Leonid YURGANOV, Frank MULLER-KARGER & Ira LEIFER

Methane increase over the Barents and Kara seas after the autumn pycnocline breakdown: satellite observations

Leonid YURGANOV1*, Frank MULLER-KARGER2& Ira LEIFER3

1University of Maryland Baltimore County (ret), Baltimore, MD, USA;2University of Southern Florida, St. Petersburg, FL, USA;3Bubbleology Research International, Inc., Solvang, CA, USA

Seven operative thermal infrared (TIR) spectrometers launched at sun-synchronous polar orbits supply huge amounts of information about Arctic methane (CH4) year-round, day and night. TIR data are unique for estimating CH4emissions from a warming Arctic, both terrestrial and marine. This report is based on publicly available CH4concentrations retrieved by NOAA and NASA from spectra of TIR radiation delivered by EU IASI and US AIRS sounders. Data were filtered for high thermal contrast in the troposphere. Validation versus aircraft measurements at three US continental sites reveal a reduced, but still significant sensitivity to CH4anomalies in the troposphere below 4 km of altitude. The focus area is the Barents and Kara seas (BKS). BKS is impacted with warm Atlantic water and mostly free of sea ice. It is a shelf area with vast deposits of oil and natural gas (~90% CH4), as well as methane hydrates and submarine permafrost. Although in summer AIRS and IASI observe no significant difference in CH4between BKS and N. Atlantic, a strong, monthly positive CH4spatial anomaly of up to 30 ppb occurs during late autumn–winter. One of explanations of this increase is a fall/winter pycnocline breakdown after a period of blocked mixing caused by a stable density seawater stratification in summer: enhanced mixing lets CH4to reach the sea surface and atmosphere.

satellite data, greenhouse gases, Arctic

1 Introduction

The Arctic has experienced the fastest warming on Earth over recent decades, termed Arctic amplification, with the Arctic Ocean warming at nearly double the rate of the world’s oceans (Hoegh-Guldberg and Bruno, 2010). Due to Arctic warming, there is concern that the vast stores of the important greenhouse gas methane (CH4) in hydrates, permafrost, and other vulnerable reservoirs can be released to the atmosphere. The radiative impact of CH4is second after carbon dioxide on century timescales, and larger on decadal timescales (Solomon et al., 2007). Warm Atlantic currents make the Barents and Kara seas (BKS) climatically important with respect to instability of seabed CH4due to the input of warming seawater. The Barents Sea is a shallow-shelf sea (average depth 230 m) with depressions to 400 m. It currently is close to ice-free year-round (Onarheim and ?rthun, 2017). The Kara Sea is even shallower (average depth 100 m) and ice-free in summer. The BKS has extensive proven oil and natural gas deposits, the latter of which are primarily CH4(Shipilov and Murzin, 2002). Although rather limited, existing data show these reservoirs drive CH4seepage into the water column (Chand et al., 2014; Platt et al., 2018). At depths greater than 250 m CH4forms hydrates that are stable in the cold BKS deep waters (~1 ℃). Additional seabed CH4is sequestered in the Kara Sea and the South Barents Sea as submarine permafrost (Osterkamp, 2010). Warming seabed water may destabilize submarine permafrost and hydrates, although specific mechanisms and timescales are uncertain.

Most seep bubble CH4dissolves in process of ebullition in deep water where methanotrophic bacteria oxidize it on weeks’ to years’ timescales (Reeburgh, 2007). Only a small fraction of the seep bubble methane directly reaches the air. The seasonal summer pycnocline prevents vertical turbulent diffusion of methane, with concentrations near the seafloor that may be extremely high (Gentz et al., 2014). They studied dissolved CH4concentrations to the West of Prins Karls Forland (Svalbard) in August 2010 (not in winter) and found that CH4bubble plumes dissolved below the pycnocline and were not vertically mixing to the upper water column. They suggested that during winter in high latitudes after the summer stratification breaks down, vertical transport of CH4from the bottom layer is not limited anymore, and CH4can reach the sea surface. The most comprehensive CH4measurements at the seafloor and in the atmosphere in the region were collected to the West of Svalbard in June–July, 2014, by Myhre et al. (2016). They found that summer CH4release from seabed sediments substantially increases CH4concentrations above the seafloor with a sharp decrease above the pycnocline. They suggest that dissolved methane captured below the pycnocline may only be released to the atmosphere when physical processes remove the pycnocline as a dynamic barrier. Mau et al. (2017) field measurements between Svalbard and Bear Island in August–September, 2015, also demonstrated the pycnocline as a barrier for CH4. All three investigations came to a conclusion on insignificant methane flux in summer-early autumn around Svalbard and between Svalbard and Bear Island. Meanwhile, as it is well known (Rudels, 1993), during winter in high latitudes convective mixing extends to the seafloor. Currently, this seasonality of potential CH4pathways has not been considered in CH4budgets (Fisher et al., 2011; AMAP, 2015). To our best knowledge, direct measurements of CH4flux in winter have not been published for the BKS to date. Summertime CH4emission density near Svalbard was estimated by Myhre et al. (2016) as 0.04 nmol·m?2·s?1. Formal extrapolation onto the whole year and total area of BKS (2.317×109km2) results in 0.047 Tg CH4·a?1, i.e. negligible flux.

