Evaluation of Sentinel-5P TROPOMI Methane Observations at Northern High Latitudes

Abstract

The Arctic and boreal regions are experiencing a rapid increase in temperature, resulting in a changing cryosphere, increasing human activity, and potentially increasing high-latitude methane emissions. Satellite observations from Sentinel-5P TROPOMI provide an unprecedented coverage of a column-averaged dry-air mole fraction of methane (XCH₄) in the Arctic, compared to previous missions or in situ measurements. The purpose of this study is to support and enhance the data used for high-latitude research through presenting a systematic evaluation of TROPOMI methane products derived from two different processing algorithms: the operational product (OPER) and the scientific product (WFMD), including the comparison of recent version changes of the products (OPER, OPER rpro, WFMD v1.2, and WFMD v1.8). One finding is that OPER rpro yields lower XCH₄ than WFMD v1.8, the difference increasing towards the highest latitudes. TROPOMI product differences were evaluated with respect to ground-based high-latitude references, including four Fourier Transform Spectrometer in the Total Carbon Column Observing Network (TCCON) and five EM27/SUN instruments in the Collaborative Carbon Column Observing Network (COCCON). The mean TROPOMI–TCCON GGG2020 daily median XCH₄ difference was site-dependent and varied for OPER rpro from −0.47 ppb to 22.4 ppb, and for WFMD v1.8 from 1.2 ppb to 19.4 ppb with standard deviations between 13.0 and 20.4 ppb and 12.5–15.0 ppb, respectively. The TROPOMI–COCCON daily median XCH₄ difference varied from −26.5 ppb to 5.6 ppb for OPER rpro, with a standard deviation of 14.0–28.7 ppb, and from −5.0 ppb to 17.2 ppb for WFMD v1.8, with a standard deviation of 11.5–13.0 ppb. Although the accuracy and precision of both TROPOMI products are, on average, good compared to the TCCON and COCCON, a persistent seasonal bias in TROPOMI XCH₄ (high values in spring; low values in autumn) is found for OPER rpro and is reflected in the higher standard deviation values. A systematic decrease of about 7 ppb was found between TCCON GGG2014 and GGG2020 product update highlighting the importance of also ensuring the reliability of ground-based retrievals. Comparisons to atmospheric profile measurements with AirCore carried out in Sodankylä, Northern Finland, resulted in XCH₄ differences comparable to or smaller than those from ground-based remote sensing.

1. Introduction

Increased greenhouse gas concentrations in the atmosphere have led to an increase of the global average temperature compared to pre-industrial times, and the change is stronger at high latitudes: the Arctic and boreal regions are warming nearly four times faster compared to the global average [1]. The changes in temperature result in a changing cryosphere [2], including contrasting regional trends of snow cover [3], degradation of permafrost [4,5], and reduced seasonal soil frost [6]. An increase in anthropogenic activity is also evident due to improved accessibility, especially for shipping [7], and expanding activity on energy production, in particular oil and natural gas extraction [8,9]. These changes may collectively result in an increase in both natural and anthropogenic high-latitude methane (CH₄) emissions that have the potential to further accelerate the ongoing changes [10,11].

Fleets of Earth-observing satellites have revolutionized the monitoring of the Arctic and boreal environments (e.g., [12,13]). For methane, passive observations in the thermal infrared spectral range have been carried out by multiple missions (e.g., AIRS, IASI, and TES). While these missions are particularly relevant for high-latitude observations because the retrievals do not require solar radiation, to infer information about the emissions near the surface demands a sensitivity of the instrument to the boundary layer of the atmosphere that is not achievable using thermal infrared wavelengths. Global observations of atmospheric methane in the near-infrared wavelengths started in 2002 with SCIAMACHY onboard ENVISAT [14]. In 2009, JAXA’s Greenhouse Gases Observing Satellite (GOSAT; [15]) became the first satellite dedicated to atmospheric greenhouse gas observations. GOSAT observations have enabled an improved understanding of methane emissions and their trends globally (e.g., [16,17]) and have contributed to the quantification of emissions, specifically over high latitudes [18]. The observational high-latitude coverage of satellites has most recently been augmented by the launch of the Dutch Tropospheric Monitoring Instrument (TROPOMI; [19]) onboard the European Space Agency’s Sentinel 5 Precursor satellite in November, 2017. A wide swath and a medium-resolution pixel size have enabled the use of these observations for data-driven emission estimations (e.g., [20,21,22]), even at higher latitudes [23].

Despite the major increase in the quantity and coverage of methane observations in the Arctic, high latitudes have not been a focus in retrieval algorithm development that has rightfully aimed for a globally optimal accuracy and precision of the retrieval products. High latitudes impose a number of challenges for passive observations of near-infrared radiation and the retrieval of a column-averaged dry-air mole fraction of methane (XCH₄) at these wavelengths. The most significant challenge is the seasonal lack of solar radiation, the solar elevation varying from large solar zenith angles (SZAs) to polar night conditions north of the Arctic circle (about 66°34^′N). Large SZAs lead to an increase in atmospheric scattering and, thus, complicate the estimation of the path length of radiation in the atmosphere. Another challenge is posed by snow-covered surfaces. While snow is highly reflective in the UV-VIS wavelengths, its reflectance decreases significantly in the near-infrared wavelengths due to the absorption by ice crystals (e.g., [24,25]). This means that, over snow-covered surfaces, the reflected radiances measured by the satellite instruments are already low due to large SZAs and are further reduced by the absorption of the surface, lowering the signal-to-noise ratio and complicating the retrievals of XCH₄. The third challenge specific to high-latitude observations of methane is the polar stratospheric vortex, which is a re-occurring meteorological phenomenon in the stratosphere. In winter, the polar vortex develops above the North Pole, and creates a stable system of little air exchange with the surrounding atmosphere. The air masses that descend within the polar vortex are depleted in methane, due to the oxidation of methane over time since the air entered the stratosphere. The descent of air affects the atmospheric profile of methane, and can thus impact retrievals through their a priori profiles that may deviate from the true profile shape [26,27,28]. An important opening to high-latitude-specific retrieval improvements was presented recently by Hachmeister et al. [29], who updated the digital elevation model (DEM) used in the TROPOMI retrievals of column-XCH₄, resulting in improved data over the high latitudes, especially over regions with steep altitude changes, such as Greenland.

Satellite observations of methane provide a valuable resource for monitoring and understanding the ongoing changes in the Arctic-boreal region but information on the reliability of the retrieval products specifically at high latitudes is necessary. In this paper, a systematic evaluation of TROPOMI XCH₄ products is performed. The products are derived from two different Level 2 processing algorithms: the operational product (OPER; [30,31,32,33]) and the scientific product (WFMD; [34,35]), including the evaluation of recent version changes of each product (OPER, OPER rpro, WFMD v1.2, and WFMD v1.8). The purpose of the paper is to benchmark the existing products and evaluate the information they provide for high-latitude emission-related studies, as well as quantify their differences. Using ground-based reference measurements, an evaluation of the product accuracy and precision is made. The present study is structured as follows. The satellite data as well as the ground-based validation data are described in Section 2. The methods used in the analysis are presented in Section 3. Results are presented and discussed in Section 4, and conclusions are drawn in Section 5.

