1. Introduction
Drylands are the lands with low precipitation and lack of soil moisture and nutrients year-round. Drylands constitute approximately 41% of Earth’s land area, where more than one-third of the world’s population lives (Mortimore 2009). The ecological environment of drylands is relatively fragile and extremely sensitive to climate change (Reed et al. 2012; Reynolds et al. 2007; Safriel 2009). In the context of global warming, extreme weather events have increased, and dryland ecosystems have encountered greater challenges and uncertainties (Reed et al. 2012; Reynolds et al. 2007; Safriel 2009; Guan et al. 2015; Huang et al. 2012). Understanding climate change and impact drivers of drylands is of great significance for the planning and achieving sustainable development.
Recent studies have revealed that the degree of climate warming in drylands is higher than the global average (Huang et al. 2012, 2017c; Guan et al. 2015; Ji et al. 2014). Drylands are expected to be more responsive to climate warming than humid lands—for example, about 44% more warming over drylands than humid lands when global warming reaches 2°C (Huang et al. 2017c). As the climate warms, drylands have expanded and would continue to expand in the twenty-first century (Feng and Fu 2013; Huang et al. 2016b; Koutroulis 2019). Dryland expansion may enhance regional warming and lead to land desertification and water scarcity, which will threaten the economy, society, and ecosystems of drylands (Huang et al. 2017b; Armah et al. 2011; Ohmura and Wild 2002; Schlaepfer et al. 2017). Enhanced climate warming, coupled with anthropogenic activities and fragile dryland ecosystems, can make the impact mechanism of dryland more complex and uncertain. Many earlier studies on the causes of climate change in drylands focused on the effects of human activities [e.g., the increased greenhouse gas emissions (Huang et al. 2017b; Lin et al. 2018; Rotenberg and Yakir 2010) and anthropogenic aerosols (Zhao et al. 2015; Huang et al. 2014)] and natural factors [e.g., El Niño–Southern Oscillation (ENSO) (Mullan 1995; Andreoli and Kayano 2005; Dong and Dai 2015; Wang et al. 2014) and sea surface temperatures (SSTs) (Bader and Latif 2003; Hoerling et al. 2006; Lu 2009; Zhao and Zhang 2016; Li et al. 2010)].
While aridity change impacts on global or regional drylands have been discussed in previous studies (Hulme 1996; Ji et al. 2015; Li et al. 2015; Zhao et al. 2014; Yin et al. 2019; Huang et al. 2017a, 2016a, 2019; Zhang et al. 2016; Pour et al. 2020), most of them were about the dryland expansion induced by a warming climate, or climate change in the specific subtypes of drylands, that is, semiarid region (Yin et al. 2019; Huang et al. 2016a, 2019; Zhang et al. 2016; Huang et al. 2012). In addition, some studies have explored the driving climate factors of dry and wet changes on a global or regional scale (Park et al. 2017; Liu et al. 2019; Lickley and Solomon 2018; Feng and Fu 2013; Li et al. 2019). Few studies emphasized the driving factors of dryland aridity changes.
In this study, an attribution analysis of the aridity changes of drylands in China was performed from the perspective of climate drivers. The main research objectives are to 1) reveal the aridity changes of drylands and its major climatic factors, 2) determine the sensitivity of dryland aridity to various climatic factors, and 3) quantify the contribution of the climatic drivers to the aridity changes of drylands. The research results are expected to provide a scientific reference for climate change adaptation in drylands, dryland planning and sustainable development.
2. Materials and methods
a. Materials
Drylands cover nearly one-half of the land surface in China. They are located in the arid and low rain areas in northern China, where water scarcity limits crop production. In general, the annual average precipitation in these areas is about 361 mm and can get below 15 mm in some extremely dry areas.
Meteorological observations including monthly precipitation, relative humidity, sunshine duration, air temperature (average, maximum, and minimum), and wind speed during the period 1960–2019 were obtained from the China Meteorological Administration. Quality control on all data sessions was performed; that is, the stations with observation data less than 11 months per year or less than 15 years total for the study period (1960–2019) were eliminated, and the missing values of other stations were filled by linear regression equations using the data of nearby stations. Last, 272 meteorological stations (Fig. 1) with high-quality monthly observations were selected to participate in the calculation. In addition, there was a spatial heterogeneity in the distribution of meteorological stations, and the inverse-distance-weighted (IDW) interpolation method was adopted to obtain the spatial distribution map.
Distributions of meteorological observation stations and dryland subtypes derived from the aridity index calculated using meteorological observations during 1960–2019 in China.
