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Understanding of future changes in drought characteristics is crucial for climate change adaptation and drought impact mitigation. We analysed the projected changes in drought characteristics relative to historical drought conditions in Central Asia using the Standardized Precipitation Evapotranspiration Index based on both gridded observations and an ensemble of bias‐corrected and spatially disaggregated global circulation models (GCMs) from phase 6 of Coupled Model Intercomparison Project (CMIP6). The results suggest that precipitation and potential evapotranspiration are projected to increase across Central Asia. Even though the change in wetness may not be significant and robust, a "dry gets drier and wet gets wetter" pattern may emerge in future under different scenarios. Drought events in Central Asia's semi‐arid and arid regions (aridity index <0.5) are projected to become more frequent (>125%), with longer duration (>55%), higher severity (>74%) and intensity (>8%) by the end of the 21st century under four Shared Socioeconomic Pathway (SSP) scenarios. In the coming period of 2021–2050, Central Asia is expected to have more drought events with a longer duration but lower intensity. Comparisons between different SSP scenarios stress the importance of climate change mitigation strategies to avoid more drought occurrence, longer drought duration and higher drought severity. The long‐term mitigation and adaptation studies for increasing drought impacts are also imperative in Central Asia. [ABSTRACT FROM AUTHOR]

Dust depositions are critical archives for understanding interior aridification and westerly climatic changes in Central Asia. Accurate and reliable dating of loess is very important for interpreting and correlating environmental records. There remains a disparity between luminescence ages and radiocarbon dating of late Quaternary loess from the Ili Basin in Central Asia. In this study, we establish a closely spaced quartz optically stimulated luminescence (OSL) chronology for the 20.5-m-thick Nilka loess section in the Ili Basin. Based on OSL ages, two intervals of higher mass accumulation rate occurred at 49–43 ka and 24–14 ka. We further compare these OSL ages with 23 accelerator mass spectrometry (AMS) 14 C ages of bulk organic matter. The results indicate that the OSL and radiocarbon ages agree well for ages younger than ca. 25 14 C cal ka BP. However, beyond 30 cal ka BP, there is no consistent increase in AMS 14 C age with depth, while the OSL ages continue to increase. These differences confirm the observation that the AMS 14 C ages obtained using conventional acid–base–acid (ABA) pretreatment are severely underestimated in other terrestrial deposits in Central Asia, which could be due to 2–4% modern carbon contamination. However, OSL dating is applicable for constructing an accurate chronology beyond 30 cal ka BP. We suggest caution when interpreting paleoenvironmental changes based on radiocarbon ages older than 25 cal ka BP. [ABSTRACT FROM AUTHOR]

Central Asia is one of the most significant loess regions on Earth, with an important role in understanding Quaternary climate and environmental change. However, in contrast to the widely investigated loess deposits in the Chinese Loess Plateau, the Central Asian loess–paleosol sequences are still insufficiently known and poorly understood. Through field investigation and review of the previous literature, the authors have investigated the distribution, thickness and age of the Xinjiang loess, and analyzed factors that control these parameters in the Xinjiang in northwest China, Central Asia. The loess sediments cover river terraces, low uplands, the margins of deserts and the slopes of the Tianshan Mountains and Kunlun Mountains and are also present in the Ili Basin. The thickness of the Xinjiang loess deposits varies from several meters to 670 m. The variation trend of the sand fraction (>63 μm) grain-size contour can indicate the local major wind directions, so we conclude that the NW and NE winds are the main wind directions in the North and South Xinjiang, and the westerly wind mainly transport dust into the Ili basin. We consider persistent drying, adequate regional wind energy and well-developed river terraces to be the main factors controlling the distribution, thickness and formation age of the Xinjiang loess. The well-outcropped loess sections have mainly developed since the middle Pleistocene in Xinjiang, reflecting the appearance of the persistent drying and the present air circulation system. However, the oldest loess deposits are as old as the beginning of the Pliocene in the Tarim Basin, which suggests that earlier aridification occurred in the Tarim Basin rather than in the Ili Basin and the Junggar Basin. [ABSTRACT FROM AUTHOR]