2. Data Description

2.1. TROPOMI XCH₄ OPER and OPER Rpro Products

The TROPOMI operational XCH₄ retrieval product is produced by the SRON Netherlands Institute for Space Research. The product provides methane column concentrations retrieved from TROPOMI radiance measurements using the RemoTeC algorithm. The retrieval algorithm RemoTeC has been extensively used to retrieve XCO₂ and XCH₄ columns from, e.g., OCO-2 and GOSAT measurements [36,37], and is also the operational algorithm for the Sentinel-5 mission. The RemoTeC algorithm deploys a full-physics forward simulation to simulate spectral measurements of the instrument for different atmospheric conditions, accounting for scattering of aerosols. It is based on a Tikhonov profile retrieval scheme in combination with an iterative optimization that adjusts the model parameters until the best fit between the model and the measured data is achieved [38,39]. The TROPOMI operational XCH₄ product uses CH₄ and CO a priori vertical profiles from the global chemical transport model TM5 [40]. The output of the inversion is a vertical profile, which is then integrated along the vertical direction to provide the total vertical column.

Methane retrievals are only performed for cloud-free ground pixels that fulfill certain criteria in terms of viewing geometry or surface roughness. To identify cloud-free scenes, the algorithm uses the re-gridded Visible Infrared Imaging Radiometer Suite (VIIRS) cloud mask from the Suomi-NPP satellite, flying in a constellation 3.5 min ahead of the Sentinel-5 Precursor. The detailed filtering criteria of the pre-filter along with other details of the algorithm are given in the Algorithm Theoretical Baseline Document for Sentinel-5 Precursor methane retrieval [41] document.

The operational TROPOMI XCH₄ retrieval product [30] consists of versions v01.02.02–v01.04.00 (1 January 2018–3 July 2021) and v02.02.00–v02.03.01 (6 July 2021–31 December 2021), and is, in this paper, referred to as OPER. A reprocessing of the full data record was carried out in 2023 following changes introduced by Lorente et al. [31], including, for example, a second-order polynomial update for an improved representation of the surface reflectance’s spectral dependence [42]. This reprocessed operational product consists of version 02.04.00, and is, in this paper, referred to as OPER rpro.

For the correct usage of the OPER and OPER rpro data products, the recommendation to use TROPOMI XCH₄ bias-corrected data associated with a quality assurance value qa_value > 0.5 is followed. The qa_value is included as part of the XCH₄ data product and provides information about the data quality and reliability of the retrieval. It ranges from 0 to 1, for non-convergent or pre-filtered pixels to convergent or clear-sky pixels, respectively.

2.2. TROPOMI XCH₄ WFMD Products

The scientific TROPOMI XCH₄ retrieval product is produced at the University of Bremen in Germany. The product provides XCH₄ retrieved from TROPOMI radiance measurements using weighting function modified differential optical absorption spectroscopy (WFMD or WFM-DOAS) retrieval method. The method is designed for a simultaneous retrieval of XCH₄ and column-averaged dry-air mole fraction of carbon monoxide, XCO [34,35]. WFMD is a linear least-squares method based on scaling (or shifting) pre-selected atmospheric vertical profiles. The vertical columns of the desired gases are determined from the measured sun-normalized radiance by fitting a linearized radiative transfer forward model to it. To enable a fast retrieval, a look-up table scheme for the radiances and their derivatives has been implemented, containing various reference spectra characterizing a variety of typical atmospheric conditions, covering combinations of different solar zenith angles, altitudes, albedos, water vapor contents, and temperatures. The reference spectra are computed with high spectral resolution in line-by-line mode and are subsequently convolved to the TROPOMI spectral resolution of the shortwave infrared wavelength bands. The linearized radiative transfer model, plus a low-degree polynomial, are linear least squares-fitted to the logarithm of the measured sun-normalized radiance. The trace gas vertical profiles (CH₄, CO, and H₂O) are scaled for the fit (i.e., the profile shape is not varied from the a priori profile). Additional fit parameters are the shift of a pre-selected temperature profile, a scaling factor for the pressure profile, and parameters for a polynomial of the second order (original settings) or third order (since v1.8; [35]).

Information on potential cloud contamination is obtained from strong H₂O absorption lines in the TROPOMI spectral band 8 by comparing the measured radiances to reference radiances for cloud-free conditions. In the retrieval post-processing, the retrieved vertical columns are converted to column-averaged dry-air mole fractions by dividing them by the dry-air columns obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) data. To adopt the high spatial resolution of the TROPOMI data, the ECMWF dry-air columns are adjusted for the actual elevation of the individual satellite scenes by accounting for the mismatch to the mean altitude of the coarser model grid.

As the look-up table is confined to specific atmospheric conditions, such as cloud-free scenes, an efficient machine learning quality filtering procedure based on a random forest classifier is trained using quasi-simultaneous cloud information from VIIRS. After the supervised learning process has been completed, the ongoing actual quality prediction of individual soundings is independent of the VIIRS data. Due to the strict quality requirements for retrieving atmospheric methane, a shallow learning random forest regressor is trained to reduce the remaining systematic coarse-scaled methane dependencies on other parameters such as albedo after quality filtering. The training is restricted to a subperiod of time and a few small regions, which are selected to cover the entire range of albedo values and all possible viewing geometries. Starting with v1.8, post-processing also includes an efficient orbit-wise destriping filter based on combined wavelet–Fourier filtering to remove stripes in flight direction in the TROPOMI XCH₄ and XCO data, while optimally preserving the original spatial trace gas features [35]. In this work, two WFMD data product versions were used: v1.2 and v1.8. The latter was the newest version at the time of writing of this paper.

2.3. TCCON Sites and XCH₄ Products

The Total Carbon Column Observing Network (TCCON) is the main validation network for greenhouse gas observations from space [43,44]. TCCON is a network of ground-based high-resolution Fourier Transform Spectrometers (FTSs) that measure direct sunlight in the near-infrared spectral region. The column-averaged dry-air mole fractions of greenhouse gases and other atmospheric trace gases (CO₂, CH₄, N₂O, HF, CO, H₂O, and HDO) are retrieved from the measured spectra. The TCCON GGG software iteratively scales an a priori trace gas profile until it finds the best fit between the observed spectrum and a forward model simulated spectrum. In the most recent major version update, the TCCON GGG2014 was updated to GGG2020. The GGG2020 version update is described in full by Laughner et al. [45]; the largest changes include improved prior trace gas profiles through a new algorithm [46], updated spectroscopy, changes in meteorological data, an updated retrieval vertical grid, improvements to the interferogram to spectrum conversion, and updated quality control.

Currently, TCCON consists of 28 operational sites, from which 23 are in the Northern Hemisphere. In this study, the focus is on the four northernmost, non-urban TCCON sites that are located to the north of 50°N and are either within or relatively close to the permafrost regions (Figure 1). In what follows, the sites are described in more detail.

