CLOUDINESS OVER THE OCEANS AT SUBARCTIC LATITUDES AS A VISIBLE PART OF ATMOSPHERIC MOISTURE TRANSPORT
Abstract and keywords
Abstract (English):
The article analyzes the climatology and interannual variability of fractional total and low cloud cover in the subarctic and subpolar regions of the North Atlantic and the North Pacific. We used surface visual observations of cloudiness from voluntary observing ships (VOS) for the period from 1950 to 2017. It is shown that in the North Atlantic and the North Pacific seasonal variations of the mean cloud cover demonstrate contrasting character. For a better identification of regional features, the probability distributions of the fractional cloud cover were analyzed. Analysis of interannual variability shows that in many areas of the North Atlantic and the North Pacific, significant linear trends in both total and low cloud cover are observed. Moreover, in the North Pacific, linear trends in the total cloudiness have pronounced seasonality.

Keywords:
Subarctic cloudiness, visual cloud observation, cloud climatology over the ocean, long-term trends in cloud cover
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References

1. Aleksandrova, M. P., S. K. Gulev, A. V. Sinitsyn (2007) , Improvement of upper-ocean shortwave radiation parameterization on the base of direct measurements in the Atlantic Ocean, Meteorology and Hydrology, 4, p. 45-54, https://doi.org/10.3103/S1068373907040048.

2. Aleksandrova, M., S. K. Gulev, K. Belyaev (2018) , Probability distribution for the visually observed fractional cloud cover over the ocean, J. Climate, 31, p. 3207-3232, https://doi.org/10.1175/JCLI-D-17-0317.1.

3. Bedacht, E., S. K. Gulev, A. Macke (2007) , Intercomparison of global cloud cover fields over oceans from the VOS observations and NCEP/NCAR reanalysis, Int. J. Climatol., 27, p. 1707-1719, https://doi.org/10.1002/joc.1490.

4. Bekryaev, R. V. (2019) , Interrelationships of the North Atlantic multidecadal climate variability characteristics, Russian Journal of Earth Sciences, 19, https://doi.org/10.2205/2018ES000653.

5. Bekryaev, R. V., I. V. Polyakov, V. A. Alexeev (2010) , Role of polar amplification in long-term surface air temperature variations and modern Arctic warming, J. Climate, 23, p. 3888-3906, https://doi.org/10.1175/2010JCLI3297.1.

6. Chernokulsky, A. V., I. Esau (2019) , Cloud cover and cloud types in the Eurasian Arctic in 1936-2012, Int. J. of Clim., 39, p. 5771-5790, https://doi.org/10.1002/joc.6187.

7. Chernokulsky, A. V., I. I. Mokhov (2012) , Climatology of total cloudiness in the Arctic: An intercomparison of observations and reanalyses, Adv. Meteor., 2012, p. 542093, https://doi.org/10.1155/2012/542093.

8. Chernokulsky, A. V., et al. (2017) , Climatology and interannual variability of cloudiness in the Atlantic Arctic from surface observations since the late nineteenth century, J. Climate, 30, p. 2103-2120, https://doi.org/10.1175/JCLI-D-16-0329.1.

9. Cronin M. F., , et al. (2019) , Air-sea fluxes with a focus on heat and momentum, Frontiers in Marine Science, 6, p. 430, https://doi.org/10.3389/fmars.2019.00430.

10. Dobson, F. W., S. D. Smith (1988) , Bulk models of solar radiation at sea, Quart. J. R. Met. Soc., 114, p. 165-182, https://doi.org/10.1002/qj.49711447909.

11. Dufour, A., O. Zolina, S. K. Gulev (2016) , Atmospheric moisture transport to the Arctic: assessment of reanalyses and analysis of transport components, J. Climate, 29, p. 5061-5081, https://doi.org/10.1175/JCLI-D-15-0559.1.

12. Foster, M. J., A. Heidinger (2013) , PATMOS-x: Results from a diurnally corrected 30-yr satellite cloud climatology, J. Climate, 26, p. 414-425, https://doi.org/10.1175/JCLI-D-11-00666.1.

13. Freeman, E., et al. (2017) , ICOADS Release 3.0: A major update to the historical marine climate record, Int. J. Climatol., 37, p. 2211-2232, https://doi.org/10.1002/joc.4775.

14. Frey, R. A., et al. (2008) , Cloud detection with MODIS. Part I: Improvements in the MODIS cloud mask for Collection 5, J. Atmos. Oceanic Technol., 25, p. 1057-1072, https://doi.org/10.1175/2008JTECHA1052.1.