Citation: Journal of Applied Meteorology and Climatology 60, 4; 10.1175/JAMC-D-20-0209.1
b. Methods
1) Aridity index
According to Eq. (1), a higher AI value indicates a more humid climate. Thus, AI is also called the humidity index (Hulme et al. 1992). Areas with AI ≤ 0.65 are classified as drylands. These drylands can be further categorized into hyperarid (AI ≤ 0.05), arid (0.05 < AI ≤0.2), semiarid (0.2 < AI ≤ 0.5), and dry subhumid (0.5 < AI ≤ 0.65) regions (Middleton and Thomas 1992). Following AI, the spatial distributions of dryland subtypes in China are displayed in Fig. 1. During the period 1960–2019, four dryland subtypes, that is, hyperarid, arid, semiarid, and dry subhumid, occupied 7.1%, 35.6%, 38.9%, and 18.4% of total drylands in China, respectively (Fig. 1).
2) Trend analysis
In this study, Sen’s slope estimator (Sen 1968) was used to evaluate the change trends of the AI and other climate factors. To test whether the change trends were significant, a nonparametric Mann–Kendall test (Mann 1945; Kendall 1975) was performed. The two methods have been widely used in climatological and hydrometeorological time series (Partal and Kahya 2006; ElNesr et al. 2010; Tabari and Marofi 2011; Gocic and Trajkovic 2013).
In this study, two significance levels (i.e., 0.01 and 0.05) were used. The null hypothesis of no trend is rejected if |ZR| > 1.96 at the 0.05 significance level and is rejected if |ZR| > 2.576 at the 0.01 significance level. Using the above methods, the interannual trends of 272 stations were calculated. In addition, when assessing the overall change trend of the region, the average value of each climate factor in the region every year was calculated first, and then the trend analysis on these average values was performed to evaluate the overall situation of the region.
3) Sensitivity and contribution analysis
The annual AI was recalculated using Eqs. (1) and (2) by decreasing by −30%, −25%, −20%, −15%, −10%, and −5% and increasing by 5%, 10%, 15%, 20%, 25%, and 30% of the initial value for one factor while other climate factors remained constant.
The sensitivity was calculated according to Eq. (10) for 12 scenarios recalculated above.
The sensitivity of 12 scenarios was averaged to get the sensitivity coefficient for AI to the climate factor.
The above three steps were repeated for each meteorological station to calculate the sensitivity of AI to P, RH, SSD, Tmax, Tmin, and WS (i.e., SP, SRH, SSSD, STmax, STmin, and SWS), and then regional mean sensitivity coefficients were calculated on the basis of the mean of all stations in the region.
3. Results
a. Aridity changes over drylands
In the past 60 years, the drylands as a whole showed a weak trend of wetting in China (Fig. 2a). The annual AI change trends were different in dryland subtypes. The regional mean AI in hyperarid and arid regions increased significantly at 0.003 and 0.004 decade−1, respectively, indicating that they were getting wetter (Figs. 2b,c). However, the regional mean AI did not change significantly in semiarid region and increased at a very weak rate of 0.000 08 decade−1 in dry subhumid region (Figs. 2d,e). Taking 104°E as the boundary in China, the climate in the west of the drylands basically showed wetting trends, with an increasing AI. Nonetheless, most areas of the eastern drylands exhibited drying trends (Fig. 3a). Over the period 1960–2019, the average of relative changes of annual AI for all drylands was very small, only −0.91%. By contrast, the average of relative changes of annual AI for hyperarid, arid, semiarid, and dry subhumid region were 44.14%, 20.88%, 1.87%, and 0.79%, respectively (Fig. 3b).
Temporal variation of the regional mean AI based on meteorological observation stations in different regions during 1960–2019: (a) drylands region, and its subtypes: (b) hyperarid, (c) arid, (d) semiarid, and (e) dry subhumid regions. A 15-yr running smoothing (blue curves) is applied to emphasize climate change.
Citation: Journal of Applied Meteorology and Climatology 60, 4; 10.1175/JAMC-D-20-0209.1
(a) Spatial distributions of relative changes in annual AI, and (b) relative changes of regional mean AI in drylands during 1960–2019.
Citation: Journal of Applied Meteorology and Climatology 60, 4; 10.1175/JAMC-D-20-0209.1
b. Changes in climatic factors
During 1960–2019, in drylands of China, the annual P exhibited an insignificant decreasing trend (i.e., −0.03 mm decade−1), the annual PET showed an insignificant increasing trend at 1.62 mm decade−1, and other climatic factors trended significantly (see Table 2). Specifically, RH, SSD, and WS decreased by 0.54% decade−1, 0.09 h decade−1, and 0.08 m s−1 decade−1, respectively, and Tmax and Tmin increased at 0.28 and 0.43°C decade−1, respectively. The change trends varied in four dryland subtypes. The annual P showed a significant increasing trend in hyperarid and arid regions but increased insignificantly in the semiarid region (2.91 mm decade−1) and decreased in the dry subhumid region (−0.76 mm decade−1). The annual PET exhibited a significant decreasing trend in the hyperarid region (−7.96 mm decade−1) but increased insignificantly in arid, semiarid, and dry subhumid regions. The annual RH and WS trended downward in these four dryland regions. The annual SSD showed a weak increasing trend at a rate of 0.01 h decade−1 in hyperarid region, decreased insignificantly in arid region (−0.02 h decade−1), and decreased significantly in semiarid and dry subhumid regions at 0.09 and 0.12 h decade−1, respectively. The annual Tmin in hyperarid, arid, semiarid, and dry subhumid regions increased significantly by 0.23, 0.40, 0.44, and 0.44°C decade−1, respectively. Except for a weak increasing trend in hyperarid region, Tmax increased significantly in other subtypes of drylands.