The TCCON station at East Trout Lake (ETL), Saskatchewan is located within the Canadian boreal forest at 54.3537°N, 104.9867°W, and 501.8 m above sea level. The nearest city is located more than 150 km away. The TCCON station is housed in an Environment and Climate Change Canada (ECCC) laboratory, which is equipped with a 60 m tall tower collecting in situ measurements of various trace gases (including CO₂ and CH₄), and a suite of surface-level aerosol measurements. The ETL TCCON spectra cover the 1800–11,000 cm⁻¹ spectral range across indium antimonide (InSb) and indium gallium arsenide (InGaAs) detectors. Both GGG2014 [47] and GGG2020 [48] data versions are available.

The TCCON Sodankylä (SO) site is located in Northern Finland at 67.3668°N, 26.6319°E, at the Finnish Meteorological Institute’s Arctic Space Centre. The Centre is situated about 6 km south of Sodankylä town (about 4900 inhabitants). Surroundings are predominantly a coniferous forest but there is also a peatland area nearby. In addition to TCCON, the site also contributes greenhouse gas measurements to other networks (e.g., COCCON and ICOS), and there are multiple measurements set up to monitor the atmosphere, cryosphere, hydrological phenomena, and the biosphere, enabling multidisciplinary studies. Regular measurements are taken with a Bruker 125HR FTS instrument [49]. The FTS at Sodankylä is equipped with indium gallium arsenide (InGaAs, covering 4000–11,000 cm⁻¹), a silicon diode (Si, covering 9000–15,000 cm⁻¹) and indium antimonide (InSb, covering 1800–6000 cm⁻¹) detectors. Both GGG2014 [50] and GGG2020 [51] data versions are available.

The Ny-Ålesund (NA) site in Svalbard (Norway) (78.9°N, 11.9°E) is hosted at the joint French–German research station AWIPEV. The TCCON FTIR spectrometer, a Bruker IFS120HR, which was updated to an IFS120-5HR in 2012, performs alternating measurements for the TCCON and NDACC networks and is installed in the AWIPEV observatory at 20 masl. The AWIPEV station hosts a large number of atmospheric research instruments, including daily radiosondes and various meteorological quantities. The station is housed on the outskirts of Ny-Ålesund on the coast of Kongsfjorden. The surface pressure data used in the retrieval is taken from the neighboring Baseline Surface Radiation Network (BSRN: [52]). In its TCCON configuration, the measurements are performed using a CaF₂ beamsplitter and a room-temperature InGaAs diode detector. There are no solar absorption measurements performed during the polar night from October to March. Data are available in two versions: the previous GGG1014 (GGG2014: [53]) and current GGG2020 (GGG2020: [54]) releases.

The Eureka (EU) TCCON site is located on Ellesmere Island in Nunavut, Canada at 80.05°N, 86.42°W, at the Polar Environment Atmospheric Research Laboratory (PEARL, [55]). The TCCON FTIR spectrometer is installed in the PEARL Ridge Lab, at an elevation of 610 m, 15 km from the Environment and Climate Change Canada Eureka Weather Station. The facility is on the shores of Slidre Fiord and the area is semi-arid and underlaid by permafrost. There is no local community. PEARL houses about two dozen instruments for atmospheric measurements, with data contributed to multiple networks, including the Network for Detection of Atmospheric Composition Change (NDACC), AERONET, MPLNET, and the Pandonia Global Network. A Bruker 125HR FTIR spectrometer was installed in 2006 as a mid-infrared NDACC instrument [56], with near-infrared capability added in 2010; since then, measurements have alternated between TCCON and NDACC. The TCCON measurements are recorded with a CaF2 beamsplitter and an InGaAs detector. No solar measurements are possible during the four months of polar night from late October to late February, and measurements have been limited since July 2020 due to restrictions on site access. Both GGG2014 [57] and GGG2020 [58] data versions are available.

2.4. COCCON XCH₄ Product

The Collaborative Carbon Column Observing Network (COCCON) is a network of portable EM27/SUN Fourier Transform Spectrometers [59]. The spectrometer measures direct solar radiation covering spectral range of 5500–11,000 cm⁻¹ with a single-detector set-up [60] and additional 4000–5500 cm⁻¹ with the dual-detector set-up [61]. The spectral resolution of the instrument is 0.5 cm⁻¹. Column mixing ratios of CO₂, CH₄, H₂O, and CO are retrieved from the spectra using PROFFAST v1.0 retrieval code (available at https://www.imk-asf.kit.edu/english/4266.php, last accessed on 9 August 2024) that employs a prior profile-scaling method for spectral fitting [62].

The EM27/SUN instruments are characterized by the instrument line shape (ILS) and calibrated against the reference EM27/SUN (SN37) and the collocated Karlsruhe TCCON instrument by the Karlsruhe Institute of Technology (KIT) [63]. In an intercomparison campaign at the Finnish Meteorological Institute’s Sodankylä facility, an EM27/SUN was found to have a bias and standard deviation of 0 ± 4 ppb in comparison to the Sodankylä TCCON instrument [62]. The EM27/SUN spectrometers have been found stable over timescales of several years and, due to the construction of the interferometer, do not require a lot of maintenance [63].

As with TCCON, we concentrated on the northernmost COCCON sites, which are Kiruna (KI), Sodankylä (SO), Fairbanks (FA), and St. Petersburg (SP; see Figure 1).

COCCON observations in Kiruna (KI, Sweden; [64]) are performed at the NDACC FTIR site and operated by KIT in cooperation with IRF. The observations have been used for detecting greenhouse gas gradients along the baseline Kiruna–Sodankylä [65].

The COCCON Sodankylä (SO) station is the same as the TCCON site at the FMI Arctic Space Centre (site description in Section 2.3). The EM27/SUN measurements at Sodankylä started in March 2017 during the Fiducial Reference Measurements for Greenhouse Gases (FRM4GHG) project, using an instrument from Karlsruhe Institute of Technology (KIT) with the serial number SN039 [66]. The time series was continued with another instrument (SN122) for which data are available from April 2020 until end of March 2021 [67]. The two Sodankylä datasets were analyzed separately, and referred to by the instruments’ serial numbers.

The Fairbanks (FA) COCCON station [68] is located in the interior of the State of Alaska, USA, at 64.859379°N, −147.849944°E and 222.5 m above mean sea level. The station is on the roof of the University of Alaska Fairbanks (UAF) Geophysical Institute and is on the western edge of the the small city of Fairbanks, Alaska, which has a city population of approximately 32,000 inhabitants and a broader regional population of approximately 100,000 people. The site is in forested hills, which generally cover most land around the site; however, to the south of Fairbanks there is a large flat basin filled with wetlands that has many lakes, which formed due to the poor drainage of frozen soils. Observations were made using EM27/SUN instrument SN050, which is owned by KIT and operated by UAF under a cooperative agreement between these institutions.

COCCON observations in Russia (St. Petersburg (SP) and Yekaterinburg; [69]) were collected in the framework of the H2020 VERIFY project. Further details of the campaign activities are provided by Alberti et al. [70].

