Radiocarbon in atmospheric CH₄ and CO₂ at Jungfraujoch in 2019–2024: influence of regional nuclear emissions and current global atmospheric ¹⁴CH₄ signal

Radiocarbon (¹⁴C) is a valuable tracer to determine the relative fossil fractions of emitted carbonaceous greenhouse gases, such as CO₂ and CH₄. While atmospheric Δ¹⁴CO₂ measurements have been conducted at multiple sites for several decades, Δ¹⁴CH₄ measurements remain more limited, mainly due to measurement challenges. In addition, ¹⁴CH₄ emissions from nuclear power plants (NPPs) can complicate data interpretation. In this study, fortnightly Δ¹⁴CH₄ and Δ¹⁴CO₂ measurements at the Swiss High-Altitude Research Station Jungfraujoch (JFJ, about 3500 m a.s.l.) between 2019 and 2024 are presented. Over this period, Δ¹⁴CH₄ values showed an increase from 350 ± 19 ‰ to 381 ± 13 ‰, while Δ¹⁴CO₂ values decreased from −2.0 ± 3.8 ‰ to −12.7 ± 2.0 ‰, respectively. The former is related to the slight increase of ¹⁴CH₄ emissions from the nuclear industry over the last years, while the latter is linked to the continued dilution of the ¹⁴CO₂ signal due to the release of ¹⁴C-devoid CO₂ from combustion of fossil fuels. Despite its high elevation, JFJ is still influenced by NPPs operating in Europe. To assess the nuclear ¹⁴C contribution to our individual measurements, we use a combination of in situ ²²²Radon measurements and Lagrangian particle dispersion model convolved with bottom-up inventory of ¹⁴C emissions from NPPs. Furthermore, our Δ¹⁴CH₄ measurements reasonably agree with simulated atmospheric values of Δ¹⁴CH₄ estimated by a global atmospheric one-box model and an estimation of global nuclear ¹⁴CH₄ emissions.

1 Introduction

Carbon dioxide (CO₂) and methane (CH₄) are the two main anthropogenic greenhouse gases (GHGs) responsible for global climate change (IPCC, 2023; WMO, 2025). Since the Industrial Revolution around 1850, their global atmospheric concentrations have been multiplied by about 1.5 and 2.7, respectively, from around 285 to 422.8 ppm in 2024 for CO₂ (Etheridge et al., 1996; Lan et al., 2025b) and from around 800 to 1930 ppb in 2024 for CH₄ (Lan et al., 2025a; MacFarling Meure et al., 2006). Anthropogenic emissions of CO₂ and CH₄ (mainly from the energy sector for fossil CO₂ and CH₄, and agriculture and waste management for biogenic CH₄) are responsible for this rise in concentrations and resulting climate change. In the period 2010–2019, CO₂ and CH₄ were responsible for a global warming of around 0.8 and 0.5 °C, respectively, relative to the period 1850–1900 (IPCC, 2023). To implement effective mitigation measures, the main emission sources of both GHGs have to be better quantified and monitored.

Radiocarbon (¹⁴C), a radioactive isotope of carbon with a half-life of 5700 ± 30 years (Kutschera, 2013; Roberts and Southon, 2007), is a valuable tracer to distinguish fossil from modern carbon sources. Naturally produced in the upper atmosphere through the interaction of thermal neutrons with nitrogen, ¹⁴C is finally oxidized to ¹⁴CO₂, which can be integrated into the biosphere via photosynthesis and into the hydrosphere via gas exchange at the water-atmosphere interface (Graven et al., 2020). On the one hand, fossil fuels (e.g. coal, oil, natural gas) that were formed millions of years ago are now devoid of ¹⁴C because of its short half-life compared to geological time scales. On the other hand, CO₂ and CH₄ derived from fresh organic matter contain ¹⁴C $/$ ¹²C ratios close to the current atmospheric ¹⁴CO₂ signature. Atmospheric ¹⁴CO₂ and ¹⁴CH₄ measurements are thus valuable proxies to study the different sources of CO₂ and CH₄ released to the atmosphere.

Atmospheric ¹⁴CO₂ measurements have a long history. Since the first measurements focused on the documentation of the atmospheric ¹⁴CO₂ bomb peak in the 1950s–1960s associated with nuclear bomb tests (Levin et al., 1985; Manning et al., 1990; Nydal and Lövseth, 1983; Rafter and Fergusson, 1957), there are now international monitoring programs following the long-term evolution of atmospheric ¹⁴CO₂ at background sites (e.g., Hammer et al., 2017; Turnbull et al., 2007). Observations at the High-Altitude Research Station Jungfraujoch (JFJ) in Switzerland have been conducted since 1986; there, two-weeks integrated CO₂ samples have been collected and further purified and analyzed at the Heidelberg laboratory (Germany) (Levin et al., 2013, 2023). On a more local scale, atmospheric ¹⁴CO₂ measurements have been used to study CO₂ emissions from urban areas to entire countries (Basu et al., 2020; Graven et al., 2018; Levin et al., 2003). Since the bomb peak in the middle of the last century, Δ¹⁴CO₂ values have been declining by the dilution of the enriched atmospheric ¹⁴C signal in both land and ocean carbon sinks and by the emissions of ¹⁴C-free fossil fuel CO₂, which depletes the atmospheric Δ¹⁴CO₂ signal (Graven et al., 2024; Oeschger et al., 1975).

Atmospheric measurements of ¹⁴CH₄ have been more limited than ¹⁴CO₂ so far. One reason for this is related to the about 200-times lower atmospheric CH₄ concentration compared to CO₂: while 2–5 L of air are sufficient to analyze ¹⁴CO₂ (e.g. yielding about 1 mgC from 5 L air at 420 ppm CO₂), several tens of liters are required for ¹⁴CH₄ analysis (e.g. yielding about 50 µgC from 50 L air at 2 ppm CH₄). The sampling and analysis of ¹⁴CH₄ is therefore difficult, and prone to CO₂ contamination. Besides this technical consideration, the interpretation of atmospheric ¹⁴CH₄ measurements may be complicated at study sites that are influenced by nuclear power plants (NPPs) (Eisma et al., 1994, 1995; Levin et al., 1992). Pressurized water reactors (PWRs), which are currently the most widely operated plants (IAEA PRIS, 2025), emit ¹⁴C mainly as ¹⁴CH₄, whereas other reactor types emit ¹⁴C mainly as ¹⁴CO₂ (Vance et al., 1995; Zazzeri et al., 2018). The annual global nuclear ¹⁴C emission rate for 2016 has been estimated to about 105 TBq for ¹⁴CO₂ and about 45 TBq for ¹⁴CH₄ (Zazzeri et al., 2018). Although ¹⁴CO₂ emissions are larger, the influence of nuclear ¹⁴CH₄ emissions on atmospheric ¹⁴CH₄ is stronger due to the much lower atmospheric CH₄ concentration compared to CO₂. This also explains why current atmospheric ¹⁴C $/$ ¹²C ratios are about 35 % higher for ¹⁴CH₄ than ¹⁴CO₂ (Emmenegger et al., 2025a; Gonzalez Moguel et al., 2022).

