scholarly journals Relationship Between Korean Monthly Temperature During Summer and Eurasian Snow Cover During Spring

Atmosphere ◽  
2017 ◽  
Vol 27 (1) ◽  
pp. 55-65 ◽  
Author(s):  
You Jin Won ◽  
Sang-Wook Yeh ◽  
Bo Young Yim ◽  
Hyun-Kyung Kim
Keyword(s):  
Snow Cover ◽  
Eurasian Snow Cover ◽  
Monthly Temperature ◽  
Eurasian Snow

North American and Eurasian snow cover co-variability

1997 ◽  
Vol 49 (4) ◽  
pp. 503-512 ◽  
Author(s):  
David J. Walland ◽  
Ian Simmonds
Keyword(s):  
Snow Cover ◽  
North American ◽  
Eurasian Snow Cover ◽  
Eurasian Snow

scholarly journals Variability of the Cold Season Climate in Central Asia. Part II: Hydroclimatic Predictability

2019 ◽  
Vol 32 (18) ◽  
pp. 6015-6033 ◽  
Author(s):  
Lars Gerlitz ◽  
Eva Steirou ◽  
Christoph Schneider ◽  
Vincent Moron ◽  
Sergiy Vorogushyn ◽  
...  
Keyword(s):  
Central Asia ◽  
Snow Cover ◽  
Large Scale ◽  
Cold Season ◽  
Hadley Cell ◽  
Quasi Biennial Oscillation ◽  
Large Variability ◽  
Forecast Performance ◽  
Eurasian Snow Cover ◽  
Eurasian Snow

Abstract Central Asia (CA) is subjected to a large variability of precipitation. This study presents a statistical model, relating precipitation anomalies in three subregions of CA in the cold season (November–March) with various predictors in the preceding October. Promising forecast skill is achieved for two subregions covering 1) Uzbekistan, Turkmenistan, Kyrgyzstan, Tajikistan, and southern Kazakhstan and 2) Iran, Afghanistan, and Pakistan. ENSO in October is identified as the major predictor. Eurasian snow cover and the quasi-biennial oscillation further improve the forecast performance. To understand the physical mechanisms, an analysis of teleconnections between these predictors and the wintertime circulation over CA is conducted. The correlation analysis of predictors and large-scale circulation indices suggests a seasonal persistence of tropical circulation modes and a dynamical forcing of the westerly circulation by snow cover variations over Eurasia. An EOF analysis of pressure and humidity patterns allows separating the circulation variability over CA into westerly and tropical modes and confirms that the identified predictors affect the respective circulation characteristics. Based on the previously established weather type classification for CA, the predictors are investigated with regard to their effect on the regional circulation. The results suggest a modification of the Hadley cell due to ENSO variations, with enhanced moisture supply from the Arabian Gulf during El Niño. They further indicate an influence of Eurasian snow cover on the wintertime Arctic Oscillation (AO) and Northern Hemispheric Rossby wave tracks. Positive anomalies favor weather types associated with dry conditions, while negative anomalies promote the formation of a quasi-stationary trough over CA, which typically occurs during positive AO conditions.

scholarly journals Effect Of Eurasian Snow Cover On Summer Climate Of The Northern Hemisphere: A GCM Study

1990 ◽  
Vol 14 ◽  
pp. 364 ◽  
Author(s):  
Tetsuzo Yasunari ◽  
Akio Kitoh ◽  
Tatsushi Tokioka
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
General Circulation ◽  
Large Scale ◽  
General Circulation Models ◽  
Late Winter ◽  
Hydrological Process ◽  
Eurasian Snow Cover ◽  
Eurasian Snow ◽  
Snow Cover Extent

Observational studies have shown that Eurasian snow-cover anomalies during winter-through-spring seasons have a great effect on anomalies in atmospheric circulation and climate in the following summer season through snow albedo feedback (Hahn and Shukla, 1976; Dey and Bhanu Kumar, 1987). Morinaga and Yasunari (1987) have revealed that large-scale snow-cover extent over central Asia in late winter, which particularly has a great effect on the circulation over Eurasia in the following season, is closely related to the Eurasian pattern circulation (Wallace and Gutzler, 1981) in the beginning of winter. Some atmospheric general circulation models (GCM) have suggested that not only the albedo effect of the snow cover but also the snow-hydrological process are important in producing the atmospheric anomalies in the following seasons (Yeh and others, 1984; Barnett and others, 1988). However, more quantitative evaluations of these effects have not yet been examined. For example, it is not clear to what extent atmospheric anomalies are explained solely by snow-cover anomalies. Spatial and seasonal dependencies of these effects are supposed to be very large. Relative importance of snow cover over Tibetan Plateau should also be examined, particularly relevant to Asian summer monsoon anomalies. Moreover, these effects seem to be very sensitive to parameterizations of these physical processes (Yamazaki, 1988). This study focuses on these problems by using some versions of GCMs of the Meteorological Research Institute. The results include the evaluation of total snow-cover feedbacks as part of internal dynamics of climatic change from 12-year GCM integration, and of the effect of anomalous snow cover over Eurasia in late winter on land surface conditions and atmospheric circulations in the succeeding seasons.

scholarly journals A Skillful Prediction Model for Winter NAO Based on Atlantic Sea Surface Temperature and Eurasian Snow Cover

2015 ◽  
Vol 30 (1) ◽  
pp. 197-205 ◽  
Author(s):  
Baoqiang Tian ◽  
Ke Fan
Keyword(s):  
Prediction Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Snow Cover ◽  
Correlation Coefficient ◽  
Cross Validation ◽  
Sea Surface ◽  
Interdecadal Variability ◽  
Eurasian Snow Cover ◽  
Eurasian Snow

Abstract A new statistical forecast scheme, referred to as scheme 1, is developed using observed autumn Atlantic sea surface temperature (SST) and Eurasian snow cover in the preceding autumn to predict the upcoming winter North Atlantic Oscillation (NAO) using the year-to-year increment prediction approach (i.e., DY approach). Two predictors for the year-to-year increment are identified that are available in the preceding autumn. Cross-validation tests for the period 1950–2011 and independent hindcasts for the period 1990–2011 are performed to validate the prediction ability of the proposed technique. The cross-validation test results for 1950–2011 reveal a high correlation coefficient of 0.52 (0.58) between the predicted and observed NAO indices (DY of the NAO). The model also successfully predicts the independent hindcasts for the period 1990–2011 with a correlation coefficient of 0.55 (0.74). In addition, scheme 0 (i.e., anomaly approach) is established using the SST and snow cover anomalies during the preceding autumn. Compared with scheme 0, this new prediction model has higher predictive skill in reproducing the interdecadal variability of NAO. Therefore, this study provides an effective climate prediction scheme for the interannual and interdecadal variability of NAO in boreal winter.

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