2.5. AirCore CH₄ Profiles

AirCore is a technique to measure profiles of atmospheric trace gases [71]. AirCore enables traceable measurements of methane and other gases from the stratosphere down to the Earth’s surface. The AirCore instrument is lifted by a meteorological balloon to the stratosphere, typically up to 30–35 km of altitude. The inlet valve of the AirCore system is open during the balloon launch and the sampler is filled during payload descent. The inlet valve of the system is closed shortly after the landing of the sampler. The gas analysis is typically performed within 2–3 h after landing of the payload, using a Cavity Ring-Down Spectrometer. The AirCore measurements are traceable by the World Meteorological Organization’s in situ trace gas scales.

In the European sector of the Arctic, AirCore observations have been made at FMI Sodankylä (SO), Finland, and at Esrange, near Kiruna, Sweden. Sodankylä was the first site in Europe to perform AirCore measurements. In Sodankylä, we have performed AirCore flights during various seasons since early September 2013. In the case of the AirCore measurements taken at Sodankylä, the precision and accuracy for CO₂, CH₄, and CO measurements are 0.05 ppm and 0.1 ppm, 0.5 ppb and 1 ppb, and 8 ppb and 3 ppb, respectively, [62]. For this study, we included all AirCore measurements carried out in Sodankylä in the years 2018–2021, totaling 18 flights.

2.6. Permafrost Extent

Permafrost is a layer of ground that remains at or below the freezing temperature of water for at least two consecutive years [72]. Permafrost areas consist of an active layer, which is subject to seasonal ground freezing, and perennially frozen ground beneath the active layer.

In this study, we used the ESA CCI Permafrost Year 3 Climate Research Data Package (CRDP v2) [73]. It provides annual datasets of permafrost extent, temperature, and active layer thickness. The extent data are gridded products in polar stereographic projections at 1 km resolution and based on the World Geodetic System 84 (WGS84) reference ellipsoid. As described in the CCI Permafrost extent data catalog webpage (https://dx.doi.org/10.5285/6e2091cb0c8b4106921b63cd5357c97c, last accessed on 9 August 2024), CCI Permafrost extent data are given as a yearly fraction of permafrost-underlain and permafrost-free areas within a grid cell. A classification according to the IPA (International Permafrost Association) zonation for the permafrost zones is as follows: isolated (0–10%), sporadic (10–50%), discontinuous (50–90%), and continuous permafrost (90–100%). Continuous permafrost is, as the name suggests, a uniform, continuous sheet of frozen material. Continuous permafrost is considered when 90% of the ground surface is permafrost, taking into account small taliks, patches of unfrozen ground that may exist within the continuous layer of frozen perennial ground, mainly under water bodies.

We re-gridded the original fraction to a 0.25° × 0.25° grid to speed up the co-location of TROPOMI Level 2 data over the permafrost regions. The re-gridded permafrost fraction is an average of the original fractions. These re-gridded values were then categorized according to the IPA permafrost zones described above. Since the permafrost data were only used in this study to estimate methane satellite data coverage and amounts, we chose permafrost data from the year 2017 for the analysis and did not update the dataset for each year.

3. Methods

3.1. Co-Location at TCCON and COCCON Sites

To make the ground-based and satellite-based observations spatially and temporally consistent, they have to be co-located. Every co-location technique is an assumption about the geographical region and temporal correspondence on which the observations can be assumed to measure the same atmospheric methane.

As the number of TROPOMI XCH₄ observations is sufficiently large, a simple method for spatial co-location can be used: we considered all TROPOMI observations $\pm 2^{\circ }$ in latitude and longitude from the TCCON and COCCON site locations. Temporally, we considered all ground-based observations within one hour of each TROPOMI overpass. In addition, to avoid spurious outliers in the analysis due to insufficient data, a minimum of three good-quality TROPOMI observations per day were required inside the co-location region, on any overpass during the day.

To compare same-day XCH₄ values, we calculated the daily median values from both TCCON and TROPOMI data for each day that had co-located observations. These daily median values were then used in the TROPOMI evaluation. For the comparison between TCCON/GGG2014 and TCCON/GGG2020 (Section 4.2.2), the temporal co-location criterion was more relaxed, allowing all same-day observations, as the prior correction (Section 3.2) was not applied in that part of the study.

3.2. Prior Correction

To account for the impact of different retrieval priors in the comparison of satellite and ground-based remote sensing products, a prior correction was applied to the TROPOMI XCH₄ observations. Following Rodgers [74] and the notations of, e.g., Schneising et al. [34], we adjusted the original TROPOMI-retrieved column-averaged dry-air mole fraction $\widehat{c}$ by assuming the TCCON or COCCON prior profile ${\overline{x}}_{a,\mathrm{ref}}$ as the common prior profile for both retrievals. The adjusted TROPOMI retrieval result ${\widehat{c}}_{\mathrm{adj}}$ is, thus,

$${\widehat{c}}_{\mathrm{adj}}=\widehat{c}+\sum _l{h}_l(1-A_l)(x_{a,\mathrm{ref}}^l-x_a^l),$$

(1)

where $h_l$ is the pressure weighting function, $A_l$ the TROPOMI column-averaging kernel, and $x_a^l$ the TROPOMI prior dry-air mole fraction at an atmospheric vertical layer l.

We quantified the impact of the prior correction (${\widehat{c}}_{\mathrm{adj}}-\widehat{c}$) against both TCCON and COCCON data. The results of the TROPOMI XCH₄ adjustment using the COCCON prior are shown in Figure 2. For OPER rpro, the COCCON prior correction impact is mostly negative, i.e., the correction leads to lower XCH₄ values. In Kiruna, the prior correction leads to up to 7.2 ppb higher XCH₄ values during spring 2019, but the impact is still mostly negative, with a minimum of −6.9 ppb in 2020. At the other COCCON sites, the correction effect varies from −5.8 ppb to 1.9 ppb. A solar zenith angle dependence can also be seen for TROPOMI OPER rpro’s COCCON-prior-adjustment: the adjustment leads to larger negative values at larger solar zenith angles (∩-shaped). For WFMD v1.8, the impact of the COCCON prior adjustment is always positive, i.e., the TROPOMI XCH₄ values are corrected upwards. The adjustment is overall smaller in magnitude when compared to OPER rpro: the differences between the prior corrected and uncorrected WFDM v1.8 daily median values are less than 4 ppb. For WFMD v1.8, a solar zenith angle dependence on the impact of the prior correction can also be seen (∪ shaped).

The results of TROPOMI XCH₄ adjustment using the TCCON GGG2020 priors were different than for the COCCON: for OPER rpro, the TCCON correction was generally smaller than for WFMD v1.8, and the solar zenith angle dependence was more visible for WFMD v1.8 than for OPER rpro. For both retrievals, the correction was ∪-shaped, more clearly so for WFMD v1.8. The adjustment varied from −5.7 ppb to 4.7 ppb for OPER rpro and from −16.2 ppb to 0.4 ppb for WFMD, depending on the site and time. For OPER rpro, the average correction was the largest at East Trout Lake (−1.2 ppb) and, for WFMD v1.8, at Ny-Ålesund (−4.4 ppb), i.e., the corrected values are generally smaller than non-corrected ones.

