Progress in Earth andPlanetary Science

Tohjima et al.
Progress in Earth and Planetary Science(2023) 10:10
https://doi.org/10.1186/s40645-023-00542-6

Open Access

METHODOLOGY

Near‑real‑time estimation of fossil fuel ­CO2
emissions from China based on atmosphericobservations on Hateruma and Yonaguni
Islands, Japan
Yasunori Tohjima1* , Yosuke Niwa1, Prabir K. Patra2, Hitoshi Mukai1, Toshinobu Machida1, Motoki Sasakawa1,
Kazuhiro Tsuboi3, Kazuyuki Saito4 and Akihiko Ito1

Abstract
We developed a near-real-time estimation method for temporal changes in fossil fuel C­ O2 ­(FFCO2) emissions from
China for 3 months [January, February, March (JFM)] based on atmospheric ­CO2 and C­ H4 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 arein 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 C­ O2 and C­ H4 (ΔCO2/
ΔCH4) observed at HAT and YON in JFM are sensitive to changes in continental emissions. From the analysis basedon 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 tothose 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 the­FFCO2 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.
Keywords Fossil fuel C­ O2 emissions, Synoptic-scale variations, Atmospheric ­CO2, Atmospheric ­CH4, COVID-19
lockdown, East Asian Monsoon*Correspondence:
Yasunori Tohjima
tohjima@nies.go.jp
1
National Institute for Environmental Studies (NIES), 16‑2 Onogawa,
Tsukuba, Ibaraki 305‑8506, Japan

2

Japan Agency for Marine-Earth Science and Technology (JAMSTEC),
3173‑25 Showa‑Machi, Kanazawa‑Ku, Yokohama, Kanagawa 236‑0001,
Japan
3
Meteorological Research Institute (MRI), 1‑1 Nagamine, Tsukuba, Ibaraki
305‑0052, Japan
4
Japan Meteorological Agency (JMA), 3‑6‑9 Toranomon, Minato‑Ku,
Tokyo 105‑8431, Japan© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Tohjima et al. Progress in Earth and Planetary Science(2023) 10:10

1 Introduction
To curb the impacts of global warming, a legally binding
international treaty, the Paris Agreement, was adopted at
the UN Climate Change Conference (COP21) in Paris on
December 13, 2015. The goal of the Paris Agreement is
to limit the temperature rise to well less than 2 °C, preferably 1.5 °C, in comparison with pre-industrial levels.
The implementation of the agreement requires a worldwide effort to immediately reduce emissions of anthropogenic greenhouse gases (GHGs) (United Nations
Environment Programme 2021). The steady implementation of the GHGs reductions pledged by individual
countries requires the development of validation methods for regional/country-scale GHGs emissions. The
national carbon dioxide ­(CO2) emissions from fossil fuelcombustion and cement manufacture ­(FFCO2) are usually derived from inventories based on various statistical
data, including the production, consumption, and tradeof fossil fuels (e.g., Gilfillan and Marland 2021). However,
it is also crucial to develop independent observational
methods for the estimation of emissions for tracking policy implementation.
Systematic observations of atmospheric GHGs, including ­CO2 and methane (­CH4), have been conducted bymany laboratories around the world. Even in the East
Asian region, intensive GHGs observation networks
have been developed using different platforms, such as
ground-based stations (e.g., Tsutsumi et al. 2006; Tohjima
et al. 2014), ships (e.g., Terao et al. 2011; Tohjima et al.
2012), aircraft (e.g., Machida et al. 2008; Tsuboi et al.
2013; Umezawa et al. 2020), and satellites (e.g., Yokota
et al. 2009; Yoshida et al. 2013). There are now denser
networks of atmospheric observations around the globe
than before. Meanwhile, the objective of the atmosphericobservations has been extended from clarification of the
global trends and temporal changes in the atmosphericburdens to the quantitative evaluation of regional/country-scale fluxes with the help of atmospheric transportmodels.
In these circumstances, a global pandemic of the novel
coronavirus disease, COVID-19, broke out in early 2020,and severe actions restricting socioeconomic activity,
including city lockdowns, were imposed by the concerned countries to prevent the spread of COVID-19.
These actions were also expected to decrease fossil fuelconsumption, resulting in a reduction in the emissionsof related species, including N­ Ox, ­CO2, and so on. For
example, ground-based and satellite-based observations
revealed that atmospheric ­NO2 concentrations decreasedby 10–70% over the cities in East China during the lockdown period from late January to March 2020 comparedwith those in 2019 (e.g., Bauwens et al. 2020; Le et al.
2020). Using such satellite-based column-averaged N­ O2

Page 2 of 14

distributions, models, and a variety of bottom-up information, Zheng et al. (2020) estimated an 11.5% decreasein China’s ­CO2 emissions during January–April 2020
compared to the same period in 2019. Meanwhile, thestudies on changes in ­CO2 emissions during the COVID19 period were conducted based on various activity data:
the change in ­FFCO2 emissions from China estimated by
Le Quéré et al. (2020) was − 242 (− 108 to − 394) ­MtCO2
during January–April 2020, which corresponds toa − 6.9% (− 3.1 to − 11.2%) decrease compared with theemissions during the same period in 2019. Such activity
data-based estimates allow us to evaluate the detailed
temporal change. For example, Liu et al. (2020) reported
that the changes in the monthly ­FFCO2 emissions in 2020
from 2019 were − 18.4% in February, − 9.2% in March,and + 0.6% in April. These results raised the question ofwhether direct observations of atmospheric ­CO2 wereable to detect the signals related to the F­ FCO2 emission
reduction caused by the COVID-19 outbreak.
Short-lived pollution constituents like N­ Ox, whose
estimated lifetime over China is less than a day even in
winter when N­ Ox has the longest lifetime (Shah et al.
2020), showed considerable decreases associated with the
COVID-19 lockdown in China, as mentioned above. In
contrast, the change in the atmospheric C­ O2 mole fraction caused by the COVID-19 pandemic is considered tobe relatively small in comparison with the atmospheric­CO2 level because of its relatively large amount in theatmosphere and a rather long lifetime. The estimateddecrease in the annual global ­FFCO2 emissions in 2020
was 5–7% relative to that in 2019, which was about 10
PgC (Le Quéré et al. 2021; Friedlingstein, et al. 2022).
Since the estimated change of 0.5–0.7 PgC corresponds
to the globally averaged atmospheric C­ O2 mole fractionof 0.2–0.3 ppm, it is rather difficult to detect such subtle
signals in the atmospheric C­ O2 trends after the emitted­CO2 is mixed globally (Lovenduski et al. 2021). Nevertheless, several studies succeeded in detecting signals related
to the ­FFCO2 reductions in China caused by the COVID19 lockdown in both local-scale observations (Zeng et al.
2022; Liu et al. 2021; Wu et al. 2021) and regional-scaleobservations (Tojima et al. 2020; Buchwitz et al. 2021;
Weir et al. 2021; Sim et al. 2022) of atmospheric ­CO2.
Tohjima et al. (2020) applied a unique method in theirstudy, which was one of the first studies to observationally detect the regional-scale signals related to ­FFCO2
emission decreases caused by the COVID-19 lockdown
in China from the synoptic-scale variability ratio of theatmospheric ­CO2 and ­CH4 (ΔCO2/ΔCH4) observed on
Hateruma Island (HAT, 24.06° N, 123.81° E). Hateruma
Island is located in the downwind area of continental
East Asia from late autumn to early spring due to the
influence of the East Asian monsoon. A previous study

