Spatial and Temporal Variations of Atmospheric CH4 in Monsoon Asia Detected by Satellite Observations of GOSAT and TROPOMI

Song, Hao; Sheng, Mengya; Lei, Liping; Guo, Ke; Zhang, Shaoqing; Ji, Zhuoyu

doi:10.3390/rs15133389

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…For example, data from the Japanese Greenhouse Gases Observing Satellite (GOSAT) revealed that the global monthly mean CH 4 concentration reached 1873 ppb in March 2023, with a growth rate of 15 ppb year −1 from March 2022 to March 2023 (https://www.gosat.nies.go.jp/recent-global-ch4.html (accessed on 6 July 2023)). Reducing methane emissions can be considered an effective way to quickly mitigate climate change because of methane's shorter lifetime than CO 2 [11]. However, the estimation of methane emissions is influenced by numerous factors (fire area, combustion type, season and wind speed) that make it challenging to accurately quantify the methane released by forest fires [12].…”

Section: Introductionmentioning

confidence: 99%

Methane Emissions in Boreal Forest Fire Regions: Assessment of Five Biomass-Burning Emission Inventories Based on Carbon Sensing Satellites

Zhao,

Wang,

Shi

et al. 2023

Remote Sensing

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Greenhouse gases such as CH4 generated by forest fires have a significant impact on atmospheric methane concentrations and terrestrial vegetation methane budgets. Verification in conjunction with “top-down” satellite remote sensing observation has become a vital way to verify biomass-burning emission inventories and accurately assess greenhouse gases while looking into the limitations in reliability and quantification of existing “bottom-up” biomass-burning emission inventories. Therefore, we considered boreal forest fire regions as an example while combining five biomass-burning emission inventories and CH4 indicators of atmospheric concentration satellite observation data. By introducing numerical comparison, correlation analysis and trend consistency analysis methods, we explained the lag effect between emissions and atmospheric concentration changes and evaluated a more reliable emission inventory using time series similarity measurement methods. The results indicated that total methane emissions from five biomass-burning emission inventories differed by a factor of 2.9 in our study area, ranging from 2.02 to 5.84 Tg for methane. The time trends of the five inventories showed good consistency, with the Quick Fire Emissions Dataset version 2.5 (QFED2.5) having a higher correlation coefficient (above 0.8) with the other four datasets. By comparing the consistency between the inventories and satellite data, a lagging effect was found to be present between the changes in atmospheric concentration and gas emissions caused by forest fires on a seasonal scale. After eliminating lagging effects and combining time series similarity measures, the QFED2.5 (Euclidean distance = 0.14) was found to have the highest similarity to satellite data. In contrast, Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and Global Fire Assimilation System version 1.2 (GFAS1.2) had larger Euclidean distances of 0.52 and 0.4, respectively, which meant that they had lower similarity to satellite data. Therefore, QFED2.5 was found to be more reliable while having higher application accuracy compared to the other four datasets in our study area. This study further provided a better understanding of the key role of forest fire emissions in atmospheric CH4 concentrations and offered reference for selecting appropriate biomass burning emission inventory datasets for bottom-up inventory estimation studies.

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Section: Introductionmentioning

confidence: 99%