3.3. Co-Location and Analysis against AirCore Measurements

AirCore observations from 2018 to 2021 (total of 18 measured profiles) were co-located with TROPOMI observations within $\pm 1$ degree in latitude and longitude from the Sodankylä TCCON/COCCON site that also serves as the launch site for the AirCore balloon-borne measurements. Temporally, only good-quality satellite observations taken within 3 h from the profile measurement were considered. For XCH₄ comparisons, the AirCore profiles were extrapolated to the surface according to the measured values at the lowest altitude and to the top of the atmosphere following the TCCON GGG2020 prior profile shape.

We applied the AirCore profiles here primarily for XCH₄ comparisons with the TROPOMI products. The profiles can, however, also give guidance about the dependence of the XCH₄ difference on the prior profile shape. In the analyses, the prior profiles of the TROPOMI retrievals were first scaled to produce the TROPOMI-retrieved XCH₄ values, and, then, the scaled profiles were quantitatively compared to the AirCore at each layer. To investigate the difference of TROPOMI XCH₄–AirCore XCH₄ and to what extent that difference was explained by the deviations of the retrieval prior profile from the measured true atmospheric methane profile, an analysis of the profile shapes was conducted. We divided the profiles into the tropospheric and stratospheric parts at 250 hPa. Then, the difference in the CH₄ layer value was compared to AirCore measurements interpolated in the retrieval layer pressure. The integral of the difference over the troposphere or stratosphere was calculated and the correlation with the TROPOMI−AirCore XCH₄ difference was evaluated.

4. Results and Discussion

4.1. Seasonal and Regional Coverage

4.1.1. Spatial Comparison of TROPOMI Products

Ground-based validation gives, spatially, a very limited coverage of high latitudes due to the sparseness of the validation sites. To analyze spatial differences between satellite products, all quality-filtered TROPOMI XCH₄ retrievals were gridded into monthly mean values in a 0.25° longitude × 0.2° latitude grid for years 2018–2021. Example XCH₄ maps for selected months in the year 2020 are shown in Figure 3, and their difference for every month in 2020 in Figure 4. Difference maps for years 2018, 2019, and 2021 were qualitatively similar to 2020.

Monthly maps of TROPOMI OPER rpro and WFMD v1.8 (Figure 3) show that the spatial coverage of retrievals varies with seasons: in winter months (from November to February) the solar zenith angle is a limiting factor for successful retrievals at high latitudes. Between March and October, cloud cover is likely a more significant factor for filtering, as estimated by the gaps in the spatial coverage. Monthly maps of TROPOMI OPER rpro show generally similar patterns of XCH₄ as WFMD v1.8 but with somewhat sparser coverage, especially in spring months. Spatial variability in XCH₄ can be seen in all seasons for both products. In particular, XCH₄ during the springtime shows a spatial variability of about 70 ppb for a given month. In the summer, atmospheric methane is first reduced in June due to the strengthening of the atmospheric OH sink with increasing solar UV radiation. Natural sources of methane from wetlands (see, e.g., [75]) cause an increase in XCH₄ towards the late summer (July and August). The largest difference in XCH₄ is between Greenland and the continents. More subtle differences of about 30 ppb can be seen regionally.

The difference between TROPOMI OPER rpro and TROPOMI WFMD v1.8 monthly-averaged XCH₄ (Figure 4) changes seasonally: in the late winter and spring months, OPER rpro produces higher XCH₄ values compared to WFMD v1.8. In May, June, and September, the differences are the most negative. In July, the best agreement is achieved between the two products. Some quite localized differences of $\pm 40$ ppb can be found but, in general, the differences between the products are approximately 10 ppb in the summer months. Towards shoulder seasons, the differences increase in magnitude. The agreement between the two products can be used as an additional measure of the reliability of spatial XCH₄ features, for instance when interpreting potential enhancements close to emissions.

4.1.2. Latitudinal Comparisons

Gridded monthly mean XCH₄ values of TROPOMI methane products were further analyzed as latitudinally binned mean values. Four retrieval products (OPER, OPER rpro, WFMD v1.2, and WFMD v1.8) were considered and binned into 5° latitudinal averages. Time series of the mean differences between products are presented in Figure 5, and the temporally averaged latitudinal differences in Figure 6.

The XCH₄ difference between OPER rpro and WFMD v1.8 is mostly systematically negative; the only positive values are seen between 50°N–65°N in January–April. At the northernmost latitudes (>80°N), the differences are the most negative, averaging to about $-25$ ppb in August. For comparison, at 50–55°N, the difference in August varies only between −5 to −10 ppb. High positive values of the mean difference in November 2019 and 2020 are based on few soundings, interpreting from the maps in Figure 4. The temporally averaged mean difference is negative, as seen in Figure 6a, and increasingly negative towards higher latitudes.

TROPOMI retrieval product development at high latitudes can be demonstrated by comparing the current products to their earlier versions. Based on the time series in Figure 5b, TROPOMI OPER differs significantly from OPER rpro, especially at latitudes >55°N. The product differences depend on latitude as well as season. The mean difference is $-9.4\pm 6.5$ ppb at 55°N–60°N and increases up to $24.5\pm 3.1$ ppb at the highest latitudes. TCCON comparisons of these products (see Section 4.2) suggest product improvement in terms of reduced biases especially at the highest latitudes with the new product version, OPER rpro.

Conversely, the difference between TROPOMI WFMD v1.2 and WFMD v1.8 is not as large as for OPER product versions. A modest seasonal cycle exists in the difference. While the difference shows positive values in the spring and negative values in autumn, the temporal average (Figure 6c) is close to zero. The largest systematic difference between WFMD product versions is at the latitude band 75°N–80°N and equals $-4.2\pm 7.4$ ppb.

4.1.3. Coverage of TROPOMI at High Latitudes

To estimate the seasonal coverage of good-quality retrievals from OPER, OPER rpro, WFMD v1.2, and WFMD v1.8, and their potential to yield information on changes in the permafrost regions, the retrievals between 2018 and 2021 (for WFMD v1.2, only 2018–2020) were classified into three categories: (1) all high-latitude retrievals north of 50°N, (2) discontinuous permafrost (permafrost extent $\in [50\%,90\%]$), and (3) continuous permafrost (permafrost extent $>90\%$), based on ESA Permafrost CCI Level 4 product for year 2017 (see Figure 1).

The number of observations in high latitudes is presented as a time series in Figure 7 for OPER, OPER rpro, WFMD v1.2, and WFMD v1.8. For both OPER and WFMD products, the number of retrievals has increased for the updated product versions, but the seasonal coverage has remained similar to the older product versions. The seasonal observational coverage at all regions to the north of 50°N is approximately Jan–Nov for OPER rpro and is, in principle, gapless for WFMD v1.8. Over the permafrost regions, OPER rpro retrievals cover months between Mar–Sep while WFMD v1.8 also shows a few retrievals over these regions in February and October. With the exception of OPER rpro in the year 2020, all retrieval products in Figure 7 systematically show the largest number of observations in April. This is probably caused by reduced cloud cover in April and, especially for WFMD products, the retrievals over the frozen Arctic Ocean (see Figure 3, top right) add to the total number of high-latitude observations in late winter and spring.

