Can We Measure a COVID-19-Related Slowdown in Atmospheric CO(2)Growth? Sensitivity of Total Carbon Column Observations

doi:10.3390/rs12152387

	Can We Measure a COVID-19-Related Slowdown in Atmospheric CO(2)Growth? Sensitivity of Total Carbon Column Observations
	Sussmann, Ralf; Rettinger, Markus
	2020-08
发表期刊	REMOTE SENSING
EISSN	2072-4292
摘要	The COVID-19 pandemic is causing projected annual CO(2)emission reductions up to -8% for 2020. This approximately matches the reductions required year on year to fulfill the Paris agreement. We pursue the question whether related atmospheric concentration changes may be detected by the Total Carbon Column Observing Network (TCCON), and brought into agreement with bottom-up emission-reduction estimates. We present a mathematical framework to derive annual growth rates from observed column-averaged carbon dioxide (XCO2) including uncertainties. The min-max range of TCCON growth rates for 2012-2019 was [2.00, 3.27] ppm/yr with a largest one-year increase of 1.07 ppm/yr for 2015/16 caused by El Nino. Uncertainties are 0.38 [0.28, 0.44] ppm/yr limited by synoptic variability, including a 0.05 ppm/yr contribution from single-measurement precision. TCCON growth rates are linked to a UK Met Office forecast of a COVID-19-related reduction of -0.32 ppm yr(-2)in 2020 for Mauna Loa. The separation of TCCON-measured growth rates vs. the reference forecast (without COVID-19) is discussed in terms of detection delay. A 0.6 [0.4, 0.7]-yr delay is caused by the impact of synoptic variability on XCO2, including a approximate to 1-month contribution from single-measurement precision. A hindrance for the detection of the COVID-19-related growth rate reduction in 2020 is the +/- 0.57 ppm/yr uncertainty for the forecasted reference case (without COVID-19). Only assuming the ongoing growth rate reductions increasing year-on-year by -0.32 ppm yr(-2)would allow a discrimination of TCCON measurements vs. the unperturbed forecast and its uncertainty-with a 2.4 [2.2, 2.5]-yr delay. Using no forecast but the max-min range of the TCCON-observed growth rates for discrimination only leads to a factor approximate to 2 longer delay. Therefore, the forecast uncertainties for annual growth rates must be reduced. This requires improved terrestrial ecosystem models and ocean observations to better quantify the land and ocean sinks dominating interannual variability.
关键词	COVID-19 lockdown fossil fuel emission reduction atmospheric CO(2)growth total carbon column observations TCCON column-averaged CO2 XCO2 annual growth rate detection delay ocean and land carbon sinks interannual variability climate variability El Nino intra-annual variability synoptic variability confidence bootstrap resampling
DOI	10.3390/rs12152387
WOS关键词	WEIGHTED MEAN CONCENTRATION ; STANDARD ERROR ; CO2 ; DIOXIDE ; OBSERVATORY-2 ; RETRIEVALS ; NETWORK ; SURFACE ; GASES ; CYCLE
WOS研究方向	Remote Sensing
WOS类目	Remote Sensing
出版者	MDPI
引用统计
文献类型	期刊论文
专题	新冠肺炎循证社会科学证据集成
作者单位	Karlsruhe Inst Technol
推荐引用方式 GB/T 7714	Sussmann, Ralf,Rettinger, Markus. Can We Measure a COVID-19-Related Slowdown in Atmospheric CO(2)Growth? Sensitivity of Total Carbon Column Observations[J]. REMOTE SENSING,2020.
APA	Sussmann, Ralf,&Rettinger, Markus.(2020).Can We Measure a COVID-19-Related Slowdown in Atmospheric CO(2)Growth? Sensitivity of Total Carbon Column Observations.REMOTE SENSING.
MLA	Sussmann, Ralf,et al."Can We Measure a COVID-19-Related Slowdown in Atmospheric CO(2)Growth? Sensitivity of Total Carbon Column Observations".REMOTE SENSING (2020).

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