Shakhova et al. (2010) investigated summer and winter methane flux from the East Siberian Arctic Shelf (ESAS) encompassing the Laptev, East Siberian seas, and Russian part of the Chuckchi Sea. More than 5000 at-sea measurements of dissolved methane have been collected. Greater than 80% of ESAS bottom waters and greater than 50% of surface waters were supersaturated with methane regarding to the atmosphere. In summer diffusive and ebullition fluxes were estimated, respectively, as 1.24 Tg CH4and 1.68 Tg CH4(total summer 2.92 Tg CH4), in winter 3.23 Tg CH4and 4.49 Tg CH4(total winter 7.72 Tg CH4), totally 10.64 Tg CH4·a?1. An assessment by Berchet et al. (2016) based on surface NOAA network and inverse modeling is significantly lower: 0.5–4.3 Tg CH4·a?1.

Satellite observations are extremely useful for characterization of Arctic CH4, especially over open sea, due to their continuous and global data record. Thermal infrared (TIR) CH4data are available globally, day and night, whereas SWIR CH4instruments are ineffective in the Arctic due to low or no sunlight, low reflectivity from water and ice, and long atmospheric optical path (Leifer et al., 2012). TIR orbital sensors include AIRS/Aqua, IASI/MetOp-A, B, C, CrIS/NPP, CrIS/NOAA-20, and GOSAT/TANSO-TIR (see a list of abbreviations at the end of the article). Example current SWIR sensors are TROPOMI and GOSAT-SWIR.

CH4retrievals for AIRS and IASI are publicly available (see links in Section 2.1). Yurganov et al. (2016) suggested a filtering technique for the Arctic data and presented data on seasonal, spacial and interannual variability of methane in the layer below 4 km. They concluded, “Seasonal increase in methane has been observed since late October–early November. This can be associated with the beginning of vertical convection in the ocean, caused by the cooling of the surface layers and the simultaneous increase in temperature of the underlying water layers. Bottom layers saturated with methane are brought to the surface” (translated from Russian). Preliminarily, Yurganov et al. (2016) assessed the annual emission of methane from the Arctic Ocean in 2010–2014 as ~2/3 of land emission to the North from 60°N. Arctic terrestrial emission is now estimated as 20–30 Tg·a?1(AMAP, 2015). Therefore, total marine CH4flux from the Arctic can be expected in the range 15–20 Tg CH4·a?1(without the Sea of Okhotsk). The most recent analysis of available measurements of the global methane flux from oceans (Weber at al., 2019) by training machine-learning models gave much lower oceanic flux (6–12 Tg CH4·a?1). However, their conclusion that very shallow coastal waters contribute around 50% of the total methane emissions from the ocean agrees with our data for the Arctic (see below, Section 3).

Here we show that CH4spatial anomaly (SA) over BKS is seasonally growing together with the Mixed Layer Depth (MLD). Therefore, an increase of emission in winter, predicted by Gentz et al. (2013) and Myhre et al. (2016) agrees with remote sensing data and seasonal cycle of MLD. However, this agreement does not exclude other explanations of the effect and a confirmation of a significant wintertime flux from Arctic seas by direct measurements is necessary. After that it may be included into the methane budget.

2 Methods

2.1 Satellite instruments, data, and retrieval techniques

The nadir cross-track scanning diffraction grating spectrometer, AIRS/Aqua, was launched in May 2002 (Xiong et al., 2008). CH4profiles and surface skin temperature () are retrieved for a 3×3 matrix of 9 circular 13.5-km diameter pixels. Version 6 of the retrieval algorithm (Susskind et al., 2012) and data (AIRS3STM.006, Aqua L3 Monthly Standard Physical Retrieval, AIRS-only) are available from https://disc.gsfc.nasa.gov/datasets/, since 2002.