4.2. TROPOMI Evaluation at the TCCON Sites

Retrievals of XCH₄ from TROPOMI OPER rpro and WFMD v1.8 were evaluated at four high-latitude TCCON sites: East Trout Lake, Sodankylä, Ny-Ålesund, and Eureka. In addition to the newest satellite-retrieval products, the earlier product versions from each retrieval system (OPER and WFMD v1.2) were considered and those results are briefly discussed. Moreover, the TCCON retrieval has recently been updated from GGG2014 to GGG2020 with significant changes, e.g., improved prior profiles [46]. Both data versions of the TCCON retrievals were considered, with the emphasis on the newest data product. The comparisons cover years 2018–2021.

4.2.1. TROPOMI XCH₄ Evaluation against TCCON GGG2020

Time series of daily median XCH₄ from ground-based TCCON GGG2020, TROPOMI OPER rpro, and WFMD v1.8, and TROPOMI−TCCON XCH₄ differences are shown in Figure 8, Figure 9, Figure 10 and Figure 11 for East Trout Lake, Sodankylä, Ny-Ålesund, and Eureka. Corresponding time series for earlier TROPOMI product versions OPER and WFMD v1.2 can be in from Appendix A, Figure A1, Figure A2, Figure A3 and Figure A4.

Time series of XCH₄ and the difference indicate that the satellite products do not contain significant outliers. Both products show systematically higher values than the TCCON at Eureka, which is the northernmost site in the comparison and, therefore, the seasonal gap in observations is extensive. In 2019, the difference with TCCON is time-dependent for both products, yielding higher values in the spring (up to about 50 ppb) and less than 10 ppb in autumn (Figure 8b,d). This seasonal dependence is also seen for the year 2020 for OPER rpro but not for WFMD v1.8. At Ny-Ålesund, the number of co-located observations is strongly limited by the vicinity of the ocean, in addition to large SZAs. The differences with TCCON are, on average, 7.5 ppb for OPER rpro and 9.2 ppb for WFMD v1.8 (Figure 9a,c). No seasonal dependence on the difference is seen, possibly due to the limited seasonal coverage. At Sodankylä, the seasonal coverage of co-located observations extends to about 7 months. The mean difference of OPER rpro with TCCON is $-0.5\pm 20.4$ ppb. The high scatter in the result is due to the temporal variability in the difference: OPER rpro shows a systematic, time-dependent difference with TCCON for the years 2019–2021 that exceeds 70–80 ppb, with high values in the spring and low values in autumn (Figure 10b). For WFMD v1.8, the mean difference is 1.2 ± 12.5 ppb. The seasonal variability in the difference is only present in the year 2019 (Figure 10d). At East Trout Lake, the results are qualitatively similar to those at Sodankylä but the seasonal gap in observations is shorter and is limited by the TROPOMI observations (Figure 11b,d). The mean differences for all sites are listed in

Table A1. TROPOMI–TCCON GGG2020 differences in the daily median XCH₄ values (calculated for 2018–2021, and including the following sampling differences: OPER rpro starts later than OPER, and WFMD v1.2 time series ends by the end of 2020).

	East Trout Lake	Sodankylä	Ny-Ålesund	Eureka
N, OPER rpro	462	270	48	79
Bias (ppb), OPER rpro	3.30	−0.47	7.54	22.42
$1\sigma$ (ppb), OPER rpro	15.45	20.43	16.7	12.98
r, OPER rpro	0.604	0.415	0.494	0.761
N, WFMD v1.8	520	234	66	113
Bias (ppb), WFMD v1.8	4.61	1.17	9.17	19.41
$1\sigma$ (ppb), WFMD v1.8	14.97	12.51	14.66	13.37
r, WFMD v1.8	0.744	0.707	0.847	0.643
N, OPER	444	259	34	56
Bias (ppb), OPER	4.22	−6.42	32.81	36.25
$1\sigma$ (ppb), OPER	18.72	17.61	13.96	18.30
N, WFMD v1.2	377	172	64	126
Bias (ppb), WFMD v1.2	5.54	−1.43	4.79	19.98
$1\sigma$ (ppb), WFMD v1.2	16.97	16.83	26.78	16.52

.

Lorente et al. [31] showed that the mean bias of OPER rpro− TCCON/GGG2014 at 13 TCCON sites was $-3.4$ ppb and the mean standard deviation of the differences was 12.7 ppb. The mean bias is in the same order of magnitude as our results and the standard deviation is smaller due to averaging over global TCCON stations. They also pointed out a similar seasonal bias at high-latitude stations, and noted that the bias was positive during spring months and negative in autumn. Lorente et al. [31] suggested that the seasonal dependence of the bias is mainly caused by retrieval errors related to snow and meteorological dynamics rather than sampling errors or different priors.

The Pearson correlation coefficients between TROPOMI and TCCON XCH₄ vary for OPER rpro from 0.415 at Sodankylä to 0.761 at Eureka, and for WFMD v1.8 from 0.643 at Eureka to 0.847 at Ny-Ålesund (Table A1). At East Trout Lake, Sodankylä, and Ny-Ålesund, the correlation is higher for WFMD v1.8 than for OPER rpro, while at Eureka, the correlation is higher for OPER rpro. The largest difference in the correlations are at Ny-Ålesund where the correlations are 0.494 and 0.847 for OPER rpro and WFMD v1.8, respectively.

4.2.2. Comparison of TCCON GGG2014 and TCCON GGG2020

Because the TCCON can be considered as the primary validation resource for greenhouse gas satellite products, it is informative to quantify how the TROPOMI evaluation results have changed with the introduction of the new TCCON GGG2020 retrieval. Therefore, TROPOMI comparisons against both TCCON GGG2014 and GGG2020 were made, considering only the days that had good-quality TCCON data in both processing versions, so that potential temporal sampling differences would not affect the results. Mean differences and standard deviations of the daily median XCH₄ values are presented in Figure 12 for all high-latitude TCCON sites. The mean difference between both TROPOMI products (OPER rpro and WFMD v1.8) and TCCON becomes systematically more positive with the new TCCON data version—it seems that the high-latitude TCCON XCH₄ values have decreased by about 7 ppb on average between old and new product versions, even more at Eureka, consistent with the results in Mostafavi Pak et al. [76]. Overall, with the newest TCCON data product, the difference between TROPOMI and TCCON results is larger positive values at Ny-Ålesund and Eureka for both TROPOMI products and at East Trout Lake for WFMD v1.8. The scatter increased for both TROPOMI products at most sites, with the exception of OPER rpro at Eureka. It should be noted that the TROPOMI products do not directly apply the TCCON data for post-processing bias correction; therefore, the TROPOMI data products are independent of the ground-based data and their product versions. In addition, for this comparison, we did not apply the prior correction to the TROPOMI data as the priors have been changed between GGG2014 and GGG2020. The statistics of the comparisons are listed in Appendix B in

Table A2. TROPOMI–TCCON GGG2020 and TROPOMI–TCCON GGG2014 differences in the daily median XCH₄ values. The sampling and temporal co-location criteria are different compared to the data in Table A1. Here, all same-day observations were considered, and only the days with both GGG versions data are included.