Change trends of regional mean climatic factors during 1960–2019 using the method of Sen’s slope estimator and Mann–Kendall test. Significant values with p < 0 0.05 and p < 0 0.01 are denoted with one and two asterisks, respectively.
The spatial distributions of change trends of various climatic factors in the past 60 years are presented in Fig. 4. The trend of annual P all over the drylands in China was mainly in the range of −20–50 mm·decade−1, and most areas trended upward; the downward trend of annual P was mainly distributed in the east of 100°E. The trend of annual PET was mainly in the range of −50–20 mm·decade−1, and the areas where PET trended downward were concentrated in the hyperarid and arid regions located in northwest China, and the eastern part of dry subhumid region. The RH trended downward in most areas (from 0% to −2.0%·decade−1), and the rising RH was basically distributed in the northwest. The SSD trended downward in most regions (as low as −1.15 h decade−1), and upward in the central and western regions. Tmax and Tmin trended upward in most areas, and the overall increase of Tmin was greater than Tmax. In the past 60 years, WS decreased significantly over most of the drylands, except for a few small sporadic area
Spatial distributions of the trends in the annual (a) P, (b) PET, (c) RH, (d) SSD, (e) Tmax, (f) Tmin, and (g) WS in drylands of China during 1960–2019.
Citation: Journal of Applied Meteorology and Climatology 60, 4; 10.1175/JAMC-D-20-0209.1
c. Sensitivity of aridity to climatic factors
According to the definition of AI and PET, the climatic factors that directly affect the humidity index mainly include P, RH, SSD, Tmax, Tmin, and WS. The definition of the sensitivity coefficient indicates that the sensitivity coefficient of AI to P is 1; that is, a 1% change in P will lead to a corresponding change of 1% in the AI. The spatial distributions of the sensitivity of AI to other climatic factors during 1960–2019 are shown in Fig. 5. In most areas of drylands, the sensitivity levels of RH, SSD, and Tmax were high. The sensitivity coefficient of WS varied from −0.39 to 0.05 over the drylands, and there were mainly two sensitivity grades, namely medium and high, showing a north–south distribution. However, the sensitivity coefficient of AI to Tmin was either small or negligible in most areas.
Spatial distributions of sensitivity coefficients of various climatic factors to aridity in drylands during 1960–2019: (a) RH, (b) SSD, (c) Tmax, (d) Tmin, and (e) WS.
Citation: Journal of Applied Meteorology and Climatology 60, 4; 10.1175/JAMC-D-20-0209.1
During 1960–2019, the sensitivity of AI to both P and RH was positive (Table 3), indicating that AI would change in accordance with P and RH. Its negative sensitivity to SSD, Tmax, Tmin, and WS indicates that AI would decrease (increase) with increase (decrease) in SSD, Tmax, Tmin, and WS. Averaged in the drylands of China, except for P, the RH had the highest sensitivity (0.39; high sensitivity), followed by Tmax (−0.38; high sensitivity), SSD (−0.25; high sensitivity), WS (−0.20; medium sensitivity), and Tmin (−0.05; small to negligible). However, the sensitivity of AI to climatic factors was a little different in four dryland subtypes. In the semiarid and dry subhumid regions, AI was most sensitive to changes in the water factors (i.e., P and RH), followed by the thermal factors (i.e., Tmax and SSD). But in the hyperarid and arid regions, AI was more sensitivity to Tmax than RH.
Regional mean sensitivity coefficients of aridity with climatic factors based on meteorological observation stations in drylands during 1960–2019.
d. Contribution analysis
The sensitivity of Tmin to AI was small to negligible in drylands, that is, changes in Tmin would hardly affect AI. Therefore, its contribution to AI was not calculated in this study. To verify the reliability of the contribution analysis, according to Eq. (11), the relative changes of AI calculated using the PM equation (RCAI), and the sum of the contributions of P, Ta, RH, SSD, Tmax, and WS to RCAI were compared at 272 stations during 1960–2019 (Fig. 6). The comparison showed that the sum of the contributions fit well with the RCAI (R2 = 0.98; RMSE = 4.3%). This indicates that the decomposition and attribution method can reasonably quantify the contributions of various climatic factors to AI changes.
Comparison between the relative changes of AI calculated using the PM equation (RCAI) and the sum of the contributions of P, Ta, RH, SSD, Tmax, and WS to RCAI (i.e., ConP + ConRH + ConSSD + ConTmax + ConWS) according to Eqs. (11) and (12) based on 272 meteorological observation stations in drylands of China during 1960–2019.
