Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Snowmelt from the Tianshan Mountains (TS) is a major contributor to the water resources of the Central Asian region. Thus, changes in snow phenology over the TS have significant implications for regional water supplies and ecosystem services. However, the characteristics of changes in snow phenology and their influences on the climate are poorly understood throughout the entire TS due to the lack of in situ observations, limitations of optical remote sensing due to clouds, and decentralized political landscapes. Using passive microwave remote sensing snow data from 1979 to 2016 across the TS, this study investigates the spatiotemporal variations of snow phenology and their attributes and implications. The results show that the mean snow onset day (Do), snow end day (De), snow cover duration days (Dd), and maximum snow depth (SDmax) from 1979 to 2016 were the 78.2nd day of hydrological year (DOY), 222.4th DOY, 146.2 days, and 16.1 cm over the TS, respectively. Dd exhibited a spatial distribution of days with a temperature of \u3c0 \u3e°C derived from meteorological station observations. Anomalies of snow phenology displayed the regional diversities over the TS, with shortened Dd in high-altitude regions and the Fergana Valley but increased Dd in the Ili Valley and upper reaches of the Chu and Aksu Rivers. Increased SDmax was exhibited in the central part of the TS, and decreased SDmax was observed in the western and eastern parts of the TS. Changes in Dd were dominated by earlier De, which was caused by increased melt-season temperatures (Tm). Earlier De with increased accumulation of seasonal precipitation (Pa) influenced the hydrological processes in the snowmelt recharge basin, increasing runoff and earlier peak runoff in the spring, which intensified the regional water crisi

Agriculture intensification increases summer precipitation in Tianshan Mountains, China

The land-use and land-cover change has a significant impact on the climate at different spatio-temporal scales. In this study, we explored the long term oasis expansion effects on regional summer precipitation in the north slope of Tianshan Mountains, China using high-resolution regional climate model. The results indicate that the oasis expansion increases the summer precipitation in the middle Tianshan Mountains while it has only a small effect over the oasis regions itself. The results indicate further that the oasis expansion affects mainly the late afternoon summer convective precipitation. The advection of air with additional moisture from the oasis areas to the mountains due to the mountain/plain circulation system during the day triggers the orographic precipitation in the middle mountain regions. These new results indicate that the oasis expansion could attribute significantly to the recent finding from observational studies about the increasing trend of precipitation in the middle Tianshan Mountains

Change in the potential snowfall phenology: past, present, and future in the Chinese Tianshan mountainous region, Central Asia

The acceleration of climate warming has led to a faster solid–liquid water cycle and a decrease in solid water storage in cold regions of the Earth. Although snowfall is the most critical input for the cryosphere, the phenology of snowfall, or potential snowfall phenology (PSP), has not been thoroughly studied, and there is a lack of indicators for PSP. For this reason, we have proposed three innovative indicators, namely, the start of potential snowfall season (SPSS), the end of potential snowfall season (EPSS), and the length of potential snowfall season (LPSS), to characterize the PSP. We then explored the spatial–temporal variation in all three PSP indicators in the past, present, and future across the Chinese Tianshan mountainous region (CTMR) based on the observed daily air temperature from 26 meteorological stations during 1961–2017/2020 combined with data from 14 models from CMIP6 (Phase 6 of the Coupled Model Intercomparison Project) under four different scenarios (SSP126, SSP245, SSP370, and SSP585, where SSP represents Shared Socioeconomic Pathway) during 2021–2100. The study showed that the SPSS, EPSS, and LPSS indicators could accurately describe the PSP characteristics across the study area. In the past and present, the potential snowfall season started on 2 November, ended on 18 March, and lasted for about 4.5 months across the CTMR on average. During 1961–2017/2020, the rate of advancing the EPSS (−1.6 d per decade) was faster than that of postponing the SPSS (1.2 d per decade). It was also found that there was a significant delay in the starting time (2–13 d) and advancement in the ending time (1–13 d), respectively, resulting in a reduction of 3–26 d for the LPSS. The potential snowfall season started earlier, ended later, and lasted longer in the north and center compared with the south. Similarly, the SPSS, EPSS, and LPSS indicators are also expected to vary under the four emission scenarios during 2021–2100. Under the highest emission scenario, SSP585, the starting time is expected to be postponed by up to 41 d, while the ending time is expected to be advanced by up to 23 d across the study area. This change is expected to reduce the length of the potential snowfall season by up to 61 d (about 2 months), and the length of the potential snowfall season will only last 2.5 months in the 2100s under the SSP585 scenario. The length of the potential snowfall season in the west and southwest of the CTMR will be compressed by more days due to a more delayed starting time and an advanced ending time under all four scenarios. This suggests that, with constant snowfall intensity, annual total snowfall may decrease, including the amount and frequency, leading to a reduction in snow cover or mass, which will ultimately contribute to more rapid warming through the lower reflectivity to solar radiation. This research provides new insights into capturing the potential snowfall phenology in the alpine region and can be easily extended to other snow-dominated areas worldwide. It can also help inform snowfall monitoring and early warning for solid water resources.</p