Despite these challenges, several analysis setups and atmospheric ¹⁴CH₄ datasets have been reported from ice cores and atmospheric samples. Pioneering atmospheric ¹⁴CH₄ measurements were reported by Lowe et al. (1988) and Wahlen et al. (1989). In the 1990s, Levin et al. (1992) reported the first sporadic ¹⁴CH₄ measurements between 1988 and 1991 at JFJ. Eisma et al. (1994, 1995) measured atmospheric ¹⁴CH₄ values from a tall tower in the Netherlands and highlighted the challenge to interpret ¹⁴CH₄ measurements, even when using an atmospheric transport model to evaluate the nuclear influence on the measurements. Lassey et al. (2007a, b) compiled more than 200 individual atmospheric ¹⁴CH₄ measurements from the Northern and Southern Hemispheres between 1986 and 2000 and deduced that about 30 % of the global CH₄ source for this period had a fossil origin. Hmiel et al. (2020) used ¹⁴CH₄ measurements from ice cores to better constrain natural geological (i.e., fossil) CH₄ emissions during the preindustrial era. By synthetizing atmospheric CH₄ concentration and its major isotopologues (¹³CH₄, CH₃D and ¹⁴CH₄) for 1750–2015, Fujita et al. (2025) estimated 30 % lower global CH₄ emissions from the fossil-fuel industry compared to previous isotope-based studies relying mostly on CH₄ and ¹³CH₄ without ¹⁴C consideration. Another output of their work was an updated inventory of the nuclear ¹⁴CH₄ emissions between 1960 and 2015 based on nuclear electricity production data.

In recent years, several novel analysis systems and studies for ¹⁴CH₄ have been reported (Espic et al., 2019; Gonzalez Moguel et al., 2022; Zazzeri et al., 2021, 2023) increasing the analysis capabilities, even in a more field-compatible way (Zazzeri et al., 2025). Despite these new studies, recent background atmospheric ¹⁴CH₄ values are still missing. At the Laboratory for the Analysis of Radiocarbon with AMS (LARA, University of Bern) (Szidat, 2020), a system exists since 2019 to analyze ¹⁴CH₄ and ¹⁴CO₂ from a single atmospheric sample (Espic et al., 2019) and was already used in several studies (Espic et al., 2025; Etiope et al., 2024; Zazzeri et al., 2025). Here, we present atmospheric ¹⁴CH₄ and ¹⁴CO₂ measurements conducted at the High-Altitude Research Station Jungfraujoch between 2019 and 2024, discuss their representativeness regarding the nuclear influence in Europe and show their relevance as worldwide background values.

2 Material & Methods

2.1 Jungfraujoch Site and Sampling Strategy

The High-Altitude Research Station Jungfraujoch (stretching from 3455 to 3585 m a.s.l., 46°32^′51^′′ N, 7°59^′7^′′ E, JFJ), established in 1931, is located on a mountain ridge in the Swiss Alps. Among others, this station is part of the Global Atmosphere Watch (GAW) network as well as of the Network for the Detection of Atmospheric Composition Change (NDACC). Furthermore, JFJ is labelled as a Class 1 Station of the European-wide Integrated Carbon Observation System (ICOS) Research Infrastructure (Heiskanen et al., 2022) since May 2018 (Yver-Kwok et al., 2021). Because of its high elevation, JFJ is a well-recognized international background station (Leuenberger and Flückiger, 2008). However, it is also intermittently impacted by direct transport from the polluted planetary boundary layer, most frequently during daytime from April to September (Henne et al., 2010).

A fortnightly air sampling program measuring ¹⁴CH₄ and ¹⁴CO₂ at JFJ every two weeks started in January 2019. Until March 2023, two morning grab air samples were taken using a membrane pump (N022AN.18, KNF Neuberger AG, Germany) that directly pumped air from the Sphinx terrasse (3580 m a.s.l.) at JFJ into two PE-Al-PE 120 L bags (Tecobag, Tesseraux Spezialverpackungen GmbH, Germany) through a dedicated sampling line (Synflex Decabon 1300 Tubing, OD =6 mm). The sampled air was dried with a magnesium perchlorate dryer (Mg(ClO₄)₂, ACS reagent, ThermoFisher, USA) to avoid condensation and further reactions between water vapor and other sampled gas species. The sampling duration per bag was commonly between 20 and 55 min depending on the ambient pressure, temperature and the flow resistance during sampling due to the dryer and the long length and small diameter of the sampling line. The morning grab samplings were performed manually and as early as possible in the morning to avoid the influence of the daytime planetary boundary layer; in practice, it commonly occurred between 07:00 and 10:00 UTC (i.e., 08:00 and 12:00 local time, UTC+1/UTC+2), depending on the seasonal train schedule ensuring the public transport up to JFJ.

To increase the reproducibility of the air sampling procedure and its representativeness as a background measurement, an automated air sampling system was developed replacing the manual sampling from 18 April 2023 onwards (Fig. 1). Its development was guided by two principles: (1) air sampling should be restricted to nighttime when the station mostly resides within the free troposphere, allowing for the collection of temporally integrated samples over multiple days; and (2) air sampling should occur as far as possible passively, i.e., without any mechanical pumps in contact with the sampled air, thereby minimizing the risk of contamination from membrane outgassing. The novel Jungfraujoch Air Sampling System (JASS) consisted of a custom-made electropolished steel sampling tank (volum = 260 L, length (L) = 140 cm, outer diameter (OD) = 51 cm, Bechtiger Edelstahl AG, Switzerland) coupled to a sampling box containing a series of valves, all controlled by a home-made control unit including a Raspberry Pi 4B module (Raspberry Pi Ltd, United Kingdom) (Fig. 1). The whole system was installed under the roof of the research station at JFJ, at about 3462 m a.s.l. (Fig. S1 in the Supplement). Its design allows a routine integrated nighttime air sampling procedure lasting 6 h every night (between 00:00 and 06:00 UTC) over 14 d, so integrating 84 h in total. Compared to the initial fortnightly morning grab sampling strategy, integrated sampling gives a more representative air mixture for every two-week period of interest (Fig. S2).

Figure 1 Jungfraujoch Air Sampling System (JASS) used for integrated nighttime air sampling over 14 d since 18 April 2023.

The inlet of the sampling line (Synflex Decabon 1300 Tubing, OD = 12 mm, L∼10 m) was protected by a dust filter (Fig. 1). A flushing membrane pump (N022AN.18, KNF Neuberger AG, Germany) continuously conveyed outside air through this sampling line and a water separator to the sampling box. Through two T-pieces (SS-12M0-3, Swagelok, USA) integrated in this sampling line, part of the sampled air could be further directed into the sampling box, either automatically using line (1), or manually using line (2) (Fig. 1). At the beginning of a sampling period, the tank was evacuated using a vacuum pump (MV 2 NT, Vacuubrand GmbH + Co KG, Germany); the end pressure was ≤ 0.7 mbar. During automatic air sampling, the sampling line (1) was open and worked as follows: both 2/2-way solenoid valves A1 and A2 (0330-A-02, Bürkert, Germany) were open and the 3/2-way solenoid valve B (0330-F-02, Bürkert, Germany) was in the position connecting this line with the tank (blue connection, Fig. 1); the mass-flow controller (MFC, F-201DV, Bronkhorst, The Netherlands) between A1 and A2 ensured a controlled air flow into the system and the increasing pressure in the tank was monitored by a pressure gauge (RPT 200 AR, Pfeiffer Vacuum, Germany); in front of the MFC, a Mg(ClO₄)₂ dryer dried the sampled air and a 0.5 µm filter (SS-4FWS-05, Swagelok, USA) protected the MFC from particulate matter. After 6 h of sampling, both A1 and A2 valves were closed. This sampling procedure was repeated every night. After 14 nighttime samplings, the pressure in the tank reached 600–650 mbar. The mean ambient atmospheric pressure at JFJ was 656 mbar over the period 2019–2024 (Emmenegger et al., 2025d); we configured our passive sampling system in a way where the tank pressure was always lower than ambient pressure, otherwise the passive sampling based on the pressure difference between the tank and the ambient pressure would not work. On-site, the transfer of the sampled air from the tank into a new bag was prepared in this manner: a dedicated transfer pump (N922SPE, KNF Neuberger AG, Germany) was turned on and the manual 3/2-way valve D2 was turned to the left (Fig. 1); the position of the 3/2-way valve D1 stayed by default turned to the left. The Python script running on the Raspberry Pi was then restarted to turn on the vacuum pump and switch the 3/2-way solenoid valve B in the position connecting the tank with the pump line (red connection, Fig. 1). At this stage, the transfer pump was evacuating the tank and after ∼30 s of line flushing, a bag was connected to the line for the transfer of the sampled air. After about one hour, enough air was transferred to the bag for further ¹⁴C analyses. It was then closed and disconnected from the transfer pump (tank pressure at around 70–90 mbar). To accelerate the evacuation of the tank and condition it again for the next sampling, the valve D2 was then turned to the right, and the vacuum pump was evacuating the tank. The system was left like this for the rest of the day and, at 23:59 UTC, valve B switched automatically back to its sampling position (blue connection, Fig. 1) (final tank pressure ≤0.7 mbar), the vacuum pump turned off and the whole system was ready to start the next sampling period. The whole sampling system was validated to be leak-tight in our laboratory in Bern before being transferred to JFJ. The low pressure achieved in the tank before each new sampling every two weeks regularly validated the tightness of the sampling system.