	East Trout Lake	Sodankylä	Ny-Ålesund	Eureka
N, OPER rpro	363	295	44	77
Bias (ppb), GGG2014	−3.8	−12.9	−1.7	6.3
$1\sigma$ (ppb), GGG2014	15.3	19.4	17.8	10.5
Bias (ppb), GGG2020	3.3	−6.3	5.6	21.9
$1\sigma$ (ppb), GGG2020	16.6	19.9	18.3	10.3
N, WFMD v1.8	416	249	46	94
Bias (ppb), GGG2014	0.8	−4.3	− 0.9	9.0
$1\sigma$ (ppb), GGG2014	14.2	12.3	18.0	13.1
Bias (ppb), GGG2020	8.9	3.3	10.5	25.3
$1\sigma$ (ppb), GGG2020	15.3	13.0	20.1	14.1

.

The difference of 0.35% between the TCCON data versions might result from the updated WMO scaling: for the scaling of the GGG2020 product, the number of available atmospheric profile measurements both from aircraft and AirCore measurements was increased almost two-fold compared to the previous scaling. In addition, sites with new profile measurements (e.g., Sodankylä) were included in the scaling. Improvements made in the TCCON XCH₄ retrieval may also contribute to explaining the difference. These improvements include the shape of the prior profiles, improved spectroscopy (by using a quadratic speed-dependent Voigt (qSDV) line shape with full line mixing), and improved air mass dependence removal methodology.

4.3. Evaluation at the COCCON Sites

The TROPOMI–COCCON comparisons were carried out at four high-latitude COCCON sites: Kiruna, Sodankylä, Fairbanks, and St. Petersburg. Most data for the comparisons were available at Fairbanks, totaling 205 (OPER rpro) and 188 (WFMD v1.8) days, for the period 2018–2021. Data from Kiruna and Sodankylä SN039 were available from 2018 to 2020, from Sodankylä SN122 from 2020 to 2021, and from St. Petersburg from 2019 to 2020.

The differences between daily median TROPOMI and COCCON XCH₄ for the TROPOMI OPER rpro and WFMD v1.8 products are shown in Figure 13, Figure 14, Figure 15, Figure 16 and Figure 17. For detailed comparison values, see

Table A3. TROPOMI–COCCON differences in the daily median XCH₄ values and correlations r for OPER rpro and WFMD v1.8.

	Kiruna	Sodankylä (SN039)	Sodankylä (SN122)	Fairbanks	St. Petersburg
N, OPER rpro	125	97	40	205	15
Bias (ppb), OPER rpro	−14.81	−6.74	−6.32	5.62	−26.51
$1\sigma$ (ppb), OPER rpro	14.36	16.02	13.96	14.61	28.73
r, OPER rpro	0.561	0.368	0.750	0.712	0.118
N, WFMD v1.8	132	78	27	188	27
Bias (ppb), WFMD v1.8	−5.03	−0.03	−3.79	17.23	2.67
$1\sigma$ (ppb), WFMD v1.8	12.94	11.54	13.02	12.05	11.45
r, WFMD v1.8	0.734	0.576	0.595	0.819	0.623

. Among the selected sites, Fairbanks is the only site where TROPOMI OPER rpro has a positive bias when compared against COCCON, with an average of 5.62 ppb. It also has the largest TROPOMI WFMD v1.8 average bias, 17.23 ppb, which makes it the only station at which WFMD performs worse than the OPER rpro product. For OPER rpro, the two Sodankylä sites have similar values of the difference, −6.74 ppb and −6.32 ppb at SN039 and SN122, respectively. Sodankylä SN039 also has the smallest difference for the WFMD v1.8, with an average of −0.03 ppb. The standard deviations range around 14 to 16 ppb for the OPER rpro product, with the exception of St. Petersburg, where the small amount of data likely leads to both high bias (−26.51 ppb) and standard deviation (28.73 ppb). However, this lack of data at St. Petersburg does not result in significantly different results in the WFMD v1.8 product compared to the other sites. The WFMD v1.8 standard deviations at all sites range from 11 to 13 ppb.

In terms of correlations between the TROPOMI and COCCON XCH₄ observations, there are significant differences between the sites; see Table A3. The Pearson correlation coefficients range from 0.118 at St. Petersburg to 0.750 at Sodankylä SN122 for OPER rpro, and from 0.576 at Sodankylä SN039 to 0.819 at Fairbanks for WFMD v1.8. Overall, WFMD v1.8 correlates better with COCCON than the OPER rpro XCH₄, with the exception of Sodankylä SN122. Sodankylä SN122 has the highest correlation with OPER rpro, 0.750, significantly higher than SN039, 0.368, though it should be noted that SN122 has fewer than half the number of days compared to SN039. At Fairbanks, despite the high biases, both OPER rpro and WFMD v1.8 correlate well with COCCON, with Pearson correlation coefficients of 0.712 and 0.819, respectively. The temporal range of the data does not seem to be connected systematically to the correlation coefficient.

4.4. AirCore Comparisons

From 18 AirCore measurements in the years 2018–2021, the TROPOMI co-location criteria (Section 3.3) yielded eleven matching AirCore cases for OPER rpro and fpur for WFMD v1.8. An example of a measured profile along with TROPOMI OPER rpro, WFMD v1.8, and TCCON GGG2020 prior profiles is shown in Figure 18. The averages XCH₄ of all co-located TROPOMI, AirCore, and TCCON results are collected in Figure 19.

By visual inspection, TROPOMI OPER rpro prior profile shapes seem to be in a good agreement with the measured profiles. It is interesting how the scaled profile sometimes deviates more from the measurement than the prior, i.e., the scaling makes the profile agreement worse. This is particularly the case for August and September samples when TROPOMI OPER rpro yields a systematically lower XCH₄ value than AirCore or TCCON. The discrepancy of the late summer profiles is seen in the differences: TROPOMI OPER rpro scaled profiles systematically underestimate CH₄ in the troposphere in August and September, and particularly in the lowest troposphere. During that season, the measured profiles (at least in the boundary layer) could show enhancements due to local-scale emissions from nearby peatlands. In addition, in the stratosphere the prior profiles can in some situations deviate from measurements by up to −300 ppb in spring.

The comparisons of TROPOMI WFMD v1.8 prior profiles to AirCore indicate that the WFMD prior profiles, which do not change temporally, can differ from the true atmospheric concentration profile significantly. However, the scaled profiles approach the measurement so that the retrieved XCH₄ does not deviate much from the AirCore extrapolated XCH₄ nor the TCCON GGG2020 retrieved values. The difference profiles (all qualitatively similar to Figure 18, fourth panel) show that the scaled WFMD profiles systematically underestimate CH₄ in the troposphere and overestimate in the stratosphere and, yet, the retrieved XCH₄ is close to the AirCore-extrapolated XCH₄.