Citation: Journal of Applied Meteorology and Climatology 60, 4; 10.1175/JAMC-D-20-0209.1
The relative contribution of P to AI was around −10%–50% over the drylands in 1960–2019 (Fig. 7). The increasing P contributed positively to the AI changes in most areas throughout the drylands, especially in the northwest, and the declining P contributed negatively to the AI changes mainly distributed in the east of 104°E. Decreasing RH decreased the AI over most of the drylands in China (ranged over −5%–0%). While the decreasing SSD increased the AI in most areas and increasing SSD contributed negatively, the AI changes mainly distributed in the west of drylands. The relative contribution of SSD to AI was mainly concentrated in −2%–5%. The increasing Tmax contributed negatively to the AI changes over most of the drylands in China (ranged over −10%–0%). The decreasing WS contributed positively to the AI changes over most of the drylands in China, and the relative contribution of WS to AI varied from 0% to 10%.
Spatial distributions of the relative contributions from (a) P, (b) RH, (c) SSD, (d) Tmax, and (e) WS to the changes in AI in drylands of China during 1960–2019.
Citation: Journal of Applied Meteorology and Climatology 60, 4; 10.1175/JAMC-D-20-0209.1
Averaged over the whole drylands, the relative contribution of declining WS to AI changes was the largest (4.8%), followed by Tmax (−4.4%) during 1960–2019 (Fig. 8). However, the relative contributions of climatic factors differed in four dryland types. The dominant climate driver contributing to the AI change over 1960–2019 was P in hyperarid and arid regions, followed by WS. In the semiarid region, the WS (4.7%) and Tmax (−4.8%) contributed the most to AI changes, followed by P (4.6%). In the dry subhumid region, the WS (4.5%) and Tmax (−4.9%) contributed the most to AI changes, while the relative contribution of P was the least (−0.8%).
Relative contributions of the P, RH, SSD, Tmax, and WS to the AI changes in drylands during 1960–2019.
Citation: Journal of Applied Meteorology and Climatology 60, 4; 10.1175/JAMC-D-20-0209.1
4. Discussion
Previous studies have reported that in the context of global warming, the drylands in China have expanded and shown a drying trend (Ma and Fu 2006; Dai 2013; Dai and Zhao 2017; Li et al. 2015). While the change in AI was not obvious, the drylands in China exhibited a weak drying trend over the period 1960–2019 (Figs. 2 and 3). This study is based on the data of meteorological observation stations. The spatial distributions of the AI value and its change trends in drylands of China based on the stations using the IDW interpolation method are consistent with the results of other grid datasets, such as CRU (Asadi Zarch et al. 2015), CPC, and GLDAS (Huang et al. 2016b; Li et al. 2015), and the overall performance is that the western part of the drylands in China mainly shows a trend of wetting, while the central and eastern part mainly exhibits a drying trend. Being the most extensive part of the drylands, the semiarid region experienced a little change in regional mean AI during 1960–2019 (Fig. 3b), but it showed a clear drying trend in its northeast area and a large-scale humidification trend in its southwest (Fig. 3a), which is consistent with previous studies (Yin et al. 2019; Huang et al. 2016a, 2019).
Furthermore, many studies have revealed that the arid regions of northwest China were significantly wetted in recent decades (Zhao et al. 2019; Li et al. 2015; Liu et al. 2013; Liu et al. 2019; Liu et al. 2018; Li et al. 2019). Consistent with these earlier works, the results of this study suggested that the hyperarid and arid regions located in northwest China showed an obvious wetting trend during 1960–2019, and the increasing trend of P was more significant than the increasing or decreasing trend of PET (Fig. 3 and Table 2). And precipitation had a relatively greater impact on AI in China’s drylands, compared with other factors, for example, from the perspective of the values of 272 meteorological observation stations, and the relative contribution of P was mainly concentrated from −10% to 50%, which was significantly greater than other factors (Fig. 7). Especially in the hyperarid and arid regions of drylands, the significantly increased P dominated the increase in AI, followed by WS, which is consistent with the findings of previous studies that increasing precipitation contributed most to the wetting trend in arid northwest China (Liu et al. 2019; Liu et al. 2018; Liu et al. 2013; Li et al. 2019). However, the results of this study also indicated that WS and Tmax contributed more to the aridity changes than P in semiarid and dry subhumid regions of drylands (Table 2, Fig. 8). Previous studies have emphasized the dominant influence of precipitation and temperature on global or regional dry–wet changes based on different data sources, such as historical observations from meteorological stations (Sun et al. 2016; Park et al. 2017; Liu et al. 2018), gridded datasets (Li et al. 2015; Ramarao et al. 2019; Sheffield et al. 2012), and future scenario data (Lickley and Solomon 2018; Feng and Fu 2013). However, results of this study showed that in drylands over China, beyond precipitation and temperature, wind speed also had a great impact on dryland aridity changes in recent decades, thus the influence of wind speed cannot be underestimated.
