Temporal-spatial variability of modern climate in the Altai Mountains during 1970-2015.

Located in the intermediate zone between the taiga forests in Siberian Plain and the deserts in Central Asia, the Altai Mountains are of scientific concern about Holocene climate change in the past decades. However, researches about modern climate changes are relatively scarce in the Altai Mountains. In this study, temporal- spatial changes of air temperature and precipitation were investigated systematically in the Altai Mountains based on fifteen meteorological records over the period of 1970-2015. The Altai Mountains experienced a rapid warming trend with a rate of 0.41°C/decade and an insignificantly wetting trend at a rate of 4.82 mm/decade during 1970-2015. The magnitude of temperature trend was negatively correlated with elevation in cold season (spring and winter), whereas that was positively correlated with elevation in warm season (summer and autumn). The cyclonic anomalies to the northwest and an anticyclonic anomalies to the southeast blocked the southward cold air and then provided the favorable condition for an increasing precipitation via the southwesternly wind in the Altai Mountains.

Introduction

The AR5 of IPCC proposes a significantly warming trend during the past century on earth [1]. There is growing evidence that the rate of warming is amplified with elevation, such that high-elevation environments experience more rapid changes in temperature than environments at low elevation [2-5]. The Asian Central Arid Zone (ACAZ) is rightly situated in the core of Eurasia. Due to few water vapor supplies from the near oceans (i.e., the North Atlantic Ocean, the Pacific Ocean and the Indian Ocean), glaciers and snow in high-elevation regions of ACAZ (i.e., the Tianshan Mountains and the Altai Mountains) become the most important solid water reservoir and support the oasis agriculture developments and urban operations. However, they are in a state of rapid melting heavily affected by the climate warming, leading to the water resources at risk [6-10]. It means that the temporal-spatial temperature and precipitation variability in ACAZ should be paid more attention on. The changes of modern temperature and precipitation in the Tianshan Mountains, the largest mountain system in ACAZ, have been detailedly investigated [11-18]. The increasing trends were revealed in mean annual temperature (MAT) [17-18] and mean annual precipitation (MAP) [14, 16] in the Tianshan Mountains over the past decades.

The Altai Mountains (Fig 1A), the second largest mountain system within ACAZ and located in the north of the Tianshan Mountains, stretch across Russia, Mongolia, China and Kazakhstan. They are of scientific concern in term of paleoclimatic studies for understanding the interaction between the westerlies and the Asian summer monsoon and for exploring the associations between climate modulation and cultural evolution along the ‘Eurasian Steppe Belt’ [19-25]. Compared with the detailed investigations about modern climate changes in the Tianshan Mountains, modern climate changes are poorly analyzed in the Altai Mountains. For example, Malygina et al. [26] detected the precipitation changes in the Russian Altai and in the Mongolian Altai in the interval spanning from 1959 to 2014 and estimated the possible driving factors. Zhang et al. [27] based on a converse moisture character during the Holocene interval and analyzed the different time-scale (i.e., season, year, multi-decades and centennial/millennial scales) climatic changes in the Russian Altai and in the Chinese Altai. It is clear that modern climate trends were not systematically investigated in terms of different sub-regions and of different elevations in the Altai Mountains.

Fig 1

Geographic location of the Altai Mountains (a) and the related meteorological stations in the Altai Mountains (white area) (b). The Altai Mountains are divided into three sub-regions (1, 2 and 3) and the detailed information of meteorological stations is showed in Table 1. The image was downloaded from the Natural Earth website (http://www.naturalearthdata.com/).

Table 1

Related information of meteorological stations in the Altai Mountains.

No.	Station Name	Latitude (°N)	Longitude (°E)	Altitude (m)	Annual temperature (°C)	Annual precipitation (mm)	Interval
1	Zmeinogorsk	51.15	82.17	354	2.81	692.78	1966–2016
2	Soloneshnoe	51.63	84.33	409	1.86	581.20	1966–2016
3	Kyzyl-Ozek	51.90	86.00	331	2.07	745.00	1966–2016
4	Yailu	51.77	87.60	480	3.73	894.95	1966–2016
5	Mugur-Aksy	50.38	90.43	1850	-2.36	142.60	1966–2016
6	Ust-Coksa	50.30	85.60	978	0.62	472.23	1940–2017
7	Kara-Tyurek	50.00	86.40	2600	-5.54	601.41	1940–2017
8	Kosh-Agach	50.00	88.04	1760	-4.96	119.93	1936–2017
9	Yalalt	48.80	89.50	2148	2.75	134.16	1970–2015
10	Olgiy	48.96	89.98	1715	1.23	111.08	1961–2015
11	Khovd	48.08	91.35	1405	1.24	132.85	1961–2015
12	Qinghe	46.67	90.38	1220	0.72	125.01	1958–2017
13	Fuyun	46.98	89.52	826.6	2.88	177.71	1962–2017
14	Aletai	47.73	88.08	736.9	4.48	190.13	1958–2017
15	Habahe	48.05	86.40	534	4.77	199.93	1958–2017
16	Zajsan	47.47	84.92	603	4.12	294.53	1967–2000
17	Semipalatinsk	50.42	80.30	196	3.58	285.66	1940–2011

Based on the selected meteorological data in the Altai Mountains, we detailedly investigated the temporal-spatial changes of modern climate of the Altai Mountains during 1970–2015. This study is very important for assessing the influences of climate changes on its physical environments and ecosystem stabilities or securities and for improving the abilities for ecological management in the whole Altai Mountains.

Regional setting

Located in the transition zone between the Siberian taiga forests and the Central Asian deserts, the Altai Mountains stand out from its surrounding territories with their unique diversity of ecosystems [28-31]. They have the largest unbroken stretches of Siberian fir, pine and larch trees in the world. The particularly remarkable species is dark coniferous taiga. Glaciers mainly occupy in the central part of Altai Mountains, especially on the ranges of Katunsky, Taldurinsky, Ak-Turu, Munku-Sardyk, South and North Chuisky, Kryzin [32-37]. They give life to two largest rivers (e.g., Ob and Yenisei river) and several large lakes (e.g., Khuvsgul, Uvs, Teletskoe and Wulungu lake). The climate in the Altai Mountains is featured by the harsh continental climate [31-37]. Due to the most of humid air masses transported by the westerlies from the west throughout a year, the Altai Mountains are featured by a strong northwest to southeast precipitation gradient [38-39]. The Siberian High controls the cold-season climate and prevents precipitation formation in the Altai Mountains [26–27, 38, 40].

Data resource and method

Data resource

The selected stations were showed in the Altai Mountains in Fig 1 and their detailed information was showed in Table 1. The eight stations are situated in the Russian Altai Mountains and they are Zmeinogorsk, Soloneshnoe, Kyzyl-Ozek, Yailu, Mugur-Aksy, Ust-Coksa, Kara-Tyurek and Kosh-Agach. The three stations are located in the Mongolian Altai Mountains and they are Olgyi, Yalalt, Khovd. The four stations are situated in the Chinese Altai Mountains and they are Habahe, Aletai, Fuyun and Qinghe. The data from Russia was obtained from the website (http://meteo.ru/english/data/) and the data from Mongolia was provided by Dr. Mariia Andreeva. The data from China was downloaded from the website (http://cma.gov.cn). These meteorological data from three countries was both pre-disposed through the strict quality control and homogenized. The data from Semipalatinsk and Zajsan meteorological station within Kazakhstan was selected and was just for calculating seasonal features of temperature and precipitation because of no long time-scale observed data. The seasonal changes were also taken into account and spring is from March to May, summer is from June to August, autumn is from September to November, and winter is from December to February in the next year.

