Vertical Distribution of Arctic Methane in 2009–2018 Using Ground-Based Remote Sensing

We analyzed the vertical distribution of atmospheric methane (CH<inline-formula> <math display="inline"> <semantics> <msub> <mrow></mrow> <mn>4</mn> </msub> </semantics> </math> </inline-formula>) retrieved from measur...

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Bibliographic Details
Main Authors: Tomi Karppinen, Otto Lamminpää, Simo Tukiainen, Rigel Kivi, Pauli Heikkinen, Juha Hatakka, Marko Laine, Huilin Chen, Hannakaisa Lindqvist, Johanna Tamminen
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Remote Sensing
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Online Access:https://www.mdpi.com/2072-4292/12/6/917
Description
Summary:We analyzed the vertical distribution of atmospheric methane (CH<inline-formula> <math display="inline"> <semantics> <msub> <mrow></mrow> <mn>4</mn> </msub> </semantics> </math> </inline-formula>) retrieved from measurements by ground-based Fourier Transform Spectrometer (FTS) instrument in Sodankyl&#228;, Northern Finland. The retrieved dataset covers 2009&#8722;2018. We used a dimension reduction retrieval method to extract the profile information, since each measurement contains around three pieces of information about the profile shape between 0 and 40 km. We compared the retrieved profiles against Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) satellite measurements and AirCore balloon-borne profile measurements. Additional comparison at the lowest tropospheric layer was done against in-situ measurements from a 50-m-high mast. In general, the ground-based FTS and ACE-FTS profiles agreed within 10% below 20 km and within 30% in the stratosphere between 20 and 40 km. Our method was able to accurately capture reduced methane concentrations inside the polar vortex in the Arctic stratosphere. The method produced similar trend characteristics as the reference instruments even when a static prior profile was used. Finally, we analyzed the time series of the CH<inline-formula> <math display="inline"> <semantics> <msub> <mrow></mrow> <mn>4</mn> </msub> </semantics> </math> </inline-formula> profile datasets and estimated the trend using the dynamic linear model (DLM).
ISSN:2072-4292