Keywords

1 Introduction

In modern era, rising levels of atmospheric greenhouse gases and subsequent climate warming are becoming a major concern for the world’s population and economy (Li et al. 2018; Bush 2020a, b; Birchall 2019; Bulkeley and Tuts 2013; Hallegatte et al. 2011). However, the impact and features of the warming process vary significantly in different parts of the globe. Hence, deeper insights into the mechanisms and consequences of regional climate changes are of key importance for physiographists, meteorologists, and climatologists.

Especially this is the case for regions exhibiting high climate warming rates, the Russian Far East (RFE) being one of them. During the last 40 years, the temperature means of summer months in some localities of the RFE have seen dramatic rise by more than 5 °C.

Since a large portion of the RFE territory lies in the permafrost zone, warming of the local climate inevitably destroys perennially frozen rocks rich in gas hydrates concentrated below the earth surface. As a result, gases (most essentially methane) escape from the permafrost into the atmosphere, which, in turn, increases fire hazards and harms fragile local ecosystems (Malkova et al. 2014).

Considering the fact that methane is a powerful greenhouse gas, we can hypothesize that increase in its total levels over the region in question due to its emission from the surface can significantly affect local temperature means of summer months.

The assumption is not straightforward as warming of the local climate is also driven by other powerful factors. One of them is the impact of blocking highs caused by arctic air outbreaks (hereinafter AAOs) into lower latitudes (Mokhov et al. 2013; Dzerdzeevskii et al. 1946).

AAOs normally manifest themselves as rapid northerly air streams formed as a result of interaction between a southern cyclone entering higher latitudes and the Arctic anticyclone. As the two vortices approach each other, the baric gradient in the cyclone’s rear sector begins to rise, which in turn leads to intensification of winds along the cyclone-anticyclone boundary. Since the air transported by the AAO is cold and dry, along its entire path (which normally extends southward to the periphery of a subtropical anticyclone) the 500-hPa geopotential height tends to rise while the atmospheric temperature tends to drop. AAO-driven air streams can capture methane emitted from the underlying surface and carry it far south.

Propagation of an AAO creates a blocking high featuring clear weather and little cloudiness (Mokhov et al. 2013; Dzerdzeevskii et al. 1946). As a result, the air inside it quickly warms (especially in summer), which can also account for substantial rise in monthly means of local temperatures. The effect is the more noticeable, the longer the total duration (TD) of AAOs over the region.

The two mentioned factors (emissions of methane and AAOs) can both be responsible for substantial climate warming in the RFE region in summer months; however, the contribution of methane is to be clarified.

In our research, we aim to check the assumption about a relationship between methane emissions from the permafrost and the temperature rise in the RFE region. The following steps are followed:

  1. 1.

    Estimation of the total duration of AAOs that crossed Russia’s arctic seacoasts during May–October of 1979–2018 period.

  2. 2.

    Identification of the RFE areas (including adjacent seas) where interannual variations in the total duration of AAOs crossing the coasts of the Chuckhi and East-Siberian seas exhibit significant correlation with the concurrent variations in monthly temperature means.

  3. 3.

    Identification of the RFE areas (including adjacent seas) where interannual variations in total column methane amount demonstrate significant correlation with the concurrent variations in the total duration of the above-mentioned AAOs.

  4. 4.

    Discussion of the findings.

2 Methods

In our research, we used reanalysis data as source material. The following datasets were chosen as most appropriate:

  1. 1.

    CAMS-reanalysis (Reanalysis 2019)—for monthly means of total column atmospheric methane (TCM);

  2. 2.

    ERA-Interim (ERA-interim 2019)—for daily means of meridional and zonal surface wind components, atmospheric temperature (Ta), and 500-hPa geopotential height (H500).

CAMS, which stands for the Copernicus Atmosphere Monitoring Service, is a new global reanalysis dataset of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). It consists of three-dimensional time-consistent atmospheric composition fields, including aerosols, chemical species and greenhouse gases (Inness et al. 2019). At the time of the research the dataset was available for the period 2003–2016. The vertical resolution of the reanalysis data included 60 model levels, with the top level at 0.1 hPa. Daily and monthly means of methane and other species at these levels are freely available from ECMWF open data service (Reanalysis 2019).

ERA-Interim reanalysis presents a variety of atmospheric parameters for 1979–2019 period. Data on wind velocities are available in spatial resolution of approximately 80 km (T255 spectral on 0.75 × 0.75° coordinate grid) and temporal resolution of 6 h, the number of vertical levels from the surface up to 0.1 hPa being 60. The data assimilation system used to produce ERA-Interim is based on a 2006 release of the Integrated Forecast System (Cy31r2), which includes a 4-dimensional variational analysis with a 12-h analysis window (Dee et al. 2011).

The areas with the highest atmospheric TCM, as established by use of CAMS data, were the coastal regions of the Chukchi Sea and the eastern part of the East-Siberian Sea, the whole area falling in 160° E–160° W sector. Daily methane emissions therein reached 30–190 g/m2. Further research was therefore carried out for the mentioned sector.

AAOs over the sector in question were identified by analyzing atmospheric patterns of H500 distributions. The fact of an AAO occurrence was established if the following conditions were met: (i) H500 values exceeded their averages for the corresponding areas, (ii) mean daily wind velocities in the identified high-pressure band had strong north components. AAO manifestations were sought for in each 2.5°-wide sector in each month of May–October period. If the fact of an AAO-occurrence was established in a certain sector, its total duration was determined. An AAO was attributed to a certain month if the period of its existence in this month was longest.

