Central Asia is one of the largest semi-arid areas in the world and argued to be a “hotspot” for climate change [1
]. Temperatures are increasing more than the global mean [3
] whereas mean precipitation only shows a minor increase [5
]. Regional trends at the level of districts and valleys can however differ greatly from large-scale observations due to the complexity of the terrain and different atmospheric forces. Biophysical consequences of altered climate regimes are likely to include melting glaciers, changes in the seasonality of river-runoff regimes inducing seasonal water shortages, or altered vegetation patterns [7
]. This may affect the livelihood of mountain communities who are mainly living on livestock-keeping and agriculture, and where natural resources are already limited [11
]. In addition to climatic challenges, historical events have led to the fact that Central Asian countries are still confronted with political instability, poverty, or insufficient infrastructure [7
]. Tackling climate change issues might, therefore, not be at the top of their priority list. Nevertheless, climate change already impacts the life of many people, particularly in rural areas, who have contributed little to anthropogenic climate change. Therefore, it is important to understand how temperature and precipitation have changed over time and space using publicly available data, to provide a basis for decision-making for impact studies and more detailed climate studies, on the level of the villages to protect the livelihood of Central Asian communities.
Previous research on Central Asians climate mostly focuses on specific subregions, like the Himalaya [14
], Tian Shan [17
], or Hindu-Kush/Karakoram [20
]. Due to their functionality as over regional water towers these mountain ranges get attention in climatological and hydrological research [23
]. Other studies analyze climatic changes over the topographically complex region of whole Central Asia, focusing on trends in either temperature [3
] or precipitation [5
]. These studies confirm a strong warming trend in Central Asia which has become accelerated in recent years. Temperatures do particularly increase in the Tian Shan and Himalayan region and precipitation in the westerly dominated Northwest. Although an overall trend of increasing precipitation can be seen, regional differences between lower and higher elevation areas are distinct [17
]. In contrast to previous studies, this paper combines the two most important climate variables, temperature and precipitation, and statistically analyzes temporal and spatial trends, using well-known methods and publicly available data. Thus far, this combined approach has only been adopted by a few studies [4
]. In addition, this paper also distinguishes between different altitudinal levels because Central Asia covers a diverse terrain.
Climate trend studies in Central Asia may obtain different results according to the dataset used. Looking at previous studies, a suite of datasets has been applied. Studies using meteorological station data [24
] or station-based gridded products [3
] benefit from the availability of station data since the beginning of the 20th century. However, the regional accuracy of these datasets is constrained by the low density of meteorological stations and their biased distribution against low-elevation areas. Further, most of the climate stations fell into disrepair after the breakdown of the Soviet Union, causing a dramatic drop in the amount of available stations after 1990 [32
]. Station-based, gridded products like the Climatic Research Unit (CRU) [35
], the Global Precipitation Climatology Centre (GPCC) [36
], or the University of Delaware (UDEL) [37
] are, therefore, better suited for temperature analyses than they are for precipitation, as temperature is rather uniform in space and fewer stations are required to get a robust result. Precipitation shows marked spatial heterogeneity needing a very dense homogenous station network [38
]. The CRU TS has already been used in temperature analyses in Central Asia [3
]. Satellite products, on the other hand, such as NASA’s Tropical Rainfall Measuring Mission (TRMM) [39
], provide spatially consistent records with high temporal resolution. While this dataset is limited to recent decades, it is suited for complex terrain [40
]. Regional differences in the performance of precipitation datasets are more pronounced than for temperature datasets, motivating earlier studies to outline their discrepancies in Central Asian regions [21
This study statistically examines seasonal temperature and precipitation trends in Central Asia from 1950 to 2016 across different terrain and altitudinal levels. By doing this it contributes to an integrative understanding of climatic change, as regional trends can differ greatly in their magnitude and temporal occurrence, inducing diverse consequences for local biophysical and social systems. To disentangle the patterns of overall changes and to differentiate their spatial intensity within the research area, linear trends have been calculated for the complete area, for the mountainous regions above 2500 m above sea level (masl) and for the lower plains under 2500 masl. To account for the problem of non-linear processes within the environmental data, we further analyzed anomaly time series to reveal low-frequency variations over time. As the applied dataset used may influence the results of trend studies, we selected publicly accessible climate datasets which provide high-spatial accuracy and long-term temporal coverage. However, the history of Central Asia and the complex terrain restrict the quality of available climate data in this region, which has to be seen as a limitation of this study.