Tohjima et al. Progress in Earth and Planetary Science(2023) 10:10

revealed that the ΔCO2/ΔCH4 ratio roughly reflected theemission ratio of C
­ O2–CH4 from continental East Asia,
especially China (Tohjima et al. 2014). In that study, theobserved ΔCO2/ΔCH4 ratios showed a gradual increaseduring 2000–2010, reflecting an unprecedented increasein ­FFCO2 emissions associated with the rapid economic
growth in China. The monthly mean ΔCO2/ΔCH4 ratio
showed a marked decrease in February 2020 when asevere lockdown was implemented almost across China(Tohjima et al. 2020). By using the observed changes in
the ΔCO2/ΔCH4 ratios and the simulated relationshipbetween the ΔCO2/ΔCH4 ratio and the ­FFCO2 emissions from China, we estimated the F­ FCO2 reductions
to be 32 ± 12% for February and 19 ± 15% for March
2020. More recently, examining the ΔCO2/ΔCH4 ratio on
Yonaguni Island (YON, 24.47° N, 123.01° E), located only
about 90 km northwest of HAT, we found that the ΔCO2/
ΔCH4 ratio also showed a marked decrease in February2020 after eliminating the local influences (Tohjima et al.
2022). These results convinced us of the reliability of theΔCO2/ΔCH4 ratio as an indicator of the relative emission
strength in China.
In this study, we revisited the ΔCO2/ΔCH4 ratios
observed at HAT and YON to develop a near-real-time
estimation method for the temporal change in the F­ FCO2emissions from China and updated the results for 2021
and 2022. In our previous study (Tohjima et al. 2020), we
used prior information about the temporal variation ofthe ­FFCO2 emissions based on a bottom-up estimation
by Le Quéré et al. (2020) to evaluate the F­ FCO2 emission
change in China in 2020. Here, we developed a methodthat does not need any prior information about the temporal emission changes. We examined the relationshipPage 3 of 14

between the ΔCO2/ΔCH4 ratio and the F­FCO2/CH4
emission ratio in China by using an atmospheric transport model and including all components of the surface fluxes. Based on the simulated relationship andthe ΔCO2/ΔCH4 ratios observed at HAT and YON, weestimated the F­ FCO2 emission changes in China during
January–March (JFM) in 2020, 2021, and 2022 under theassumption of invariable biospheric ­CO2 fluxes and total­CH4 emissions. These results were then compared with
reported estimations for earlier times.

2 Methods/experimental
2.1 Atmospheric observations at HAT and YON

The National Institute for Environmental Studies (NIES)
and the Japan Meteorological Agency (JMA) beganmonitoring the atmospheric GHGs, including ­CO2 and­CH4 at HAT and YON, respectively, in the 1990s (Fig. 1).
The technical details for the measurements of atmospheric ­CO2 and ­CH4 were given in Tohjima et al. (2002)
and Tohjima et al. (2010) for HAT, and Watanabe et al.
(2000) and Tsutsumi et al. (2006) for YON. Both islands
belong to the western part of the Ryukyu Islands, locatedbetween the East China Sea and the western Pacific. Air
masses are predominantly transported from the continental region of East Asia during winter and from the
Pacific region during summer due to the East Asianmonsoon (Wada et al. 2013; Tohjima et al. 2014). Since
HAT and YON are relatively closely located to each otherwithin a distance of about 90 km, almost identical seasonal cycles and trends of the atmospheric ­CO2 and ­CH4
have been observed on both islands (Zhang et al. 2007).
Additionally, similar synoptic-scale variations withperiods of several hours to several days have also beenFig. 1 Map showing the locations of Hateruma Island (HAT) and Yonaguni Island (YON)Tohjima et al. Progress in Earth and Planetary Science(2023) 10:10

observed at both sites, especially during winter, as shownin Fig. 2. Enhanced mole fractions of GHGs and related
species are often observed when the continental air
masses are transported to the islands. However, a previous study revealed that a substantial diurnal variation
was superimposed on the synoptic-scale variation of ­CO2
at YON, preventing us from extracting the continental
emission signals from the variability ratio (Tohjima et al.
2022). This diurnal cycle with a deep trough in the daytime was attributed to the local biospheric ­CO2 exchangeon the island. The rather large local influences at YON
were attributed to the differences in the site conditions:the monitoring station with a sampling tower at HAT was
built at the eastern tip of the island, whereas that at YON
is located inland. We needed a different treatment for thedata at YON to suppress the local influences (Tohjima
et al. 2022). In this study, we used hourly ­CO2 and ­CH4
data at HAT and YON up to April 2022.
2.2 Data processing procedure

The ratio of the synoptic-scale variations of theatmospheric ­CO2 and C­ H4 was calculated using thesame methods as those adopted by previous studies(Tohjima, et al. 2014, 2020). Here, we provide a brief
explanation of the calculation procedure. The variability ratio, ΔCO2/ΔCH4, was computed as a slopeof reduced major axis regression (RMA) (Hirsch andGilroy 1984) for a scatter plot of the consecutive time