2.2 Sample preparation and measurements of Δ¹⁴CH₄ and Δ¹⁴CO₂

For both morning grab and integrated nighttime sampling strategies, the air collected in bags was transferred to the LARA laboratory at the University of Bern (Szidat, 2020), where a dedicated extraction line for ¹⁴CH₄ and ¹⁴CO₂ analysis was available (see Espic et al., 2019 for details). The extraction was generally done within one week after sampling and most of the time the day after. The typical air volume for one analysis was 60 L. During a preconcentration step, this air was pumped successively through three traps filled either with fiber glass or activated charcoal and cooled down with liquid nitrogen; this allowed us to preserve the whole amount of CH₄ contained in the initial 60 L of air by trapping or pumping away the main other gas species, especially nitrogen and oxygen. CO₂ was quantitatively trapped in the first trap and could be collected separately (see below). After that, the gas sample volume was about 10 mL, small enough to be run through a gas chromatograph (GC, 7890B, Agilent, USA; ShinCarbon ST 80/100 packed column; thermal conductivity detector, He as carrier gas) to purify the sample. CH₄ was isolated quantitatively from the remaining CO and CO₂ present in trace quantities due to different elution times in the GC (CO: ∼ 2 min; CH₄: ∼ 8 min; CO₂: ∼ 13 min). Downstream of the GC, the extracted pure CH₄ was converted into CO₂ by combustion in a flow oven at 950 °C using copper oxide wires as a catalyst. The CH₄-derived CO₂ (∼ 60 to 70 µg of carbon, µgC) was transferred into glass ampoules (OD = 4 mm) and sealed for gas radiocarbon measurements. After the CH₄ extraction procedure, the CO₂ of the sample held in the first trap was recovered by cryogenic transfer into a ∼ 55 mL glass flask. The carbon mass of the recovered CO₂ was typically greater than 1 mgC, which was transformed into graphite using an automated graphitization equipment (AGE) (Němec et al., 2010). Concerning the quality control of the ¹⁴CH₄ extraction system, Espic et al. (2019) reported a cross-contamination of 0.4 ± 0.2 % (n=2) from the previous sample and an extraction yield of 101.2 ± 1.4 (n=13).

Radiocarbon analyses of the gaseous CH₄-derived CO₂ samples and the graphite CO₂ samples were performed either at LARA or at the Laboratory of Ion Beam Physics, ETH Zürich, Switzerland, using the same type of AMS (MIni CArbon DAting System, MICADAS), equipped with a gas ion source (Ruff et al., 2007; Synal et al., 2007). Glass ampoules were cracked in a dedicated gas inlet system (Wacker et al., 2013) and the CH₄-derived CO₂ was diluted with He to ∼ 5 %, transferred into a syringe, and then fed into the ion source using a constant gas flow. Graphite samples were introduced directly to the MICADAS source. For both types of samples, raw ¹⁴C $/$ ¹²C as well as ¹³C $/$ ¹²C ratios were converted into F¹⁴C and δ¹³C values, respectively, by performing a blank subtraction as well as a standard normalization and correction for isotope fractionation (only for F¹⁴C) using ¹⁴C-free CO₂ (Blank) (F¹⁴C =0) and CO₂ produced from the primary NIST standard oxalic acid II (OX-II) (SRM 4990C) (F¹⁴C =1.34066, δ¹³C $=-\mathrm{17.8}\mathrm{‰}$), respectively. For the gas measurements, standards came from two gas bottles directly attached to the gas inlet system; both mixtures consisted of 5 % CO₂ with the F¹⁴C value of interest and 95 % He. For the graphite measurements, both standards underwent the same sample preparation as the CO₂ samples and were measured with them in the same magazine. Gas samples were commonly measured starting with 2–4 OX-II and 2 Blank standards, then with the samples in chronological sampling order and finishing with 2–4 OX-II and 2 Blank to account for potential drifts. The dataset presented here was built over 40 measurement days in 6 years. Per measurement day, about 24–32 standards and samples, including about 5–8 from JFJ, were measured; the remaining samples came from other sampling sites or further projects. The average standard deviation of all the OX-II standards used for normalization within a single measurement day was ± 0.007 (in F¹⁴C) whereas the average single OX-II precision derived from counting statistics was ± 0.010 (n=204). Graphite samples were generally measured in a 39-position magazine containing 3–4 OX-II and 3 Blank samples. The dataset presented here was built over 35 magazines. The average standard deviation of all the OX-II standards used for normalization within a single measurement day was ± 0.0018 (in F¹⁴C) whereas the average single OX-II precision derived from counting statistics was ±0.0025 (n=117). The final data evaluation was done using the BATS tool (Wacker et al., 2010). The typical measurement precision is 8 ‰ for ¹⁴CH₄ and 1.5 ‰ for ¹⁴CO₂ (see below). Throughout the current work, ¹⁴C results are reported using the notation Δ¹⁴C, and calculated with age correction as the parameter Δ in Stuiver and Polach (1977).

2.3 Ancillary measurements and datasets

At JFJ, in the framework of the ICOS measurement program, CO₂ and CH₄ concentrations are measured continuously using cavity ringdown spectroscopy; corresponding hourly average values were used in the present study (Emmenegger et al., 2025b, c). These values were used, among other purposes, to determine the stability of CH₄ and CO₂ concentrations in the transfer bags between sampling and extraction. CH₄ and CO₂ concentrations were measured in the remaining sampled air after extraction with a cavity ringdown spectrometer (Picarro G2401, Picarro Inc., USA) calibrated with three gas bottles with known CH₄ and CO₂ concentrations (1861.2, 1955.9, and 2206.7 ppb for CH₄; 379.21, 418.38, and 458.62 ppm for CO₂) (Carbagas, Switzerland). Mean differences between concentrations measured after extraction and measured in situ were 1.3 ± 2.8 ppb and 0.7 ± 1 ppm, for CH₄ and CO₂, respectively (n=13, between January and July 2024).

