Unveiling the impact of soil methane sink on atmospheric methane concentrations in 2020
Data files
Jun 10, 2024 version files 1.91 MB
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Fig.1a.tif
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Fig.1b.tif
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Fig.2a.tif
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Fig.3a.tif
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Fig.3b.tif
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Fig.S1a.tif
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Fig.S1b.tif
1.35 MB
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otherfigs.xlsx
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README.md
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Abstract
In 2020, anthropogenic methane (CH4) emissions decreased due to COVID-19 containment policies, but there was a substantial increase in the concentration of atmospheric CH4. Previous research suggested that this abnormal increase was linked to higher wetland CH4 emissions and a decrease in the atmospheric CH4 sink. However, the impact of changes in the soil CH4 sink remained unknown. To address this, we utilized a process-based model to quantify the alterations in the soil CH4 sink of terrestrial ecosystems between 2019 and 2020. By implementing the model with various datasets, we consistently observed an increase in the global soil CH4 sink, reaching up to 0.35 ± 0.06 Tg in 2020 compared to 2019. This increase was primarily attributed to warmer soil temperatures in northern high latitudes. These findings emphasize the importance of considering the CH4 sink in terrestrial ecosystems, as neglecting it can lead to an underestimation of both emission increases and reductions in atmospheric CH4 sink capacity. Furthermore, they highlight the potential role of increased soil warmth in terrestrial ecosystems in slowing the growth of CH4 concentrations in the atmosphere.
https://doi.org/10.5061/dryad.70rxwdc67
The dataset includes the raw data for all the figures in the article, as well as the R code for the Soil Methanotrophy Model(MEMO).
Description of the data and file structure
Fig.1a: The global soil methane sink for the year 2019, computed by the MEMO model. The unit is milligrams per square meter per year (mg m-2 year-1).The data has a resolution of 1x1 degree raster, covering global land areas, with the geographic coordinate system being the World Geodetic System 1984 (WGS84). It can be opened using the raster package in R.
Fig.1b: The global soil methane sink for the year 2020, computed by the MEMO model. The unit is milligrams per square meter per year (mg m-2 year-1).The data has a resolution of 1x1 degree raster, covering global land areas, with the geographic coordinate system being the World Geodetic System 1984 (WGS84). It can be opened using the raster package in R.
Fig.2a: Differences in soil methane sink between 2020 and 2019. The unit is milligrams per square meter per year (mg m-2 year-1).The data has a resolution of 1x1 degree raster, covering global land areas, with the geographic coordinate system being the World Geodetic System 1984 (WGS84). It can be opened using the raster package in R.
Fig.3a: The impact of changes in soil temperature on increases in CH4 sink between 2019 and 2020. The unit is milligrams per square meter per year (mg m-2 year-1).The data has a resolution of 1x1 degree raster, covering global land areas, with the geographic coordinate system being the World Geodetic System 1984 (WGS84). It can be opened using the raster package in R.
Fig.3b: The impact of changes in soil moisture contents on increases in CH4 sink between 2019 and 2020. The unit is milligrams per square meter per year(mg m-2 year-1).The data has a resolution of 1x1 degree raster, covering global land areas, with the geographic coordinate system being the World Geodetic System 1984 (WGS84). It can be opened using the raster package in R.
Fig.S1a: Spatial changes in average soil temperature in 2020 were compared to those in 2019. The unit is Celsius(℃). The data has a resolution of 1x1 degree raster, covering global land areas, with the geographic coordinate system being the World Geodetic System 1984 (WGS84). It can be opened using the raster package in R.
Fig.S1b: Spatial changes in average soil moisture content in 2020 were compared to those in 2019. The unit is cubic meter per cubic meter(m3 m-3).The data has a resolution of 1x1 degree raster, covering global land areas, with the geographic coordinate system being the World Geodetic System 1984 (WGS84). It can be opened using the raster package in R.
otherfigs: The original data for other figures in the article.The file is an .xlsx spreadsheet, with the sheet names corresponding to the figure numbers in the article.
Code/Software
memo: R language code for the MEMO model. 1)Library loading; 2)Dataset loading.