The IASI/MetOp-A is a cross-track-scanning Michelson interferometer that measures spectra of outgoing long wave radiation with an apodized resolution of 0.5 cm?1at the ν4 CH4band near 7.65 μm wavelength (Razavi et al., 2009). The MetOp-A satellite, operated by EUMETSAT, was launched in 2006. Like AIRS, IASI has a 2200-km swath with a scan swath angle of ±48.3°. The IASI retrieval algorithm NUCAPS was built at NOAA/NESDIS to emulate the AIRS Version 5 code and has been in operation since 2008 (Maddy et al., 2009). Level 2 data for 2×2 matrices of 4 circular, 12-km diameter pixels are available from the NOAA’s CLASS site https://www.avl.class.noaa. gov/saa/products/.

Both satellites are currently operative and have sun-synchronous polar orbits: a geometry that favors polar coverage. Aqua and MetOp-A cross the equator at approximately 01:30 and 13:30; 09:30 and 21:30 local time, respectively. Retrieval algorithms for these two instruments are similar but not identical. For both, CH4retrieval accuracy depends on the accuracy of temperature, humidity, and surface emissivity retrievals. The latter is highly important for land, but unimportant for the water surface. Pixel matrices combined with microwave radiometer field of view allow retrieval of CH4profiles for cloud-free and partially-clouded pixels (Susskind et al., 2003). The significantly higher spectral resolution of IASI (0.5 cm?1) compared to that of AIRS (~1.5 cm?1) is a reason for giving preference to IASI data.

Data over the North Atlantic and BKS with sufficiently high vertical thermal contrast () were analyzed.is defined as the temperature difference betweenand at the 4 km altitude; a criterion> 10 ℃ was suggested by Yurganov et al. (2016).

As it will be presented below, Arctic CH4data for these two instruments and retrieval techniques are in agreement. At the same time, it should be noted that both retrieval techniques were developed primarily for weather prediction purposes and global conditions. Atmospheric gases data from them are still qualified as “research” products. Arctic environment is very specific: there are seasonal changes in sea ice area and thickness, winter temperature inversions are often, the network of “ground truth” information is sparse, etc. This paper should be considered as a pilot one; a specific retrieval procedure should be developed for Arctic with inclusion radiation data from as many instruments as possible and the most comprehensive atmospheric state information (temperature and humidity profiles, surface emissivity, etc).

A comprehensive analysis of NUCAPS and AIRS v6 uncertainties for specific Arctic conditions requires the retrieval code. It is unavailable for public: we used just results of the retrievals. A measure of uncertainty in this situation is standard deviation of individual retrievals. It is between 20 and 30 ppb over BKS. Normally there are ~30 points per 0.5°×0.5° grid cell per one month or 120 points per 1°×1° grid cell per month and ~2000 points for box #8 per month. Therefore, standard deviations of the monthly means are: 5–6 ppb for 0.5°×0.5° grid cell and ~0.7 ppb for box #8. More information about accuracy of the data can be found in a response to a reviewer of a discussion paper by Leifer et al. (2019).

2.2 CH4 retrieval validation

For comparison we used CH4mixing ratios near the surface that were sampled weekly at coastal stations in Iceland (ICE) and Svalbard, Norway (ZEP). Validation versus aircraft profiles of CH4have been implemented at three US sites for entire period of 2003–2018: Trinidad Head (THD) in Northern California, Southern Great Plains (SGP) in Oklahoma and near Charleston (SCA) in South Carolina. The surface and aircraft samples were analyzed at the NOAA/GMD/ESRL laboratory in Boulder, Colorado, surface data are available at ftp://aftp.cmdl.noaa.gov/data/ trace_gases/ch4/flask/surface/; aircraft data are kindly supplied by Colm Sweeney (NOAA/GMD/ESRL).

AIRS- and IASI-derived CH4concentrations and anomalies below 4 km altitude (termed Low Troposphere, LT) and above 4 km (Mid-upper Troposphere, MUT) were validated with airborne CH4measurements at three sites in the USA (Figure 1). For MUT, satellite and airborne measurements agree better than for LT with slopes close to 1. For the lower troposphere, data show good correlation but with slopes close to 0.5. We propose that this slope, calculated from a least squares linear regression analysis (Table 1 calculated by MATLAB) provides an empirical sensitivity ().was derived for both the LT and MUT (between 4 and 8 km of altitude):

= Δret/ Δreal, (1)

whereretandrealare the retrieved CH4and aircraft, altitude-averaged, CH4concentrations, respectively. Δis the anomaly of concentration, spatial (SA) or temporal (TA).