Method

The treated method (i.e., the Mann-Kendall trend test) was applied to probe the temperature and precipitation trends during the observed period [16, 38–42]. If the slope value is >0, the climate data has a positive trend and the climate data shows a negative trend when the slope value is <0. The data of slope value indicates the rate or the magnitude at which the climate data increases or decreases.

Results

Temperature trend characters

The temperature variations in annual and seasonal scale of the Altai Mountains were showed in Fig 2. The result shows that MAT experienced an increasing trend during the studied interval (1970–2015), which had a significance level of 0.01. The increased rate was 0.41°C/decade in the Altai Mountains during 1970–2015. In the studied interval, about 1.85°C was the value which the MAT increased (Fig 2A). A significant increase of air temperature was also showed in spring, summer and autumn (Fig 2B–2D), whereas air temperature in winter was statistically insignificant (Fig 2E). Their increased rates were 0.64°C/decade for spring, 0.43°C/decade for summer, 0.41°C/decade for autumn and 0.27°C/decade for winter, respectively (Fig 2B–2E). The rising rate of seasonal temperature was the quickest in spring.

Fig 2

Variations of annual and seasonal mean temperature in the Altai Mountains during 1970–2015.

Fig 3 presented the spatial divergent rates of MAT and seasonal temperature in the Altai Mountains. The significantly positive trends (p<0.05) were observed in MAT among all fifteen stations. The MAT were divergent and the value spanning from 0.28°C/decade in Zmeinogorsk to 0.75°C/decade in Fuyun between 1970 and 2015. The rates in majority of stations fall spanning from 0.20 to 0.40°C/decade, that in four stations waved between 0.40 and 0.60°C/decade, and that in two stations fluctuated between 0.60 and 0.80°C/decade. The fastest increased rate of MAT was observed at Fuyun and Qinghe in the southeastern Altai Mountains. All stations significantly increased (from 0.43 to 0.84°C/decade) in spring, larger than the increased trend of MAT. The rising rates in summer varied spanning from 0.05 (Altai and Qinghe) to 0.61 (Fuyun)°C/decade, while the insignificant changes of some stations (i.e., Zmeinogorsk, Soloneshnoe, Altai and Qinghe) were observed. In autumn, except Soloneshnoe (0.04°C/decade, P>0.05) station, others showed a warming rate ranging from 0.20 to 0.65°C/decade. In winter, a decreasing trend of temperature (−0.12°C/decade) with no significance was detected at Zmeinogorsk. The winter air temperature at Ust-Coksa, Fuyun and Khovd experienced increasing trends which exceeded the 95% confidence level. The rates insignificantly increased between 0.02 and 0.20°C/decade in other stations. As a whole, the temporal and spatial rates of temperature were varied in annual and seasonal scale and the warming climate was found in the whole Altai Mountains during the past decades.

Fig 3

Spatial trends for annual and seasonal temperature during 1970–2015 over the Altai Mountains.

Precipitation trend characteristics

Fig 4 showed the trends of MAP and seasonal precipitation during 1970–2015 in the Altai Mountains. An increased rate about 4.82 mm/decade (P>0.05) was detected for MAP (Fig 4A). The MAP in 2015 increased 21.69 mm comparing with that in 1970. The changes of seasonal precipitation all did not exceed the significance level of 0.05 and their rates were 2.10 mm/decade in spring, 1.60 mm/decade in summer, 0.25 mm/decade in autumn and 0.93 mm/decade in winter, respectively (Fig 4B–4E).

Fig 4

Variations of annual and seasonal mean precipitation in the Altai Mountains during 1970–2015.

The spatial changes of precipitation in annual and seasonal scale were presented in Fig 5. Four stations (Zmeinogorsk, Kyzyl-Ozek, Kosh-Agach and Olgiy) had decreasing trends of MAP with significant levels (P<0.05) and their rates were −4.68, −8.66, −7.29 and −4.99 mm/decade, respectively. Although the positive trends of MAP were detected among other stations, the significant trends were only found in Soloneshnoe, Habahe, Aletai, Fuyun and Qinghe and their rates ranged from 1.14 (Khovd) to 17.97 (Soloneshnoe) mm/decade. In spring, two stations (Zmeinogorsk and Kosh-Agach) exhibited decreasing trends with no significance. Other remaining stations showed increasing trends with rates spanning from 0.16 to 6.40 mm/decade, but most of them did not past 95% confidence level. In summer, five stations (Mugur-Aksy, Kara-Tyurek, Yailu, Olgiy and Khovd) revealed declining trends with no significance. Except Soloneshnoe, the precipitation at other remaining nine stations was featured by insignificant climbing trends (P>0.05). The increasing amplitude in Soloneshnoe was the most evident and the largest with a rate of 11.27 mm/decade. The autumn precipitation exhibited insignificantly decreasing trends among eight stations in the northern Altai and in the eastern Altai. Insignificant trends were also observed in other stations with increasing rates spanning from 0.14 (Yalalt) to 4.71 (Habahe) mm/decade. In winter, only Zmeinogorsk, Mugur-Aksy and Kara-Tyurek showed decreasing trends with insignificant levels (P>0.05). All stations in the southern Altai Mountains had a significant increasing trend of precipitation in winter, while no significance was found among other stations. Overall, the Altai Mountains experienced a slightly wetting trend since 1970, especially in the southern Altai.

Fig 5

Spatial trends for annual and seasonal precipitation during 1970–2015 over the Altai Mountains.

Evolution of temperature and precipitation in sub-regions

The monthly temperature in the Altai Mountains is characterized by the highest temperature in June-August and the lowest temperature in November-January during 1970–2015 (Fig 6A). The highest temperature (22.97°C) was recorded in Zajsan and the lowest value (-28.78°C) was in Kosh-Agach (Fig 6A). The three features of monthly precipitation were characterized in the Altai Mountains (Fig 6B). Firstly, the monthly precipitation was unimodal and was mainly concentrated in warm season (summer and autumn) with 55–84%. The related stations are Soloneshnoe, Kyzyl-Ozek, Yailu, Kara-Tyurek and Ust-Coksa. The MAP in these stations was relatively high (average about 649.73 mm). Secondly, the feature of monthly precipitation was also unimodal, but the MAP was relatively low (average about 133.86 mm). The associated stations are Mugur-Aksy, Kosh-Agach within Russia, three stations within Mongolia and Qinghe within China. Thirdly, being different to the former two features of precipitation, the distribution of monthly precipitation was bimodal characterized by two peaks at April-September (50–68%) and at November- December (13–21%). The associated stations include Zmeinogorsk, Habahe, Aletai, Fuyun, Semipalatinsk and Zajsan.

Fig 6

Variations of monthly temperature (a) and monthly precipitation (b) in the Altai Mountains during 1970–2015.

Based on the features of monthly precipitation, the Altai Mountains was classified into three sub-regions: sub-region 1, 2 and 3 (Fig 1). The related trends of MAT and MAP among three sub-regions during 1970–2015 were presented in Fig 7. The result suggests that MAT all significantly increased among three sub-regions (Fig 7A). The most rapidly increased temperature occurred in sub-region 2 at a rate of 0.45°C/decade. The next was in sub-region 3 at a rate of 0.42°C/decade and the third in sub-region 1 at 0.38°C/decade. Being different to the temperature fluctuations, the trends of MAP were insignificantly and their rates were 7.7 mm/decade in sub-region 1, 1.9 mm/decade in sub-region 2 and 8.2 mm/decade in sub-region 3, respectively (Fig 7B).