It is interesting to test which part of the prior profile is more important in explaining the difference in the total column: the tropospheric or the stratospheric part. To estimate this, the individual TROPOMI prior profiles co-located with the AirCore were divided into two parts, separated at 250 hPa, and, for each pressure level of the retrieval’s vertical scale, the corresponding CH₄ was interpolated from the AirCore measurement. The integrated difference through all levels of the atmosphere corresponds to the overall TROPOM–AirCore difference in XCH₄. The integrated differences were computed separately for both tropospheric and stratospheric parts, and the results were compared against the TROPOMI–AirCore difference. The results systematically indicated that the tropospheric CH₄ differences correlated better with the overall difference: for the tropospheric difference, Pearson’s correlation coefficient was $R^2=0.90$ for OPER rpro and $R^2=0.86$ for WFMD v1.8, whereas, for the stratospheric difference, the results were $R^2=0.20$ for OPER rpro and $R^2=0.45$ for WFMD v1.8. A total of 1172 (OPER rpro) and 411 (WFMD v1.8) retrievals were considered. Based on the correlations, the tropospheric part is more significant in explaining the difference in the total column, even though the differences in the stratosphere could be much larger per layer. This finding is understandable considering that most of the air is in the troposphere but also highlights the relative importance of the prior profile shape in the troposphere, which could be particularly relevant where emissions take place. In addition, other metrics (such as the mean difference and standard deviation in the troposphere and stratosphere) were tested, and they resulted in similar conclusions.

5. Conclusions

High latitude methane emissions and their evolution in the warming Arctic are an intriguing and important subject of research, especially using novel Earth observations for added information. To support and enhance the use of TROPOMI satellite observations of methane in flux-inversion studies as well as to facilitate satellite-data-driven emission estimation, we have, in this work, carried out a systematic evaluation of the TROPOMI methane products and their recent version development, focusing exclusively on high latitudes (>50°N). The evaluation covers the years 2018–2021.

TROPOMI OPER rpro and WFMD v1.8 XCH₄ products were compared spatially, temporally, and latitudinally. The inter-product differences showed a systematically lower average XCH₄ for OPER rpro than for WFMD v1.8, except in the late winter and early spring months when OPER rpro exhibited higher values. In May, the difference (OPER rpro–WFMD v1.8) became systematically negative, persisting for most of the year. The spatial differences were the smallest in July. In the spatial scales of a few hundred kilometers, approximately ±30–40 ppb differences between the products could be found, especially over Canada as well as Russia. These spatial difference patterns were more profound in the first 5 months of each year, which might suggest snow-related interference. In the latter part of each year, the largest differences were found along the coasts of Greenland, suggesting a surface topography-related difference in the retrievals [29]. While an inter-product difference cannot be used for the validation of either product, the availability of several XCH₄ products based on different retrievals is important for a more reliable interpretation of spatial patterns and anomalous XCH₄ values as well as in supporting the evaluation of the overall reliability and robustness of the observations and retrieval approaches.

Comparisons against the ground-based reference measurements from four high-latitude TCCON and five COCCON stations were carried out in order to quantify the accuracy and precision of the TROPOMI products. The averages of the daily median XCH₄ differences are collected in Figure 20. The key findings are summarized as follows. TROPOMI products showed systematically mostly higher XCH₄ values compared to the TCCON, especially at the highest latitudes. The TROPOMI products were mostly in agreement with each other, except at St. Petersburg’s campaign-related, shorter-term site. The similarity of the residual biases may refer to systematic errors in the products. The persistent seasonal bias in the OPER rpro data shown in this work and also pointed out by Lorente et al. [31] is reflected in the systematically larger scatter of OPER rpro compared to WFMD v1.8. Any residual and seasonal biases in the retrieval products can significantly affect the emission estimates from flux inversions and complicate their interpretation, especially when emissions with a strong seasonal variability, such as high-latitude wetland emissions, are considered [75].

A few remarks regarding high-latitude reference data are necessary. First, the accuracy and precision of satellite-retrieval products have improved significantly with matured algorithms, to the level where the accuracy and precision of the ground-based reference measurements have become essential. The recent updates in the TCCON GGG retrieval algorithm [45] had a decreasing impact of about 7 ppb (even larger at Eureka) to the high-latitude TCCON data between versions GGG2014 and GGG2020. This is a significant fraction of the mean satellite-to-TCCON difference. While comparisons against the GGG2020 did not result systematically in improved TROPOMI-to-TCCON differences, the various updates made in the TCCON XCH₄ retrieval might give guidance to assess whether similar updates in prior profiles, spectroscopy, or other data processing would be necessary for the TROPOMI products, for collective improvements.

Second, localized site-to-site differences in the validation results also increase the importance of reliable reference data. In this study, the COCCON comparisons yielded systematic results that were quantitatively comparable to the TCCON, especially when the site latitude was taken into account. Shorter measurement periods (e.g., at St. Petersburg) can affect the representativeness and comparability of the result. Because the number of ground-based measurements at high latitudes remains severely limited, it is important to stress the value of the continuity, maintenance, characterization, and calibration of the measurements. As these reference measurements provide the primary validation data for multiple satellites, any temporal changes in their quality may indirectly affect the interpretation and even potential bias corrections of satellite data.

Measured atmospheric profiles of methane have proven useful in addition to the ground-based total column retrievals: they can be extrapolated to total atmospheric columns that provide another reference for the evaluation of TROPOMI XCH₄ products. The measured profiles can also give guidance to assessing the retrieval prior profile shapes for ground-based remote sensing as well as satellite observations. The analysis initiated in this study for evaluating the prior profile shape’s significance at different altitudes for explaining the XCH₄ difference could be developed further, yet keeping in mind that the satellite XCH₄ retrievals are sensitive to a number of other factors as well. A key finding from the AirCore comparisons was that the tropospheric part of the prior profile shape was more important than the stratospheric part for explaining the differences in the total column, which is reasonable given that a majority of air (and methane) is in the troposphere. However, what about the variability within the troposphere—how significant is it to consider enhancements by local emissions in the boundary-layer part of the profile? The measured profiles can also be used for independent validation of ground-based retrievals and for linking to the WMO trace gas scales. This underlines the emerging need for more atmospheric profile measurements that would cover all seasons, either from balloon-borne AirCore or aircraft measurements. In addition, it would be important to further advance methane profile retrieval development for ground-based instruments into a more operational direction, perhaps using principal components and dimension reduction methods as presented by Karppinen et al. [27]. These would also serve as independent model evaluation data to the inverse modeling community.

To conclude, satellite observations of atmospheric methane from TROPOMI OPER rpro and WFMD v1.8 products enable seasonal analyses at high latitudes, even over permafrost regions. The data products have significantly improved over recent version updates, and the differences between the two products are mostly comparable in magnitude to their differences against the ground-based references. While some remaining spatial differences may complicate the interpretation of methane anomalies, especially in the winter and spring months, the agreement between the products in the summer and autumn suggests the possibility to make reliable satellite observations during the season of the highest natural methane emissions and the warming autumn, including the soil freezing period. Addressing the uncertainties related to the Arctic methane emissions and their evolution requires, in addition to reliable satellite observations, an improved understanding of complex carbon cycle and cryosphere interaction processes and their transfer to advanced models, as well as the disentanglement of anthropogenic and natural emissions.