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

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.

Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation

“…High interannual hydroclimatic fluctuations in these mountain ranges impose substantial burdens on irrigated crop production that dominates the southern parts of Central Asia (Karthe et al 2017). Better preparing for variable downstream river runoff has long been called for, such as through implementing early warning systems based on seasonal forecasts (Karthe et al 2017, Apel et al 2018, World Bank 2018, Gerlitz et al 2019, Xenarios et al 2019. In this regard, global and regional climate oscillations have been suggested as viable indicators for long-range seasonal hydrological outlooks in the region (Gerlitz et al 2020).…”

“…Much of the previous research on the relationship between climate oscillations and precipitation variability focused on entire Central Asia and explored the effects of the El Niño-Southern Oscillation (ENSO). It was reported that ENSO during its El Niño phase increases precipitation intensity across Central Asia, with the amplitude of this effect being particularly strong from the autumn to summer (Mariotti 2007, Chen et al 2018, Gerlitz et al 2019. In contrast, La Niña state is associated with below-average precipitation in the region.…”

“…These teleconnections manifest as wave trains that modulate the location and strengths of the westerlies (Bothe et al 2012). The Atlantic Multidecadal Oscillation (AMO) is a periodic variation in currents in the northern Atlantic Ocean, and its positive state in the winter is associated with higher precipitation in Central Asia (Gerlitz et al 2019).…”

“…The SH can impact winter precipitation variability across the mountainous region of South Asia that neighbours the Pamir (Riaz and Iqbal 2017). Finally, the quasi-Biennial oscillation (QBO), which regulates the direction of stratospheric winds in the equatorial zones, is positively associated with precipitation in some areas of Central Asia, but this relationship appears statistically insignificant across the larger part of Central Asia (Brönnimann et al 2016, Gerlitz et al 2019.…”

“…In addition, climate teleconnections are known to exhibit nonstationary behaviour in space and time. For instance, the relationship between SCAN and cold season precipitation in Central Asia may inverse depending on the location (Gerlitz et al 2019). In the mountain areas, a positive relationship between SCAN and precipitation during the winter can become negative during the summer (Schiemann 2007).…”