Our study confirms the strong warming trend of Central Asia, with an abrupt acceleration of its intensity in the mid-1990s. The significant increase in annual and seasonal temperatures has already been identified in previous studies, however with different trend magnitudes [3
]. Our results indicated an annual temperature increase of 0.28 °C per decade (1950–2016), compared to the outcomes of [3
] and [24
], who found an annual temperature increase of 0.39 °C per decade (1979–2011) and 0.16 °C per decade (1901–2003), respectively. Discrepancies in the trend magnitudes are likely caused by different regional extents, time periods, and data sources. This methodological constraint might also affect the results of other Central Asian studies [3
], and global studies [67
], as their time series also show an abrupt change of the mean in the late 20th century. It can be summarized that, despite different time periods and different datasets, all large-scale studies agreed on a strong warming trend averaged over Central Asia, which has accelerated in recent years.
Seasonal investigations in trend characteristics showed that spring and winter are under the greatest change. Spring and winter are important seasons in Central Asian because (1) two thirds of the annual precipitation is falling during that time, and (2) water reservoirs in the form of snow and ice are built up [68
]. Altered precipitation patterns during these months can induce water shortages in summer, effect agricultural yields in autumn, and modify snow-regimes [9
]. However, our data does not reveal any significant changes in precipitation. In terms of temperature, both seasons show a significant increase. According to our results, winter displays the highest rate of temperature increase (0.32 °C per decade), followed by spring. These data are consistent with earlier studies that also show winter as the most rapidly warming season in this region [13
]. Some studies, however, show the contrary, with spring as the greatest warming season, directly followed by winter [3
]. According to [49
], it can be summarized that the colder seasons of the year are warming the most in semi-arid regions.
As temporal trends are averaged over the whole research area, it is important to assess their local characteristics by using grid-based trend maps. The accuracy of the trend maps is defined by the spatial resolution of the input data. Therefore, to reveal spatial trend differences the choice of data is important. Our data has a resolution of 0.5 degree and can account for general differences between geographical regions (Figure 4
and Figure 7
). Using this data we identified a general increase in temperature across the whole research area, whereas the northern parts do warm more than the southern parts. Finding a possible explanation for this north–south gradient was beyond the scope of this study and should be further investigated in future research. In terms of precipitation no clear pattern could be identified using the visual trend maps. However, as earlier studies already assessed the importance of elevation dependent temperature and precipitation rates [71
] but gained controversial results, we looked at this in more detail, calculating additional trend rates. Due to the scarce coverage of meteorological stations in high-altitude areas, it is difficult to gain significant evidence for elevation dependent gradients. Therefore, earlier study results are often contradicting. Whereas [19
] does not recognize altitudinal effects on temperature change, [3
] found that lower elevations warm more than higher elevations, but only in some regions in Central Asia. For precipitation it is even more difficult, as it can be spatially and temporally highly variable. However, both [17
] detected a slight tendency towards higher trend rates in lower Central Asian plains. Our results also show higher trend magnitudes for plains, but not at a significant level (Table 3
). The same situation can be seen for temperature, where plains tend to increase at a stronger rate. These results might be affected by the lack of observational data in high-elevation areas. In addition, looking at the spatial distribution of temperature and precipitation trends in Figure 5
and Figure 8
, no clear difference between trend tendencies in plains and mountains can be seen. As the mountains of Central Asia are inhabited at many different altitudinal levels and because the impacts of climate change can have different characteristics from valley to valley, it is important to build up the station network to higher altitudes. This would allow to obtain more precise information about elevation-dependent trends and impacts, especially because communities at high-altitudes are less resilient against climatic changes, as their food and water sources are limited.
The accuracy of the results is limited by the availability of accurate climate data in high altitudes and missing long-term records. The complexity of the terrain makes regional trends differ greatly from large-scale observations and the sparse meteorological stations coverage impacts the accuracy of Central Asian gridded climate datasets. Due to the low resolution of the applied gridded products, climatic processes at finer spatial and temporal scales, like extreme events or low-frequency variations are neglected [72
]. To obtain information about fine-scale processes, which are important for assessing climate change impacts and vulnerability scenarios, various downscaling methods or stochastic models could be applied [72
]. By applying a combined dataset of the gridded station product CRU TS and the satellite product TRMM 3B43, we attempted to account for the problem of precipitation data accuracy. Nevertheless, our trend magnitudes differed from previous studies, whereas the general tendency was in accordance. It is a challenge to analyze precipitation trends in complex and data sparse regions. However, understanding climatic change in Central Asia and investigating its regional differences is important because of the subsistence-based and natural-controlled lifestyle of the rural communities and the dependence on glacier and snow packs for the regional water supply. To provide a suitable basis for adaptation strategies, 0.5 degree resolution is insufficient in a complex mountainous terrain. Therefore, future studies should amplify their spatial resolution and focusing on the level on the villages, because that is the level where people are impacted by the consequences of climate change.