Furthermore, integrated samples have been collected at JFJ since 1986 to analyze atmospheric Δ¹⁴CO₂, first started by the University of Heidelberg, now run as part of the ICOS Research Infrastructure (Emmenegger et al., 2025a; Hammer et al., 2017; Levin and Kromer, 2004). The dedicated setup draws ambient air throughout 14 d through a sodium hydroxide solution, in which CO₂ is chemically absorbed. We chose the same 14 d schedule for our nighttime sampling as the ICOS schedule to enable a comparison between both datasets. ICOS Δ¹⁴CO₂values are also reported as Δ values according to Stuiver and Polach (1977). Furthermore, atmospheric Δ¹⁴CO₂ values from the Mace Head Atmospheric Research Station (MHD, Ireland) are used here for comparison (Hammer and Levin, 2023); due to its location exposed to westerly winds from the North Atlantic Ocean, MHD is often used as European background station.

We also made use of continuous ²²²Radon (in the following referred as Rn) measurements at JFJ with a two-filter dual loop alpha particle detector, operated by the University of Basel since 2009 (Griffiths et al., 2014) and which is meanwhile part of ICOS (Fig. S1). Rn is emitted from land surfaces into the atmosphere and because of its half-life of 3.8 d, it is a potential tracer of recent land contact. In this study, Rn was used as a proxy to distinguish free tropospheric samples from those originating from the planetary boundary layer (i.e., with recent land contact). To account for variations in ambient temperature and pressure, the raw hourly Rn concentrations (Conen, 2025) were converted into Rn values at STP conditions (i.e., T=0 °C, P=101 325 Pa).

To evaluate the accuracy and stability of our Δ¹⁴CH₄ and Δ¹⁴CO₂ measurements over time, regular measurements of a pressurized air bottle (PAB) (Carbagas, Switzerland) considered as an internal standard have been performed since March 2022 in parallel to the measurements of the JFJ samples; this air strictly underwent the same extraction and measurement procedures as the JFJ samples, commonly just before them. A first bottle was measured until end of July 2023 (CH₄ = 1997 ± 2 ppb, and CO₂ =434.5 ± 0.1 ppm), replaced by a second one in August 2023 (CH₄ = 2178 ± 2 ppb, and CO₂ = 455.8 ± 0.1 ppm). It should be noted that the Δ¹⁴CH₄ and Δ¹⁴CO₂ values of both bottles were a priori unknown and that we used them to evaluate the stability of the values derived from our measurement procedure.

2.4 Atmospheric modeling of nuclear ¹⁴C influence at JFJ

One goal of the present study was to evaluate the influence of nuclear ¹⁴CH₄ and ¹⁴CO₂ emissions on our atmospheric Δ¹⁴CH₄ and Δ¹⁴CO₂ measurements at JFJ. For this purpose, the amount and transport of ¹⁴CH₄ and ¹⁴CO₂ molecules from nuclear emissions were simulated using the Lagrangian particle dispersion model FLEXible PARTicle (FLEXPART) (Pisso et al., 2019) in the version adopted for inputs from the numerical weather prediction model COSMO (Henne et al., 2016). Here, we use the COSMO analysis product of the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss), based on high spatial resolution (1 km × 1 km) model simulations and using a local ensemble transform Kalman filter (LETKF) meteorological data assimilation system (Schraff et al., 2016). Meteorological analysis fields from this product were available at 1 h temporal resolution for the Alpine domain (approximately 0–17° E, 42–50° N). The FLEXPART-COSMO model was used in previous atmospheric studies including the verification of the Swiss CH₄ emission inventory (Henne et al., 2016), the influence of nuclear ¹⁴CO₂ emissions on Δ¹⁴CO₂ at a Swiss tall tower (Berhanu et al., 2017), the analysis of CO₂ and δ¹³CO₂ at JFJ (Pieber et al., 2022), and for inverse modeling of halocarbon emissions over Switzerland (Katharopoulos et al., 2023). Here, the model was operated in the same way as in Katharopoulos et al. (2023). In short, 50 000 model particles were released continuously from JFJ for every 3 h interval and traced backwards in time for 4 d or until they reached the domain boundaries. Afterwards, the integration of the particles' path was continued for up to 10 d in a European scale FLEXPART-IFS simulation driven by hourly inputs from the European Centre for Medium-Range Weather Forecasts (ECMWF) HRES operational forecast/analysis product available at 0.1°×0.1° resolution. The residence time of particles below a height of 50 m above model ground is then estimated in 3 h intervals and divided by air density provides so called source sensitivities (or concentration footprints).

Nuclear ¹⁴C emissions between 2019 and 2023 from the nuclear power plants (NPPs) located in the modeling area (Fig. S3) were mostly estimated using the compilation of nuclear ¹⁴C emissions of Laemmel et al. (2025). The atmospheric transport modeling was performed only for the period 2019–2023, as the input parameters for 2024 were not yet fully available. For most countries (i.e. Bulgaria, the Czech Republic, Romania, Slovakia, Slovenia, Spain, the Netherlands, and the United Kingdom) only annual total ¹⁴C emissions for the NPPs were available. For some countries, more detailed information could be used including quarterly ¹⁴C emissions from NPPs in France and Germany and monthly emissions for the Swiss NPPs Leibstadt and Gösgen, the Swedish NPPs Forsmark, Oskarshamn, and Ringhals, some NPPs in the United Kingdom and the French nuclear fuel reprocessing plant (NFRP) La Hague. Monthly inorganic and organic ¹⁴C emissions from the Hungarian NPP Paks were also available for 2019 in this dataset. In addition to these published values, monthly inorganic and organic ¹⁴C emissions for 2020–2023 were kindly made available by the operating company of the NPP Paks for this simulation. Reported emissions from the NPPs located in Belarus, Belgium, Russia, and Ukraine were not available; we estimated the annual ¹⁴C emissions for each of these plants by multiplying the annual electricity production (Laemmel and Szidat, 2025) by reactor-specific emission factors (EF) of 0.19, 0.41, and 1.3 TBq GWa⁻¹ (GWa = gigawatt × 1 year) for PWR, VVER (water-cooled water-moderated energy reactor), and LWGR (light water graphite reactor), respectively. EF values for PWR and VVER were derived from the work of Fujita et al. (2025) and EF value for LWGR from the work of Zazzeri et al. (2018).

In a further step, ¹⁴CH₄ and ¹⁴CO₂ emissions per NPP were derived, as the reported values were mainly for total ¹⁴C. We used data for inorganic and organic ¹⁴C emissions that were reported in some countries (e.g., for NPPs in Germany, Hungary, Spain, Sweden, and Swizerland). For other countries we assumed that PWRs and VVERs emit 75 % of the total ¹⁴C amount in organic form (so 25 % in inorganic form) and that all the other reactors (e.g. boiling water reactor (BWR), LWGR, and pressurized heavy water reactor (PHWR)) emit ¹⁴C entirely in inorganic form, as also was assumed by Fujita et al. (2025). Furthermore, we assumed that the inorganic ¹⁴C form is entirely composed of ¹⁴CO₂ and that ¹⁴CH₄ represents 72.5 % of the organic ¹⁴C form; this last value was derived from the study of Kunz (1985) who analysed the composition of hydrocarbons of gaseous effluents at two PWRs in the USA. They reported values of 68 % and 77 % at the PWR Ginna and PWR Indian Point, respectively. Finally, the annual, quarterly or monthly emission amount of ¹⁴CO₂ and ¹⁴CH₄ available for each plant was equally divided into three-hour intervals to compute an emission rate per output step of the FLEXPART transport simulation. FLEXPART-derived source sensitivities, convolved with these emission rates yield ¹⁴C mole fractions at the sampling site and a final nuclear correction Δ¹⁴C^Nuc can be calculated assuming a mass balance model for C and ¹⁴C (Graven et al., 2019) (See detailed equations in the Supplement).