Improved atmospheric modelling of the oasis-desert system in Central Asia using WRF with actual satellite products

Because of the use of outdated terrestrial datasets, regional climate models (RCMs) have a limited ability to accurately simulate weather and climate conditions over heterogeneous oasis-desert systems, especially near large mountains. Using actual terrestrial datasets from satellite products for RCMs is the only possible solution to the limitation; however, it is impractical for long-period simulations due to the limited satellite products available before 2000 and the extremely time- and labor-consuming processes involved. In this study, we used the Weather Research and Forecasting (WRF) model with observed estimates of land use (LU), albedo, Leaf Area Index (LAI), and green Vegetation Fraction (VF) datasets from satellite products to examine which terrestrial datasets have a great impact on simulating water and heat conditions over heterogeneous oasis-desert systems in the northern Tianshan Mountains. Five simulations were conducted for 1-31 July in both 2010 and 2012. The decrease in the root mean squared error and increase in the coefficient of determination for the 2 m temperature (T2), humidity (RH), latent heat flux (LE), and wind speed (WS) suggest that these datasets improve the performance of WRF in both years; in particular, oasis effects are more realistically simulated. Using actual satellite-derived fractional vegetation coverage data has a much greater effect on the simulation of T2, RH, and LE than the other parameters, resulting in mean error correction values of 62%, 87%, and 92%, respectively. LU data is the primary parameter because it strongly influences other secondary land surface parameters, such as LAI and albedo. We conclude that actual LU and VF data should be used in the WRF for both weather and climate simulations

Accelerating upward treeline shift in the Altai Mountains under last-century climate change

Treeline shift and tree growth often respond to climatic changes and it is critical to identify and quantify their dynamics. Some regions are particularly sensitive to climate change and the Altai Mountains, located in Central and East Asia, are showing unequivocal signs. The mean annual temperature in the area has increased by 1.3–1.7 °C in the last century. As this mountain range has ancient and protected forests on alpine slopes, we focus on determining the treeline structure and dynamics. We integrated in situ fine-scale allometric data with analyses from dendrochronological samples, high-resolution 3D drone photos and new satellite images to study the dynamics and underlying causal mechanisms of any treeline movement and growth changes in a remote preserved forest at the Aktru Research Station in the Altai Mountain. We show that temperature increase has a negative effect on mountain tree growth. In contrast, only younger trees grow at higher altitudes and we document a relatively fast upward shift of the treeline. During the last 52 years, treeline moved about 150 m upward and the rate of movement accelerated until recently. Before the 1950s, it never shifted over 2150–2200 m a.s.l. We suggest that a continuous upward expansion of the treeline would be at the expense of meadow and shrub species and radically change this high-mountain ecosystem with its endemic flora. This documented treeline shift represents clear evidence of the increased velocity of climate change during the last century

How are Interannual Variations of Land Surface Phenology in the Highland Pastures of Kyrgyzstan Modulated by Terrain, Snow Cover Seasonality, and Climate Oscillations? An Investigation Using Multi-Source Remote Sensing Data

