Does elevation dependent warming exist in high mountain Asia?

In this paper, we used the annual air temperature during the period from 1961 to 2017 from these 128 stations to analyze the temperature change in HMA (figure 1). All the meteorological stations selected for this study had been maintained for following the standard of the China Meteorological Information Center. The standard requires strict quality control processes including extreme inspection, consistency check, and others before releasing these data (Li et al 2012).

Mann–Kendall statistical test (Mann 1945, Kendall 1975) was employed to test the significances of trend in the annual mean temperatures of HMA. The nonparametric Mann–Kendall statistical test has been commonly used to assess the significance of monotonic trends in meteorological series (Li et al 2012, Chen et al 2014).

To reveal the relationship between temperatures at different altitudes, we calculated the Pearson's correlation coefficient between them before and after 1985. The correlation coefficient (r) is usually calculated as follows:

$$\begin{eqnarray}&&r=\displaystyle \frac{\sum \left({x}_{i}-\overline{x}\right)\left({y}_{i}-\overline{y}\right)}{\sqrt{\sum }}.\end{eqnarray} \tag{ 1 }$$

Considering the relationship between the trend of temperature and elevation, we calculate Pearson's correlation coefficient (r) to detect the association between them.

The Mann–Kendall statistical test showed a significant rising trend in the air temperature in HMA (figure 2(a)) from 1961 to 2017, with a rate of 0.323 °C/decade (figure 2(b), P < 0.001). A significant rising trend (P < 0.001) was found in four different areas. Compared with SETP (0.269 °C/decade) and TSM (0.308 °C/decade), there were higher changing rates in the NTP (0.381 °C/decade) and SWTP (0.36 °C/decade). Additionally, only two weather stations have a downward trend in temperature from 1961 to 2017.

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Figure 3(b) shows that there is a significant positive correlation (r > 0.55, P < 0.01) among temperatures at all different altitudes from 1986 to 2017. Meanwhile, there is a significant (r > 0.60, P < 0.01) positive correlation in temperatures at different altitude of only above 2500 m during 1961–1985 (figure 3(a)). However, the change trend in temperatures has no correlation between at altitudes below 2500 m and above 3000 m before 1985. It is worth mentioning that the temperature at 0–1500 m has a significant (P < 0.05) negative correlation with the temperature at 4000–5000 m in 1961–1985. Figure 2 (c) also showed that the temperature at the 0–1500 m and 4500–5000 m have an almost perfect 'mirroring' relationship during 1961–1985. It is suggesting that there are significant differences in the influence factors of temperature in the two altitude regions.

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Many studies showed that there was EDW phenomenon in the HMA (Qin et al 2009, Wang et al 2012, Yan and Liu 2014, Palazzi et al 2017). Conversely, Lu et al (2010) found that the higher the altitude, the less the warming magnitude, the higher the altitude, the later the climate warming happens, and vice versa from 1960 to 2005 over the Tibetan Plateau. Hu et al (2014) suggested that there are significant negative correlations between temperature increase rate and elevation in Central Asia between 1979 and 2011. Ouyang et al (2019) found that cooling trend was even found in the northeast Tibetan Plateau from 2003–2011. Pepin et al (2019) found cooling was observed at highest elevations >6000 m in the Himalaya during the period from 2002 to 2017. Guo et al (2019b) found that annual air temperature in the southwestern Tibetan Plateau has decreased by 0.15 °C/decade from 2001 to 2015. They also suggested (Guo et al 2019a) that annual mean warming rates in temperature showed rapid decrease above 4500 m from 2001 to 2015. It can be seen that the results vary if scholars attempted to adopt different data to analyze the temperature in different temporal and spatial scales. It remains difficult to sufficiently assess whether mountains have warmed at a higher rate than the rest of the land surface primarily because we lack adequate observations to resolve it conclusively (Rangwala and Miller 2012).

We compared the relationship between temperature change and elevation at different times and spatial scales over HMA. The results showed that the larger temporal and spatial scales, the more difficult it was for EDW to appear. In other words, there was EDW phenomenon only in specific mountainous and time scales. For example, SETP almost always has EDW phenomenon, however, the phenomenon occurred only in the NTP, SWTP and TSM in specific times. There was no EDW in HMA in the time scale of 1961–2017 and on the spatial scale of the altitude of 3500–5000 m.