Fig 7

Variations of mean annual temperature (a) and mean annual precipitation (b) in three sub-regions of the Altai Mountains during 1970–2015.

Temperature/precipitation trend-elevation relationship

We further analyzed the temperature/precipitation trends in different elevations (i.e., high elevation >2000 m, middle elevation at 2000–1000 m and low elevation <1000 m) (Fig 8). The stations in high elevation are Kara-Tyurek and Yalalt. Mugur-Aksy, Kosh-Agach, Olgiy, Khovd and Qinghe are contained in middle elevation and the remaining stations are included in low elevation. A significant warming during 1970–2015 with different rates was found in different elevations of the Altai Mountains (Fig 8A). Their increased rates were 0.37°C/decade in high elevation, 0.44°C/decade in middle elevation and 0.39°C/decade in low elevation, respectively.

Fig 8

Variations of mean annual temperature (a) and mean annual precipitation (b) in different elevations of the Altai Mountains during 1970–2015.

Being consistent with the synchronously increasing trend of MAT in different elevations, the MAP in different elevations was also increasing in the studied period (Fig 8B) and their rates were 7.27 mm/decade in high elevation, 0.86 mm/decade in middle elevation and 7.49 mm/decade in low elevation, respectively. All trends of MAP were insignificant (P>0.05). Overall, we found that the variations of MAP were complex than that of MAT in different sub-regions and different elevations of the Altai Mountains, being similar with their changes in the Tianshan Mountains [9]. It should be noted that the the fastest warming rightly corresponds to the smallest increased precipitation in sub-region 2 and in middle elevations.

Discussions

Analysis of divergent trends of climate change and elevations

It is very important to reveal the relationship between temperature and elevation for understanding regional climate response to global warming. Two proposals about relationships between temperature trend and elevation were existed. Firstly, the warming rate in higher elevations was much larger than that in lower elevations [4–7, 43–45]. Secondly, the divergent magnitude of temperature trend was not associated with elevation [9, 46]. The relationships between the magnitude of MAT and seasonal temperature trend and elevation in the Altai Mountains were showed in Fig 9. The slight positive correlations between MAT trend magnitude and elevation were found in annual, summer and autumn, the relationship was only significant in summer. The negative relationship occurred in spring and winter, and they were not significant. Overall, the magnitude of temperature trend was negatively correlated with elevation in cold season (spring and winter), whereas that was positively correlated with elevation in warm season (summer and autumn), being similar with that in the Tianshan Mountains [9]. This elevation-dependent temperature depends upon the changes of snow cover and surface albedo feedback in the Altai Mountains [9, 47–48]. In details, the larger snow cover and its stronger albedo feedback in cold season accelerate the transit of upward turbulent heat, the cooling air has a lower probability of being heated by latent heat in high elevation than that in low elevation. In warm season, the decreased snow cover and the weakening surface albedo can not inhibit the warming air in high elevation, i.e., high-elevation environments experience more rapid changes in temperature than environments at low elevations [2-5].

Fig 9

Relationships between annual and seasonal air temperature trend magnitude and elevation in the Altai Mountains.

Furthermore, understanding the elevation dependent wetting or not is also of significance. Previous studies revealed precipitation increased with elevation climbing in the mountains on earth [49-51]. The similar wetting trend with elevation was also observed in the adjacent Tianshan Mountains [9, 16]. The relationships between MAP and seasonal precipitation trend magnitude and elevation in the Altai Mountains were showed in Fig 10. There were slight negative correlations between precipitation trend magnitude and elevation for annual, spring, summer and winter, and they were insignificant. The only positive relationship occurred in autumn. The strong elevation dependency of seasonal precipitation appeared in autumn with a correlation efficient of 0.23 which was statistically insignificant at the 95% confidence level. The autumn precipitation from 1970 to 2015 displayed an increasing tendency at a rate of 0.9 mm/decade each 1000 m. It suggests larger changes of autumn precipitation in low elevation than in high elevation of the Altai Mountains during 1970–2015. Being inconsistent with the wetting trend with elevation in summer and autumn in the Tianshan Mountains [9, 16], the elevation dependency of MAP and seasonal precipitation trends are more intricate in the Altai Mountains.

Fig 10

Relationships between annual and seasonal precipitation trend magnitude and elevation in the Altai Mountains.

Association between atmospheric circulations and climate information

The temperature trends in the Altai Mountains were similar with that in the Northern Hemisphere in recent decades and both experienced a warming climate. The rising rate of temperature was the quickest in spring, which was attributable to the substantially declined snow cover in the past 30 years, especially in early spring through summer interval [52-54]. Fossil fuel and biofuel emissions of black carbon plus organic matter were mainly responsible for spring-time snow cover loss over Eurasia including the Altai Mountains [55]. Although three features of monthly precipitation were existed in the Altai Mountains, MAP was featured by an insignificantly increasing trend, which is inconsistent with the wetting Tianshan Mountains [9].

Regional precipitation variability in the Altai Mountains could be closely related with atmosphere circulation. Xu et al. [9] calculated that the differences of geopotential height (shaded) and wind speed (vector) at 500 hPa in the Asian Central Arid Zone including the Altai Mountains through calculating the circulation using NCEP/NCAR reanalysis data (Fig 11). As shown in Fig 11, the Altai Mountains were controlled by a cyclonic anomalies to their northwest and an anticyclonic anomalies to their southeast. This kind of atmospheric modes blocked the southward cold air and in turn provided favorable environment for water vapor transport via the westerlies from the North Atlantic, the Mediterranean and Caspian seas to the Altai Mountains [56-57]. The horizontal moisture advection and wind convergence terms has a significant positive contribution to the wetting trend in the northwest China [58]. The southwesternly wind between the anomaly cyclone and the anomaly anticyclone driven the water vapor flux from the Mediterranean and Caspian sea into the Altai Mountains, which resulted in an increasing precipitation. Compared with the precipitation changes in sub-region 2, the larger increased precipitation in sub-region 1 and 3 could result from the effect of windward slope [27].

Fig 11

Differences of geopotential height (shaded) and wind speed (vector) at 500 hPa between 1996–2016 and 1960–1995 (modified from Fig 12 in Xu et al. (2018)).

The length and direction of arrows mean the flux and the direction of wind.

The combination of complex topography and other local effects may be responsible for different relationships between precipitation trend and elevation in the Altai Mountains [16-17]. Several uncertainties can influence the relationship of precipitation trend with elevation in mountainous regions over recent decades. Firstly, the long-term observed data of precipitation are extremely sparse at high elevations. For example, only two stations (Kara-Tyurek and Yalalt) are above 2000 m and no observed data is above 3000 m in the Altai Mountains. Meanwhile, there is no available long-term precipitation data in the Altai Mountains within Kazakhstan. The CRU (Climatic Research Unit) and GPCC (Global Precipitation Climatology Centre) data are able to describe the temporal-spatial variations of precipitation, but their uncertainties limit their applicability in the mountainous areas [59-60]. Secondly, there is a lack of consistency in the observation instruments and environments, such as vegetation changes, human grazing and relocation of stations because the Altai Mountains cover over four countries (China, Kazakhstan, Russia and Mongolia). The uncertainties would influence the relationships between climate and elevation to a certain extent, and the climate-elevation relationships should be observed and be investigated in future through (1) establishing more high-elevation stations and (2) combining three main methods including surface in-situ climate observations, satellite remote-sensing data and high-resolution climatic modeling [2–5, 16].