Page 4 of 14series of the two species within a certain time window.
The standard deviations and the correlation coefficientwere also computed at the same time. These calculations were repeated for the whole data set by shifting
the time window by one hour. Then, if the standard
deviation and the correlation coefficient were lower
than certain criteria, those regression slopes were discarded. Finally, the selected regression slopes were used
to compute the monthly average or the moving averages of ΔCO2/ΔCH4.
We set 0.1 ppm and 0.7 as the criteria for the standard deviation of ­CO2 and the correlation coefficient,
respectively, as was done in our previous study. As forthe time window for the correlation analysis, a durationof 24 h was used for HAT, while a much longer duration was used for YON. As mentioned in the previous
section, the ­CO2 diurnal cycle at YON showed a larger
decrease in the daytime than that at HAT, which canbe attributed to a larger local ­CO2 uptake at YON. The
larger diurnal cycle, enlarging the absolute value of theΔCO2/ΔCH4 ratio at YON, made it difficult to extract
the signals related to the relative emission strengths in
the upwind region. However, since such a local influence was effectively eliminated by using a longer time
window (84 h) and only nighttime data in a previous
study (20-06 LST), we adopted the same approach as
Tohjima et al. (2022) to calculate the ΔCO2/ΔCH4 ratioat YON in this study.

Fig. 2 Time series of (a) atmospheric ­CO2 and (b) ­CH4 hourly mole fractions. The data obtained at YON (gray lines) and HAT (red lines) during theperiods from December 15, 2019, to April 15, 2020, and from December 15, 2020, to April 15, 2021, are depicted. The simulated C­ O2 and ­CH4 for HAT
are plotted as blue lines (see text)

Tohjima et al. Progress in Earth and Planetary Science(2023) 10:10

2.3 Model simulation

To quantitatively evaluate the relationship between thecontinental ­CO2 and ­CH4 emissions and the ΔCO2/ΔCH4
ratio at HAT and YON, we used a Nonhydrostatic ICosahedral Atmospheric Model (NICAM)-based atmospheric transport model (NICAM-TM: Niwa et al. 2011).
The NICAM dynamical framework inherently guarantees
the conservation of tracer mass in the atmospheric transport process without any numerical mass fixer (Satoh2002), which makes NICAM-TM suitable for studying long-lived species like greenhouse gases (e.g., Niwa
et al. 2012). The Japanese 55-year Reanalysis data (JRA55: Kobayashi et al. 2015) for the period between 2000
and 2021 were used to nudge horizontal winds in the
NICAM-TM simulation, and the horizontal resolution of
NICAM-TM used was approximately 112 km.
For the simulation of the atmospheric C­ O2, we used allcomponents of the global surface C­ O2 fluxes, which consist of fluxes of ­FFCO2, ocean ­CO2, and land biosphere­CO2 ­(BioCO2). For the F­ FCO2, we used global high-resolution flux maps from the Open-source Data Inventoryfor Anthropogenic ­CO2, version 2019 (ODIAC2019),
which were available for the period from 2000 to 2018
(Oda and Maksyutov 2011; Oda et al. 2018). For theocean ­CO2, we used monthly air-sea flux maps developedby the Japan Meteorological Agency for the period from2000 to 2018 (Takatani et al. 2014; Iida et al. 2015, 2021).
For the ­BioCO2, we used averaged monthly flux maps
based on the inversion for the period of 2006–2008, conducted with NICAM-TM (Niwa et al. 2012). As for the
global surface ­CH4 fluxes, we also used monthly inversion flux maps computed by the NICAM-TM 4D-Var
system with a set of global surface observations including
HAT and YON for the period from 2000 to 2017 (Niwaet al. 2017a, b; Saunois et al. 2020). We did not use theinversion ­BioCO2 fluxes in this study because the objective of the simulation was only to investigate the relationship between the ΔCO2/ΔCH4 ratio at the observationsites and the ­CO2/CH4 emission ratio in China. Thus,the ­CO2 and ­CH4 fluxes did not need to exactly represent
the actual temporal variations during the analysis period.
Only the seasonal cycles of those ­CO2 and ­CH4 fluxes
were made consistent with the observations by using theinversion results of Niwa et al. (2012) and Saunois et al.
(2020), respectively.
In this study, we simulated the atmospheric ­CO2 and­CH4 mole fractions at HAT for 2000–2021 by using thecorresponding climate dataset from the JRA-55 reanalysis and the above ­CO2 and ­CH4 flux data. When there
were no flux data for the corresponding years, we repeatedly used the latest flux maps instead: ­FFCO2 emission
and ocean ­CO2 flux maps from 2018, and ­CH4 flux maps
from 2017. The temporal changes in the monthly fluxes

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of the F­ FCO2, land ­BioCO2, and ­CH4 from China usedin the simulation are plotted in Additional file 1: Fig. S1.
This set of temporally increasing F­FCO2 emissions ishereinafter referred to as Set A. It should be noted that
we do not necessarily need prior information on emissions for the target years 2020, 2021, and 2022 in thisstudy, because only relative emission changes are estimated based on the simulated relationship between theemission ratios and the variability ratios.
The time series of the simulated atmospheric ­CO2 and­CH4 mole fractions at HAT during JFM in 2020 and 2021
are plotted in Fig. 2. The simulations generally well reproduced the observed synoptic-scale variations of both ­CO2and ­CH4. Using these simulated time series based on the­FFCO2 emissions of Set A, we examined the relationship between the ΔCO2/ΔCH4 ratio and the C­ O2/CH4
emission ratio in China. Additionally, we simulated theatmospheric ­CO2 and ­CH4 at HAT in January, February,
and March 2020 and 2021 by changing the ­FFCO2 emissions from China in 2018 to 55, 70, 85, 115, and 130%emissions and examined the relationship between thesimulated variability ratio and emission ratio using thesemodified ­FFCO2 emissions. This set of modified F­ FCO2emissions is hereinafter referred to as Set B. Note that weused the emissions from the whole mainland of China tocalculate the emission ratio and we multiplied the emissions from the entire China by single factors to prepare
the modified F­ FCO2 emissions. The simulated relationship between the variability ratio and the emission ratio
can also be applied to the observations at YON because
the simulated ΔCO2/ΔCH4 ratios at HAT and YON were
almost the same, reflecting the relatively short distance
between HAT and YON (about 90 km) compared to thegeographical relation between both sites and continental China. The observed data for both sites show almost
identical synoptic-scale variations except for diurnal
cycles.

