Statistical characterization of urban CO2 emission signals observed by commercial airliner measurements

Near-real-time estimation of fossil fuel CO2 emissions from China based on atmospheric observations on Hateruma and Yonaguni Islands, Japan

We developed a near-real-time estimation method for temporal changes in fossil fuel CO₂ (FFCO₂) emissions from China for 3 months [January, February, March (JFM)] based on atmospheric CO₂ and CH₄ observations on Hateruma Island (HAT, 24.06° N, 123.81° E) and Yonaguni Island (YON, 24.47° N, 123.01° E), Japan. These two remote islands are in the downwind region of continental East Asia during winter because of the East Asian monsoon. Previous studies have revealed that monthly averages of synoptic-scale variability ratios of atmospheric CO₂ and CH₄ (ΔCO₂/ΔCH₄) observed at HAT and YON in JFM are sensitive to changes in continental emissions. From the analysis based on an atmospheric transport model with all components of CO₂ and CH₄ fluxes, we found that the ΔCO₂/ΔCH₄ ratio was linearly related to the FFCO₂/CH₄ emission ratio in China because calculating the variability ratio canceled out the transport influences. Using the simulated linear relationship, we converted the observed ΔCO₂/ΔCH₄ ratios into FFCO₂/CH₄ emission ratios in China. The change rates of the emission ratios for 2020-2022 were calculated relative to those for the preceding 9-year period (2011-2019), during which relatively stable ΔCO₂/ΔCH₄ ratios were observed. These changes in the emission ratios can be read as FFCO₂ emission changes under the assumption of no interannual variations in CH₄ emissions and biospheric CO₂ fluxes for JFM. The resulting average changes in the FFCO₂ emissions in January, February, and March 2020 were 17 ± 8%, - 36 ± 7%, and - 12 ± 8%, respectively, (- 10 ± 9% for JFM overall) relative to 2011-2019. These results were generally consistent with previous estimates. The emission changes for January, February, and March were 18 ± 8%, - 2 ± 10%, and 29 ± 12%, respectively, in 2021 (15 ± 10% for JFM overall) and 20 ± 9%, - 3 ± 10%, and - 10 ± 9%, respectively, in 2022 (2 ± 9% for JFM overall). These results suggest that the FFCO₂ emissions from China rebounded to the normal level or set a new high record in early 2021 after a reduction during the COVID-19 lockdown. In addition, the estimated reduction in March 2022 might be attributed to the influence of a new wave of COVID-19 infections in Shanghai.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s40645-023-00542-6.

Global nature run data with realistic high-resolution carbon weather for the year of the Paris Agreement

The CO₂ Human Emissions project has generated realistic high-resolution 9 km global simulations for atmospheric carbon tracers referred to as nature runs to foster carbon-cycle research applications with current and planned satellite missions, as well as the surge of in situ observations. Realistic atmospheric CO₂, CH₄ and CO fields can provide a reference for assessing the impact of proposed designs of new satellites and in situ networks and to study atmospheric variability of the tracers modulated by the weather. The simulations spanning 2015 are based on the Copernicus Atmosphere Monitoring Service forecasts at the European Centre for Medium Range Weather Forecasts, with improvements in various model components and input data such as anthropogenic emissions, in preparation of a CO₂ Monitoring and Verification Support system. The relative contribution of different emissions and natural fluxes towards observed atmospheric variability is diagnosed by additional tagged tracers in the simulations. The evaluation of such high-resolution model simulations can be used to identify model deficiencies and guide further model improvements.

Measurement(s)	atmospheric carbon dioxide, methane and carbon monoxide
Technology Type(s)	numerical simulation
Factor Type(s)	None
Sample Characteristic - Organism	long-lived greenhouse gases
Sample Characteristic - Environment	atmosphere
Sample Characteristic - Location	global atmosphere

The Impact of COVID-19 on CO2 Emissions in the Los Angeles and Washington DC/Baltimore Metropolitan Areas

Responses to COVID-19 have resulted in unintended reductions of city-scale carbon dioxide (CO2) emissions. Here, we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. We present three lines of evidence using methods that have increasing model dependency, including an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course-correct CO2 reduction activities efficiently.

Detection of fossil-fuel CO2 plummet in China due to COVID-19 by observation at Hateruma

The COVID-19 pandemic caused drastic reductions in carbon dioxide (CO₂) emissions, but due to its large atmospheric reservoir and long lifetime, no detectable signal has been observed in the atmospheric CO₂ growth rate. Using the variabilities in CO₂ (ΔCO₂) and methane (ΔCH₄) observed at Hateruma Island, Japan during 1997–2020, we show a traceable CO₂ emission reduction in China during February–March 2020. The monitoring station at Hateruma Island observes the outflow of Chinese emissions during winter and spring. A systematic increase in the ΔCO₂/ΔCH₄ ratio, governed by synoptic wind variability, well corroborated the increase in China’s fossil-fuel CO₂ (FFCO₂) emissions during 1997–2019. However, the ΔCO₂/ΔCH₄ ratios showed significant decreases of 29 ± 11 and 16 ± 11 mol mol⁻¹ in February and March 2020, respectively, relative to the 2011–2019 average of 131 ± 11 mol mol⁻¹. By projecting these observed ΔCO₂/ΔCH₄ ratios on transport model simulations, we estimated reductions of 32 ± 12% and 19 ± 15% in the FFCO₂ emissions in China for February and March 2020, respectively, compared to the expected emissions. Our data are consistent with the abrupt decrease in the economic activity in February, a slight recovery in March, and return to normal in April, which was calculated based on the COVID-19 lockdowns and mobility restriction datasets.