2.5 Background atmospheric Δ¹⁴CH₄ modeling

Another goal of the present study was to evaluate the representativeness of our atmospheric Δ¹⁴CH₄ measurements at JFJ on a global level. For this goal, we compared our data with calculated atmospheric Δ¹⁴CH₄ values from a one-box model developed by Fujita et al. (2025). In this study, we extended their simulation until 2024 (Fig. S4) by updating their posterior CH₄ emission scenarios since 2013 (i.e., average of posterior CEDS, EDGARv5, and EDGARv6 scenarios; see Fujita et al., 2025). The anthropogenic CH₄ emissions for 2013–2022 were extended by using EDGARv8 (European Commission Joint Research Centre, 2023). To match the consistency with the posterior anthropogenic biogenic (BIO) and fossil fuel (FF) emissions in Fujita et al. (2025), the mean differences between EDGARv8 and Fujita et al. (2025) were calculated for 2008–2012 (BIO: 2.1 Tg yr⁻¹, FF: 9.8 Tg yr⁻¹) and then added to the values of EDGARv8 after 2013, respectively. For 2023–2024, the emissions of the Shared Socioeconomic Pathways (SSPs) were used. Here, we adopted SSP5-8.5 scenario in 2030 (Gidden et al., 2019) and linearly interpolated it between the EDGARv8 2022 emissions and the 2030 scenario emissions to the years 2023 and 2024. Natural biogenic CH₄ emissions were optimized based on the CH₄ mass balance equation to keep the consistency between our simulations and observed global mean CH₄ mole fractions by NOAA/GML (Lan et al., 2025a). To evaluate the ¹⁴C signature of the biospheric CH₄ sources, Δ¹⁴CO₂ values for the time period 2013–2024 were derived from the SSP5-8.5 scenario (Graven et al., 2020). To extend the nuclear ¹⁴CH₄ emissions beyond 2013, we considered the posterior annual nuclear ¹⁴CH₄ emissions derived by Fujita et al. (2025). Assuming that only PWRs and VVERs are emitting ¹⁴CH₄, a mean annual emission factor Φ (GBq GWa⁻¹) was computed by dividing these emissions by the annual total electricity production by PWRs and VVERs derived from the data compilation by Laemmel and Szidat (2025). Considering the years 2008–2012, the mean Φ value was 250 GBq GWa⁻¹, the min Φ value was 243 GBq GWa⁻¹, and the max Φ value was 259 GBq GWa⁻¹. Considering these three Φ values and the annual total electricity production by PWRs and VVERs in 2013–2024 (the value for 2024 was chosen equal to 2023 as the real value was not yet available), three projections of annual nuclear ¹⁴CH₄ emissions were computed and used as variable input parameter in three different simulations of global atmospheric Δ¹⁴CH₄ values (Fig. S4g). Posterior geologic emissions, biospheric turnover time, total CH₄ lifetime, carbon and hydrogen kinetic isotope effects, and carbon and hydrogen CH₄ isotopic signatures for respective sources in Fujita et al. (2025) were repeated by the values in 2012 over 2013–2024.

The simulated Δ¹⁴CH₄ values were compared to our annual mean Δ¹⁴CH₄ values at JFJ for 2019–2024 and previously reported measurements from samples (ice cores and/or firn air) of Greenland and Antarctica (Hmiel et al., 2020) and from atmospheric samples (Gonzalez Moguel et al., 2022; Lassey et al., 2007b; Levin et al., 1992; Quay et al., 1999; Sparrow et al., 2018; Townsend-Small et al., 2012; Wahlen et al., 1989). Note that the data in Hmiel et al. (2020) was used in Fujita et al. (2025) as observational constraints. Several other studies were found but not used here because of the large scatter in the reported data (Lowe et al., 1988; Manning et al., 1990) or difficulties in the unit conversion into current radiocarbon parameters (Ehhalt, 1974).

Figure 2 (a) All Δ¹⁴CH₄ values (black points) measured at JFJ since 2019; (b) same Δ¹⁴CH₄ values excluding the points showing a clear nuclear influence (black points, Δ¹⁴CH${}_{\mathrm{4}}^{\le \mathrm{420}\mathrm{‰}}$). (c) Rn and (d) CH₄ concentrations corresponding to the sampling periods of the Δ¹⁴CH₄ measurements. In subplot (b), black error bars show the statistical uncertainty of the individual Δ¹⁴CH₄ measurements. The vertical black dashed line in all the four subplots on 18 April 2023 represents the beginning of air sampling using the new JASS system. The horizontal dashed line in (a) and (b) is shown for Δ¹⁴CH₄ =420 ‰, i.e., the chosen threshold for a clear influence of nuclear contamination. The horizontal red dotted line in (c) is shown at 1.5 Bq m⁻³ Rn, the chosen threshold between free-troposphere conditions and conditions influenced by the planetary boundary layer at JFJ. Red points in all the subplots represent Δ¹⁴CH₄, Rn, and CH₄ values where corresponding Rn values are lower than 1.5 Bq m⁻³. Blue lines in (c) and (d) correspond to the monthly means measured in situ.

3 Results

3.1 Δ¹⁴CH₄ measurements at Jungfraujoch

Δ¹⁴CH₄ values at JFJ between 2019 and 2024 were between 322 ‰ and 767 ‰ and showed a slightly increasing trend over the last six years (Fig. 2a, b). Some exceptionally high Δ¹⁴CH₄ values were measured (i.e., on 13 June and 25 July 2019; 13 May, 10 June and 24 June 2020; 12 May 2021) and were attributed to local and strong nuclear ¹⁴CH₄ releases. Rn values were lower than 5 Bq m⁻³ over 2019–2024 with generally lower values during winter months compared to summer months (Fig. 2c), which is consistent with a potentially stronger influence of the planetary boundary layer in summer. Annual mean CH₄ concentrations increased from 1930 ppb in 2019 to 1996 ppb in 2024 (Fig. 2d). Rn and CH₄ concentration data shown here represent the corresponding average values during the sampling intervals.

Reduced scatter in Δ¹⁴CH₄ values since the installation of the JASS in April 2023 is visible (Fig. 2a, b). Also, hourly Rn and CH₄ concentrations averaged over the JASS sampling periods show less amplitude variation after this date. For both parameters, corresponding values are closer to the monthly means measured in situ by the ICOS instruments (blue curves in Fig. 2c, d). The temporal stability of our Δ¹⁴CH₄ measurements from March 2022 onwards is evaluated using the regular Δ¹⁴CH₄ measurements of our two internal standard PAB bottles. The standard deviation of the Δ¹⁴CH₄ value for all 40 CH₄ measurements over 15 months for each PAB bottle (so about 80 CH₄ measurements in total) is 8 ‰ (Fig. S5a, b), which is lower than the instrumental uncertainty of a single Δ¹⁴CH₄ measurement (12 ‰) indicating the satisfactory long-term reproducibility of our Δ¹⁴CH₄ measurements.