3 Results and discussion
3.1 Temporal change in the ΔCO2/ΔCH4 ratios at HAT
and YON

The monthly mean values of the ΔCO2/ΔCH4 ratio
observed at HAT and YON between 1998 and 2022 areplotted in Fig. 3. As our previous study (Tohjima et al.
2022) suggested, there are considerable similarities inthe temporal change between HAT and YON; the ΔCO2/
ΔCH4 ratios at both sites show a gradual increase in the
2000s and rather stable values after 2011. These trends inthe variability ratio are mostly attributed to the changes
in the F­ FCO2 emissions from China (Tohjima et al. 2014,2020). In fact, the pattern of the temporal changes in theannual ­FFCO2 emissions from China taken from estimations of the Global Carbon Project (GCP) (FriedlingsteinTohjima et al. Progress in Earth and Planetary Science(2023) 10:10

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Fig. 3 Temporal changes in the monthly ΔCO2/ΔCH4 ratios based on the observations at HAT and YON. The monthly averages at HAT (closed
red symbols) and at YON (open black symbols) for January (triangles), February (circles), and March (squares) from 1998 to 2022 are plotted. Solid
red and dashed black lines represent the average values of the monthly ΔCO2/ΔCH4 ratios during a 9-year period (2011–2019) for HAT and YON,
respectively. The gray-shaded area represents the 95% confidence interval for the average for YON. The dashed and solid blue lines represent the­FFCO2 emissions from China (right blue Y-axis) taken from GCP (Friedlingstein, et al. 2022) and ODIAC (Oda et al. 2018), respectively. The orange
circles connected with the dashed orange line represent the temporal change in the F­ FCO2/CH4 emission ratios, which were averages based on theemissions from China used in the model simulation in this study (Additional file 1: Fig. S1) for the 3 months

et al. 2022) and ODIAC (Oda et al. 2018) generally agrees
with that of the ΔCO2/ΔCH4 ratio (Fig. 3). In Fig. 3, we
also plotted the average ratios of the estimated F­ FCO2and ­CH4 emissions from China for JFM between 2000
and 2018. Note that these emission ratios were based on
the emission estimates of Set A used in the model simulation of this study (Additional file 1: Fig. S1). The estimated ­CH4 emissions have a gradually increasing trend(Additional file 1: Fig. S1), but the ­FFCO2/CH4 emission
ratio for China shows a similar temporal pattern to the­FFCO2 emissions from China (Fig. 3), indicating thatthe trend in emission ratio is dominated by the trend in­FFCO2 emissions.
Previous studies also showed marked decreases in the
ΔCO2/ΔCH4 ratios at HAT and YON in February 2020,
when the COVID-19-related nationwide lockdown
in China considerably reduced the ­FFCO2 emissions(Tohjima et al. 2020; 2022). In Fig. 3, the averages of theΔCO2/ΔCH4 ratios for HAT and YON during the preceding 9-year period (2011–2019) are drawn with a 95%
confidence interval for YON. The ΔCO2/ΔCH4 ratios atboth sites fall below the 95% confidence limit in February
2020. In contrast, the monthly mean ΔCO2/ΔCH4 ratios
at HAT and YON during JFM in 2021 and 2022 returnedto the previous 9-year (2011–2019) level or higher. Thissuggests that the ­FFCO2 emissions from China in early2021 returned to the same level as or higher than before
the COVID-19 lockdown.
To take a closer look at the temporal changes in the
ΔCO2/ΔCH4 ratio during JFM in 2020, 2021, and 2022,
the 30-day moving averages of the ΔCO2/ΔCH4 ratios
for HAT and YON are depicted in Fig. 4. For comparison, averages of the ΔCO2/ΔCH4 ratios for the preceding 9 years (2011–2019) are also drawn together with thestandard deviations in the figure. The ΔCO2/ΔCH4 ratios
show decreases between January and February 2020,
minima in the middle of February, and gradual increases
toward the preceding 9-year averages in March 2020.
Previous studies (Tohjima et al. 2020, 2022) suggested
that the above temporal patterns of the consecutive
ΔCO2/ΔCH4 ratios were consistent with the estimatedchange in the F­ FCO2 emissions from China based on a
study by Le Quéré et al. (2020). On the other hand, theconsecutive ΔCO2/ΔCH4 ratios during JFM in 2021 arelarger than the preceding 9-year average and spread. The
local minimum in the middle of February 2021 might berelated to the reduction in economic activity during the
Chinese New Year holidays, which were from February
11 to 17, 2021 (see Fig. 4). The period of the Chinese New
Year holidays was from January 24 to February 2 in 2020,

Tohjima et al. Progress in Earth and Planetary Science(2023) 10:10

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Fig. 4 Consecutive changes in the ΔCO2/ΔCH4 ratio during January–March 2020, 2021, and 2022. The changes based on the observations at (a)HAT and (b) YON are depicted. The pink dots with lines and the red squares represent the 30-day moving averages and the monthly averagesof the ΔCO2/ΔCH4 ratio, respectively. The gray open circles are the averages of the 30-day moving averages of the ΔCO2/ΔCH4 ratio for thepreceding 9 years (2011–2019) and the vertical bars are their uncertainties. The temporal resolution of the consecutive changes is ± 15 days. The
light-blue-shaded areas correspond to the Chinese New Year holidaysof which the last 3 days were extended holidays to fightthe spread of COVID-19. These results seem to be consistent with the recovery of economic activity in China
from the influence of the COVID-19 pandemic in early2021.
It is noteworthy that the consecutive ΔCO2/ΔCH4
ratios in 2022, which were higher than the preceding
9-year average in January and decreased in February,reached the preceding 9-year level or lower in March.
Since the COVID-19 infection spread again mostly inShanghai after March, the reduced ΔCO2/ΔCH4 ratios
might reflect the decrease in F
­ FCO2 emissions associatedwith the confinements of the socioeconomic activities in
China (Myllyvirta 2022).
3.2 Simulated ΔCO2/ΔCH4 ratio