Conclusions and implications

The Altai Mountains experienced a rapid warming trend with a rate of 0.41°C/decade and an insignificantly wetting trend at a rate of 4.82 mm/decade during 1970–2015. Being inconsistent with the precipitation increasing trend with elevation in the Tianshan Mountains over recent decades [9, 16], the insignificantly increasing trend was showed in different elevations. The magnitude of temperature trend was negatively correlated with elevation in cold season (spring and winter), whereas that was positively correlated with elevation in warm season (summer and autumn). Additionally, the trends of precipitation magnitude with elevation in the Altai Mountains were complex. Two warning signs we should focus on. The first is that no obvious increasing precipitation poses a potential threat to the regional forest developments in middle elevations under the warming condition. The second is that no obvious increasing precipitation directly affects storage of water resource in the Altai Mountains under the warming condition. In addition with significant mass loss of glaciers in the Altai Mountains [36–37, 61], a decreasing water storage may be a heavy threat to human survival through impacts on water availability. This urge us to take action to protect the safety of ecology and water resource in the Altai Mountains.

(RAR)

Click here for additional data file.

14 Nov 2019

PONE-D-19-22736

Temporal-spatial variability of modern climate in the Altai Mountains during 1970-2015

PLOS ONE

Dear Dr. Zhang,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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One reviewer raised serious concerns, and thought this manuscript is similar with the paper published in Scientific Reports from the same research team in 2018. Therefore, I would suggest you have to address this concern carefully. Specific attention should also be paid as follows:

You suggested that this anticyclone mode blocked the water vapour transport from the west. Why does this weakened water vapour transport not cause a drying trend? Why does the strengthening of Siberian High in winter not cause a cooling in the Altai Mountains?

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Reviewer #1: The reviewer congratulates the authors for this extensive analysis of “Temporal-spatial variability of modern climate in the Altai Mountains during 1970-2015”. This is a noble piece of work and will be very useful for the scientific community. This will provide the first hand information regarding the climate of Altai mountains. The comments are attached in the attachment.

Reviewer #2: Review of paper titled “Temporal-spatial variability of modern climate in the Altai Mountains during 1970-2015” by Li et al.

General comment: accepted after minor revisions

In this paper, the author investigated the air temperature and precipitation trends in Altai Mountains based on 15 meteorological records over the period 1970-2015. Significant temperature increasing trends and insignificant precipitation trends were found over the Altai Mountains, with increased rate of precipitation trend and decreased rate of temperature trend. These trends were attributed to the enhancing anticyclone circulation and increasing geopotential height.

This study is straightforward and meaningful, climate changes over the high-elevation regions and arid regions, and their societal and economic influences are concerned by both research community and general public. However, I do have some concerns about the analyses and results.

For the introduction section, I believe some recent literatures about climate changes over the high-elevation regions, would also be helpful to strengthen the motivation and importance of this manuscript, such as:

Pepin, N., R. S. Bradley, H. F. Diaz, et al., 2015: Elevation-dependent warming in mountain regions of the world. Nature - Climate Change, 5, 424-430, DOI: 10.1038/NCLIMATE2563

Diaz, H. F., R. S. Bradley, and L. Ning, 2014: Climatic changes in mountain regions of the American Cordillera and the tropics: historical changes and future outlook. Arctic Antarctic & Alpine Research, 46(4), 735-743.

Bradley, R. S., F. T. Keimig, H. F. Diaz, and D. R. Hardy, 2009: Recent changes in freezing level heights in the tropics with implications for the deglacierization of high mountain regions. Geophys. Res. Lett., 36, L17701, doi: 10.1029/2009GL037712

Bradley, R. S., M. Vuille, H. F. Diaz, and W. Vergara, 2006: Threats to water supplies in the tropical Andes. Science, 312, 1755-1756

Line 137, any physical explanations about the largest increasing trend in spring?

The three sub-regions used in Fig. 7 should be marked in a map.

In Fig. 8, which stations are categorized as high-, middle-, low-elevations?

About the section of “temperature/precipitation trend-elevation relationship”, first, I am not surprised about no significant relationship between precipitation trends and elevations. But, for the relationship between temperature trends and elevation, usually larger increasing trends over high-elevation regions are more acceptable to the whole community, because of several potential mechanisms: snow albedo, water vapor changes, and aerosols etc. (Pepin et al., 2015). In Fig. 8, 0.037 C/decade, 0.044 C/decade, and 0.039C/decade are not very different, especially how many stations in each category are not given. Therefore, I would recommend the authors use another data (e.g., CRU data) to to repeat this analysis and confirm their results.

In the following discussion, the authors tried to use the snow cover to explain the converse relationships between temperature trends and elevations. But, I think seasonal snow cover extents can only be used to explain the climatology, rather than the changes. While, the changes of snow cover and surface albedo feedback will support the elevation dependent warming. Deeper discussion about changes of snow cover and corresponding mechanisms is needed in the revision.

In the section of “association between atmospheric circulations and climate information”, when using correlations between precipitation changes and large-scale climate variability to explain the precipitation changes, the authors mixed the concepts of inter-annual variations and trends. Meanwhile, AO, NAO, and ENSO are inter-annual scale climate variability, while AMO and PDO are decadal scale climate variability, so they influence precipitation variability on different scales. Therefore, the authors need to clarify which of these two changes (or both) they want to discuss, then discuss the corresponding mechanisms. Moreover, the mechanisms behind influences from the AO, NAO, and AMO are easy to understand through the water vapor transportation through the westerly. While, the authors should provide more details about mechanisms behind influences from PDO and ENSO to the precipitation over the Altai Mountains.

In Fig. 11, why the whole period is divided into 1996-2016 and 1960-1995? Any sudden change or shift around 1995/96?

Maybe I missed some files, but I did not see Table 1 and Table 2.

Caption of Fig. 10, should it be mean annual precipitation?

Reviewer #3: General comments:

This study analyzed the temporal-spatial variability of precipitation and temperature in the Altai Mountains during 1970-2015. Meanwhile, the authors intend to provide interpretations for the climate variations. However, the interpretations are improper. The English is not really good, and the paper would greatly benefit from a thorough language editing.

Detailed comments:

1.Lines 35-36: Why does the warming result in the wetting in mid-latitude regions?

2.Lines 37-38: Revise “the mid-latitude regions” to “Eurasia”.

3.Where are the tables?

4.Line 108: AMO and PDO are not atmosphere circulation indices.

5.Lines 107-116: Why do you select these five climate indices, which cannot explain the results in this study as analyzed in the Discussions of this paper?

6.Lines 113-116: Why do you unconventionally divide the seasons?

7.Lines 212-218: It is better to display the sub-regions in Figure 1.

8.Lines 279-281: Figure 10c indicates larger changes in lower elevations than that in the higher elevations.

9.Lines 285-299: The correlations reflect the interannual relationships between the indices and precipitation in the Altai Mountains. However, this study focused on the decadal variability of precipitation in the Altai Mountains. Hence, this analysis is improper for this study.

10.Lines 300-315: This study explored the climate changes during 1970-2015, whereas Figure 11 analyzed the circulation changes from 1960 to 2016. Hence, this analysis is also improper for this study.

11.Lines 285-315: The first paragraph suggests that global circulations have weak relationship with the precipitation. The second paragraph indicates that local circulation might modulate the influence of global circulations on precipitation. However, the detailed mechanism is not proposed in this study.

The authors suggest that this anticyclone mode blocked the water vapour transport from the west. Why does this weakened water vapour transport not cause a drying trend? Why does the strengthening of Siberian High in winter not cause a cooling in the Altai Mountains?