The validation (Figure 1 and Table 1) shows a common feature for both instruments: MUT slopes are ~1.0 whereas LT slopes are between 0.3 and 0.5. Correlation coefficients (2) range from 0.75 to 0.92. The higher sensitivityabove 4 km relative to 0–4 km is probably due to a greater thermal contrastin MUT compared to LT.

One should discriminate between the anomaly accuracy and the concentration accuracy. For MUT our validation effort shows a good TA accuracy and a moderate bias in concentration (Table 1 calculated by MATLAB). For LT a significant underestimation in concentration up to 40 ppb and a 50% underestimation of TA may occur. The LT TA error, however, is persistent for all three sites and both instruments; this systematic error should be just taken into account at the analysis stage. A great advantage of the LT data is a proximity of the atmospheric domain to the surface sources. MUT is characterized much higher winds that mix the atmosphere effectively. Further details of the satellite CH4validation and accuracy can be found in Yurganov et al. (2016) and in a response to a reviewer of a discussion paper by Leifer et al. (2019).

Figure 1 April–September IASI CH4 data for LT (blue) and MUT (red) compared with concentrations measured from NOAA aircrafts at 3 sites in USA: Trinidad Height, CA (THD), Southern Great Plain, OK (SGP), and Charleston, SC (SCA) (a–c). The same, but for AIRS. Error bars correspond to STD/(N?1)1/2, where N is number of points included into the mean (d–f).

Table 1 Parameters of linear regression for three NOAA/ESRL validation sites

2.3 Ocean mixed layer depth calculations

MLD was estimated from the ECCO2 global model. ECCO2 is a Phase II of the High-Resolution Global– Ocean and Sea–Ice Data Synthesis, sponsored by the NASA (Wunsch et al., 2009). The model has spatial resolution of 0.25° with 50 vertical levels. Daily average MLD was estimated as the depth at which the potential density relative to the surface is larger than surface densityby using the Δ=0.8 ℃ ×criterion, whereis the density andis the seawater thermal expansion coefficient at the surface (Kara et al., 2003). Comparisons with field measurements confirm that model accuracy is better than 20 m (Kara et al., 2003).

3 Results

Monthly mean maps of IASI LT CH4(Figure 2) demonstrate a surprisingly spatially homogeneous CH4distribution over the entire Arctic Ocean between May and September. This is in contrast to the heterogeneity observed beginning in October where strongly enhanced methane is observed over various Arctic seas.

Figure 2 Monthly mean IASI LT CH4 concentrations in 2018.

Monthly mean BKS maps for June and December (Figure 3, a–d) for both IASI and AIRS show similar patterns and trends, with IASI showing structures in CH4at higher fidelity and higher enhancement—likely the result of higher spectral resolution. Seasonal variation of LT CH4from IASI was analyzed for selected boxes in the BKS, designated 1 to 7 on a path from Iceland to the North Kara Sea (Figure 3b). IASI found increased methane in narrow bands along coasts of Norway, Novaya Zemlya, and Svalbard in December. These local coastal enhancements need further evaluation and comparison withdata to avoid misinterpretation; they are not analyzed further.

The annual cycle amplitudes (from minimum to maximum, a kind of TA) for selected boxes (Figure 3e) increases gradually and persistently from box #1 (~25 ppb) to box #7 (~55 ppb), i.e., a factor of 2. These remote sensing data are in a qualitative agreement withNOAA flask measurements at the Svalbard (Zeppelin, ZEP) observatory and at the Iceland NOAA site (ICE), thick lines at Figure 3e (an offset of 18 ppb is subtracted from all surface data for comparability). The cycle amplitude of measured surface methane in Svalbard is ~30% higher than for Iceland.

Box #8 to the SW from Svalbard is adjacent to the area investigated by Gentz et al. (2014), Figure 3b. This area is at the edge of the continental shelf and also is known for numerous seabed seeps mapped by ships. We calculated the CH4seasonal SA as the differences between IASI data for box #8 and the average of boxes #1 and #2 (the Barents Sea Opening), then we compared them withSA between ZEP and ICE (Figure 3f): both curves have two maxima, in November–December and in March, but amplitudes of seasonal cycles were different; that may be explained by different techniques and/or different atmospherics domains.