15. Gulev, S. K., T. Jung, E. Ruprecht (2007a) , Estimation of the impact of sampling errors in the VOS observations on air-sea fluxes. Part I: Uncertainties in climate means, J. Climate, 20, p. 279-301, https://doi.org/10.1175/JCLI4010.1.

16. Gulev, S. K., T. Jung, E. Ruprecht (2007b) , Estimation of the impact of sampling errors in the VOS observations on air-sea fluxes. Part II: Impact on trends and interannual variability, J. Climate, 20, p. 302-315, https://doi.org/10.1175/JCLI4008.1.

17. Josey, S. (2003) , A new formula for determining the atmospheric longwave flux at the ocean surface at mid-high latitudes, J. Geophys. Res., C4, p. 3108, https://doi.org/10.1029/2002JC001418.

18. Karlsson, K.-G., et al. (2013) , CLARA-A1: A cloud, albedo, and radiation dataset from 28 yr of global AVHRR data, Atmos. Chem. Phys., 13, p. 5351-5367, https://doi.org/10.5194/acp-13-5351-2013.

19. Kay, J. E., A. Gettelman (2009) , Cloud influence on and response to seasonal Arctic sea ice loss, J. Geophys. Res., 114, p. D18204, https://doi.org/10.1029/2009JD011773.

20. Liu, Y., J. R. Key (2016) , Assessment of Arctic cloud cover anomalies in atmospheric reanalyses products using satellite data, J. Climate, 29, p. 6065-6083, https://doi.org/10.1175/JCLI-D-15-0861.1.

21. Pisareva, M. N. (2018) , An overview of the recent research on the Chukchi Sea water masses and their circulation, Russian Journal of Earth Sciences, 18, https://doi.org/10.2205/2018ES000631.

22. Rossow, W. B., R. A. Schiffer (1991) , ISCCP cloud data products, Bull. Amer. Meteor. Soc., 72, p. 2-20, https://doi.org/10.1175/1520-0477(1991)072%3C0002:ICDP%3E2.0.CO;2.

23. Rossow, W. B., R. A. Schiffer (1999) , Advances in understanding clouds from ISCCP, Bull. Amer. Meteor. Soc., 80, p. 2261-2287, https://doi.org/10.1175/1520-0477(1999)080%3C2261:AIUCFI%3E2.0.CO;2.

24. Serreze, M. C., R. G. Barry (2011) , Processes and impacts of Arctic amplification: A research synthesis, Global Planet. Change, 77, p. 85-96, https://doi.org/10.1016/j.gloplacha.2011.03.004.

25. Stubenrauch, , et al. (2013) , Assessment of global cloud datasets from satellites: Project and database initiated by the GEWEX Radiation Panel, Bull. Amer. Meteor. Soc., 94, p. 1031-1049, https://doi.org/10.1175/BAMS-D-12-00117.1.

26. Tilinina, N., A. Gavrikov, S. Gulev (2018) , Association of the North Atlantic surface turbulent heat fluxes with midlatitude cyclones, Mon. Wea. Rev., 146, p. 3691-3715, https://doi.org/10.1175/MWR-D-17-0291.1.

27. Travkin, V. S., T. V. Belonenko (2019) , Seasonal variability of mesoscale eddies of the Lofoten Basin using satellite and model data, Russian Journal of Earth Sciences, 19, https://doi.org/10.2205/2019ES000676.

28. Ushakov, K. V., R. A. Ibrayev (2018) , Assessment of mean world ocean meridional heat transport characteristics by a high-resolution model, Russian Journal of Earth Sciences, 18, https://doi.org/10.2205/2018ES000616.

29. Vlasova, G., S. Marchenko, N. Rudykh (2019) , Modeling spring hydrodynamic regime of surface waters in Kamchatka Strait, Russian Journal of Earth Sciences, 19, https://doi.org/10.2205/2019ES000674.

30. Wille, et al. (2019) , West Antarctic surface melt triggered by atmospheric rivers, Nature Geoscience, 12, p. 911-916, https://doi.org/10.1038/s41561-019-0460-1.

31. WMO, (1974) , Manual on Codes, Vol. 1, 348 pp., World Meteorological Organization Publ., Geneva.

32. Woodruff, S. D., et al. (2011) , ICOADS Release 2.5: Extensions and enhancements to the surface marine meteorological archive, Int. J. Climate, 31, p. 951-967, https://doi.org/10.1002/joc.2103.

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