Figure 11 indicates that the Altai Mountains are controlled by cyclonic anomalies to their northwest and an cyclonic anomalies to their southeast, rather than an anticyclone circulation.

11. Fig. 10: Revise “air temperature” to “precipitation”.

Reviewer #4: The manuscript attempted to discuss spatiotemporal characteristics of the modern climate, specifically precipitation and temperature, in the Altai Mountains and associated atmospheric circulations. This research is important and meaningful. The results reveal that the Altai Mountains experience a rapid warming, while no significant trend was found. The Altai Mountains was divided into 3 subregions based on the characters of monthly precipitation, and the trends of annual and seasonal precipitation and temperature were also studied for each subregion. However, the method used to divide these subregions is not very convincing. Meanwhile, the explanation about the atmospheric circulations associated with the climate variability over the Altai Mountains is not clear, and even contradictory to the observed trends. Furthermore, this manuscript is similar with the paper published in Scientific Reports from the same research team in 2018. Therefore, I suggest that this manuscript can’t be accepted by this journal.

General comments:

1. Tables that mentioned in the manuscript are missing.

2. The trends of annual and seasonal mean temperature/precipitation for the fifteen stations in the Altai Mountains have been investigated and compared with each other. A table containing this information can be added to make readers easier to follow. In the analysis of trend-elevation relationship, which stations are included in the high elevations, middle elevations, and low elevations need to be clarified.

3. The three subregions are divided based on the three characters of monthly precipitation. For the northern Altai Mountains (subregion 1), the monthly precipitation in Zmeinogorsk station shows two peaks (Fig.6), but the other five stations in this sub-region all shows one peak. Why is the Zmeinogorsk station included in this subregion? More explanation is needed. At the same time, I have the similar concern about the southern Altai Mountains (subregion 3). This subregion includes Qinghe, Aletai, Fuyun, and Habaha stations. The monthly precipitation of Qinghe station shows one peak, but two peaks can be found in other three stations. ¬¬The division of sub-regions need more justifications.

4. For the atmospheric circulations responsible for the climate variability in the Altai Mountains is not clear. This manuscript stated that the Altai Mountains experienced a rapid warming (line 318, line 22), but it also argued that no rapid warming in the Altai Mountains due to the strengthening of Siberian High in winter (lines 308-309). This is contradictory. Meanwhile, how does the anticyclone mode block less water vapor transported by the westerlies from the North Atlantic Ocean into the Altai? I don’t think the reference 35 talks about it.

Specific comments:

1. Lines 125: the subtitle “Temperature and precipitation during 1970-2015” is redundant.

2. There is a small figure on the left bottom of figure 1. The description about it needs to be added to the caption of figure 1

3. For figure 3, there is a small histogram and ”0.47” on the top of the legend. What does that stand for? Figure 5 has the same problem.

4. The figure 6 doesn’t have “a” on the temperature (left figure) and “b” on the precipitation (right figure)

5. For the caption of figure 8, change “annual mean precipitation” to” mean annual precipitation”

6. The “seasonal air temperature” needs to be added to the caption of figure 9 and figure 10.

7. In figure 10, the left string (annual, summer, etc.) covered the data point.

5. The Altai Mountains has been divided into 3 subregions (northern, eastern and southern). Showing these subregions in a figure will be clearer. Maybe the division of subregions can be added to the figure 1.

**********

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Submitted filename: PONE.docx

Click here for additional data file.

15 Dec 2019

Plos One (PONE-D-19-22736)

Dear Professor Yang Bao:

Thanks very much for taking your and the reviewers’ time to review this manuscript. We really appreciate all your comments and suggestions! Please find our responses in below and my revisions/corrections in the re-submitted files.

Thank you very much for your attention and consideration again.

Sincerely yours

Zhang Dongliang

2019-12-11

Replies to Editor (thank you!):

One reviewer raised serious concerns, and thought this manuscript is similar with the paper published in Scientific Reports from the same research team in 2018. Therefore, I would suggest you have to address this concern carefully. Specific attention should also be paid as follows:

Reply: Yes, we published the paper in Scientific Reports in 2018. That paper revealed the out-of- phase relationship of precipitation changes at different time-scales (i.e., season, year, multi-decades, centennial and millennial scales). In this study, we systematically investigated the temporal-spatial changes of modern climate in the Altai Mountains during 1970-2015, including temperature and precipitation trends and their changes in different sub-regions, temperature and precipitation trend-elevation relationship and possible reasons.

Replies to Reviewer 1 (thank you!):

Line 78: I think it is species not ‘specie’

Reply: Modified.

Provide a better figure 2. The years at the bottom is not clear.

Reply: Added.

Line 169-170: please rephrase “Around 21.69 mm of annual mean precipitation was increased from 1970 to 2015”.

Reply: Done.

This MAP in 2015 increased 21.69 mm comparing with that in 1970.

Line 176: “with significant levels:. You need to mention the value.

Reply: Added.

Line 190: change ‘trends was’ to ‘trends were’

Reply: Added.

Line 198: please change the heading to “mean annual cycle or temporal evolution of temperature and precipitation”.

Reply: Thank you. We changed the heading to “evolution of temperature and precipitation in sub-regions”.

The tables are missing from the manuscript. Please provide.

Reply: Added.

The elevation dependent temperature depends upon the various factor, such as snow-albedo feedback, nocturnal cloud, change in surface energy balance, aerosol and change in water vapour and fluxes. The authors must look into these to confirm which mechanism dominates over Altai mountains in deciding the elevation dependent warming.

Reply: Done.

This character should result from the lakes (e.g., Wulungu Lake, Zaysan Lake, Teleskoyel Lake and Achit Lake) in low-elevation regions of the Altai Mountains, which significantly alter the surface energy and then influence on regional climate via mediating the fluxes of energy, moisture and momentum [9, 47-48]. In details, during the warm season (summer and autumn), the lakes absorb incoming solar radiation and inhibit upward turbulent heat transport, the humid air has a lower probability of being heated by latent heat released from condensation at low elevations which induce cooling and lower trend in low elevation. During the cold season (spring and winter), the air is warmed and showed a greater trend magnitudes in low elevations due to the heat is released from lakes.

Replies to Reviewer 2 (thank you!):

1, For the introduction section, I believe some recent literatures about climate changes over the high-elevation regions, would also be helpful to strengthen the motivation and importance of this manuscript, such as:

Reply: We added the several papers you suggested.

2, Line 137, any physical explanations about the largest increasing trend in spring?

Reply: Thank you for your question. We investigated the possible causes.

The rising rate of temperature was the quickest in spring, which was attributable to the substantially declined snow cover inferred from satellites in the past 30 years, especially in early spring through summer interval (Zhai and Zhou, 1997; Qian et al., 2011; Huang et al., 2012). Fossil fuel and biofuel emissions of black carbon plus organic matter were mainly responsible for springtime snow cover loss over Eurasia (Flanner et al., 2009).

Huang JP, Guan XD, Ji F. Enhanced cold-season warming in semi-arid regions. Atmospheric Chemistry and Physics, 2012; 12(12): 5391-5398.

3, The three sub-regions used in Fig. 7 should be marked in a map.

Reply: Thank you! We showed these sub-regions in Figure 1b.

4, In Fig. 8, which stations are categorized as high-, middle-, low-elevations?

Reply: Thank you! We added them in the revised manuscript.