In general, in the past 60 years, AI was most sensitive to changes in water factors (P and RH), followed by thermal factors (SSD and Tmax), and dynamic factor (WS). The influence of water factors had a positive effect on AI changes, while thermal and dynamic factors mostly had negative effects on AI changes in drylands over China. It is worth noting that in drylands, the sensitivity of AI to Tmin was very weak and can be ignored during 1960–2019. Moreover, the sensitivity of AI to climatic factors varied among subtypes of drylands. For example, compared with the semiarid and dry subhumid regions, AI had a higher sensitivity to Tmax than RH in the hyperarid and arid regions (Table 3). This also indicated the necessity and importance of regional research. Results of this study also showed that the aridity changes had obvious regional differences during 1960–2019. And the climate in the west of the drylands basically shifted to wetter conditions, while most areas of the eastern drylands exhibited drying trends (Fig. 3a), which was similar to the spatial distribution of the change trends of P (Fig. 4a). In the hyperarid and arid regions, the significantly reduced RH and the weak increase Tmax had a small negative contribution to the increase of AI, while the significantly increased P and reduced WS led to a significant increase in AI, thus the hyperarid and arid regions eventually showed a significant tendency to get wet. In the semiarid region, the insignificantly increased P, significantly reduced WS and significantly reduced SSD contributed positively to an increase in AI, but this positive effect had been partly offset by the significantly reduced RH and significantly increased Tmax, thus the AI in this area showed an insignificant increasing trend. In the dry subhumid region, the change trend of AI was small or even negligible. This is mainly because the positive contributions of the significantly reduced WS and significantly reduced SSD to AI had been basically offset by the negative contributions of the insignificantly decreased P, the significantly reduced RH and significantly increased Tmax to AI.
In recent decades, the enhanced climate warming, coupled with the impact of human activities, has increased the occurrence of drought events and aggravated land degradation in drylands (Prăvălie 2016). The development and management of drylands are expected to encounter greater uncertainties and challenges. The knowledge of aridity changes and its driving factors of drylands can provide important references for drylands to adapt to climate change, formulate planning policies, and achieve sustainable development. This will also be the research direction and goal for a long time in the future.
5. Conclusions
In this study, the contributions of major climatic factors to the aridity changes in drylands over China were decomposed and quantified. Here AI was used to evaluate the aridity changes in drylands. Over the past 60 years, the entire drylands showed a weak but wetting trend, and the aridity changes varied in the four drylands subtypes. The regional mean AI exhibited a significant increase trend in the hyperarid and arid regions, indicating an obvious climate wetting. However, the semiarid region showed a weak wetting trend, and the dry subhumid region experienced a drying trend. Average over drylands, the change trend of P was weak, but RH, WS, and SSD significantly reduced, and the temperature factors (i.e., Tmax and Tmin) significantly increased. From the sensitivity coefficient, the sensitivity level of AI to different climatic factors was determined. By averaging across all drylands in China, AI had the highest sensitivity to P and RH, followed by Tmax, SSD and WS, and the lowest sensitivity to Tmin (about −0.05, small to negligible). Based on the sensitivity analysis, combined with the relative changes of climatic drivers, the relative contributions of key climatic drivers to AI were quantified. Results showed that the significantly increased P dominated the increase of AI in the hyperarid and arid regions. While in the semiarid and dry subhumid regions, WS contributed more to AI change than P in the past 60 years. These results could provide a scientific reference for understanding the impact mechanism of climate change in drylands, formulating climate adaptation strategies, and supporting dryland sustainable development.
Acknowledgments
This work was supported by several grants from the National Key R&D Program of China (Grant 2019YFB2102003), the National Natural Science Foundation of China (Grants 41805049, 41805083), and the Graduate Research and Innovation Projects of Jiangsu Province (Grant SJKY19_0949).
REFERENCES
Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop evapotranspiration: Guidelines for computing crop requirements. FAO Irrigation and Drainage Paper 56, 300 pp., http://www.fao.org/3/X0490E/X0490E00.htm.
Andreoli, R. V., and M. T. Kayano, 2005: ENSO-related rainfall anomalies in South America and associated circulation features during warm and cold Pacific decadal oscillation regimes. Int. J. Climatol., 25, 2017–2030, https://doi.org/10.1002/joc.1222.
Armah, F. A., J. O. Odoi, G. T. Yengoh, S. Obiri, D. O. Yawson, and E. K. A. Afrifa, 2011: Food security and climate change in drought-sensitive savanna zones of Ghana. Mitig. Adapt. Strategies Global Change, 16, 291–306, https://doi.org/10.1007/s11027-010-9263-9.
Asadi Zarch, M. A., B. Sivakumar, and A. Sharma, 2015: Assessment of global aridity change. J. Hydrol., 520, 300–313, https://doi.org/10.1016/j.jhydrol.2014.11.033.