Figure 3 Mean LT CH4 concentrations measured by AIRS for 2013–2015 in June and December, respectively (a–b); mean LT CH4 concentrations measured by IASI for 2014–2018 in June and December, respectively (c–d). Locations of surface stations ICE and ZEP are shown by a circle and a triangle, respectively. Monthly mean LT concentrations measured by IASI and averaged over 2014–2018 for boxes shown on the maps above. Surface concentrations has been moved downward by 18 ppb for comparability (e). Red line is a difference between concentrations for the box #8 and combined boxes #1–#2 (f). Black line is for a difference between surface CH4 at ZEP and ICE. Blue line is for the box #8 seawater MLD. All three lines are 2014–2016 averages. Error bars correspond to STD/ (N?1)1/2.

Patches of increased methane in winter can be explained by the density stratification breakdown allowing CH4from deeper water including sources to reach the atmosphere. To validate this hypotheses we extracted MLD for the box #8 located to the SW from Svalbard (Figure 3f, blue curve). The shape of the MLD cycle is very similar to the shape of methane SA for the same location. The maximum mixing depth up to 400 m is sufficient to transport methane from the seabed to the surface.

A good agreement between methane monthly anomalies and MLD is observed also for the total period of AIRS measurements (Figure 4). Note an agreement between monthly SA measured by AIRS and IASI during their common period of measurements (2010–2016). On the same graph monthly meanfor the box #8 are plotted. The variations ofare found to be out of phase with MLT, as expected: in autumn, after breakdown of stratification, cooler and CH4-richer seawater replaces the summer warmer and fresher seawater.

4 Discussion

A combination of two conditions are necessary and sufficient for a significant flux of CH4from sea to air. First, there should be seabed or seawater CH4sources either in the region or an upcurrent region. Second, there should be an effective transport of CH4from the source to the surface seawater layer. A significant emission of CH4from the seafloor to deep water layers around Svalbard and along a path Svalbard–Bear Isl. has been documented (see the Introduction). All these results show negligible CH4flux and are obtained in summer months or in September, i.e., before a breakdown of the pycnocline. Gentz et al. (2014) and Myhre et al. (2016) predict a much higher methane flux in winter, when a transport by an intense turbulent diffusion and convection occurs.

Figure 4 CH4 monthly anomalies (LT concentration differences between box #8 and combined boxes #1 and #2) according to AIRS and IASI measurements. Blue line is smoothed daily MLD calculated for the box #8 (accuracy ± 20 m). Also shown are monthly mean SST for the box #8. Error bars correspond to STD/ (N?1)1/2.

The BKS is known for extensive methane seeps explained by oil and natural gas deposits (Shipilov and Murzin, 2002). Remote sensing data on CH4in the lower troposphere over the winter Arctic Ocean presented in this article confirm predictions of Gentz et al. (2014) and Myhre et al. (2016) and evidence in favor of a significant emission of CH4from BKS. Moreover, satellites allow one not only to assume a significant winter methane flux, but also to give an idea on areas where this emission may occur, as well as about its annual cycle. Specifically, maximum CH4SA for BKS is observed in November–December with a secondary maximum in March. Noteworthy is that the pycnocline is not a barrier for bubbles: this kind of CH4transport should be effective year-round, it’s a barrier to turbulent transport of dissolved CH4. Negligible summer CH4suggests direct ebullition transport to the atmosphere is not sufficient to create significant anomalies on monthly bases. Numerousspikes observed by Leifer et al. (2019) at the Barents Sea in August–September, 2013, probably are explained by local seeps.

IASI data (Figure 3d) (Yurganov and Leifer, 2016a) show increased CH4SA in narrow bands along the coasts of Norway, Novaya Zemlya, and Svalbard, but not other coasts. Unfortunately, as Leifer et al. (2019) noted, there may be problems in the retrieval algorithm (NUCAPS) for partially land/sea pixel retrievals. This raises the potential for biases in coastal results and argues for the need for improved retrieval technique.

Other vast areas of strong CH4SA include Baffin Bay, seas along the East Siberia coast (Figure 2), the Bering Sea and the Sea of Okhotsk (Yurganov and Leifer, 2016b). Methane hydrates exist beneath the sea bottom near cold seeps NE off the Sakhalin in the Sea of Okhotsk (Shoji et al., 2005). This sea, like BKS (Shipilov and Murzin, 2002), is a target area of gas hydrates exploration. One may speculate that methane hydrates of geologic origin, including direct seeps and methane hydrates at BKS and Sea of Okhotsk, is a significant source of CH4in the Arctic.