Precipitation outputs from 30 Global Circulation Models (GCMs) of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) were evaluated from 1951 to 2014 over six climate zones in arid Central Asia (ACA) using the Climate Research Unit TS 4.04 (CRU) precipitation datasets as reference. An evaluation framework was constructed taking into account metrics of annual precipitation patterns, annual cycle precipitation statistics, categorical validation and long-term precipitation trend. The performance of GCMs vary from region to region. Compared to CRU, the 30 selected GCMs present considerable wet bias in the Southern Xinjiang (SX) and Hexi Corridor (HC) regions which can be larger than 100%. The simulated precipitation from most GCMs shows a consistent annual cycle shape and closer with that of observations in the western ACA, but fails to capture precipitation peak in NK and NX regions. Compared to NK, CD, TM and NX, all models and the full model ensemble have limited ability in reproducing the Probability Density Function (PDF) of observations over SX and HC. Most GCMs show limited competence to reproduce the long-term precipitation trend with high wet bias over the Hexi Corridor. Based on the comprehensive performance ranking, it is discovered that CESM2, CESM2-FV2, CESM2-WACCM and CESM2-WACCM-FV2 from NCAR, ACCESS-CM2 from CSIRO and CanESM5 from Canadian Centre for Climate Modeling demonstrate better performances across ACA. The ensemble of 30 selected GCMs has limited ability to accurately simulate precipitation according to the above four types of metrics. The results of this paper may provide scientific guidance to CMIP6 end-users when selecting the most suitable GCMs for their specific applications over ACA. • 1. The performance of CMIP6 Global Circulation Models (GCMs) varies from region to region. • 2. GCMs suffer from large wet bias in Southestern Xinjiang and Hexi Corridor. • 3. Simple mean ensemble has limited skills in simulating precipitation. • 4. CESM2, ACCESS-CM2, and CanESM5 show better skills in simulating precipitation over Arid Central Asia. • 5. The ensemble based on the top-ranked 5, 12 and 14 GCMs have better performances over six sub-regions. [ABSTRACT FROM AUTHOR]

Abstract The continuous rise in summer temperature and consequent glacier wastage have led to a significant loss of frozen water resources in the mountain headwaters of the major river systems in Central Asia. High mountain lakes in the headwaters of the Syr Darya River serve as sensitive indicators of climate change in the region. In this study, lakes in the headwaters of the Syr Darya River are examined using a Landsat image series. A total of 959 lakes covering a total area of 328.39 ± 5.51 km2 were identified in 2015, of which 91% were distributed in the larger Naryn River basin, and the remaining lakes were in the smaller Kara Darya River basin. During the period of the 1990s to 2015, a clear expansion trend in both lake area (+6.64 km2) and number of lakes (+205) was observed. Glacial lakes contributed 69% of the increase in the number of lakes. Among the four lake types, proglacial lakes showed the fastest area expansion with an average relative growth rate of 94% from the 1990s to 2015, 5–8 times that of the other three lake types: nonglacial lakes, unconnected-fed lakes and unconnected-nonfed lakes. Lake Petrov was the fastest growing glacial lake (+0.05 km2 yr−1) during the past century and will continue to expand as its supply glacier continues to retreat. The continuous expansion and potential outburst risk of the Lake Petrov poses a serious threat to downstream toxic tailing ponds of the Kumtor Gold Mine. Lake Song Kol, the largest natural lake in this region, presented a steady status during 1972–2017. The warming climate-caused acceleration of glacial melt is thought to be the primary reason for the lake enlargements in this region. The lake inventory prepared here is valuable baseline dataset for the evaluation of water resources in the Syr Darya River drainage basin and for the potential risk and hazard assessments of glacial lake outburst floods. [ABSTRACT FROM AUTHOR]

The Scaled Drought Condition Index (SDCI) is an agricultural drought index derived from multiple remote sensing datasets including precipitation from the Tropical Rainfall Measurement Mission (TRMM) and temperature and vegetation index from the Moderate Resolution Imaging Spectroradiometer (MODIS). The SDCI can be used for large-scale drought monitoring at a high spatial resolution in dry and humid regions. However, it is not applicable to certain areas for numerous reasons, including the discontinued service and limited coverage of the TRMM, uncertainties associated with the coarse resolution of precipitation input, the fixed lag time between precipitation deficit and vegetation responses, experimental weights and an arbitrary classification scheme. Therefore, this study aims to propose an optimal SDCI (OSDCI) using an alternative Global Satellite Mapping of Precipitation (GSMaP) as precipitation input, adding a varied lag time between precipitation and vegetation response, determining the optimal weights of variables and revising the severity classification. In Central Asia, OSDCI is calibrated using the 3-month Standardized Precipitation Evapotranspiration Index (SPEI) during the 2001–2008 growing seasons as drought reference. The SPEI from 2009 to 2016, soil moisture anomalies (SMAs) derived from both the Global Land Data Assimilation System (GLDAS) and the Climate Prediction Center (CPC), and the annual crop production of wheat and barley are used for cross-validation. For comparison, three original SDCIs are also included. The results show that the OSDCI displays better performance than the three experimental SDCIs with higher correlation coefficients relative to SPEI and SMA from the CPC for different cover types, and performance is comparable to that of SMA from the GLDAS and annual crop production. Additionally, the OSDCI based on the revised classification scheme (OSDCI_rev) shows significant superiority over the OSDCI based on the original classification system (OSDCI_org) with lower drought frequency and area error. Furthermore, the OSDCI_rev can capture both the temporal evolution and the spatial patterns of droughts exemplified by the SPEI and SMAs. Overall, the results of this study show the potential use of the OSDCI with a revised classification scheme as a remote sensing drought index for agricultural drought monitoring. • An Optimal Scaled Drought Condition Index (OSDCI) is developed. • Lag times and optimal weights for different land covers are considered in OSDCI. • OSDCI is well correlated with SPEI, soil moisture anomalies and crop production. • A new drought severity classification system is proposed for the OSDCI. • The new classification system is more accurate for drought area and frequency. [ABSTRACT FROM AUTHOR]