Bader, J., and M. Latif, 2003: The impact of decadal-scale Indian Ocean sea surface temperature anomalies on Sahelian rainfall and the North Atlantic Oscillation. Geophys. Res. Lett., 30, 2169, https://doi.org/10.1029/2003GL018426.
Beven, K., 1979: A sensitivity analysis of the Penman–Monteith actual evapotranspiration estimates. J. Hydrol., 44, 169–190, https://doi.org/10.1016/0022-1694(79)90130-6.
Dai, A., 2013: Increasing drought under global warming in observations and models. Nat. Climate Change, 3, 52–58, https://doi.org/10.1038/nclimate1633.
Dai, A., and T. Zhao, 2017: Uncertainties in historical changes and future projections of drought. Part I: Estimates of historical drought changes. Climatic Change, 144, 519–533, https://doi.org/10.1007/s10584-016-1705-2.
Dong, B., and A. Dai, 2015: The influence of the interdecadal Pacific oscillation on temperature and precipitation over the globe. Climate Dyn., 45, 2667–2681, https://doi.org/10.1007/s00382-015-2500-x.
ElNesr, M., M. Abu-Zreig, and A. Alazba, 2010: Temperature trends and distribution in the Arabian Peninsula. Amer. J. Environ. Sci., 6, 191–203, https://doi.org/10.3844/ajessp.2010.191.203.
Feng, S., and Q. Fu, 2013: Expansion of global drylands under a warming climate. Atmos. Chem. Phys., 13, 10 081–10 094, https://doi.org/10.5194/acp-13-10081-2013.
Gao, Y., X. Li, L. Ruby Leung, D. Chen, and J. Xu, 2015: Aridity changes in the Tibetan Plateau in a warming climate. Environ. Res. Lett., 10, 034013, https://doi.org/10.1088/1748-9326/10/3/034013.
Gocic, M., and S. Trajkovic, 2013: Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Global Planet. Change, 100, 172–182, https://doi.org/10.1016/j.gloplacha.2012.10.014.
Gong, L., C.-Y. Xu, D. Chen, S. Halldin, and Y. D. Chen, 2006: Sensitivity of the Penman–Monteith reference evapotranspiration to key climatic variables in the Changjiang (Yangtze River) basin. J. Hydrol., 329, 620–629, https://doi.org/10.1016/j.jhydrol.2006.03.027.
Guan, X., J. Huang, R. Guo, H. Yu, P. Lin, and Y. Zhang, 2015: Role of radiatively forced temperature changes in enhanced semi-arid warming in the cold season over East Asia. Atmos. Chem. Phys., 15, 13 777–13 786, https://doi.org/10.5194/acp-15-13777-2015.
Hoerling, M., J. Hurrell, J. Eischeid, and A. Phillips, 2006: Detection and attribution of twentieth-century northern and southern African rainfall change. J. Climate, 19, 3989–4008, https://doi.org/10.1175/JCLI3842.1.
Huang, J., X. Guan, and F. Ji, 2012: Enhanced cold-season warming in semi-arid regions. Atmos. Chem. Phys., 12, 5391–5398, https://doi.org/10.5194/acp-12-5391-2012.
Huang, J., T. Wang, W. Wang, Z. Li, and H. Yan, 2014: Climate effects of dust aerosols over East Asian arid and semiarid regions. J. Geophys. Res. Atmos., 119, 11 398–11 416, https://doi.org/10.1002/2014JD021796.
Huang, J., M. Ji, Y. Xie, S. Wang, Y. He, and J. Ran, 2016a: Global semi-arid climate change over last 60 years. Climate Dyn., 46, 1131–1150, https://doi.org/10.1007/s00382-015-2636-8.
Huang, J., H. Yu, X. Guan, G. Wang, and R. Guo, 2016b: Accelerated dryland expansion under climate change. Nat. Climate Change, 6, 166–171, https://doi.org/10.1038/nclimate2837.
Huang, J., and Coauthors, 2017a: Dryland climate change: Recent progress and challenges. Rev. Geophys., 55, 719–778, https://doi.org/10.1002/2016RG000550.
Huang, J., Y. Xie, X. Guan, D. Li, and F. Ji, 2017b: The dynamics of the warming hiatus over the Northern Hemisphere. Climate Dyn., 48, 429–446, https://doi.org/10.1007/s00382-016-3085-8.
Huang, J., H. Yu, A. Dai, Y. Wei, and L. Kang, 2017c: Drylands face potential threat under 2°C global warming target. Nat. Climate Change, 7, 417–422, https://doi.org/10.1038/nclimate3275.
Huang, J., J. Ma, X. Guan, Y. Li, and Y. He, 2019: Progress in semi-arid climate change studies in China. Adv. Atmos. Sci., 36, 922–937, https://doi.org/10.1007/s00376-018-8200-9.
Hulme, M., 1996: Recent climatic change in the world’s drylands. Geophys. Res. Lett., 23, 61–64, https://doi.org/10.1029/95GL03586.