We further analyzed the temperature/precipitation trends in different elevations. The Altai Mountains were divided into three gradients (i.e., >2000 m, 1000-2000 m and <1000 m) (Fig. 8). The stations within >2000 m were Kara-Tyurek and Yalalt. Mugur-Aksy, Kosh-Agach, Olgiy, Khovd and Qinghe were contained elevation between 2000 and 1000 m and the remaining stations were considered elevation <1000 m.

5, About the section of “temperature/precipitation trend-elevation relationship”, first, I am not surprised about no significant relationship between precipitation trends and elevations. But, for the relationship between temperature trends and elevation, usually larger increasing trends over high-elevation regions are more acceptable to the whole community, because of several potential mechanisms: snow albedo, water vapor changes, and aerosols etc. (Pepin et al., 2015). In Fig. 8, 0.037 C/decade, 0.044 C/decade, and 0.039C/decade are not very different, especially how many stations in each category are not given. Therefore, I would recommend the authors use another data (e.g., CRU data) to to repeat this analysis and confirm their results.

Reply: Thank you. Your suggestion is very valuable. According to your suggestion, we checked CRU and GPCC data. The study showed that both CRU and GPCC products are able to describe the temporal-spatial variation of precipitation in China. However, the CRU data showed large biases in the Tibetan Plateau and some large-mountain areas including the Tianshan Mountains and the Altai Mountains (Wang and Wang, 2017). The study also showed that GPCC is a better choice compared to CRU for studying the long-term precipitation trend in China (Wang and Wang, 2017). Although GPCC agrees well with observed data, the uncertainty of the satellite rainfall products limits their applicability in mountainous areas (Jin et al., 2016). Therefore, we pay more attention on the observed data in the Altai Mountains.

Wang D, Wang AH. Applicability assessment of GPCC and CRU precipitation products in China during 1901 to 2013. Climatic and Environmental Research, 2017; 22(4): 446-462 (in Chinese).

Jin XL, Shao H, Zhang C, Yan Y. The Applicability Evaluation of Three Satellite Products in Tianshan Mountains. Journal of Natural Resources, 2016; 31(12): 2074-2085 (in Chinese).

6, In the following discussion, the authors tried to use the snow cover to explain the converse relationships between temperature trends and elevations. But, I think seasonal snow cover extents can only be used to explain the climatology, rather than the changes. While, the changes of snow cover and surface albedo feedback will support the elevation dependent warming. Deeper discussion about changes of snow cover and corresponding mechanisms is needed in the revision.

Reply: Done.

This character should result from the lakes (e.g., Wulungu Lake, Zaysan Lake, Teleskoyel Lake and Achit Lake) in low-elevation regions of the Altai Mountains, which significantly alter the surface energy and then influence on regional climate via mediating the fluxes of energy, moisture and momentum [9, 47-48]. In details, during the warm season (summer and autumn), the lakes absorb incoming solar radiation and inhibit upward turbulent heat transport, the humid air has a lower probability of being heated by latent heat released from condensation at low elevations which induce cooling and lower trend in low elevation. During the cold season (spring and winter), the air is warmed and showed a greater trend magnitudes in low elevations due to the heat is released from lakes.

7, In the section of “association between atmospheric circulations and climate information”, when using correlations between precipitation changes and large-scale climate variability to explain the precipitation changes, the authors mixed the concepts of inter-annual variations and trends. Meanwhile, AO, NAO, and ENSO are inter-annual scale climate variability, while AMO and PDO are decadal scale climate variability, so they influence precipitation variability on different scales. Therefore, the authors need to clarify which of these two changes (or both) they want to discuss, then discuss the corresponding mechanisms. Moreover, the mechanisms behind influences from the AO, NAO, and AMO are easy to understand through the water vapor transportation through the westerly. While, the authors should provide more details about mechanisms behind influences from PDO and ENSO to the precipitation over the Altai Mountains.

Reply: Thank you very much. According to your valuable suggestion, we didn’t discuss the associations between precipitation and driving factors (including AO, NAO, ENSO and so on) because no obvious correlations were found in the revised manuscript. In this study, we pay more attention on the effect of atmospheric circulations on the climate changes of the Altai Mountains.

As shown in Fig. 11, the Altai Mountains were controlled by cyclonic anomalies to their northwest and an anticyclonic anomalies to their southeast. This kind of atmospheric modes blocked the northward cold air and in turn provided favorable environment for an increasing temperature in the Altai Mountains. The water vapor from the North Atlantic, the Mediterranean and Caspian sea was transported to the Altai Mountains via the westerlies (Aizen et al., 2001, 2006). Previous study indicated that the sum of horizontal moisture advection and wind convergence terms has a significant positive contribution to the wetting trend in the northwest region of China (Peng and Zhou, 2017). As shown in Fig. 11, the southwestern wind between the anomaly cyclone and the anomaly anticyclone driven the water vapor flux from the Mediterranean and Caspian sea into the Altai Mountains, which in turn, increases the precipitation. The larger increased precipitation appeared in sub-region 1 and 3 during 1970-2015 due to the effect of windward slope with a significant change in southern Altai Mountains within China [27].

8, In Fig. 11, why the whole period is divided into 1996-2016 and 1960-1995? Any sudden change or shift around 1995/96?

Reply: Thank you for your question. Yes, you are right. The mutations of MAT occurred in 1995 and the large atmospheric circulation influenced on the mutations of climate. This result was inferred from the analysis of temperature in the nearby Tianshan Mountains by Xu et al. (2018).

9, Maybe I missed some files, but I did not see Table 1 and Table 2.

Reply: We are sorry we missed them in the original manuscript. Now we added the table in the resubmitted manuscript.

10, Caption of Fig. 10, should it be mean annual precipitation?

Reply: Done.

Replies to Reviewer 3 (thank you!):

1. Lines 35-36: Why does the warming result in the wetting in mid-latitude regions?

Reply: Thank you. Sorry, we made a mistaken and modified it. The AR5 of IPCC (2013) pointed out that in the 21st century global warming will further intensify the Earth’s water cycles, making the high-latitude areas even wetter and mid- and low-latitude areas even drier, melting more glaciers and reducing spring snow covers in the Northern Hemisphere.

Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). Summary for Policymakers: The Physical Science Basis, Contribution of Working Group I to the IPCC Fifth Assessment Report Climate Change, 2013.

2. Lines 37-38: Revise “the mid-latitude regions” to “Eurasia”.

Reply: Done.

3. Where are the tables?

Reply: Added.

4. Line 108: AMO and PDO are not atmosphere circulation indices.

Reply: Deleted.

5. Lines 107-116: Why do you select these five climate indices, which cannot explain the results in this study as analyzed in the Discussions of this paper?

Reply: Thank you for your question. Based on no obvious correlation between five indices and climate changes, we deleted this part. We focused on the analysis of association between atmospheric circulations and climate information.

6. Lines 113-116: Why do you unconventionally divide the seasons?

Reply: Modified and reanalyzed data.

Winter, spring, summer and autumn are defined as extending from December to the following February (DJF), March to May (MAM), June to August (JJA) and September to November (SON), respectively.

7. Lines 212-218: It is better to display the sub-regions in Figure 1.

Reply: Displayed.

8. Lines 279-281: Figure 10c indicates larger changes in lower elevations than that in the higher elevations.

Reply: Modified.

9.Lines 285-299: The correlations reflect the interannual relationships between the indices and precipitation in the Altai Mountains. However, this study focused on the decadal variability of precipitation in the Altai Mountains. Hence, this analysis is improper for this study.

Reply: Thank you for your valuable suggestion. We deleted the correlation analysis and paid more attention on association between atmospheric circulations and climate information.

