GCloudl: Arctic clouds (Research Collaboration)

Global warming is rapidly transforming Arctic clouds. This collaboration documents the changes.


The Arctic is one of the most cloudy regions on the Earth.

The Arctic Cloud research collaboration is set to deliever:

  • Combined, homogenized and quality controlled historical in situ cloud observations from the Eurasian Arctic stations
  • Improved methodology to create more rigorous Arctic cloud climatology
  • Extended statistical processing and analysis of the cloud cover, cloud types and their historical variability
  • Documented relations between the Arctic clouds and both global and local climate processes, circulation patterns and transformation mechanisms and feedbacks
  • Experienced support and guidance for planning contemporary and future observational field campaigns and model development, e.g. in the frameworks of the Year of the Polar Prediction (YOPP)
  • Advanced understanding on the cloud transformation processes and feedbacks in the context of the Arctic Amplification of the Global Warming

Our hypothesis emphasizes the role of cloud spatial self-organization and transformation between cloud types. Restructuring of the clouds has a significant effect on the air-sea-land interaction, precipitation, radiation processes, cloud feedback mechanisms and extreme weather events.

Project Summary

The final report for the Norwegian Research Council reserach exchange project on the Arctic Clouds [pdf]

Data sets [download] (Data set is free for noncommercial use).


The processing of raw data is described in Chernokulsky et al. (2017), Chernokulsky and Esau (2019) for cloud cover; and in Esau and Chernokulsky (2015) for cloud morphological type amount. Those papers should be referred to when data are used.

Dataset contains seasonally averaged information on cloud characteristics (such as cloud cover and amount of morphological type of low-level clouds) from 104 Russian and Norwegian Arctic stations.

Zip-file includes the list of station (with the coordinates (lat-lon) and country belonging to (R)ussia or (N)orway), and four folders for each season (JFM - January-February-March, AMJ - April-May-June, JAS - July-August-September, OND - October-November-December).

Each folder contains 104 files (each station is in own file) with 163 rows (for each year for 1861–2013) and 11 columns.

Columns are:
total cloud cover (percentage)
frequency of clear sky (percentage)
frequency of scattered clouds (percentage)
frequency of broken clouds (percentage)
frequency of overcast (percentage)

low cloud cover (percentage)
amount of cumulus and cumulonimbus clouds (percentage)
amount of stratocumulus clouds (percentage)
amount of stratus and nimbostratus clouds (percentage)
number of observations

-9.9 stands for undefined values.


Total cloudiness over the ArcticTotal cloudiness over the Arctic

        Chernokulsky, A., & Mokhov, I. I. (2012). Climatology of Total Cloudiness in the Arctic: An Intercomparison of Observations and Reanalyses. Advances in Meteorology, 2012, 1–15. doi:10.1155/2012/542093 [pdf]

Abstract. Total cloud fraction over the Arctic (north of 60◦N) has been evaluated and intercompared based on 16 Arctic cloud climatologies from different satellite and surface observations and reanalyses. The Arctic annual-mean total cloud fraction is about 0.70 ± 0.03 according to different observational data. It is greater over the ocean (0.74 ± 0.04) and less over land (0.67 ± 0.03). Different observations for total cloud fraction are in a better agreement in summer than in winter and over the ocean than over land. An interannual variability is higher in winter than in summer according to all observations. The Arctic total cloud fraction has a prominent annual cycle according to most of the observations. The time of its maximumconcurs with the time of the sea ice extent minimum (early summer–late autumn) and vice versa (late spring). The main reason for the discrepancies among observations is the difference in the cloud-detection algorithms, especially when clouds are detected over the ice/snow surface (during the whole year) or over the regions with the presence of strong low-tropospheric temperature inversions (mostly in winter). Generally, reanalyses are not in a close agreement with satellite and surface observations of cloudiness in the Arctic.





Fig. 1. June-July-August mean of TCF over the Arctic (north of 60◦N) from different data. The error bars correspond to the standard deviation (in the interannual variability) of each data (separately for land and the ocean). The abscissa corresponds to TCF over the ocean, and the ordinate corresponds to TCF over land. The inclined long-dashed lines correspond to TCF over land and the ocean (their slope is equal to the land-ocean ratio in the Arctic).


Climatology of precipitationsClimatology of precipitationsChernokulsky, Alexander  A. Kozlov, F G. Zolina, O N. Bulygina, O A. Semenov, V. (2018). Climatology of Precipitation of Different Genesis in Northern Eurasia. Russian Meteorology and Hydrology, 43, 425-435. doi:10.3103/S1068373918070014.