Using the simulated atmospheric ­CO2 and ­C H4 at HAT
for the period from 2000 to 2021 based on the ­FFCO2
emissions of Set A, we calculated the monthly averages of the ΔCO2/ΔCH4 ratio from January to Marchin the same way as the observed data at HAT werecomputed. The simulated and observed monthly averaged ΔCO2/ΔCH4 ratios during 2000–2021 are shownin Fig. 5. The simulated ΔCO2/ΔCH4 ratios roughly

Tohjima et al. Progress in Earth and Planetary Science(2023) 10:10

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Fig. 5 Temporal change in the monthly ΔCO2/ΔCH4 ratios during January–March from 2000 to 2021. The simulated monthly ΔCO2/ΔCH4 ratios
based on the F­ FCO2 emissions of Set A are plotted as purple circles. The observed monthly ΔCO2/ΔCH4 ratios at HAT are plotted as gray circles
for comparison. The purple circles surrounded by the dotted line represent the outliers, which are more than 20 mol ­mol−1 larger than thecorresponding observed ratios. The color-coded triangles represent the simulated results based on the F­ FCO2 emissions of Set B from 55 to 130%
for the meteorological fields of 2020 and 2021

trace the observed increasing trend in the 2000s andplateau after 2011 except for five points enclosed bydotted lines, which are more than 20 mol ­mol−1 larger
than the corresponding observed ratios. These discrepancies were caused by the fact that the monthly ΔCO2/
ΔCH4 ratios included some extraordinarily large values with very small ­C H4 variability. These erroneous
ΔCO2/ΔCH4 ratios might be attributed to uncertainties in model transport or flux distributions used in thesimulation or both. Thus, we rejected these five dataas outliers in the subsequent analysis. The observed
and simulated ΔCO2/ΔCH4 ratios without the abovementioned five outliers show a clear positive correlation with a correlation coefficient of 0.74 and a slopeof 1.2 ± 0.1 (Additional file 1: Fig. S2). The regression
slope was determined by the RMA method, and theuncertainties (1σ) of the parameters were evaluated bya bootstrap method, in which the regression calculations were repeatedly applied to the datasets preparedby iterative resampling with replacement (n = 10,000).
We used the above approach also for the regression
analyses in the following section. Additional sensitivity
simulations are also shown for the ΔCO2/ΔCH4 ratio
based on the F­ FCO2 emissions of Set B and the meteorological reanalysis data during 2020–2021 in Fig. 5.

The relationship between the simulated ΔCO2/ΔCH4
ratios and the F­ FCO2/CH4 emission ratios in China isshown in Fig. 6, where the simulated results for 2000–
2021 and 2020–2021 based on the F­ FCO2 emissions of
Set A and Set B, respectively, are plotted. As expected,
both datasets show positive and consistent correlations and slopes. From the linear regression analysis,
we obtained slopes and y-intercepts of 1.10 ± 0.08 and
45 ± 5 (mol ­mol−1), respectively, for Set A and thoseof 1.07 ± 0.10 and 45 ± 7 (mol ­mol−1), respectively, for
Set B. There are no statistically significant differences
between the two regression lines (p = 0.63), suggesting that the contribution of the year-to-year differencesin atmospheric transport does not strongly influence
the relationship between the ΔCO2/ΔCH4 ratios andthe ­FFCO2/CH4 emission ratios. Such characteristics
are brought about by the very fact that calculating thevariability ratio cancels out the transport influences.
Therefore, by combining Set A and Set B for the simulated relationship between the ΔCO2/ΔCH4 ratio and­FFCO2/CH4 emission ratio, we obtained a single regression line with a slope of 1.08 ± 0.07 and a y-intercept of
47 ± 4 (mol ­mol−1) (Fig. 6). The results of the regression
analysis are summarized in Table 1.

Tohjima et al. Progress in Earth and Planetary Science(2023) 10:10

Page 9 of 14

Fig. 6 Scatter plot between the ­FFCO2/CH4 emission ratios in China
and the simulated ΔCO2/ΔCH4 ratios for HAT. The plots based on the­FFCO2 emissions of Set A and Set B are shown as red and blue circles,
respectively. The red, blue, and black lines represent linear regression
lines for the plots based on Set A, Set B, and their combined set,
respectively. The values for “a” and “b” in the figure represent therelated slopes and y-axis intercepts, respectively. The gray vertical bars
are estimated uncertainties (1σ) for the regression line based on the
total ­FFCO2 data. Gray crosses are the outliers

The simulated ΔCO2/ΔCH4 ratios also showed linear relationships to the ­(FFCO2 + ­BioCO2)/CH4 emission ratios and F­FCO2 emissions in China, as shown
in Additional file 1: Fig. S3. These results are summarized in Table 1. The difference of the y-intercepts forthe ­FFCO2/CH4 from those for the ­(FFCO2 + ­BioCO2)/
CH4 corresponds to the influence of the ­BioCO2 fluxes
on the ΔCO2/ΔCH4 ratios. The regression slopes for theemission ratios are close to unity, and the y-intercepts
for the ­(FFCO2 + ­BioCO2)/CH4 are roughly close to theorigin, indicating that the ΔCO2/ΔCH4 ratios at HAT