10.Lines 300-315: This study explored the climate changes during 1970-2015, whereas Figure 11 analyzed the circulation changes from 1960 to 2016. Hence, this analysis is also improper for this study.

Reply: Thank you. We referenced the study between atmospheric circulations and climate changes during 1960-2016 in the nearby Tianshan Mountains, which was finished by Xu et al. (2018).

11. Lines 285-315: The first paragraph suggests that global circulations have weak relationship with the precipitation. The second paragraph indicates that local circulation might modulate the influence of global circulations on precipitation. However, the detailed mechanism is not proposed in this study.

Reply: Thank you. We paid more attention on association between atmospheric circulations and climate information.

The authors suggest that this anticyclone mode blocked the water vapour transport from the west. Why does this weakened water vapour transport not cause a drying trend? Why does the strengthening of Siberian High in winter not cause a cooling in the Altai Mountains?

Reply: Thank you. We modified it.

As shown in Fig. 11, the Altai Mountains were controlled by cyclonic anomalies to their northwest and an anticyclonic anomalies to their southeast. This kind of atmospheric modes blocked the northward cold air and in turn provided favorable environment for an increasing temperature in the Altai Mountains.

Figure 11 indicates that the Altai Mountains are controlled by cyclonic anomalies to their northwest and an cyclonic anomalies to their southeast, rather than an anticyclone circulation.

Reply: According to your suggestion, we modified it.

As shown in Fig. 11, the Altai Mountains were controlled by cyclonic anomalies to their northwest and an anticyclonic anomalies to their southeast. This kind of atmospheric modes blocked the northward cold air and in turn provided favorable environment for an increasing temperature in the Altai Mountains.

11. Fig. 10: Revise “air temperature” to “precipitation”.

Reply: Changed.

Replies to Reviewer 4 (thank you ! ):

1. Tables that mentioned in the manuscript are missing.

Reply: Added.

2. The trends of annual and seasonal mean temperature/precipitation for the fifteen stations in the Altai Mountains have been investigated and compared with each other. A table containing this information can be added to make readers easier to follow. In the analysis of trend-elevation relationship, which stations are included in the high elevations, middle elevations, and low elevations need to be clarified.

Reply: Thank you! We added them.

We further analyzed the temperature/precipitation trends in different elevations. The Altai Mountains were divided into three gradients (i.e., >2000 m, 1000-2000 m and <1000 m) (Fig. 8). The stations within >2000 m were Kara-Tyurek and Yalalt. Mugur-Aksy, Kosh-Agach, Olgiy, Khovd and Qinghe were contained elevation between 2000 and 1000 m and the remaining stations were considered elevation <1000 m.

3. The three subregions are divided based on the three characters of monthly precipitation. For the northern Altai Mountains (subregion 1), the monthly precipitation in Zmeinogorsk station shows two peaks (Fig. 6), but the other five stations in this sub-region all shows one peak. Why is the Zmeinogorsk station included in this subregion? More explanation is needed. At the same time, I have the similar concern about the southern Altai Mountains (subregion 3). This subregion includes Qinghe, Aletai, Fuyun, and Habaha stations. The monthly precipitation of Qinghe station shows one peak, but two peaks can be found in other three stations. The division of sub-regions need more justifications.

Reply: Thank you very much for pointing out the mistake. We did not notice it. Based on your suggestion, we redivided the sub-regions.

Fig. 6 showed monthly temperature and monthly precipitation changes of the Altai Mountains during 1970-2015. The highest temperature appears in June-August and the lowest temperature in November-January. The highest temperature (22.97 °C) in summer was recorded in Zajsan and the lowest value (-28.78 °C) in winter was in Kosh-Agach (Fig. 6a). Three features of monthly precipitation are characterized in the Altai Mountains (Fig. 6b). Firstly, the monthly precipitation is unimodal and is mainly concentrated in warm season (summer and autumn) with 55-84%. The related stations are Soloneshnoe, Kyzyl-Ozek, Yailu, Kara-Tyurek and Ust-Coksa. Their MAP is also relatively abundant (average about 649.73 mm). Secondly, the feature of monthly precipitation is also unimodal, but the MAP is relatively low (average about 133.86 mm). The associated stations are Mugur-Aksy, Kosh-Agach within Russia, three stations in Mongolia and Qinghe in China. Thirdly, different to the former two features of precipitation, the distribution of monthly precipitation is bimodal characterized by two peaks at April-September (50-68%) and at November-December (13-21%). The associated stations include Zmeinogorsk in Russia, Habahe, Aletai and Fuyun in China, Semipalatinsk and Zajsan in Kazakhstan.

4. For the atmospheric circulations responsible for the climate variability in the Altai Mountains is not clear. This manuscript stated that the Altai Mountains experienced a rapid warming (line 318, line 22), but it also argued that no rapid warming in the Altai Mountains due to the strengthening of Siberian High in winter (lines 308-309). This is contradictory. Meanwhile, how does the anticyclone mode block less water vapor transported by the westerlies from the North Atlantic Ocean into the Altai? I don’t think the reference 35 talks about it.

Reply: Thank you very much. We rewritten them.

Regional precipitation variability in climate trends could be closely related with atmosphere circulation. Xu et al. [9] calculated that differences of geopotential height (shaded) and wind speed (vector) at 500 hPa in the Asian Central Arid Zone including the Altai Mountains through calculating the circulation using NCEP/NCAR reanalysis data (Fig. 11). As shown in Fig. 11, the Altai Mountains are controlled by cyclonic anomalies to their northwest and an anticyclonic anomalies to their southeast. This kind of atmospheric modes blocked the northward cold air and in turn provided favorable environment for an increasing temperature in the Altai Mountains. The water vapor from the North Atlantic, the Mediterranean and Caspian sea is transported to the Altai Mountains via the westerlies (Aizen et al., 2001, 2006). Previous study indicated that the sum of horizontal moisture advection and wind convergence terms has a significant positive contribution to the wetting trend in the northwest region of China (Peng and Zhou, 2017). As shown in Fig. 11, the southwestern wind between the anomaly cyclone and the anomaly anticyclone drives the water vapor flux from the Mediterranean and Caspian sea into the Altai Mountains, which resulted in an increasing precipitation. The larger increased precipitation appeared in sub-region 1 and 3 due to the effect of windward slope with a significant change in the southern Altai Mountains within China [27].

Specific comments:

1. Lines 125: the subtitle “Temperature and precipitation during 1970-2015” is redundant.

Reply: Deleted.

2. There is a small figure on the left bottom of figure 1. The description about it needs to be added to the caption of figure 1.

Reply: Added.

3. For figure 3, there is a small histogram and ”0.47” on the top of the legend. What does that stand for? Figure 5 has the same problem.

Reply: Thank you very much. We modified them. The numbers in a small histogram of Figure 3 and 5 indicate the changeable values of temperature and precipitation.

4. The figure 6 doesn’t have “a” on the temperature (left figure) and “b” on the precipitation (right figure)

Reply: Added.

5. For the caption of figure 8, change “annual mean precipitation” to” mean annual precipitation”

Reply: Modified.

6. The “seasonal air temperature” needs to be added to the caption of figure 9 and figure 10.

Reply: Added.

7. In figure 10, the left string (annual, summer, etc.) covered the data point.

Reply: Modified.

5. The Altai Mountains has been divided into 3 subregions (northern, eastern and southern). Showing these subregions in a figure will be clearer. Maybe the division of subregions can be added to the figure 1.

Reply: Added.

Submitted filename: Authors Replies to Review Comments.doc

Click here for additional data file.

