An observational study was conducted of winter snow anomaly in Mongolia and the associated atmospheric circulation. Monthly data of snow depth and temperature at 23 Mongolian stations from 1940 to 1992 were used for a statistical analysis. The Mongolian snow amount is fairly large in the northern mountainous regions and decreases to the south. On average, there is a substantial seasonal increase in snow depth during November and December. The deepest snow occurs in January, with an average depth of 3.4 cm averaged for all 23 stations. The correlation coefficients for November and December between the snow depth and temperature are significant and negative over wide areas.A principal component analysis was applied to interannual anomalies of January snow depth. The first mode (PC1) reveals widespread loadings of the same sign, and the second mode (PC2) shows a northeast-southwest dipole pattern. A correlation analysis was employed to examine relationships between PC1, PC2 and the preceding atmospheric circulation modes derived from a principal component analysis of the Northern Hemisphere 500 hPa geopotential heights. The PC1 time series for January is highly correlated with the North Atlantic oscillation for November, and the PC2 time series is significantly correlated with the Eurasian pattern for December. These relationships will provide a potential basis for long-range forecasts of mid-winter anomalous snow that leads to a significant loss of livestock (so-called white dzud ) in Mongolia.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.