straightforwardly reflect the ­CO2/CH4 emission ratios
in China, as was indicated in a previous study (Tohjima
et al. 2014). As for the relationship to the ­FFCO2 emissions, there is a significant difference (p < 0.05) between
the regression lines for the ΔCO2/ΔCH4 ratios derived
from the ­FFCO2 emissions of Set A and Set B, indicatingthat the different rate of increase in the C­ H4 emissions
and potentially the spatial heterogeneity in the change
rates of the realistic ­FFCO2 emissions of Set A contribute to the temporal change in the ΔCO2/ΔCH4 ratios. In
the following sections, assuming that the land biospheric­CO2 fluxes from China have no interannual variations,
we used the linear relationship between the ΔCO2/ΔCH4
ratios and the F­ FCO2/CH4 emission ratio to evaluate thechange in the F­ FCO2/CH4 emission ratio in China.
Although the JFM chosen for the analysis corresponds
to the period when the biotic activities are relatively dormant, there can be a measurable interannual variability
in the B­ ioCO2 and C­ H4 fluxes (Additional file 1: Fig. S1).
From the inversely estimated ­BioCO2 and ­CH4 fluxes
from China after 2011 based on NICAM-TM (see Additional file 1: Fig. S1), we obtained averages and standard
deviations (1σ) of 2.0 ± 1.3 TgC d­ ay−1 and 0.118 ± 0.008−1­TgCH4 ­day for the ­BioCO2 and ­CH4 fluxes, respectively. These standard deviations for the ­BioCO2 and­CH4 fluxes correspond to about ± 14% and ± 7% variations in the (­FFCO2 + ­BioCO2)/CH4 emission ratio in
China in the recent decade, respectively. Meanwhile, the­CH4 emissions from China for the 3 months are mostly
derived from anthropogenic sources, including coal mining, landfills, enteric fermentation, and other anthropogenic sources, except for paddy fields (e.g., Ito et al.
2019). Among these anthropogenic sources, coal miningis the largest source in China, contributing about 40% of
the total emission during JFM. Since the increase in coalconsumption historically enhanced the ­FFCO2 and ­CH4
emissions in China, it was inferred that these emissions
positively correlated, as was pointed out by Saeki andPatra (2017). Therefore, such a positive correlation of theTable 1 Summary of the regression analysis of the simulated ΔCO2/ΔCH4 ratio at HAT using three sets of ­FFCO2 emissions (A, B, andA + B) against three sets of emission ratios, ­FFCO2/CH4, ­(FFCO2 + ­BioCO2)/CH4, and ­FFCO2 in China
Emission ratio or emission

FFCO2/CH4(FFCO2 + ­BioCO2)/CH4
FFCO2
a

Set A (2000–2021)

Set B (2020–2021)

Slope

Intercepta

SlopeIntercepta

1.10 ± 0.08

45 ± 51.07 ± 0.10

0.36 ± 0.02b

58 ± 40.40 ± 0.04b

1.19 ± 0.08
−1

Units of intercepts are given in mol ­mol

16 ± 71.07 ± 0.10

Set A + B

p ­valuec

Slope

Intercepta

45 ± 71.08 ± 0.07

47 ± 40.63

46 ± 70.37 ± 0.02b

56 ± 04

0.04

26 ± 91.12 ± 0.07

22 ± 6b

Units of slopes for ­FFCO2 emission are given in (mol ­mol−1)/TgCcp values are for the hypothesis that there is no significant difference between the slopes for the time-dependent fluxes and modified fluxes

0.51

Tohjima et al. Progress in Earth and Planetary Science(2023) 10:10

emissions might, to some extent, attenuate the temporalchange in the observed ΔCO2/ΔCH4 ratios at HAT and
YON. The influence of the correlative change in the C­ H4emissions on the ΔCO2/ΔCH4 ratios in this model simulation can be evaluated from the linear relationships of
the ΔCO2/ΔCH4 ratios against the F­ FCO2 emissions of
Set A and Set B listed in Table 1. For the same change
in the ­FFCO2 emissions from 220 TgC, the change in thesimulated ΔCO2/ΔCH4 ratios for Set A is about 8% lower
than that for Set B.
3.3 Estimation of the ­FFCO2/CH4 emission ratio in China
3.3.1 Conversion of the ΔCO2/ΔCH4 ratio to ­FFCO2/CH4
emission ratio

Using the observed monthly averages of the ΔCO2/
ΔCH4 ratios at HAT and YON for JFM in 2020, 2021,
and 2022 and the preceding 9-year (2011–2019) averages for JFM, we evaluated the changes in the ­FFCO2/
CH4 emission ratio in China in 2020, 2021, and 2022
in comparison with the preceding 9-year (2011–2019)averages (Table 2). Here, we adopted the standard
errors of the monthly averages of the selected regression slopes (Sect. 2.2) and the standard deviations forthe 9-year averages of the monthly ΔCO2/ΔCH4 ratios
as the respective uncertainties. These ΔCO2/ΔCH4
ratios were translated into the ­FFCO2/CH4 emission
ratio in China by the linear function deduced in theprevious section. Then, we calculated the change rateof the F­ FCO2/CH4 emission ratios compared to the
9-year averages at HAT and YON and their weighted
averages. Here we set the reciprocal of the square ofPage 10 of 14

the uncertainty associated with each ­FFCO2/CH4 emission ratio for the weight. Table 2 lists these estimatedchanges in the F­FCO2/CH4 emission ratio ranging
from − 36 to 48%.
In our previous study (Tohjima et al. 2020), using thesame monthly average ΔCO2/ΔCH4 ratios at HAT and
an atmospheric model simulation, we estimated therelative changes in the F­ FCO2 emissions from China tobe − 32 ± 12% for February and − 19 ± 15% for March
2020 under the assumption of invariable ­C H4 emissions. The slight differences in the ­FFCO2 emission
changes in this study are attributed to the different
approach of the previous study, in which the ­FFCO2
emissions from China were reduced in proportion to
the bottom-up estimate based on the economic activity
data of Le Quéré et al. (2020
We also estimated the consecutive changes in the­FFCO2/CH4 emission ratio based on the 30-day moving
averages of the ΔCO2/ΔCH4 ratio at HAT and YON for
the JFM in 2020, 2021, and 2022. As was done for themonthly averages, we converted the consecutive variability ratios to emission ratios and computed the rate ofchange in the emission ratios for the preceding 9-year
averages. The estimated rates of change in the ­FFCO2/
CH4 emission ratio for HAT and YON are depicted in
Additional file 1: Fig. S4, and their weighted averages
with the propagated uncertainties (1σ) are shown in
Fig. 7. Hereinafter, we discuss the weighted averages ofthe estimated ­FFCO2/CH4 emission ratios, which canbe interpreted as the change in the F­ FCO2 emissionsin China under the assumption of no interannual ­C H4emission change during JFM.