Hulme, M., R. Marsh, and P. D. Jones, 1992: Global changes in a humidity index between 1931-60 and 1961-90. Climate Res., 2, 1–22, https://doi.org/10.3354/cr002001.
Hupet, F., and M. Vanclooster, 2001: Effect of the sampling frequency of meteorological variables on the estimation of the reference evapotranspiration. J. Hydrol., 243, 192–204, https://doi.org/10.1016/S0022-1694(00)00413-3.
Ji, F., Z. Wu, J. Huang, and E. P. Chassignet, 2014: Evolution of land surface air temperature trend. Nat. Climate Change, 4, 462–466, https://doi.org/10.1038/nclimate2223.
Ji, M., J. Huang, Y. Xie, and J. Liu, 2015: Comparison of dryland climate change in observations and CMIP5 simulations. Adv. Atmos. Sci., 32, 1565–1574, https://doi.org/10.1007/s00376-015-4267-8.
Kendall, M. G., 1975: Rank Correlation Methods. Griffin, 202 pp.
Koutroulis, A. G., 2019: Dryland changes under different levels of global warming. Sci. Total Environ., 655, 482–511, https://doi.org/10.1016/j.scitotenv.2018.11.215.
Li, H., A. Dai, T. Zhou, and J. Lu, 2010: Responses of East Asian summer monsoon to historical SST and atmospheric forcing during 1950-2000. Climate Dyn., 34, 501–514, https://doi.org/10.1007/s00382-008-0482-7.
Li, Y., J. Huang, M. Ji, and J. Ran, 2015: Dryland expansion in northern China from 1948 to 2008. Adv. Atmos. Sci., 32, 870–876, https://doi.org/10.1007/s00376-014-4106-3.
Li, Y., W. Yu, K. Wang, and X. Ma, 2019: Comparison of the aridity index and its drivers in eight climatic regions in China in recent years and in future projections. Int. J. Climatol., 39, 5256–5272, https://doi.org/10.1002/joc.6137.
Lickley, M., and S. Solomon, 2018: Drivers, timing and some impacts of global aridity change. Environ. Res. Lett., 13, 104010, https://doi.org/10.1088/1748-9326/aae013.
Lin, L., A. Gettelman, Q. Fu, and Y. Xu, 2018: Simulated differences in 21st century aridity due to different scenarios of greenhouse gases and aerosols. Climatic Change, 146, 407–422, https://doi.org/10.1007/s10584-016-1615-3.
Liu, C., W. Huang, S. Feng, J. Chen, and A. Zhou, 2018: Spatiotemporal variations of aridity in China during 1961–2015: Decomposition and attribution. Sci. Bull., 63, 1187–1199, https://doi.org/10.1016/j.scib.2018.07.007.
Liu, L., Y. Wang, N. You, Z. Liang, D. Qin, and S. Li, 2019: Changes in aridity and its driving factors in China during 1961–2016. Int. J. Climatol., 39, 50–60, https://doi.org/10.1002/joc.5781.
Liu, X., D. Zhang, Y. Luo, and C. Liu, 2013: Spatial and temporal changes in aridity index in northwest China: 1960 to 2010. Theor. Appl. Climatol., 112, 307–316, https://doi.org/10.1007/s00704-012-0734-7.
Lu, J., 2009: The dynamics of the Indian Ocean sea surface temperature forcing of Sahel drought. Climate Dyn., 33, 445–460, https://doi.org/10.1007/s00382-009-0596-6.
Ma, Z., and C. Fu, 2006: Some evidence of drying trend over northern China from 1951 to 2004. Chin. Sci. Bull., 51, 2913–2925, https://doi.org/10.1007/s11434-006-2159-0.
Mann, H. B., 1945: Nonparametric tests against trend. Econometrica, 13, 245–259, https://doi.org/10.2307/1907187.
McCuen, R. H., 1974: A sensitivity and error analysis CF procedures used for estimating evaporation. J. Amer. Water Resour. Assoc., 10, 486–497, https://doi.org/10.1111/j.1752-1688.1974.tb00590.x.
Middleton, N. J., and D. S. G. Thomas, 1992: World Atlas of Desertification. United Nations Environment Programme, Edward Arnold, 69 pp.book
Mortimore, M., 2009: Dryland Opportunities: A New Paradigm for People, Ecosystems and Development. Global Drylands Imperative, 86 pp.
Mullan, A. B., 1995: On the linearity and stability of Southern Oscillation–climate relationships for New Zealand. Int. J. Climatol., 15, 1365–1386, https://doi.org/10.1002/joc.3370151205.
Ohmura, A., and M. Wild, 2002: Climate change: Is the hydrological cycle accelerating? Science, 298, 1345–1346, https://doi.org/10.1126/science.1078972.
Park, C. E., S. J. Jeong, C. H. Ho, H. Park, S. Piao, J. Kim, and S. Feng, 2017: Dominance of climate warming effects on recent drying trends over wet monsoon regions. Atmos. Chem. Phys., 17, 10 467–10 476, https://doi.org/10.5194/acp-17-10467-2017.