The EDW phenomenon only occurred in specific mountain areas and time scales, the main reasons are as follows. First, as it is well known that the warming rate is amplified at high latitudes (Rangwala et al 2013, Du et al 2019, Tabari et al 2019). The HMA has a large range of latitudes (N26°–44°). Therefore, the latitude is a significant factor in temperature change. Second, different land cover is another reason. For example, snow cover increased over the southwestern Tibetan Plateau leads to the annual temperature has decreased by 0.15 °C/decade during 2001–2015 (Guo et al 2019b). Third, Shi et al (2019) found that obviously lower summer minimum temperature warming trend on the southern Tibetan Plateau (2.3 × 10⁻³ °C/year) than over the Eurasian continent (5.1 × 10⁻³ °C/year) from 1850 to 1950 could be due to increased evaporative cooling. However, there are temporal and spatial differences in the degree of evaporative cooling at different altitudes. So, changes in some climate factors can also affect temperature changes. Fourth, tall terrain blocks the movement of atmospheric circulation, such as Pacifc Decadal Oscillation (PDO), Siberian High. Therefore, the influence of the same atmospheric circulation on the HMA is significantly different. For example, the Siberian High has an important effect on temperature changes in TSM (Li et al 2012), but it is difficult to have an effect on the southern part of the Tibetan plateau. Fifth, it may also be influenced by the local environment changes, such as human activities, or the glacier and permafrost melting at different altitudes (Du et al 2019, Yin et al 2019).

There are some physical mechanisms that can bring the change of warming rate in different elevation areas, such as snow/ice albedo feedback, cloud cover, water vapor modulation of longwave heating, aerosols, and soil moisture (Rangwala and Miller 2012).

Snow/ice albedo feedbacks was one of importance feedbacks in the climate system (Minder et al 2018). Giorgi et al (1997) believed that the melting water of ice and snow in high altitude areas reduced the surface albedo, and thus the solar radiation absorbed by the surface increased, thereby improving the snow and ice feedback. Palazzi et al (2019) found that the more frequent EDW driver in Tibetan Plateau-Himalayas was the change in albedo. The albedo feedback should be strongest at elevations associated with the snow line or the 0 °C isotherm (Rangwala and Miller 2012).

Duan and Wu (2006) suggested that the nocturnal cloud cover of low level had been increasing over Plateau during the period from 1961 to 2003. They believed that these increases can explain part of the upwards in temperature over Tibetan Plateau in recent 50 years. They also pointed out that if cloud cover in the daytime had been decreasing, it would have increased the absorption of solar radiation and speed up warming. Liu et al (2009) suggested that the elevation dependency was most probably caused by the combined effects of cloud-radiation with snow-albedo feedbacks among various influencing factors in the Tibetan Plateau and its surroundings from 1961 to 2006. Guo et al (2019a) found that trends in nighttime cloud and snow cover were correlated with patterns of EDW on the Tibetan Plateau from 2001 to 2015.

Minder et al (2018) found that EDW in Rocky Mountains was primarily caused by the snow albedo feedback. They also pointed out that no evidence was found for a contribution from elevation-dependent water vapor feedbacks. However, Rangwala et al (2009) suggested that increases in surface water vapor and the related changes in downward longwave radiation appeared to be part of the reason for the warming in the Tibetan Plateau from 1961 to 2000. Palazzi et al (2019) also suggested that in the Tibetan Plateau-Himalayas, the changes in specific humidity were an additional driver factor for EDW.

Aerosols, black carbon (soot), and dust were additional reasons to EDW (Rangwala and Miller 2012). Xu et al (2009) suggested that atmospherically deposited black carbon can increase the absorption of visible radiation by 10%–100% in the Tibetan Glaciers. Black carbon can also lead cloud cover to decrease and affect the radiation budget at the surface (Hansen et al 1997). Rangwala et al (2010) indicated that there were higher aerosol concentrations at lower elevations in the Tibetan Plateau, which would lead the rate of increase in temperature to be lower at these lower elevations and consequently contribute to an elevation gradient in warming rates.

