Abstract
The COVID-19 pandemic is causing projected annual CO2 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 Niño. 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 ≈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 ≈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.
Highlights
As recently documented by the Intergovernmental Panel on Climate Change (IPCC), human-induced warming reached approximately 1 ◦C above preindustrial levels in 2017 [1]
The Paris agreement to limit warming to 1.5 ◦C implies emission reductions beginning immediately and CO2 emissions reaching zero by 2055. This implies an urgent need to verify bottom-up emission estimates independently via atmospheric measurements. This is a demanding task, because the response of atmospheric concentrations to emission changes are masked by a much stronger effect from the interannual variability of the ocean and land sinks driven by climate variability, in particular El Niño [2]
Sink variability must be accurately taken into account by related measurements and models, in order to obtain closure between emissions, sinks, and measured atmospheric concentration changes [3]
Summary
As recently documented by the Intergovernmental Panel on Climate Change (IPCC), human-induced warming reached approximately 1 ◦C above preindustrial levels in 2017 [1]. The Paris agreement to limit warming to 1.5 ◦C implies emission reductions beginning immediately and CO2 emissions reaching zero by 2055. This implies an urgent need to verify bottom-up emission estimates independently via atmospheric measurements. Sink variability must be accurately taken into account by related measurements and models, in order to obtain closure between emissions, sinks, and measured atmospheric concentration changes [3]. This is a demanding task at the edge of the current state of the art in carbon cycle research [4]
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