Partal, T., and E. Kahya, 2006: Trend analysis in Turkish precipitation data. Hydrol. Processes, 20, 2011–2026, https://doi.org/10.1002/hyp.5993.
Pour, S. H., A. K. A. Wahab, and S. Shahid, 2020: Spatiotemporal changes in aridity and the shift of drylands in Iran. Atmos. Res., 233, 104704, https://doi.org/10.1016/j.atmosres.2019.104704.
Prăvălie, R., 2016: Drylands extent and environmental issues. A global approach. Earth-Sci. Rev., 161, 259–278, https://doi.org/10.1016/j.earscirev.2016.08.003.
Ramarao, M. V. S., J. Sanjay, R. Krishnan, M. Mujumdar, A. Bazaz, and A. Revi, 2019: On observed aridity changes over the semiarid regions of India in a warming climate. Theor. Appl. Climatol., 136, 693–702, https://doi.org/10.1007/s00704-018-2513-6.
Reed, S. C., K. K. Coe, J. P. Sparks, D. C. Housman, T. J. Zelikova, and J. Belnap, 2012: Changes to dryland rainfall result in rapid moss mortality and altered soil fertility. Nat. Climate Change, 2, 752–755, https://doi.org/10.1038/nclimate1596.
Reynolds, J. F., and Coauthors, 2007: Global desertification: Building a science for dryland development. Science, 316, 847–851, https://doi.org/10.1126/science.1131634.
Rotenberg, E., and D. Yakir, 2010: Contribution of semi-arid forests to the climate system. Science, 327, 451–454, https://doi.org/10.1126/science.1179998.
Safriel, U., 2009: Deserts and desertification: Challenges but also opportunities. Land Degrad. Dev., 20, 353–366, https://doi.org/10.1002/ldr.935.
Schlaepfer, D. R., and Coauthors, 2017: Climate change reduces extent of temperate drylands and intensifies drought in deep soils. Nat. Commun., 8, 14196, https://doi.org/10.1038/ncomms14196.
Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall’s tau. J. Amer. Stat. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934.
Sheffield, J., E. F. Wood, and M. L. Roderick, 2012: Little change in global drought over the past 60 years. Nature, 491, 435–438, https://doi.org/10.1038/nature11575.
Sun, S., and Coauthors, 2016: Shift in potential evapotranspiration and its implications for dryness/wetness over southwest China. J. Geophys. Res. Atmos., 121, 9342–9355, https://doi.org/10.1002/2016JD025276.
Tabari, H., and S. Marofi, 2011: Changes of Pan evaporation in the west of Iran. Water Resour. Manage., 25, 97–111, https://doi.org/10.1007/s11269-010-9689-6.
Wang, S., J. Huang, Y. He, and Y. Guan, 2014: Combined effects of the Pacific decadal oscillation and El Niño-Southern Oscillation on global land dry–wet changes. Sci. Rep., 4, 6651, https://doi.org/10.1038/SREP06651.
Yin, Y., S. Wu, G. Chen, and E. Dai, 2010: Attribution analyses of potential evapotranspiration changes in China since the 1960s. Theor. Appl. Climatol., 101, 19–28, https://doi.org/10.1007/s00704-009-0197-7.
Yin, Y., D. Ma, and S. Wu, 2019: Enlargement of the semi-arid region in China from 1961 to 2010. Climate Dyn., 52, 509–521, https://doi.org/10.1007/s00382-018-4139-x.
Zhang, H., Q. Zhang, P. Yue, L. Zhang, Q. Liu, S. Qiao, and P. Yan, 2016: Aridity over a semiarid zone in northern China and responses to the East Asian summer monsoon. J. Geophys. Res. Atmos., 121, 13 901–13 918, https://doi.org/10.1002/2016JD025261.
Zhao, H., X. Pan, Z. Wang, S. Jiang, L. Liang, X. Wang, and X. Wang, 2019: What were the changing trends of the seasonal and annual aridity indexes in northwestern China during 1961–2015? Atmos. Res., 222, 154–162, https://doi.org/10.1016/j.atmosres.2019.02.012.
Zhao, S., H. Zhang, S. Feng, and Q. Fu, 2015: Simulating direct effects of dust aerosol on arid and semi-arid regions using an aerosol-climate coupled system. Int. J. Climatol., 35, 1858–1866, https://doi.org/10.1002/joc.4093.
Zhao, T., L. Chen, and Z. Ma, 2014: Simulation of historical and projected climate change in arid and semiarid areas by CMIP5 models. Chin. Sci. Bull., 59, 412–429, https://doi.org/10.1007/s11434-013-0003-x.
Zhao, Y., and H. Zhang, 2016: Impacts of SST warming in tropical Indian Ocean on CMIP5 model-projected summer rainfall changes over central Asia. Climate Dyn., 46, 3223–3238, https://doi.org/10.1007/s00382-015-2765-0.