Table 2 Observed ΔCO2/ΔCH4 ratio and estimated change in the ­FFCO2/CH4 emission ratio in China from the preceding 9-year
average
Date (year/month)

2020/01
2020/022020/032021/01
2021/022021/032022/01
2022/022022/03
2011–2019/01
2011–2019/02
2011–2019/03

Monthly ΔCO2/ΔCH4 (mol ­mol−1)

Estimated change in the F­ FCO2/CH4 emission ratio from thepreceding 9 years (%)

HAT

YON

HAT

147 ± 2145 ± 1100 ± 2117 ± 2146 ± 2126 ± 2147 ± 2146 ± 2126 ± 3117 ± 2131 ± 7129 ± 11133 ± 1197 ± 2126 ± 2149 ± 1126 ± 1170 ± 2156 ± 1124 ± 1132 ± 2133 ± 11126 ± 12130 ± 1118 ± 10− 35 ± 9− 18 ± 1118 ± 10− 4 ± 1316 ± 1618 ± 10− 4 ± 13

− 19 ± 11–YON
14 ± 15− 36 ± 10− 5 ± 1319 ± 15

1 ± 1548 ± 2027 ± 16− 2 ± 142 ± 13–Weighted Ave
17 ± 8− 36 ± 7− 12 ± 8

18 ± 8− 2 ± 1029 ± 1220 ± 9− 3 ± 10

− 10 ± 9

–

–

–

–

–

–

–

Tohjima et al. Progress in Earth and Planetary Science(2023) 10:10

Page 11 of 14

Fig. 7 Estimated ­FCO2/CH4 emission change in China based on the ΔCO2/ΔCH4 ratios observed at HAT and YON. The estimated results for 3 months(January, February, and March) in 2020, 2021, and 2022 are depicted in the left, middle, and right panels, respectively. The red circles with red lines
and pink squares represent the estimates based on the 30-day moving averages and monthly averages, respectively, of the observed ΔCO2/ΔCH4
ratios. The vertical bars represent the uncertainties. For comparison, the temporal changes in F­ FCO2 emissions from China based on the bottom-up
estimates of Le Quéré et al. (2020) (orange line) and Liu et al. (2020) (light blue line) are also shown. The gray-shaded areas correspond to theChinese New Year holidays

3.3.2 FFCO2/CH4 emission change in 2020

The estimated monthly change in the ­FFCO2/CH4 emission ratios for 2020 compared with the preceding 9-year
averages was 17 ± 8% for January, − 36 ± 7% for February,
and − 12 ± 8% for March (Table 2). The average change
for the 3 months is − 10 ± 9% compared with the preceding 9-year average. The value is consistent with previous estimates based on bottom-up approaches: − 10.1%(− 4.6 to − 16.5%) by Le Quéré et al. (2020) and − 13% by
Liu et al. (2020) for JFM. Note that these bottom-up values were reported as the changes in emissions from theprevious year (2019).
As was discussed in Tohjima et al. (2020), the marked
decrease in February 2020 corresponded to the periodof the nationwide lockdown in China, and the slightrecovery in March corresponded to the transition period
to normal conditions. Such a temporal change is moreclearly shown in the plot of the consecutive estimationof the ­FFCO2/CH4 emission ratio (Fig. 7). For comparison, the temporal changes in the ­FFCO2 emissions from
China based on bottom-up estimates (Le Quéré et al.
2020; Liu et al. 2020) are also depicted in the figure. Bothbottom-up estimates begin to decrease in late January,reach a minimum in the middle of February, then gradually return to the previous emission level (0%), although
the estimate of Liu et al. (2020) shows a sharp maximum

at the beginning of February. The sharp maximum corresponds to the estimated F­FCO2 emission minimumof the previous year (2019), which was attributed by Liu
et al. (2020) to the reduction in economic activity in
China during the Chinese New Year holidays from February 4 to 10, 2019. Except for the sharp maximum, ourestimation agrees well with the bottom-up estimations.
These results seem to support the reliability of our simple
estimation approach based on the atmospheric ΔCO2/
ΔCH4 ratio.
3.3.3 FFCO2/CH4 emission change in 2021

The estimated ­FFCO2/CH4 emission ratios for 2021 wereequal to or larger than the preceding 9-year average;
the average change was 18 ± 8% for January, − 2 ± 10%
for February, and 29 ± 12% for March, and the averagechange for JFM is 15 ± 10%. The relatively lower ratio
for February than those for January and March may be
attributed to the temporal decrease in F
­FCO2 emissions during the Chinese New Year holidays. From theextended estimates of the bottom-up study of Liu et al.
(2020) (https://​www.​carbo​nmoni​tor.​org.​cn), we obtained
a monthly change of 16% for January, − 1% for February, and 14% for March relative to 2019, and the averagechange for JFM was 10%, which is again consistent withthe estimations of this study. The consecutive variations

Tohjima et al. Progress in Earth and Planetary Science(2023) 10:10

in 2021 are shown in Fig. 7. Compared with the bottomup estimate of Liu et al. (2020), the temporal variabilityof our estimation is rather suppressed, especially during the period related to the Chinese New Year holidays.
The difference is partially explained by the low temporal
resolution (± 15 days) of our estimation. In addition, ourestimated ­FFCO2 emissions for March 2021 are slightly
larger than the bottom-up estimates of Liu et al. (2020).
These enhanced estimates were supported by the ΔCO2/
ΔCH4 ratios observed at both HAT and YON (see Additional file 1: Fig. S4). Note that the ΔCO2/ΔCH4 ratios
at YON were more than double those at HAT. Possibly,
influences from local emissions were not sufficiently
eliminated (see Sect. 2.2), enhancing the ΔCO2/ΔCH4
ratios at YON in March 2021. Our observational result,although still having a large uncertainty, suggests that the­FFCO2 emissions from China considerably reboundedin early 2021 despite the global effort to reduce GHGs
emissions.
3.3.4 FFCO2/CH4 emission change in 2022

The estimated changes in the monthly F­FCO2/CH4
emission ratios for 2022 were 20 ± 9%, − 3 ± 10%,and − 10 ± 9% for January, February, and March, respectively, and the average change for JFM is 2 ± 9%. The
monthly ­FFCO2 emission changes relative to 2019 takenfrom the extended estimates of the bottom-up study of
Liu et al. (2020) were 15% for January, 3% for February,
and 8% for March, and the average change for JFM was
9%, which are again consistent with the estimations of
this study except for March. The consecutive variations
in our emission estimate for 2022 shown in Fig. 7 persistently decrease in February even after the Chinese New
Year holidays and maintain a low level in March, whereas
the bottom-up result of Liu et al. (2020) shows a gradualincrease after the Chinese New Year holidays. The emissions from Shanghai strongly affected the ΔCO2/ΔCH4
ratios observed at HAT and YON because of the relatively short distance. Therefore, our estimated changes
based on atmospheric observations might reflect the­FFCO2 emission decreases due to the spread of COVID19 in Shanghai after March.