The MLD global maps (Kara et al., 2002) clearly indicate that enhanced vertical mixing to depths of 250 m or deeper occurs in winters of both hemispheres in Northern Atlantic (40°N and above), and in the Southern Ocean (40°S and below). One may expect a significant flux of CH4from the Southern Ocean to the atmosphere between June and November, if CH4sources present (e.g., microbiological ones, as proposed by Kelley and Jeffrey, 2002).

There are significant regional climate implications of enhanced Arctic winter marine CH4emissions which depend on the nature of the source(s). Seeps from extensive hydrocarbon reservoirs at BKS (Shipilov and Murzin, 2002) could lead to formation of methane hydrates of geological origin, submerged permafrost, methane hydrates of biological sources, microbial production in the benthic zone. Satellite data supply little or no information about the nature of the sources. Given that increasing seabed temperatures will lead to increased hydrate dissociation, there are important implications for future emissions. As such, the elucidation of methane hydrate amounts and locations, and vulnerability to warming should be a matter of further research. A valuable feature of satellite data is a possibility to track changes in methane over areas with different sources over a long period of time since 2003.

5 Conclusions

Satellite measurements of CH4concentration in the lower 4 km of the troposphere reveal a significant, up to 30 ppb, monthly anomaly referenced to North Atlantic maximized in November–December–January over vast areas of BKS, as well as a part of the Greenland Sea. Satellite observations show similar, but less pronounced, anomalies in autumn–winter over other Arctic seas (Yurganov et al., 2016; Yurganov and Leifer, 2016a). This CH4anomaly may be interpreted as intensification of CH4flux induced by turbulent diffusion and/or convection after a breakdown of the summer stratification. After a confirmation of a significant wintertime flux from Arctic seas by direct measurements the marine CH4emission may be included into the methane budget.

Abbreviations

AIRS: Atmospheric Infrared Sounder

AMSL: Above Mean Sea Level

BKS: Barents and Kara seas

CLASS: Comprehensive Large Array-data Stewardship System

CrIS: Cross-track Infrared Sounder

ESRL: Earth System Research Laboratory

EUMETSAT: European Organization for the Exploitation of Meteorological Satellites

GMD: Global Monitoring Division

GOSAT: Greenhouse Gases Observing Satellite

GSFC: Goddard Space Flight Center

IASI: Infrared Atmospheric Sounding Interferometer

MLD: Mixed layer depth

NASA: National Aeronautics and Space Administration

NESDIS: National Environmental Satellite, Data, and Infor-mation Service

NOAA: National Oceanic and Atmospheric Administration

NPP: National Polar-orbiting Partnership

NUCAPS: NOAA Unique Combined Atmospheric Proces-sing System

ROSES: Research Opportunities in Earth and Space Science

SA: Spatial anomaly

SCIAMACHY: SCanning Imaging Absorption Spectro-Meter for Atmospheric CHartographY

: Surface skin temperature

: Standard deviation

TA: Temporal anomaly

TROPOMI: TROPOspheric Monitoring Instrument

NOAA/ESRL sampling stations

ICE: Storhofdi, Vestmannaeyjar, Iceland, 63.4oN, 20.3oW, 118 m AMSL

SCA: Charleston, South Carolina, USA, 32.77oN, 79.55oW, 0 m AMSL

THD: Trinidad Head, California, USA, 41.054oN, 124.151oW, 107 m AMSL

SGP: Southern Great Plains, Oklahoma, USA, 36.607oN, 97.489oW, 314 m AMSL

ZEP: Zeppelin at Svalbard, Ny-?lesund, Norway, 78.9oN, 11.9oE, 474 m AMSL

This work was supported by the NASA ROSES- 2013 grant: “A.28, The Science of Terra and Aqua: Long-term Satellite Data Fusion Observations of Arctic Ice Cover and Methane as a Climate Change Feedback”. Thanks to Colm Sweeney (NOAA/GMD/ ESRL) for providing aircraft CH4data.

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1 July 2019;

31 October 2019;

9 December 2019

: Yurganov L, Muller-Karger F, Leifer I. Methane increase over the Barents and Kara seas after the autumn pycnocline breakdown: satellite observations. Adv Polar Sci, 2019, 30(4): 382-390,doi: 10.13679/j.advps.2019.0024

, E-mail: leonid.yurganov@gmail.com

10.13679/j.advps.2019.0024