In second and third steps, we used statistical approaches. Time series of the indicators under study were formed; their linear trends were properly made up for. Correlation analysis and Student’s t-test were then applied to estimate the correlation significance corresponding to 95% confidence interval and 0.34 correlation coefficient threshold level.

The results of the correlation analysis were presented on contour maps using Delaunay triangulation (Skvortsov 2002).

3 Results and Discussion

The above approach was used to identify all AAO-events in each 2.5°-wide sector of the region in question from May to October in 1979–2018 period. Their total durations were determined for each sector and each year, which enabled to build the time series. Angular coefficients of their linear trends plotted against mean longitudes of the corresponding sectors are presented in Fig. 1a. Two periods were analyzed: 1979–2018 and 1998–2018. Mean values of AAO TDs are presented on a similar plot in Fig. 1b.

Fig. 1
figure 1

Dynamics of arctic air outbreaks over the RFE region in August: a angular coefficients of AAO total duration linear trends; b mean values of the total duration of AAOs

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As seen from Fig. 1, in 1979–2018 total durations of AAOs exhibited significant rise in all sectors of the region under study. The highest rise rates were detected in the eastern part of the East-Siberian Sea and the Bering Strait area. AAO TDs increased in eastward direction peaking in the Bering Strait sector (165° W).

In step 2 of the research, we evaluated the statistical significance of correlation between interannual variations in AAO TDs and variations in mean monthly Ta in different areas of the region in question. The strongest AAO TD—Ta correlation was established for the AAOs crossing 175–172.5° W sector. The latter corresponds to the Chukchi Sea coast extending from the Kolyuchin Bay to the Cape Serdtse-Kamen. The areas of significant positive AAO TD—Ta correlation in August are shown in Fig. 2.

Fig. 2
figure 2

Areas of significant positive correlation between the total duration of AAOs that crossed 175–172.5° W sector and mean monthly August temperatures

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As follows from Fig. 2, the whole area of strong AAO TD—Ta correlation for AAOs crossing 175–172.5° W sector lies within 50–69° N and 172.5° E–167.5° W. The area is quite extensive and includes a large part of the mainland RFE, southern parts of the Chukchi Sea, as well as the central part of the Bering Sea. The strongest correlation within this area is found above the Chukchi Peninsula and off its south coast (particularly, the Anadyr Bay), as well as off the north coasts of the Aleutian Islands (particularly, the Andreanof Islands, Rat Islands, and Near Islands). The adjacent waters of the Pacific are also affected.

Distribution of mean monthly Ta change rates in August in 1979–2018 is shown in Fig. 3.

Fig. 3
figure 3

Distribution of mean monthly Ta change rates in August in 1979–2018

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In Fig. 3, a stable increase in mean monthly Ta is seen almost over the whole area. For the last 40-year period, Ta in some parts of the area has risen by dramatic values (up to 5 °C). Blocking highs can partly provide an explanation for the phenomenon. The reason for Ta increase inside a blocking high is that little or zero cloud cover leads to absorption of solar radiation by the earth surface and consequent surface air heating. This happens both over the land and over the sea; however, the sea is less subject to heating due to water’s much larger heat capacity. Therefore, despite the rise in the total duration of blocking highs, increase in surface temperatures of the Chukchi and the Bering seas by such dramatic values has not happened. Hence, general temperature increase in the region cannot be attributed to blocking highs only.

Similar research was performed for other months of May–October period. Significant positive AAO TD—Ta correlation has been detected over several areas of the region in question only in July–September. It has also been established that the actual temperature rise in these months cannot be explained solely by blocking highs.

In step 3, we proceeded to identify the parts of the RFE region and the adjacent seas, where interannual variations in total column methane amount demonstrated significant correlation with the concurrent variations in AAO TD monthly means.

As an example, in Fig. 4 we show the areas featuring significant positive correlation between the total duration of AAOs that happened in August in 175–172.5° W sector and monthly means of TCM over the region in question.

Fig. 4
figure 4

Areas of significant positive correlation between the total duration of AAOs crossing 175–172.5° W sector and mean monthly values of TCM in August

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As follows from Fig. 4, a significant TCM—AAO TD correlation is present in near-coastal areas of the Chukchi Sea, around the Chukchi Peninsula, and in the central part of the Bering Sea. The correlation pattern is very similar to that shown in Fig. 2. This fact tells in favor of the proposed hypothesis assuming possible connection of methane emissions with the local climate warming and emphasizes the role of AOOs in the southward transport of methane.

Analogous studies carried out for other months of May–October period have revealed a similar correlation pattern in September. No similarities have been detected in other months (May, June, July, and October).

4 Conclusion

Our research has proved the existence of interconnection between rise in mean temperatures of August and September in the RFE and methane emissions from the permafrost of the East-Siberian and Chukchi seacoasts. Arctic air outbreaks have been shown to play a major role in southward transport of methane. However, similar mechanisms apparently do not work directly during other warm months, since no significant correlation between air temperatures and atmospheric methane levels has been found. Better understanding of the origins and mechanisms of the local climate warming throughout the year will require further studies.

Since permafrost degradation is likely to continue in the near future, methane emissions into the atmosphere will grow. The faster the growth rate, the more noticeable the methane contribution to the warming process will become.