Additionally, Palazzi et al (2019) found that the role of internal climate variability can be significant in modulating the EDW signal. Zhu et al (2019) pointed out that surface wind speed was consistently the major contributor to the variation in sensible heat trend from lower to higher altitude areas over Tibetan Plateau. They also stated that surface wind speed was mainly attributed to the dynamic process related to the PDO. So, atmospheric circulation was another natural factor affecting climate system changes. Furthermore, the changes of sensible heat may be influenced by the diminution in sunshine duration at higher-altitude regions (Zhu et al 2019).

Chen et al (2003) suggested that when CO₂ doubled, the high-altitude cloud cover in the eastern Tibetan Plateau decreased, the low-altitude cloud increased. Duan and Wu (2006) showed that nocturnal cloud cover of low layer had been increasing over the eastern parts of the Plateau from 1961 to 2003. Therefore, more solar radiation reached the ground at high altitudes. Chen et al (2003) also found that there was less snow in the eastern Tibetan Plateau, so cloud cover was the main reason for the EDW in the SETP. The decrease in reflectivity caused by ice/snow in the western and southwestern Tibetan Plateau was the main factor affecting the surface energy balance, so the snow/ice feedback was the main reason for the EDW in the western and southwestern part of the Tibetan Plateau. Gao et al (2012) and Li et al (2018) illustrated that there was less snow in the east than that in the west. They also suggested that the snow cover period of low-altitude in the eastern part of the Tibetan Plateau shortened while that of the high altitude lengthened, which also proved that the snow/ice cover was not the main reason for EDW in the SETP.

Zhu et al (2019) found that sensible heat changes were related to the Pacific Decadal Oscillation. In the recent decades, the warming in the northwestern Pacific in relation to the switch of the PDO from a warm phase to a cold phase led to divergence anomalies in the upper troposphere, which would have caused an enlargement in divergence of the upper troposphere and subsequently a boost in surface convergence and rising motion over Tibetan Plateau. Obviously PDO has a greater impact on sensible heat in the eastern part of Tibetan Plateau than that in the western.

The meteorological stations with an altitude of 0–1500 m and 4000–5000 m are mainly located in TSM and SWTP, respectively (figure 1).

Previous studies have shown that the temperatures in Xinjiang (including TSM) (Wang et al 2013a, 2013b, Chen et al 2014) and Tibetan Plateau (Ding and Li 2008) have abrupt change in the mid-1980s. Before the abrupt change, temperature changes were mainly affected by natural factors. After the change, although the temperature trends in the two regions were similar, the driving factors for warming were not exactly the same. In particular, their local environments are different, making their temperature changes significantly different. Firstly, the TSM belongs to the arid area, while the SWTP belongs to the semi-arid area. Secondly, the SWTP is higher in altitude, and snow, glaciers and permafrost are more widely distributed than in TSM. Thirdly, Li et al (2012) found that that the weakening of the Siberian High during the 1980s–1990s was the main reason for the higher rate of the temperature rise in TSM. However, snow/ice albedo feedback, cloud cover, and water vapor were the main reasons for the temperature rise in the SWTP (Duan and Wu 2006, Duan and Xiao 2015, Palazzi et al 2019).

We found that the EDW was more obvious during the period from 1998 to 2012. This was consistent with Yan and Liu (2014), who indicated EDW and extreme climate events have strengthened over the Tibetan Plateau since 2001 because of rapid warming from 2001 to 2012.

There was no warming of the global mean surface temperature during 1998–2012, as noted by many studies (Easterling and Wehner 2009, IPCC 2013, Kosaka and Xie 2013, Trenberth et al 2014). But, Huang et al (2017) suggested that the amplified Arctic warming over the past decade has significantly contributed to a continual global warming trend, rather than a hiatus or slowdown. Cheng et al (2017) also showed that all ocean basins examined have experienced significant warming since 1998. Duan and Xiao (2015) found that the climate warming hiatus did not occur over the Tibetan Plateau from 1998 to 2013. According to the principle of global energy balance, this may indicate that the slowdown in global land surface warming was mainly attributed the convergence of heat into the oceans, the Arctic and high-altitude mountains, because the warming in these areas has never stagnation or slowdown.