For 2019–2024, mean annual atmospheric Δ¹⁴CH₄ values can be deduced from our fortnightly air sampling program in different ways (Table 1). Firstly, all Δ¹⁴CH₄ measurements clearly influenced by nuclear contamination were excluded. Choosing a threshold of 420 ‰ (Δ¹⁴CH${}_{\mathrm{4}}^{\le \mathrm{420}\mathrm{‰}}$, Figs. 2b and 3) 129 of the 137 measurements initially available are retained (94 %). Over the six years, a slightly increasing Δ¹⁴CH${}_{\mathrm{4}}^{\le \mathrm{420}\mathrm{‰}}$ tendency rising from 350 ± 19 ‰ in 2019 to 381 ± 13 ‰ in 2024 (i.e., by a rate of $\sim +\mathrm{6}$ ‰ yr⁻¹) is observed (Table 1, Fig. 3).

Secondly, only Δ¹⁴CH₄ measurements whose corresponding Rn values are lower than 1.5 Bq m⁻³ STP (Δ¹⁴CH${}_{\mathrm{4}}^{\text{Rn-filt}}$) were retained; analyzing the probability density function of more than five years of Rn values at JFJ (November 2015 to December 2020), Conen and Zimmermann (2020) found that air masses with corresponding Rn values below 1.5 Bq m⁻³ STP belong mostly to the free troposphere (with 77 % confidence). Compared to the first set of mean annual values (n = 129), mean annual Δ¹⁴CH₄ values are 1 ‰–7 ‰ lower and the total number of points used for these mean values is almost halved (n = 71) (Table 1, Fig. 3). During the period of integrated sampling the observations removed with the Rn threshold are mainly from the summer.

The third processing step additionally corrects for the influence of NPPs by subtracting the nuclear Δ¹⁴CH₄ signal simulated with FLEXPART-COSMO from the Δ¹⁴CH₄ measurements at JFJ. Figure 3 shows for each Δ¹⁴CH${}_{\mathrm{4}}^{\text{Rn-filt}}$ value (red point) the corresponding value corrected for the nuclear influence (Δ¹⁴CH${}_{\mathrm{4}}^{\text{Rn-filt + Nuc-corr}}$) (blue point). Overall, the mean nuclear Δ¹⁴CH₄ influence is 7 ± 9 ‰ (min = 0 ‰, max = 58 ‰, n = 60).

Table 1 Mean annual Δ¹⁴CH₄ values with standard deviations derived from our fortnightly Δ¹⁴CH₄ measurements at JFJ between 2019 and 2024 with four different computations. The first column of mean values considers all Δ¹⁴CH₄ measurements; the second column only considers Δ¹⁴CH₄ measurements ≤ 420 ‰ (Δ¹⁴CH${}_{\mathrm{4}}^{\le \mathrm{420}\mathrm{‰}}$). The third column considers only Δ¹⁴CH₄ values whose corresponding Rn values are lower than 1.5 Bq m⁻³ STP (Δ¹⁴CH${}_{\mathrm{4}}^{\text{Rn-filt}}$). The fourth column subtracts from each Δ¹⁴CH${}_{\mathrm{4}}^{\text{Rn-filt}}$ value the modelled nuclear ¹⁴CH₄ influence (Δ¹⁴CH${}_{\mathrm{4}}^{\text{Rn-filt +Nuc-corr}}$). The numbers in brackets in columns 2–5 represent the number of values per year considered for the annual mean calculation. For 2023, the last sampling period finished early 2024 when no modelled nuclear ¹⁴CH₄ influence was yet available so that the corresponding Δ¹⁴CH₄ value was not considered.

Figure 3 Nuclear influence on background Δ¹⁴CH₄ measurements at JFJ with values ≤ 420 ‰ (Δ¹⁴CH${}_{\mathrm{4}}^{\le \mathrm{420}\mathrm{‰}}$) (black points). Red symbols denote points where Rn is lower than 1.5 Bq m⁻³ STP (Δ¹⁴CH${}_{\mathrm{4}}^{\text{Rn-filt}}$) (same as in Fig. 2a, b). Black error bars show the statistical uncertainty of the individual Δ¹⁴CH₄ measurements. Blue points correspond to the red points after subtraction of the simulated nuclear influence (Δ¹⁴CH${}_{\mathrm{4}}^{\text{Rn-filt + Nuc-corr}}$). Blue error bars represent the standard deviation of the nuclear influence. Horizontal black, red and blue lines show the annual mean values derived from the black (Table 1, Δ¹⁴CH${}_{\mathrm{4}}^{\le \mathrm{420}\mathrm{‰}}$), red (Table 1, Δ¹⁴CH${}_{\mathrm{4}}^{\text{Rn-filt}}$) and blue points (Table 1, Δ¹⁴CH${}_{\mathrm{4}}^{\text{Rn-filt + Nuc-corr}}$), respectively. Δ¹⁴CH${}_{\mathrm{4}}^{\text{Rn-filt + Nuc-corr}}$ values were not available for 2024 due to missing input parameters for the nuclear simulation for this year.

3.2 Comparison with previous observations and simulated global Δ¹⁴CH₄ signal

Our annual mean Δ¹⁴CH₄ values at JFJ are similar to previous observations in the Northern Hemisphere from 2005–2020 (Fig. 4). Direct observations from Los Angeles, Canada and Alaska showed values of 340 ‰–350 ‰, comparable to our JFJ data of 338 ‰–366 ‰ for Δ¹⁴CH${}_{\mathrm{4}}^{\text{Rn-filt + Nuc-corr}}$ from 2019–2023 (Fig. 4, Table 1). Firn air observations from Greenland for 2005–2013 were slightly higher, 350 ‰–380 ‰, but still consistent with our data.

Figure 4 Simulated global atmospheric Δ¹⁴CH₄ signal based on the one-box model of Fujita et al. (2025) extended until 2024 (see Sect. 2.5) with observations from JFJ and other studies, for 1980–2024. The solid black line is the global Δ¹⁴CH₄ posterior until 2013 from Fujita et al. (2025). The grey area surrounding the line shows a 68 % confidence interval. The dotted black lines denote the global observational target ranges, corresponding to a 99 % confidence interval of the global averages, defined by Fujita et al. (2025). The three blue lines represent the extrapolated global atmospheric Δ¹⁴CH₄ based on the three emission factor values Φ (see text). Individual points represent atmospheric Δ¹⁴CH₄ values from atmospheric or ice-core samples. Our annual mean JFJ values between 2019 and 2024 are shown as black diamonds (Δ¹⁴CH${}_{\mathrm{4}}^{\le \mathrm{420}\mathrm{‰}}$), purple squares (Δ¹⁴CH${}_{\mathrm{4}}^{\text{Rn-filt}}$), and blue circles (Δ¹⁴CH${}_{\mathrm{4}}^{\text{Rn-filt + Nuc-corr}}$).

A rather stable global background Δ¹⁴CH₄ is simulated for the period 2005–2024, supporting the consistency between the observations over this period. This stable period followed an increase from ∼ 140 ‰ in 1980 to ∼ 350 ‰ in 2005 (see Fig. 4 and Fujita et al., 2025), mainly driven by increasing nuclear ¹⁴CH₄ emissions since 1970 (Fig. S4h).

All the Northern Hemisphere measurement data is higher than the simulated Δ¹⁴CH₄ (Fig. 4), which reflects global atmospheric Δ¹⁴CH₄ accounting for contributions from both hemispheres (Fujita et al., 2025). Due to a lack of data, the current difference between the hemispheres is presently not well-known, but previous data indicate an excess Δ¹⁴CH₄ in the Northern Hemisphere, which is consistent with stronger nuclear power plant emissions there (Fig. 4). The observational targets from Fujita et al. (2025) were constructed to allow for this hemispheric difference and for uncertainty due to lack of data. The mean offset between the observations in Greenland for 2005–2013 and the simulated global Δ¹⁴CH₄ value is 22 ± 9 ‰, comparable to the offset between our JFJ observations for 2019–2024 and the global simulation of 0 ‰ to 33 ‰.