In the semiarid, continental climates of montane Central Asia, with its constant moisture deficit and low relative humidity, agropastoralism constitutes the foundation of the rural economy. In Kyrgyzstan, an impoverished, landlocked republic in Central Asia, herders of the highlands practice vertical transhumance—the annual movement of livestock to higher elevation pastures to take advantage of seasonally available forage resources. Dependency on pasture resource availability during the short mountain growing season makes herds and herders susceptible to changing weather and climate patterns. This dissertation focuses on using remote sensing observations over the highland pastures in Kyrgyzstan to address five interrelated topics: (i) changes in snow cover and its seasonality from 2002 through 2016; (ii) pasture phenology from the perspective of land surface phenology using multi-scale data from 2001 through 2017; (iii) relationships between snow cover seasonality and subsequent land surface phenology; (iv) terrain effects on the snow-phenology interrelations; and (v) the influence of atmospheric teleconnections on modulating the relationships between snow cover seasonality, growing season duration, and pasture phenology. Results indicate that more territory has been experiencing earlier snow onset than earlier snowmelt, and around equivalent areas with longer and shorter duration of snow seasons. Significant shifts toward earlier snow onset (snowmelt) occurred in western and central (eastern) Kyrgyzstan, and significant duration of the snow season shortening (extension) across western and eastern (northern and southwestern) Kyrgyzstan. Below 3400 m, there was a general trend of significantly earlier snowmelt, and this area of earlier snowmelt was 15 times greater in eastern than western rayons. In terms of land surface phenology, there was a predominant and significant trend of increasing peak greenness, and a significant positive relationship between snow covered dates and modeled peak greenness. While there were more negative correlations between snow cover onset and peak greenness, there were more positive correlations between snowmelt timing and peak greenness, meaning that a longer snow cover season increased the amplitude of peak greenness. The amount of thermal time (measured in accumulated growing degree-days) to reach peak greenness was significantly negatively correlated both with the number of snow covered dates and the snowmelt date. Thus, more snow covered dates translated into fewer growing degree-days accumulated to reach peak greenness in the subsequent growing season. Terrain features influenced the timing of snowmelt more strongly than the number of snow covered dates. Slope was more important than aspect, but the strongest effect appeared from the interaction of aspect and the steepest slopes. The influence of atmospheric teleconnection arising from climate oscillation modes was marginal at the spatial resolutions of this study. Thermal time accumulation could be largely explained with Partial Least Squares (PLS) regression models by elevation and snow cover metrics. However, explanation of peak greenness was less susceptible to terrain and snow cover variables. This research study provides a comprehensive evaluation of the spatial variation of interannual phenology in the highland pastures that serve as the foundation of rural Kyrgyz economy. Finally, it contributes to a better understanding of recent environmental changes in remote highland Central Asia

Central Asia’s Changing Climate: How Temperature and Precipitation Have Changed across Time, Space, and Altitude

Changes in climate can be favorable as well as detrimental for natural and anthropogenic systems. Temperatures in Central Asia have risen significantly within the last decades whereas mean precipitation remains almost unchanged. However, climatic trends can vary greatly between different subregions, across altitudinal levels, and within seasons. Investigating in the seasonally and spatially differentiated trend characteristics amplifies the knowledge of regional climate change and fosters the understanding of potential impacts on social, ecological, and natural systems. Considering the known limitations of available climate data in this region, this study combines both high-resolution and long-term records to achieve the best possible results. Temperature and precipitation data were analyzed using Climatic Research Unit (CRU) TS 4.01 and NASA’s Tropical Rainfall Measuring Mission (TRMM) 3B43. To study long-term trends and low-frequency variations, we performed a linear trend analysis and compiled anomaly time series and regional grid-based trend maps. The results show a strong increase in temperature, almost uniform across the topographically complex study site, with particular maxima in winter and spring. Precipitation depicts minor positive trends, except for spring when precipitation is decreasing. Expected differences in the development of temperature and precipitation between mountain areas and plains could not be detected