Leading modes of interannual soil moisture variability in European Russia and their relation to regional climate during the summer season
Soil moisture (SM) data from the Global Land Evaporation Amsterdam Model dataset for 1980–2014 are used to investigate interannual variability of SM in European Russia during the summer season. An Empirical Orthogonal Function (EOF) analysis performed on monthly-mean data (i.e., separately for June, July and August) revealed three leading modes of SM variability, characterized by monopole (EOF-1), zonal dipole (EOF-2) and meridional dipole (EOF-3) patterns. Together these modes explain more than half of the total SM variability in each summer month. Analysis of correlations between the leading PCs (principal components) of SM in European Russia and indices of regional teleconnections suggests that the monopole pattern is associated with the Polar—Eurasia teleconnection, whereas the zonal and meridional dipole patterns are linked respectively to the East Atlantic—West Russia and Scandinavian teleconnections. These links are subject to change over the summer season. The leading PCs broadly capture the large SM anomalies associated with regional climate extremes (such as the Russian summer heat wave in 2010). Correlation analysis revealed generally consistent patterns in which positive (negative) SM anomalies are linked to cyclonic (anti-cyclonic) anomalies of sea level pressure, above (below) normal precipitation and negative (positive) anomalies of air temperature. Locally we find differing roles of air temperature and precipitation in land-atmosphere interaction. Specifically, while precipitation is the dominant driver of interannual SM variability in early summer, air temperature plays a larger role in late-summer land-atmosphere interaction (at some time scales) over the southern part of European Russia, where moisture availability is limited.
This is a contribution to the IORAS St.-Assig. # 0149-2019-0002. Analysis of soil moisture variability and its links to key regional climate variables was performed with support of the project 14.B25.31.0026 funded by the Ministry of Education and Science of the Russian Federation. IIZ benefited from the support by Helmholtz-RSF grant 710 #18-47- 06202. We thank the anonymous reviews for their constructive comments and suggestions that have greatly improved the manuscript. The soil moisture data were downloaded from the GLEAM project website: http://www.gleam.eu. The GPCP data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at http://www.esrl.noaa.gov/psd/. The NCEP data were extracted from NOAA-CIRES Climate Diagnostics Center.
- Bendat JS, AG Piersol (1966) Measurement and Analysis of random data, 390. pp., Wiley, HobokenGoogle Scholar
- Cherenkova EA (2011) The use of satellite data for analysis of variations in soil moisture and vegetation cover state in the southern part of European Russia in the late 20th century—early 21st century. Issledovanie Zemli iz Kosmosa No 6:80–87 (in Russian)Google Scholar
- Dirmeyer PA, Schlosser CA, Brubaker KL (2008) Precipitation, recycling and land memory: An integrated analysis. COLA Technical Report 257, 24 ppGoogle Scholar
- Hurrell JW, CK Folland (2002) A change in the summer atmospheric circulation over the North Atlantic. CLIVAR Exch 7(3–4):52–54Google Scholar
- Marsh TJ, Hannaford J (2007) The summer 2007 floods in England and Wales—a hydrological appraisal. NERC/Centre for Ecology & Hydrology, pp 32Google Scholar
- Meshcherskaya AV, Boldyreva NA, Shapaeva ND (1982) Average regional soil productive moisture and snow depth. Statistical analysis and examples of application, Gidrometeoizdat, Leningrad, p 243 [in Russian]Google Scholar
- Meshcherskaya AV, Mirvis VM, Golod MP (2011) Drought in 2010 against a background of long-term variations in aridity in the major grain-sowing regions of the European part of Russia, Trudy GGO, No. 563, pp 94–121 [in Russian]Google Scholar
- Sitnov SA, Mokhov I (2013) Water vapor content in the atmosphere over European Russia during the 2010 summer fires. Atmos Ocean Phys 49:413–429Google Scholar
- Strashnaya AI, Maksimenkova TA, OV Chub (2011) Agrometeorological features of the drought of 2010 in Russia in comparison with the droughts of past years. Trudy Gidromettsentra Rossii No 345:194–214 (in Russian)Google Scholar
- van den Dool H, Huang J, Fan Y (2003) Performance and analysis of the constructed analogue method applied to US soil moisture applied over 1981–2001. J Geophys Res 108:1–16Google Scholar
- Wilks DS (1995) Statistical methods in the atmospheric sciences. Academic Press, Boston, p 467Google Scholar