Spatiotemporal characteristics of future changes in precipitation and temperature in Central Asia

Abstract

The arid and semi‐arid areas in Central Asia have scarce water resources and fragile ecosystems that are especially sensitive and vulnerable to climate change. Reliable information regarding future projections of change in climate is crucial for long‐term planning of water resources management and structural adjustment of agriculture in this region. However, the low‐density meteorological observation network brings great challenges for investigating the effects of climate variations. In this study, variations of precipitation and temperature in Central Asia were examined by a combination of gridded climate dataset of the Climate Research Unit and the latest general circulation models (GCMs) under a representative concentration pathway 4.5. Three downscaling methods, Delta, Advanced Delta, and Bayesian model averaging (BMA) methods, translate the coarse GCMs to local climatic variations for the period 2021–2060 relative to 1965–2004. Major results suggested that the Advanced Delta and BMA methods outperformed the Delta method in precipitation downscaling. Projected precipitation exhibited a general increasing trend at a rate of 4.63 mm/decade for entire Central Asia with strong spatiotemporal heterogeneity. While a declining trend was observed in the southwestern and central parts of Central Asia in summer. The projected temperature was revealed an obvious ascending at 0.37°C/decade, while the warming rate accelerated in higher latitude and mountainous areas. [Correction added on 03 December 2018, after first online publication: The preceding statement has been corrected in this version.] The surface land coverage had significant effects on the variations of precipitation and temperature, respectively. The driven factors of local climate were suggested by analysing the relationships between climate variations and large‐scale atmospheric circulation fields anomalies. The findings of this study aims to provide useful information to improve our understanding for future climate change and benefit local decision makers.

Projected Rainfall Erosivity Over Central Asia Based on CMIP5 Climate Models

“…The Tien Shan, Pamir-Alay and Pamir mountains experience more torrential rainfall compared to the surrounding low-lying deserts. Changes in precipitation mainly depend on changes in the water content in the atmosphere, which is transferred from the oceans to the earth through large-scale atmospheric circulation [40]. Atmospheric circulation over Central Asia is characterized by the predominance of the west-east transfer of air masses when the main moisture that gives precipitation comes from the North Atlantic Ocean [37,[48][49][50].…”

“…ENSO-induced precipitation is related to large-scale atmospheric circulation changes caused by sea surface temperature (SST) [52]. Previous studies have shown that changes in SST have a significant impact on the transport of water vapor from the oceans to land [40,53]. The main feature in the distribution of precipitation in Central Asia is their small annual amount of the lower part of the territory, resulting in vast deserts.…”

“…Projected precipitation data from GCMs BCCCSM1-1, IPSLCM5ALR, MIROC5, and MPIESMLR for the RCP2.6 and RCP8.5 greenhouse emission scenarios were used [39] (Table 1; Figure 2). The GCMs were selected because of their relative independence, good performance in precipitation simulation for Central Asia [40] and Tibetan Plateau [33]. Global precipitation with 1 km 2 horizontal resolution was obtained from the WorldClim database [36].…”

Variations in Sediment Grain Size from a Lake in the Tianshan Mountain of Central Asia: Implications for Paleoprecipitation Reconstruction

“…Over the past 100 years, air temperature in the arid region of Central Asia, which is mainly controlled by westerly winds, has clearly shown an increasing trend [5,6]. Annual precipitation in this region also shows an overall increasing trend, but with spatial differences [7,8]. Under the combined effect of climate change and human activities, regional surface processes are undergoing strong and brand-new changes, which are highlighted by changes in arid hydrological processes and consequent changes in the ecological environment [9][10][11].…”

Applications of artificial neural networks in three agro-environmental systems: microalgae production, nutritional characterization of soils and meteorological variables management

“…Nonetheless, some changes have occurred, climatic anomalies and the modification of precipitations regimens are striking the agriculture (Luo et al, 2019). In recent years, weather and climate have become an issue of interest for scientists, media and population because of the harmful effects associated with global warming (Ahrens, 2012;Webster, 2013), and humans activities have been reported as one of the leading causes (Barry and Chorley, 2009).…”