The three simulated Δ¹⁴CH₄ trends from 2013 to 2024 (blue lines in Fig. 4) based on three different emission factors Φ (Sect. 2.5 and Fig. S4g) show a decrease followed by a stabilization (for Φ = 243 GBq GWa⁻¹), or a slight increase (for Φ ≥ 250 GBq GWa⁻¹). The initial decrease was caused by a decrease in nuclear ¹⁴CH₄ emissions following the Fukushima accident in 2011 (Fig. S4h). Afterwards, the stabilization or slight increase has arisen from nuclear ¹⁴CH₄ emissions that have increased again in particular after 2017 (Fig. S4h). Our measurement data show a slight positive trend that is reduced after accounting for regional influences from nuclear power plant emissions (Fig. 4, Sect. 3.1). The simulations seem to be more consistent with this slight positive trend from our measurement data using the higher emission factors than the lowest emission factor, where a slight decrease in Δ¹⁴CH₄ is simulated.

We can also compare with observations at JFJ in 1988–1991 by Levin et al. (1992) (Fig. 4). A large spread in individual measurements of 210 ‰–255 ‰ was found at that time. We also found a large scatter in samples collected in the morning before the installation of integrated nighttime sampling (Fig. 2, Sect. 3.1). The number of operating PWRs worldwide passed from about 240 in 1991 to about 310 in 2024 (Laemmel and Szidat, 2025), suggesting that the influence of nuclear ¹⁴CH₄ emissions has increased. Overall, the increase from ∼ 230 ‰ in the 1980s to 360 ‰ in the early 2020s is consistent with other data and with the simulated change (Fig. 4).

Figure 5 (a) In black, Δ¹⁴CO₂ values measured at JFJ since 2019 and in blue, Δ¹⁴CO₂ values reported by ICOS. (b) In black, CO₂ values related to the sampling periods of our Δ¹⁴CO₂ measurements and in blue, mean monthly CO₂ values measured continuously in situ. The vertical black dashed line in both subplots on 18 April 2023 represents the change in the sampling method, passing from fortnightly morning grab air samples to 14 d nighttime integrated samples with the JASS system.

3.3 Δ¹⁴CO₂ measurements at Jungfraujoch

Between 2019 and 2024, Δ¹⁴CO₂ values measured at JFJ ranged from +3 ‰ to −17 ‰, following a decreasing trend over these six years; this trend is mostly due to the emissions of ¹⁴C-free fossil fuel CO₂ which depletes the global atmospheric Δ¹⁴CO₂ signal (Fig. 5a). Annual mean CO₂ concentrations increased from 411.9 ppm in 2019 to 424.7 ppm in 2024 (Fig. 5b). The standard deviation of the Δ¹⁴CO₂ value for all 40 CO₂ measurements over 15 months for each PAB bottle (so about 80 CO₂ measurements in total) is 1.5 ‰ (Fig. S5c, d), which is lower than the instrumental uncertainty of a single Δ¹⁴CO₂ measurement (2 ‰) indicating the satisfactory long-term reproducibility of our Δ¹⁴CO₂ measurements.

We compare our annual mean Δ¹⁴CO₂ and individual Δ¹⁴CO₂ observations with measurements from ICOS at JFJ (Emmenegger et al., 2025a) in Figs. 5a and 6 and in Table 2. The average trend in Δ¹⁴CO₂ is similar in both datasets: −2.1 ± 1.9 ‰ yr⁻¹ for our data and −2.8 ± 1.9 ‰ yr⁻¹ for ICOS data. Annual mean values differ by less than 1.5 ‰ except for 2019, when our annual mean Δ¹⁴CO₂ value was 3.6 ‰ lower than the ICOS annual mean. Larger individual differences are visible especially in the first half of 2019 (Fig. 5a). Both annual mean ICOS Δ¹⁴CO₂ values for 2019 and 2020 at JFJ are consistent with the equivalent means at the MHD station (Table 2), indicating that the offset in 2019 between our values and the ICOS ones was probably due to a small fossil contamination in our early Δ¹⁴CO₂ measurements that was remediated during the year.

Table 2 Annual mean Δ¹⁴CO₂ (in units of ‰) with standard deviations at JFJ from this study and from ICOS (Emmenegger et al., 2025a). The third line of the table gives the annual mean Δ¹⁴CO₂ at Mace Head Atmospheric Research Station (MHD, Ireland). The numbers in brackets represent the number of values per year considered for the annual mean.

Figure 6 (a) Comparison between nighttime-integrated Δ¹⁴CO₂ values from our LARA program (black points) and all-day-integrated Δ¹⁴CO₂ values from the ICOS program (blue lines). (b) Difference between LARA and ICOS Δ¹⁴CO₂ values for simultaneous samples (i.e., ΔΔ¹⁴CO₂). The solid black line represents the zero line and the typical Δ¹⁴CO₂ measurement uncertainty of ± 2 ‰ are shown as dotted black lines. The red line shows the mean difference between both datasets of −0.04 ± 1.74 ‰ (n = 20).

After the JASS installation in 2023, a more detailed comparison of Δ¹⁴CO₂ with ICOS became possible, as we use the same 14 d sampling as the ICOS integrated sodium hydroxide solution sampling (Fig. 6). Over about 20 months the mean difference was −0.04 ± 1.74 ‰ (n = 20) with (insignificantly) lower values in our data. A few large differences up to ± 4 ‰ were observed. We emphasize that even though the fortnightly periods are the same, our JASS sampler integrates only nighttime hours whereas the ICOS sampler integrates all day; moreover, the air inlet of our system is situated about 3 m higher than the one from the ICOS sampler. Therefore, the measurements are not conducted on the exact same air. However, the insignificant mean difference suggests that the different sampling conditions affect the measured Δ¹⁴CO₂ only marginally.

Figure 7 Raw Δ¹⁴CO₂ values measured at JFJ (black points) and corresponding nuclear-corrected Δ¹⁴CO${}_{\mathrm{2}}^{\text{Nuc-corr}}$ values from which the mean nuclear influence was subtracted (blue points). The vertical blue bar around each blue point corresponds to the standard deviation of the nuclear influence over the sampling period. Annual horizontal black lines correspond to the annual mean raw Δ¹⁴CO₂ value derived from the black points. Annual horizontal blue lines correspond to the annual mean Δ¹⁴CO${}_{\mathrm{2}}^{\text{Nuc-corr}}$ value derived from the blue points. Simulations for 2024 have not been available yet due to missing input parameters for this year.

Similar to the correction we made using the simulated ¹⁴CH₄ nuclear influence on Δ¹⁴CH₄ measurements, we also make a correction using the simulated ¹⁴CO₂ nuclear influence on Δ¹⁴CO₂ measurements (Fig. 7). The mean nuclear Δ¹⁴CO₂ influence is 0.2 ± 0.4 ‰ (min = 0 ‰, max = 2.3 ‰). The overall effect of nuclear ¹⁴C emissions on atmospheric Δ¹⁴CO₂ is less than for Δ¹⁴CH₄, i.e., 0.2 ± 0.4 ‰ compared to 7 ± 9 ‰, respectively. Moreover, nuclear ¹⁴CO₂ emissions (from BWRs and NFRP) are known to be less sporadic than ¹⁴CH₄ emissions from PWR reactors (Stenström et al., 1995b), so our atmospheric model based on monthly nuclear ¹⁴C releases simulates better nuclear ¹⁴CO₂ contributions than ¹⁴CH₄ contributions.