4 Conclusions
We developed a near-real-time estimation method forthe change in the ­FFCO2 emissions from China basedon the synoptic-scale variability ratio of atmospheric­CO2 and C­ H4 (ΔCO2/ΔCH4 ratio) in January, February,
and March (JFM) on two remote islands in Japan, HAT
and YON. From simulation results based on an atmospheric transport model (NICAM-TM) with all components of realistic ­CO2 and ­CH4 fluxes, we found a linear
relationship between the monthly averaged ΔCO2/ΔCH4

Page 12 of 14

ratios and the F­ FCO2/CH4 emission ratios in China. We
used this simulated linear relationship to translate theobserved ΔCO2/ΔCH4 ratio into ­FFCO2/CH4 emission
ratios under the assumption of no interannual B­ ioCO2change during JFM. The change in the estimated F­ FCO2/CH4 emission ratio can be interpreted as the change inthe ­FFCO2 emissions by assuming no interannual C­ H4emission change during JFM. Because this method is
simple compared to the inverse method, near-real-timemonitoring is feasible.
Using the developed method, we estimated the change
in the ­FFCO2/CH4 emission ratios for 2020, 2021, and2022 with respect to the average emission ratios for thepreceding 9-year period (2011–2019), during which
relatively stable ΔCO2/ΔCH4 ratios were observed
at both HAT and YON. The resulting changes in the­FFCO2 emissions for January, February, and March were
17 ± 8%, − 36 ± 7%, and − 12 ± 8%, respectively, in 2020(− 10 ± 9% for JFM overall), 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). The estimations for 2020of not only the average change but also the temporal pattern of the F­ FCO2 emission change agreed well with thereported estimations based on bottom-up studies (LeQuéré et al. 2020 and Liu et al. 2020). Therefore, our estimations for 2021 strongly suggest that ­FFCO2 emissions
from China rebounded with the recovery of the socioeconomic activities after the COVID lockdown in China.
However, our estimated F­ FCO2 change showed a slightdecrease in March 2022, suggesting that the ­FFCO2 emissions from China were still affected by the infection status of COVID-19 in China.
This early estimation method proposed in this study
only gives us quick but rough estimations because of avariety of assumptions. Particularly, the assumption that
­BioCO2 and ­CH4 have no interannual variations should
be validated in future studies to refine the estimated­FFCO2 change based on more comprehensive analyses.
Additionally, we could probably overcome the limitationof the short estimation period of this study by analyzing
the atmospheric observations from tall towers and aircraft with reduced interferences from the local biosphere
and with large footprint areas in continental China. Nevertheless, we consider this estimation method useful forthe verification of the GHG emission mitigation strategy
in China or elsewhere with strategically positioned measurement sites.
Abbreviations
HAT	Hateruma Island
YON	Yonaguni Island
NIES	National Institute for Environmental Studies

Tohjima et al. Progress in Earth and Planetary Science(2023) 10:10

Page 13 of 14

JMA	Japan Meteorological Agency
RMA	Reduced major axis regression
NICAM-TM	Nonhydrostatic ICosahedral Atmospheric Model (NICAM)-basedtransport model
ODIAC	Open-source Data Inventory for Anthropogenic ­CO2

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Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40645-​023-​00542-6.
Additional file 1. Fig. S1. Temporal changes in the monthly emissions of(a) ­FFCO2, (b) ­BioCO2, and (c) C­ H4 from China used in the simulation of thisstudy.Fig. S2. Scatter plot of the relationship of the ΔCO2/ΔCH4 ratios (redcircles) between the observation and simulation.Fig. S3. Scatter plots ofthe simulated ΔCO2/ΔCH4 ratios for HAT to (a) the ­(FFCO2+BioCO2)/CH4
emission ratios and (b) the FFCO2 emissions in China.Fig. S4. Temporalchanges of the ­FFCO2/CH4 emission ratio relative to the preceding 9-year(2011-2019) averages during the three-month period (January-March) in
2020, 2021, and 2022 for (a) HAT and (b) YON.
Acknowledgements
We are grateful to the staff members of the Global Environment Forum andthe Center for Global Environmental Research, as well as the local staff members for their continued support in conducting the in situ measurements of­CO2 and ­CH4 at HAT. We also thank the staff members of the Japan Meteorological Agency for their work in the long-term observations of atmospheric­CO2 and ­CH4 at YON. The NICAM-TM simulations were performed using the
NIES supercomputer system (NEC SX-Aurora).
Author contributions
YT conceived and designed the study, YT, HM, TM, MS, KT, and KS conducted
the measurements, YN carried out the model simulation, and YT, YN, and PKP
developed the analysis strategy. All authors participated in the discussions
and preparation of the manuscript. All authors read and approved the final
manuscript.
Funding
This study was supported by funds provided by the Environment Research
and Technology Development Fund (JPMEERF21S20800) and the Global Environmental Research Coordinate System from the Ministry of the Environment,
Japan (grant no. E1451).
Availability of data and materials
The time series of the atmospheric C­ O2 and C­ H4 mole fractions at HAT are
available through the NIES database, Global Environmental Database (GED)(https://​db.​cger.​nies.​go.​jp/​ged/​en/​index.​html). The time series of the atmospheric ­CO2 and ­CH4 mole fractions at YON are available through the Web siteof the World Data Centre for Greenhouse Gases (WDCGG). (https://​xml.​kishou.​go.​jp/).

Declarations
Competing interests
The authors declare that they have no competing interests.
Received: 29 August 2022 Accepted: 17 February 2023

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