
EMME campaign
OCO-2 The Orbiting Carbon Observatory-2 (OCO-2) is a NASA satellite, launched in
MUSICA IASI The Infrared Atmospheric Sounding
CAMS reanalysis (control run)
Results and discussion
Yekaterinburg The measurement period covered winter and spring, from 5
Correlation between COCCON and satellite products Figure 10 to
Generation of the CAMS fields adjusted to COCCON observations
Method
Selection criteria for the best number of windows
Verification of the method
Combined data results by using the scaling method
Peterhof
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