4 Discussion

Our Δ¹⁴CH₄ measurements at JFJ represent the first direct multi-annual time-series of atmospheric measurements in the Northern Hemisphere published within the last 25 years. With the introduction of integrated nighttime sampling, the collected air samples are less likely to show elevated Δ¹⁴CH₄ values due to sporadic NPP emissions. Four sets of annual Δ¹⁴CH₄ means for 2019–2024 are reported in Table 1, based on different filtering approaches. The first two columns present annual values based on raw measurements and an upper threshold (Δ¹⁴CH₄ = 420 ‰) chosen to remove the 6 % highest values measured, respectively. The third column uses simultaneous ²²²Rn measurements and only considers Δ¹⁴CH₄ values with a corresponding ²²²Rn ≤ 1.5 Bq m³. Based on this previous filtered Δ¹⁴CH₄ dataset, the fourth column further adds a correction of the regional nuclear influence based on atmospheric modeling. From these four sets, the third one is probably the most representative of a well-mixed mid-latitude background site as ²²²Rn is a well-known proxy to identify origin of air masses, e.g. from the free troposphere. The fourth set, based on ¹⁴C nuclear emission inventory, would represent the ideal set but it still so far needs a better knowledge and more high-resolution data of nuclear ¹⁴CH₄ emissions to be validated. As the measurements at JFJ are consistent with previous measurements and global model simulations, continued observations at JFJ will provide an important constraint on the global background Δ¹⁴CH₄ trends and the global CH₄ budget (Fujita et al., 2025).

The correction for the NPP influence on Δ¹⁴CH₄ that we apply to the measurements may even be improved with more information on regional nuclear ¹⁴C emissions. Our current atmospheric simulation uses monthly constant ¹⁴CH₄ emission rates to describe the NPP releases in Switzerland and less frequent data for reactors in other countries. However, it is known that radioactive emissions from PWRs are rather sporadic (Stenström et al., 1995b). Espic et al. (2025) collected 18 grab air samples for atmospheric ¹⁴CH₄ and ¹⁴CO₂ analyses around the Swiss PWR Gösgen during the first day of its annual revision period in 2019 (sampling duration per bag: 20–75 min) and observed a ¹⁴CH₄ and ¹⁴CO₂ release event that lasted only a few hours but included ∼ 8 % of the total annual ¹⁴CH₄ emissions of that year. Comparing this former study using grab samples to the present study using integrated samples also illustrates the importance of the choice of sampling duration and setup to gain knowledge about two processes with different timescales. Furthermore, Espic et al. (2025) found that the activities of noble gases measured at a 10 min temporal resolution at the PWR stack may be a valuable proxy to identify sporadic ¹⁴C releases. A generalized use of this kind of high-frequency data would be beneficial to refine temporal variation in estimates of ¹⁴C emissions from NPPs.

The composition of ¹⁴C is another important uncertainty for estimates of ¹⁴C emissions from NPPs. Here, only PWRs and VVERs were considered to emit organic ¹⁴C (e.g., ¹⁴CH₄); however, small organic ¹⁴C emissions have also been reported for other reactor types: up to 7 % for BWRs (Kunz, 1985; Stenström et al., 1995a), 1–4 to 25 %–30 % for PHWRs (Bharath et al., 2022; IAEA, 2004; Joshi et al., 1987; Milton et al., 1995), and up to 30 % for LWGRs (Gaiko et al., 1985; Konstantinov et al., 1989). Organic ¹⁴C emissions from these reactor types may be significant as about 13 % of the total nuclear electricity is produced by BWRs (which is the third-most important reactor type after PWRs and VVERs based on nuclear electricity) and the emission factors for PHWRs and LWGRs (1.6 and 1.3 TBq GWa⁻¹, respectively, Zazzeri et al., 2018) are even several times higher than for PWRs and VVERs. LWGR emissions are particularly uncertain, and radiocarbon measurements of tree rings around LWGRs suggested emission factors could be two to four times higher than the assumed value of 1.3 TBq GWa⁻¹ (Juodis et al., 2022; Nazarov et al., 2023). In addition, PWRs themselves exhibit a broad range (i.e., 44 %–95 %) for the organic ¹⁴C fraction at PWRs and VVERs. Furthermore, there are only few 40-year old measurements of the speciation of the individual fractions of the organic ¹⁴C emissions that may involve (besides ¹⁴CH₄) relevant portions of e.g. ¹⁴C₂H₆, ¹⁴C₃H₈ and ¹⁴C₄H₁₀ (Kunz, 1985). More recently, Espic et al. (2025) found for the Swiss PWR Gösgen (see above) that the measured ratio between ¹⁴CH₄ and ¹⁴CO₂ emissions and the reported ratio between organic and ¹⁴CO₂ emissions agreed with each other, implying that almost all the organic emissions are in form of ¹⁴CH₄. More measurements focusing on the hydrocarbon composition of the organic ¹⁴C fraction are needed at PWRs, VVERs and LWGRs.

This work demonstrates that our measurements of Δ¹⁴CO₂ at JFJ are generally consistent with concurrent measurements from the ICOS program. The installation of the JASS system in 2023 furthermore constitutes an improvement of the long-running ICOS Δ¹⁴CO₂ measurements at JFJ, since it integrates nighttime periods which mostly are dominated by air from the free troposphere, whereas the ICOS measurements rely on all-day air sampling. Even though the insignificantly low mean difference between ICOS sodium hydroxide-based integrated sampling vs. our JASS system suggests that the different sampling conditions affect the measured Δ¹⁴CO₂ only marginally (Fig. 6), this observation requires a longer duration for the comparison of both datasets to prove their consistency.

5 Conclusions

We conducted fortnightly atmospheric Δ¹⁴CH₄ and Δ¹⁴CO₂ measurements at the Swiss High-Altitude Research Station Jungfraujoch (about 3500 m a.s.l.) between 2019 and 2024. Initially based on 20–60 min air samples commonly collected in the early morning, a novel air sampling setup automatically collecting ambient air during nighttime was installed in April 2023. Over the six years 2019–2024, Δ¹⁴CH₄ values at JFJ have shown a slight increase from 350 ± 19 ‰ to 381 ± 13 ‰ (i.e., by a rate of $\sim +\mathrm{6}$ ‰ yr⁻¹) while Δ¹⁴CO₂ values decreased from −2.0 ± 3.8 ‰ to −12.6 ± 2.0 ‰ (i.e., by a rate of $\sim -\mathrm{2}\mathrm{‰}$ yr⁻¹). Our Δ¹⁴CO₂ values generally agree well with the integrated Δ¹⁴CO₂ measurements from the ICOS program. Accounting for nuclear ¹⁴CH₄ and ¹⁴CO₂ emissions on the European scale within the atmospheric transport model FLEXPART-COSMO, we simulate the nuclear signal on our individual measurements at JFJ and estimate an average nuclear influence of 7 ± 9 ‰ and 0.2 ± 0.4 ‰ for ²²²Rn-filtered Δ¹⁴CH₄ and raw Δ¹⁴CO₂ values, respectively, which we use to correct the observed data. Our Δ¹⁴CH₄ data are consistent with an atmospheric one-box model for Δ¹⁴CH₄ that simulates slightly increasing or decreasing Δ¹⁴CH₄ over 2013–2024, depending on the strength of nuclear power plant emissions. Our new observations at JFJ will help to refine the global background Δ¹⁴CH₄ and Δ¹⁴CO₂ and to constrain CH₄ and CO₂ sources and sinks.