15 Jan 2020

PONE-D-19-22736R1

Temporal-spatial variability of modern climate in the Altai Mountains during 1970-2015

PLOS ONE

Dear Dr. Zhang,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR:

One reviewer suggested that it still needs major revising, and I agree. Please revised it carefully. Additionally I would recommend that the text be polished carefully.

==============================

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Additional Editor Comments (if provided):

This manuscript has been revised greatly. However, the reviewer suggested that it still needs revising, and I agree. There are some wording errors in the text additionally. I would recommend that the text be polished carefully.

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Reviewers' comments:

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Comments to the Author

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Reviewer #2: (No Response)

Reviewer #3: (No Response)

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Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #2: (No Response)

Reviewer #3: Yes

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Reviewer #2: (No Response)

Reviewer #3: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: The authors have successfully addressed all my concerns, and I can now recommend this manuscript to be accepted.

Reviewer #3: General comments:

This manuscript has remarkably improved compared with the original version. However, some further modifications should be required.

Detailed comments:

1.Lines 27-28: Revise “in the northwest” to “ to the northwest”, “in the southeast” to “ to the southeast”, and “northward” to “southward” (northward air is warm air, also in Line 296).

2.Lines 30 and 302: “southwestern” to “southwesterly”

3.Lines 45-46: “leading to put the security of water resources at risk” has syntax error.

4.Line 48 and other places in this paper: “divergent” is more appropriate than “changeable”.

5.Line 68: “detailed” to “detailedly”

6.Line 70: “are” to “is”

7.Fig. 2e: p>0.01

8.Lines 119-123 and Lines 153-157: The comparisons are meaningless because they are not in the same period. The trends would be different even in the same area between different periods.

9.Lines 127-128: “Fig. 2b-d” to “Fig. 2b-e”

10.Line 161: “Fig. 4b-d” to “Fig. 4b-e”

11.Line 209: “both” is inappropriate

12.Lines 247-259: The interpretation is not convincing. The lake areas are very small compared with the study area. The effect of the lake on the temperature is local and very weak. In addition, the lakes are freezing up in cold seasons, which would cool rather than warm the air via reflecting solar radiation.

13.Fig. 10b: p>0.01

14.Lines 271-273: Negative trend in lower elevation also indicates precipitation change, the range of which is larger than that in higher elevation.

15.Fig. 11: which season? In addition, the differences can be calculated by the authors according the study period rather than modifying from Xu et al. (2018).

16.Line 315: CRU and GPCC are not satellite-based data.

17.Lines 100-101 and Lines 318-320: In the Data Section, it is suggested that meteorological data is pre-disposed through strict quality control and homogenized. However, this data is doubted in the Discussion Section.

**********

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Reviewer #2: No

Reviewer #3: No

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10 Feb 2020

From: zhdl@ms.xjb.ac.cn

Authors replies to the review comments

Plos One (PONE-D-19-22736R1)

Dear Prof. Yang Bao:

Thanks very much for taking your and the reviewers’ time to review this manuscript. We really appreciate all your comments and suggestions! Please find our responses in below and my revisions/corrections in the re-submitted files.

Thank you very much for your attention and consideration again.

Sincerely yours

Zhang Dongliang

2020-2-10

Detailed comments:

1.Lines 27-28: Revise “in the northwest” to “ to the northwest”, “in the southeast” to “ to the southeast”, and “northward” to “southward” (northward air is warm air, also in Line 296).

Reply: Done.

2.Lines 30 and 302: “southwestern” to “southwesterly”

Reply: Done.

3.Lines 45-46: “leading to put the security of water resources at risk” has syntax error.

Reply: Done.

4.Line 48 and other places in this paper: “divergent” is more appropriate than “changeable”.

Reply: Done.

5.Line 68: “detailed” to “detailedly”

Reply: Done.

6.Line 70: “are” to “is”

Reply: Done.

7.Fig. 2e: p>0.01

Reply: Modified.

8.Lines 119-123 and Lines 153-157: The comparisons are meaningless because they are not in the same period. The trends would be different even in the same area between different periods.

Reply: Deleted.

9.Lines 127-128: “Fig. 2b-d” to “Fig. 2b-e”

Reply: Done.

10.Line 161: “Fig. 4b-d” to “Fig. 4b-e”

Reply: Done.

11.Line 209: “both” is inappropriate

Reply: Deleted.

12.Lines 247-259: The interpretation is not convincing. The lake areas are very small compared with the study area. The effect of the lake on the temperature is local and very weak. In addition, the lakes are freezing up in cold seasons, which would cool rather than warm the air via reflecting solar radiation.

Reply: Thank you very much. We neglect this important information about the freezing lakes in winter.

This elevation-dependent temperature depends upon the changes of snow cover and surface albedo feedback in the Altai Mountains [9, 47-48]. In details, the larger snow cover and its stronger albedo feedback in cold season accelerate the transit of upward turbulent heat, the cooling air has a lower probability of being heated by latent heat in high elevation than that in low elevation. In warm season, the decreased snow cover and the weakening surfce albedo can not inhibit the warming air in high elevation, i.e., high-elevation environments experience more rapid changes in temperature than environments at lower elevations [2-5].

13.Fig. 10b: p>0.01

Reply: Done.

14.Lines 271-273: Negative trend in lower elevation also indicates precipitation change, the range of which is larger than that in higher elevation.

Reply: Modified.

15.Fig. 11: which season? In addition, the differences can be calculated by the authors according the study period rather than modifying from Xu et al. (2018).

Reply: Thank you for your question. Fig. 11 shows the annual-scale differences of geopotential height (shaded) and wind speed (vector) at 500 hPa between 1996-2016 and 1960-1995. We referenced the Xu et al. (2018)’s results based on two reasons: firstly, the climate in the Tianshan Moutains and the Altai Mountains within the Central Asia are consistently influenced by the westerlies throughout a year and by the Siberian High in winter; secondly, the destination of this manuscript is from our study about Holocene climate change in the Altai Mountains. We recognized a delayed temperature increase at high elevations during the early Holocene, resulting in more humid conditions at high elevations compared to low elevations (Zhang and Feng, 2018). So we want to investigate the temporal and spatial change of climate in the observed interval. Honestly, we have difficulty in doing that. Thank you very much.

16.Line 315: CRU and GPCC are not satellite-based data.

Reply: We deleted the word “satellite”.

17.Lines 100-101 and Lines 318-320: In the Data Section, it is suggested that meteorological data is pre-disposed through strict quality control and homogenized. However, this data is doubted in the Discussion Section.

Reply: Thank you for your question. Indeed, the collected data from stations in the Altai Mountains is pre-disposed through strict quality control and homogenized. In the Discussion section, we doubted the fact that there has been a lack of consistency in the observation instrument and environments, such as vegetation changes, human grazing and relocation of stations because the Altai Mountains stretch over four countries (Kazakhstan, China, Mongolia and Russia).

Submitted filename: Authors Replies to Review Comments2.doc

Click here for additional data file.

25 Feb 2020

Temporal-spatial variability of modern climate in the Altai Mountains during 1970-2015

PONE-D-19-22736R2

Dear Dr. Zhang Dongliang,

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Bao Yang, Ph.D, Prof.

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PLOS ONE

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Reviewers' comments:

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Comments to the Author

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Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #2: Yes

Reviewer #3: (No Response)

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Reviewer #2: Yes

Reviewer #3: Yes

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

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Reviewer #3: Yes: Xiaojian Zhang

2 Mar 2020

PONE-D-19-22736R2

Temporal-spatial variability of modern climate in the Altai Mountains during 1970-2015

Dear Dr. Zhang:

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