Abstract. A method for discriminating among different types of precipitation is presented. The method is based on surface observations of precipitation, present and past weather, and the morphological types of clouds. The climatology of showery, nonshowery, and drizzle precipitation in Northern Eurasia is studied using the data of 529 Russian weather stations for the period of 1966–2014. Showery precipitation dominates in Northern Eurasia. In general, showery precipitation has greater temporal (monthly and diurnal) and spatial variability than nonshowery precipitation. The majority of showers are registered in summer (the maximum is in July), whereas the high est total monthly nonshowery precipitation is observed in autumn (the maximum is in October). The daily intensity values of showery and nonshowery precipitation are generally close, the maximum intensity is recorded in July–August. For three-hour in tervals, the shower in tensity is by 1.1–1.5 times higher. The drawbacks of the presented methodology are discussed.






Fig. 2. The climatology of different characteristics of (a) showery and compound, (b) nonshowery, and (c) drizzle precipitation rate: annual precipitation rate (colored), the frequency of days with the corresponding precipitation type (the white dash line), and the contribution to total precipitation (%) (the black dotted line). The dots mark the location of weather stations. The discrete spline interpolation was utilized.



Cloud types statistics at the sea ice marginCloud types statistics at the sea ice margin

        Esau, I. N., & Chernokulsky, A. V. (2015). Convective cloud fields in the Atlantic sector of the Arctic: Satellite and ground-based observations. Izvestiya, Atmospheric and Oceanic Physics, 51(9), 1007–1020. doi:10.1134/S000143381509008X

Abstract. Convective cloudiness in the Atlantic sector of the Arctic is considered as an atmospheric spatially selforganized convective field. Convective cloud development is usually studied as a local process reflecting the convective instability of the turbulent planetary boundary layer over a heated surface. The convective cloudiness has a different dynamical structure in high latitudes. Cloud development follows cold air outbreaks into the areas with a relatively warm surface. As a result, the physical and morphological characteristics of clouds, such as the type of convective cloud, and their geographical localization are interrelated. It has been shown that marginal sea ice and coastal zones are the most frequently occupied by Cu hum, Cu med convective clouds, which are organized in convective rolls. Simultaneously, the open water marine areas are occupied by Cu cong, Cb, which are organized in convective cells. An intercomparison of cloud statistics using satellite data ISCCP and groundbased observations has revealed an inconsistency in the cloudiness trends in these data sources: convective cloudiness decreases in ISCCP data and increases in the ground based observation data. In general, according to the stated hypothesis, the retreat of the seaice boundary may lead to an increase in the amount of convective clouds.

Fig. 5. Repeatability of convective rolls (black circles) and cells (white circles) observations in winter period (November to March) above Greenland and Barents seas depending on the distance to the ice edge. The plot is drawn based on NOAA9, 11, and 14 satellite data processed in Bruemmer, Pohlman (2000).




Cloud change in Atlantic ArcticCloud change in Atlantic Arctic

        Chernokulsky, A. V., Esau, I., Bulygina, O. N., Davy, R., Mokhov, I. I., Outten, S., & Semenov, V. A. (2017). Climatology and Interannual Variability of Cloudiness in the Atlantic Arctic from Surface Observations since the Late Nineteenth Century. Journal of Climate, 30(6), 2103–2120. [pdf]

Abstract. A long-term climatology of cloudiness over the Norwegian, Barents, and Kara Seas (NBK) based on visual surface observations is presented. Annual mean total cloud cover (TCC) is almost equal over solid-ice (SI) and open-water (OW) regions of the NBK (73% 6 3% and 76% 6 2%, respectively). In general, TCC has higher intra- and interannual variability over SI than over OW. A decrease of TCC in the middle of the twentieth century and an increase in the last few decades was found at individual stations and for the NBK as a whole. In most cases these changes are statistically significant with magnitudes exceeding the data uncertainty that is associated with the surface observations. The most pronounced trends are observed in autumn when the largest changes to the sea ice concentration (SIC) occur. TCC over SI correlates significantly with SIC in the Barents Sea, with a statistically significant correlation coefficient between annual TCC and SIC of 20.38 for the period 1936–2013. Cloudiness overOWshows nonsignificant correlation with SIC. An overall increase in the frequency of broken and scattered cloud conditions and a decrease in the frequency of overcast and cloudless conditions were found over OW. These changes are statistically significant and likely to be connected with the long-term changes of morphological types (an increase of convective and a decrease of stratiform cloud amounts).

Fig. 5. Interannual variations of 5-yr running means of total cloud cover from 1938 to 2011 over the entire solid-ice (blue curves) and open-water (green curves) regions of the NBK for (a) winter, (b) spring, (c) summer, and (d) autumn. Cloud breaks are considered as 7 (solid lines) and 8 oktas (dashed lines). Linear regressions are shown for TCC when cloud breaks are considered as 7 oktas. Regressions counted for two periods: 1) from the maximum TCC in the early twentieth century warming to the absolute minimum (for the entire period) and 2) from this minimum to the last year. Italic, bold italic, and bold fonts indicate the confidence at the 0.1, 0.05, and 0.01 levels, respectively. Note that the absolute values for the ordinate axis are different for different seasons (however, the range of 30% is kept for all seasons).


