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Atmosphere
Special Issues
Long-Term Observation of Greenhouse Gases and Reactive Gases
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Long-Term Observation of Greenhouse Gases and Reactive Gases

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (31 May 2019) | Viewed by 9836

Special Issue Editor

Dr. Dmitry Belikov

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Guest Editor
Hokkaido University, Sapporo, Japan
Interests: carbon dioxide; carbon cycle; atmospheric modeling

Special Issue Information

Dear Colleagues,

The steady rise in atmospheric long-lived greenhouse gas (GHG) concentrations is the main driver of contemporary climate change. Many GHGs occur naturally in the atmosphere (i.e., carbon dioxide, methane, and nitrous oxide), while others are anthropogenic. The latter include, for example, chlorofluorocarbons (CFCs), hydrofluorocarbons (HFCs), and perfluorocarbons (PFCs), as well as sulfur hexafluoride (SF6). Human activities increase atmospheric concentrations of both natural and synthetic GHGs. GHGs remain in the atmosphere for different amounts of time and some of them are more effective than others at warming the atmosphere. Anthropogenic GHG emissions have increased since the pre-industrial era and are now higher than ever. Long-term, high-quality, atmospheric measurements are crucial for quantifying trends in greenhouse gas fluxes and attributing them to fossil fuel emissions, changes in land use and management, or the response of natural land and ocean ecosystems to climate change. In this Special Issue, we seek to publish innovative papers which investigate long-term observations of GHGs, the influence of atmospheric transport patterns, and the key processes driving measured concentration levels.

Dr. Dmitry Belikov
Guest Editor

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Keywords

  • Greenhouse gases
  • Global warming potential
  • Carbon dioxide
  • Methane
  • Chlorofluorocarbons
  • Hydrofluorocarbons
  • Nitrous oxide
  • Sulfur hexafluoride
  • Long-term trends
  • Emission inventories
  • Biomass burning
  • Fuel combustion

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Published Papers (2 papers)

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Research

18 pages, 3543 KiB  
Article
Analysis of the Diurnal, Weekly, and Seasonal Cycles and Annual Trends in Atmospheric CO2 and CH4 at Tower Network in Siberia from 2005 to 2016
by Dmitry Belikov, Mikhail Arshinov, Boris Belan, Denis Davydov, Aleksandr Fofonov, Motoki Sasakawa and Toshinobu Machida
Atmosphere 2019, 10(11), 689; https://doi.org/10.3390/atmos10110689 - 8 Nov 2019
Cited by 20 | Viewed by 4116
Abstract
We analyzed 12 years (2005–2016) of continuous measurements of atmospheric CO 2 and CH 4 concentrations made at nine tower observation sites in the Japan–Russia Siberian Tall Tower Inland Observation Network (JR-STATION), located in Siberia. Since the data are very noisy and have [...] Read more.
We analyzed 12 years (2005–2016) of continuous measurements of atmospheric CO 2 and CH 4 concentrations made at nine tower observation sites in the Japan–Russia Siberian Tall Tower Inland Observation Network (JR-STATION), located in Siberia. Since the data are very noisy and have a low temporal resolution due to gaps in instrument operation, we used the recently developed Prophet model, which was designed to handle the common features of time series (multiple strong seasonalities, trend changes, outliers) and has a robust performance in the presence of missing data and trend shifts. By decomposing each sampled time-series into its major components (i.e., annual trend and seasonal, weekly, and hourly variation), we observed periodically changing patterns of tracer concentrations. Specifically, we detected multi-year variability of tracers and identified high-concentration events. The frequency of such events was found to vary throughout the year, reaching up to 20% of days for some months, while the number of such events was found to be different for CO 2 and CH 4 . An analysis of weather conditions showed that, in most cases, high-concentration events were caused by a temperature inversion and low wind speed. Additionally, wind directions were found to be different for high- and low-concentration events. For some sites, the wind direction indicated the location of strong local sources of CO 2 and CH 4 . As well as elucidating the seasonality of greenhouse gas concentrations, this study confirmed the potential of the Prophet model for detecting periodicity in environmental phenomena. Full article
(This article belongs to the Special Issue Long-Term Observation of Greenhouse Gases and Reactive Gases)
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23 pages, 8987 KiB  
Article
Spatio-Temporal Consistency Evaluation of XCO2 Retrievals from GOSAT and OCO-2 Based on TCCON and Model Data for Joint Utilization in Carbon Cycle Research
by Yawen Kong, Baozhang Chen and Simon Measho
Atmosphere 2019, 10(7), 354; https://doi.org/10.3390/atmos10070354 - 27 Jun 2019
Cited by 25 | Viewed by 4888
Abstract
The global carbon cycle research requires precise and sufficient observations of the column-averaged dry-air mole fraction of CO 2 (XCO 2 ) in addition to conventional surface mole fraction observations. In addition, assessing the consistency of multi-satellite data are crucial for joint utilization [...] Read more.
The global carbon cycle research requires precise and sufficient observations of the column-averaged dry-air mole fraction of CO 2 (XCO 2 ) in addition to conventional surface mole fraction observations. In addition, assessing the consistency of multi-satellite data are crucial for joint utilization to better infer information about CO 2 sources and sinks. In this work, we evaluate the consistency of long-term XCO 2 retrievals from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2) in comparison with Total Carbon Column Observing Network (TCCON) and the 3D model of CO 2 mole fractions data from CarbonTracker 2017 (CT2017). We create a consistent joint dataset and compare it with the long-term model data to assess their abilities to characterize the carbon cycle climate. The results show that, although slight increasing differences are found between the GOSAT and TCCON XCO 2 in the northern temperate latitudes, the GOSAT and OCO-2 XCO 2 retrievals agree well in general, with a mean bias ± standard deviation of differences of 0.21 ± 1.3 ppm. The differences are almost within ±2 ppm and are independent of time, indicating that they are well calibrated. The differences between OCO-2 and CT2017 XCO 2 are much larger than those between GOSAT and CT XCO 2 , which can be attributed to the significantly different spatial representatives of OCO-2 and the CT-transport model 5 (TM5). The time series of the combined OCO-2/GOSAT dataset and the modeled XCO 2 agree well, and both can characterize significantly increasing atmospheric CO 2 under the impact of a large El Niño during 2015 and 2016. The trend calculated from the dataset using the seasonal Kendall (S-K) method indicates that atmospheric CO 2 is increasing by 2–2.6 ppm per year. Full article
(This article belongs to the Special Issue Long-Term Observation of Greenhouse Gases and Reactive Gases)
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