Total Arctic cloudiness correlation with selected circulation indicesTotal Arctic cloudiness correlation with selected circulation indicesChernokulsky, Alexander A., Esau Igor (2019). Cloud cover and cloud types in the Eurasian Arctic in 1936–2012. International Journal of Climatology, doi:10.1002/joc.6187 [pdf] 

Abstract. The Arctic is a cloudy place. It has been recognized that the Arctic cloud cover is sensitive to different climatic factors such as sea ice extent and atmospheric circulation indices. Moreover, several influential climate feedbacks, for example, the summertime cloud-radiation feedback, have been recognized. Yet, the cloud cover studies were limited in time to the satellite era observations and fragmentary data sets from meteorological stations. Here, we present the complete long-term cloud records from 86 meteorological stations in the Eurasian Arctic. The stations are located on the coast and islands of the region from the Barents to Chukchi Seas. Thus, this study is complementing and extending the study by Chernokulsky et al. (2017) where the cloud data from the Norwegian through Kara Seas were presented. Our data set comprises the entire period of observations at each station. However, we present the area-wide analysis only over the historical period of 1936–2012 when there were sufficient density of stations and cloud records for the coherent analysis. The total cloud cover, which on multiannual average constitutes 69–74% in different areas, increases in the warmer periods. The strongest increase is found in the convective cloud cover, particularly in the Chukchi Sea. We observe statistical evidence of transition between stratiform and convective cloud types. The cloud characteristics reveal the strongest correlations with the Atlantic circulation indices and the sea ice concentration in all Eurasian Arctic areas. The correlations with the Pacific circulation indices are much less significant. The obtained cloud data sets disclose much smaller scale features and variability, which deserve further research.

Fig. 7. The Mann-Kendall correlation coefficients (colour shading) of ntot and the selected climate indices (see the Supplementary for the detailed description). Dots denote statistically significant (at the .05 level) correlations.



Meteorological stations observing Arctic clouds

Stations BKSStations BKS

Stations EurasiaStations Eurasia

Location of Norwegian (blue-rimmed circles) and Russian (red-rimmed circles) weather stations used in Chernokulsky et al. (2017). Stations with open water conditions are shown by the green circles, stations with solid-ice conditions are shown by the blue circles. The boundaries for March (dashed lines) and September (solid lines) sea ice extent based on HadISST data (Rayner et al. 2003) are shown for the years 1981–90 (light blue lines) and 2004–13 years (dark blue lines). Locations of the Russian weather stations used in Chernokulsky and Esau (2019). Circle colour denotes three selected regions. Circle size represents the duration of station operation. The boundaries of sea ice extent for March and September based on SIBT1850 data (Walsh et al., 2016) are shown for the 1981–1990 and 2004–2013 periods. U and W letters denote Uelen and Wrangel island stations, respectively.

Historical variability of the cloud observations

Historical variability of cloud observationsHistorical variability of cloud observationsThe
annual number of the reporting stations is
shown with the red solid line; the total
annual number of the cloud reports—
with the black dashed line; the number of
the qualified cloud reports with the
moonlight criterion applied—with the
black solid line; the amount of the lowlevel
cloud reports with an undefined
type—with the black dotted line. The
blue text explains key changes in the
observations. The horizontal colour lines
indicate the periods that were analysed in
the previous works, which used the
station data in these areas. (Stanhill,
1995) (S95) study is shown as the purple
line, which covers the period of
1964–1991; (Przybylak, 1999) (P99)
study (light magenta line) covers the
period of 1951–1990; (Eastman and
Warren, 2010a) (EW10) study (green
line)—1971–2007; (Chernokulsky et al.,
2011) (ChBM11) study (red line)—
1991–2010; (Khlebnikova et al., 2014)
(KhMS14) study (orange line)—
1951–2010; C17 study (blue line) covers
the period of 1936–2013


Climatology of the Arctic Eurasian cloudsClimatology of the Arctic Eurasian clouds

Changes in the total Arctic cloudines by stations and seasons

Arctic cloud climate changesArctic cloud climate changes


Total cloud cover correlation with selected circulation indices

Total Arctic cloudiness correlation with selected circulation indicesTotal Arctic cloudiness correlation with selected circulation indices


Chernokulsky, A.V., Esau, I., Bulygina, O.N., Davy, R., Mokhov, I.I., Outten, S., Semenov, V.A., 2017: Climatology and interannual variability of cloudiness in the Atlantic Arctic from surface observations since the late 19th century. J. Climate, 30, 2103–2120, doi: 10.1175/JCLI-D-16-0329.1.

Esau, I.N., Chernokulsky, A.V., 2015: Convective Cloud Fields in the Atlantic Sector of the Arctic: Satellite and Ground-Based Observations. Izvestiya, Atmos. Oceanic Phys., 51, 1007–1020, doi: 10.1134/S000143381509008X.

Project Details
Funding Agency: 
Research Council of Norway
NERSC Principal Investigator: 
Igor Ezau
Coordinating Institute: 
Nansen Environmental and Remote Sensing Center
Project Status: