Assessment of the elevation-dependent warming of land surface temperatures in the Qinling-Daba Mountains and its relationship with land surface albedo and aerosol optical depth from 2001 to 2021

Assessment of the elevation-dependent warming of land surface temperatures in the Qinling-Daba Mountains and its relationship with land surface albedo and aerosol optical depth from 2001 to 2021 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Assessment of the elevation-dependent warming of land surface temperatures in the Qinling-Daba Mountains and its relationship with land surface albedo and aerosol optical depth from 2001 to 2021 Yuanyuan Lian, Jiale Tang, Yanli Zhang, Fang Zhao, Haifang Yu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4399888/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract In this paper, we examined the elevation-dependent warming (EDW) patterns of MODIS LST across different seasons in the Qinling-Daba Mountains, further investigate the connections between the EDW patterns of LST and ALB as well as AOD. The key findings include: 1) Our study reveals a robust correlation between LST and air temperature in the Qinling-Daba Mountains, suggesting the feasibility of using MODIS LST to predict the temperature trends 2) During the period from 2001 to 2010, MODIS LST shows a significant EDW trend, primarily in the spring season. In contrast, a negative EDW is observed in the period during 2011–2021, which is contrary to the earlier decade, particularly during the autumn and winter seasons. 3) EDW of MODIS LST is affected by the combination of ALB and AOD. The former has a negative influence on the change of LST, particularly above 2500 m in elevation. However, the latter is negatively correlated with the trend of MODIS LST, primarily at lower and middle altitudes (0-2500 m). This study gives a comprehensive explanation for the EDW of the temporal variations of LST in the Qinling-Daba Mountains to improve our understanding of the complex interactions and potential future climate scenarios in the region. Earth and environmental sciences/Climate sciences/Atmospheric science Earth and environmental sciences/Climate sciences/Climate change The Qinling-Daba Mountains MODIS LST Elevation-dependent warming Land surface albedo Aerosol optical depth Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction The rise in global surface temperatures, amounts to a 1.1°C in the period 2011–2020 compared to 1850–1900, and climate patterns indicate an anticipated escalation to 1.5°C, indicating the continuous global warming trend from 2011 to 2040 1 . Climate warming is influenced by various factors, including cloudiness, water vapor feedbacks, surface albedo, aerosols, soil moisture, and others, exhibiting complex seasonal variations 2,3 . Altitude is suggested to impact the seasonal variations of surface temperatures and contribute to the complexity of temperature change 4–6 . Elevation-dependent warming (EDW) is a well-documented phenomenon characterized by temperature trends rising faster in high altitude mountains 7,8 . This phenomenon is supported and validated in numerous major mountain ranges, such as the Tibetan Plateau 9–12 , Rocky Mountains of North America 13,14 , Yunnan-Kweichow Plateau 15 , Tropical Andes mountain range 16 . However, it should be noted that the occurrence of the phenomenon of AEDW depends on the season, the study period and mountainous areas 17–20 . Negative EDW was observed in central Europe and eastern North America through the monitoring of global weather station sites, with a higher warming trend identified at lower altitudes than at higher altitudes 21 . Hence, some studies suggested that there was no clear relationship between elevation and the degree of warming 17,22–24 . It is simplistic to dismiss the link between elevation and climate warming, since the complex variations in EDW contribute to the complexity in climate warming in mountainous regions, including seasonal and interannual variations 25 . This complexity of EDW is particularly pronounced in ecological transition zones, where there is a horizontal zonation of climate and vegetation, coupled with vertical zonation corresponding to elevation changes 26 . Unfortunately, the occurrence of EDW and its seasonal difference have not been confirmed in the transition mountain ranges between the north and south of China, although it is crucial for a comprehensive understanding of climate warming trends of China. Surface albedo (ALB) and aerosol optical depth (AOD) are considered as significant drivers of elevation-dependent warming 27–29 . ALB constitutes a key variable influencing the Earth's climate system and plays a crucial role in numerical climate models and surface energy balance equations 30 . Variations in ALB have been linked to anomalous warming at high altitudes during the spring and winter seasons 31 . ALB emerges as primary contributors to heightened warming at high altitudes like the Tibetan Plateau, with warming associated with ALB ranging from about 0.26 to 0.50 K in winter and from 0.27 to 0.77 K in spring 32 . Under warming conditions, receding snowpack on the ground leads to decreased ALB, increased absorption of solar radiation by the surface, and enhanced warming at high altitudes 33,34 . AOD is an important parameter of aerosol optical properties. It signifies the integration of aerosol extinction coefficients in the vertical direction under clear and cloudless weather, depicting the absorption and scattering of atmospheric radiation by aerosols 35,36 . Aerosol particles influence the atmospheric radiation balance through both direct and indirect radiative forcing mechanisms 37 . Aerosol absorption and scattering contribute to the reduction of solar radiation over the ground, subsequently impacting the surface thermal environment 28 , and consequently, the EDW phenomenon 20,21,38 . In contrast to ALB, the impact of AOD on EDW is predominant at low altitudes 37 . Prior studies have predominantly focused on the influence of surface albedo on changes in EDW at high altitudes 27,39 or on the impact of aerosols on changes in EDW at low altitudes 40,41 . In fact, the emergence of EDW pattern in a huge mountain systems is a result of the combined effects of spatial and temporal variations in LST at low altitude and high altitude, However, the influence of AOD and ALB on EDW is different in season 31 . The purpose of this paper is to investigate the existence of the EDW phenomenon in an ecologically transitional zone characterized by complex climate and terrain, specifically analyzing its presence in different seasons and altitudes. Additionally, the study aims to identify the underlying causes of the observed EDW patterns in these varied conditions. The Qinling-Daba Mountains, located in the ecological transition zones between the northern subtropical zone and the warm-temperate zone of China 42,43 , is considered a potentially vulnerable area in terms of ecology 44–46 . Between 1969 and 2017, the mean annual temperature in the Qinling-Daba Mountains increased by about 1.2°C 47 , potentially causing a northward shift in its temperature zone 48 . However, climate warming in the Qinling-Daba Mountains varies significantly between high and low altitudes, due to the complex topography and Underlying surface 49 . Consequently, this region serves as an ideal location for studying EDW of the temporal variations of LST in the ecological transition zone. However, the scarcity of meteorological stations in mountainous areas presents a challenge in exploring temperature variations, which hinders the study of EDW changes and their influencing mechanisms in the Qinling-Daba Mountains. The Moderate Resolution Imaging Spectroradiometer (MODIS) product compensates for the lack of mountain sites and has been widely used to estimate changes in quantitative mountain air temperatures 50–52 . It has been applied to address the challenges in studying EDW (Palazzi, Mortarini et al., 2019). In this study, we assessed the characteristics of surface temperature trends in the Qinling-Daba Mountains from 2001 to 2021 based on MODIS LST, and analyzed the relationship between LST trends and elevation in different years, aiming to explore the roles of ALB and AOD in the EDW of LST in the mountains on seasonal and annual scales. 2 Material and method 2.1 Study area The Qinling-Daba Mountains are located between 30 ° N and 36 ° N latitude and 101 ° E and 114 ° E longitude (Fig. 1 ). The mountain range consists of the Qinling Mountain Range and the Daba Mountain Range, with the Han Shui Valley lying between them. The topography of the Qinling-Daba mountains gradually decreases from west to east. The western part connected to the Tibetan Plateau, representing the highest point of the Qinling-Daba mountain range, reaching an altitude of 5528 m. In contrast, the eastern part connects to the plains and has altitudes ranging from 1000 to 1500 m. The Qinling-Daba Mountains serve as the boundary between the warm-temperate zone and the northern subtropical zone in China 53 . This region is characterized by transitions from a deciduous broadleaved forest belt to an evergreen broad-leaved forest belt. On the north of the Qinling Mountain Range, the vegetation is primarily a warm-temperate deciduous broadleaved forest zone, while on the south of the Qinling Mountain Range, it transitions to a mixed evergreen and deciduous broad-leaved forest belt. Moving further south to the southern slope of the Daba Mountain Range, the vegetation predominantly consists of subtropical evergreen broadleaved forests. 2.2 Data 2.2.1 Land Surface Temperature In this study, the MODIS LST product (MOD11A1) was acquired from the NASA website ( https://ladweb.nascom.nasa.gov ). The MOD11A1 product provides daily per-pixel surface temperature and emissivity at a 1km spatial resolution, deriving from various parameters including MODIS geographic information data, emissivity, cloud mask, atmospheric temperature, water content, snow cover, and surface cover parameters 54 . The product also offers pertinent quality control assessments, observation time, apparent zenith angle, and clear-sky coverage. Data preprocessing comprises two main components: data filtering and data merging. Initially, outliers were addressed based on the product's quality assurance of the MOD11A1 product, followed by merging the qualified daily data for four seasons—spring, summer, autumn, and winter—to calculate their mean values. Subsequently, the seasonal mean value at a 1km resolution for the years 2001–2021 was determined. In contrast to previous studies on EDW conducted at an annual scale 55–57 , focusing on seasonal scales enables a more nuanced understanding of seasonal differences in EDW, facilitating the identification of significant EDW patterns 58,59 . 2.2.2 Surface Albedo MODIS surface reflectance products, specifically the MOD09A1 dataset, provide estimates of surface spectral reflectance, which were obtained from USGS website ( https://earthexplorer.usgs.gov/ ). The MOD09A1 data provides estimates of surface spectral reflectance in an 8-day gridded Level 3 product in a sinusoidal projection at 500 m resolution. The scientific dataset within the MOD09A1 product consists of reflectance values in bands 1–7, quality assurance assessments and pixels for a given day of the year. It also includes solar angle, angle of view and zenith angle, for the period from January 1, to December 31, 2021. To process the MODIS data for analysis, a mosaic projection transformation of the MODIS data was performed using the MRT software. This transformation converted the projected coordinates to WGS-84 coordinate system. Anomalies in the data were processed, and the individual datasets were summed and averaged to create seasonal averages. Furthermore, all the data was resampled to a 1-kilometer resolution to ensure spatial consistency. To calculate the Albedo (ALB) values, the method developed by Shunlin Liang 60 , as Equation (A.1), was used to compute the ALBs based on MOD09A1. The Equation (A.1) is as follows: ALB = 0.160a1 + 0.291a2 + 0.243a3 + 0.116a4 + 0.112a5 + 0.081a7-0.0015 (A.1) Where: a1, a2, a3, a4, a5, and a7 represent the reflectance values in band 1, 2, 3, 4, 5, and 7, respectively. 2.2.3 Aerosol Optical Depth MODIS is an important instrument for detecting atmospheric aerosols, and various aerosol satellite inversion methods are grounded in different principles tailored to diverse surface types and aerosol compositions. Within the MODIS AOD algorithm for land, two independent algorithms are employed: the dark image element algorithm for dark backgrounds like vegetation-covered land 61 and the dark blue algorithm for bright backgrounds 62,63 . In 2018, NASA introduced the Multi-Angle Atmospheric Correction Algorithm for Atmospheric AOD (MAIAC) product, known as, MCD19A2. MAIAC is a popular algorithm that incorporates time-series analyses with pixel- and image-based processing to enhance the accuracy of cloud detection, aerosol inversion, and atmospheric corrections 64 . MCD19A2 is derived from the direct fusion of MODIS observations from two satellites, offering high temporal and spatial resolutions and generating daily AOD data at spatial resolutions of 5km x 5km and 1km x 1km. In this paper, we acquired a product with a wavelength of 550 nm from the MCD19A2 data version C6, covering the period from January 1, 2001, to December 31, 2021 from NASA's website ( https://search.earthdata.nasa.gov ). The data has a daily temporal resolution and a spatial resolution of 1 km x 1 km. The daily data was processed to calculate the seasonal mean values of AOD. 2.2.4 Meteorological stations The meteorological stations utilized in this study were sourced from the monthly dataset of surface climate data in China provided by the National Meteorological Information Centre ( http://data.cma.cn/ ). There are 100 stations, located within the study area, providing the daily air temperature, spanning from 2001 to 2021 (Fig. 1 ). These stations were primarily located in six provinces and municipalities, namely, Gansu, Shaanxi, Henan, Sichuan, Hubei, and Chongqing. We processed the temperature anomalies and aggregated the daily data into monthly average data using the monthly averaging method to assess the accuracy of MODIS LST. 2.2.5 DEM The digital elevation model (DEM) data utilized in this paper were downloaded from the USGS website ( https://eaethexplorer.usgs.gov/ ), with a spatial resolution of 30 m. The DEM is stable, digitally processed, and maintains a constant accuracy. It undergoes real-time updates, making it easy to automate and providing real-time information. It serves as the fundamental data for national geographic information, widely used 65–67 . To obtain the DEM data for the Qinling-Daba Mountains, we extracted the data based on the vector boundary (Fig. 1 ) and reprojected it to the WGS-84 coordinate system. The data was resampled to a 1-kilometer resolution to ensure spatial consistency with the MODIS data we utilized. 2.3 Methods In this study, LST was compared with air temperatures recorded at meteorological stations in different months from 2001 to 2021 to assess the accuracy of MODIS LST in the Qinling-Daba mountains. The evaluation criteria consisted of R 2 (coefficient of determination), root mean square error (RMSE), and ratio of standard deviations (RSD). To examine the surface temperature trends in the Qinling-Daba Mountains over the study period, we employed the Mann-Kendall test and Theil-Sen Median method in the Qinling-Daba Mountain from 2001 to 2021. The Mann-Kendall test is a nonparametric test that does not assume a specific probability distribution for the series under examination 68 . It is commonly used for long term trends in temperature, precipitation, runoff, etc 69 . The test evaluates the correlation between data points in the time series to determine if there is a significant trend change 70,71 . The Theil-Sen Median method, also known as Sen’s slope estimation, is a robust non-parametric statistical approach used for trend analysis. The method is computationally efficient, insensitive to measurement error and outlier data, and commonly applied in the analysis of long time series data 72 . Furthermore, we conducted a Pearson correlation analysis to examine the relationship between altitude and climate warming, determining the EDW. Additionally, we explored the relationship between LST and surface albedo and aerosol optical depth in the mountains, Finally, we discussed the mechanisms underlying the variation of the EDW at low and high altitudes in the Qinling-Daba Mountains. 3 Results 3.1 Assessment of the accuracy of MODIS LST in the Qinling-Daba Mountains The time series analysis of MODIS LST and air temperature from 2001 to 2021 reveals that the monthly average LST follows a similar variations trend to the air temperature in the Qinling-Daba Mountains (Fig. 2 ). Both LST and air temperature reach their highest average values in July and the lowest values in January and December. However, the LST is slightly higher than the air temperature across all months. The average LST in the Qinling-Daba Mountains from 2001 to 2021 is 17.03°C, while the average air temperature is 13.56°C. The correlation between air temperature and surface temperature remains strong throughout the 21-year period with R² above 0.9 at all stations in the Qinling-Daba Mountains. Specifically, 47% of the stations had R² between 0.9 and 0.95, and 53% had R² values above 0.95. The root means square error (RMSE) of the air temperature and LST was 3.83, and the ratio of standard deviation (RSD) is 1.31, These results suggest that MODIS LST effectively captures the trend changes in air temperature in the Qinling-Daba Mountains. 3.2 Spatiotemporal variations of LST in the Qinling-Daba Mountains from 2001 to 2021 The spatiotemporal variations in LST trends from 2001 to 2021 indicate a decline in LST across most regions, except for certain areas in the eastern fringe, which show an increasing trend in LST (Figure 3a). However, there is a notable distinction between the rates of change in mean and maximum LST for the periods 2001-2010 and 2011-2021, suggesting a cooling trend in LST during the first decade and a significant warming trend in the subsequent period (Figures 3b and c). To further validate the disparity in LST between the two study periods, we calculated Sen’s slope estimates of LST for 2001-2010 and 2011-2021. The results demonstrate that the LST trends in the two study periods exhibit contrasting changes in the rate of the LST variations during winter compared to other seasons (bottom right of Fig. 3a). In winter, the Qinling-Daba Mountains experienced a noticeable warming trend from 2001 to 2010, while in spring, summer and autumn, the Sen slopes were -0.06 ℃/a, -0.19 ℃/a and -0.0685 ℃/a, respectively, showing a cooling trend. However, in the winter of 2011-2021, the temperature tended to decrease (-0.036 ℃/a). On the other hand, the Sen slope of LST in other seasons was positive, with spring, summer, and autumn exhibiting climatic tendency rates of 0.0057 °C/a, 0.029 °C/a, 0.031 °C/a, and 0.031 °C/a, respectively. These findings highlight significant changes in the climate propensity rate of LST during the two distinct time periods, 2001-2010 and 2011-2021. The relationships between spatiotemporal variations in LST trends and altitudes were explored based on these two time periods with opposite LST trends. 3.3 EDW and its seasonal differentiations in the spatiotemporal variations of LST in the Qinling-Daba Mountains from 2001to 2021 The relationship between altitude and the climate propensity rate of LST reveals a significant EDW pattern during the spring of the period 2001–2010 (Fig. 4 ). The results indicate that pixels reflecting the temporal variations in LST are primarily located in quadrant Ⅲ (<0 ℃), presenting a cooling trend, in the low elevation region, particularly at 500-1500m, during the spring. The primary rate of change in LST in this region is approximately − 0.2 ℃/a. Conversely, there is a significant warming rate at higher altitudes, with pixels reflecting temporal variations in LST distributed in quadrant Ⅰ (>0 ℃), above 2500m, during the spring. In these high-altitude areas, the range of LST change mainly between 0.2–0.4 ℃/a. Furthermore, Pearson correlation analyses of surface temperature trends and elevation for the periods 2001–2010 across different seasons reveal that the warming trend of LST becomes more apparent, with a correlation coefficient of 0.742 (P < 0.01), between overall elevation and LST change trend during the spring in the Qinling-Daba Mountains (Fig. 5 ). The relationship between altitude and the climate propensity rate of LST reveals a significant negative EDW pattern during the autumn and winter of the period 2011–2021 (Fig. 4 ). The pixels reflecting temporal variations in LST are primarily located in the quadrant Ⅳ (>0 ℃) within the altitude range of 0-2500 m, indicating a pronounced warming phenomenon. The primary rate of change in LST is mainly concentrated in the range of 0.1–0.25 ℃/a across the four seasons. However, in spring and summer, the pixels reflecting the temporal variations in LST are discretely distributed at different altitudes. On the other hand, a wider range of cooling trends occurs at high altitudes during autumn and winter (LST change trend values mainly falling in quadrant Ⅱ), with the main trend values ranging from − 0.15 to -0.25°C/a. Additionally, Pearson correlation analyses of surface temperature trends and elevation for the periods 2011–2021 across different seasons reveal that the cooling trend of LST becomes more apparent with the increase in altitude during autumn and winter. The correlation coefficient reaches − 0.860 (P < 0.01) in autumn and − 0.888 (P < 0.01) in winter (Fig. 5 ). 3.4 Effects of ALB and AOD on the EDW of LST in the Qinling-Daba Mountains 3.4.1 Relationship between ALB and EDW of LST in the Qinling-Daba Mountains To systematically elucidate the mechanism causing the occurrence of EDW in LST, we selected the spring of 2001–2010 and the autumn and winter of 2011–2021, which were marked by pronounced EDW and negative EDW trends, and explored the effects of ALB on the EDW of LST in the Qinling-Daba Mountains. Initially, we employed Theil-Sen Median trend analysis to analyze the trends in ALB during the selected time periods. The results indicated a downward trend with increasing altitude in the spring from 2001 to 2010, especially, a more pronounced decline trend in ALB was observed at higher altitudes (Figure. 6a). Concurrently, the temporal variations of LST exhibited an increasing trend, particularly noticeable above 2500 m. This trend in LST was opposite to the variations observed in ALB. Correlation analysis between the temporal trend of LST and ALB revealed a significant negative correlation, reaching − 0.32 (P < 0.01) (Figure. 7a). These findings suggest that ALB had a negative influence on the occurrence of EDW of LST in regions above 2500 m. The results indicate that there is no significant correlation between the change rates of LST and ALB (P>0.05) during the autumn of 2011-2021 (Figure. 6b and Figure. 7b). However, in the winter of 2011-2021, the temporal trend of LST exhibited a significant negative correlation with ALB (Figure. 7c), with a correlation coefficient reaching to -0.346 (P < 0.01). Notably, during the period of 2011-2021, a distinct "mirror" relationship was observed between the temporal trends of LST and ALB at altitudes of 2500-5000min the winter (Figure. 6c). Specifically, the temporal trend of LST demonstrated a decreasing pattern with increasing altitude between 2500 m and 5000 m. At its lowest value at 4500 m, the trend of LST reversed and started increasing. On the other hand, during the same period, ALB initially showed an increasing pattern and reached its highest point at 4500 m before decreasing with increasing altitude. Overall, whether it is positive or negative in EDW, ALB has a negative effect on the temporal trend of LST during the spring of 2001-2010 and the winter of 2011-2021 at high altitudes (above 2500 m) 3.4.2 Relationship between AOD and EDW of LST in the Qinling-Daba Mountains During the spring from 2001 to 2010, when significant EDW events were observed, the temporal trend of LST exhibited an inverse relationship with AOD as the elevation increased from 0 to 2500 m (Fig. 8). Specifically, the trend of LST showed an increasing pattern, ranging from while the trend of AOD decreased, as the elevation increased from 0 to 2500 m. Furthermore, correlation analysis demonstrated a significant negative correlation between AOD and LST trends during spring in the period of 2001–2010 with correlation coefficients reaching to -0.28 (P < 0.05) (Fig. 9 ). In the autumn and winter from 2011 to 2021, during which negative EDW events were observed, the temporal trend of LST exhibited an inverse relationship with AOD as the elevation increased from 0 to 2500 m (Fig. 8). In autumn, the trend of LST decreased from 0.04 /a to -0.0038 /a, while the trend of AOD increased from − 0.023 /a to -0.001 /a as the elevation increased from 0 to 2500 m. Similarly, in winter, the trend of LST decreased from 0.024 /a to -0.001 /a, while the trend of AOD increased from − 0.03 /a to -0.0039 /a with the increase in elevation from 0 to 2500 m. Furthermore, correlation analysis demonstrated significant negative correlations between AOD and LST trends during the autumn and winter from 2011 to 2021. The correlation coefficients reached notable of -0.504 (P < 0.01) and − 0.421 (P < 0.05), respectively, as shown in Fig. 9 . Overall, regardless of whether it is positive or negative in EDW, AOD has a negative effect on the temporal trend of LST during the spring of 2001–2010, as well as in the autumn and winter of 2011–2021, particularly at low altitudes (0-2500 m). 4 Discussion 4.1 Spatiotemporal variations of EDW in the Qinling-Daba Mountains from 2001 to 2021 The results indicate that MODIS LST effectively captures the spatiotemporal variations of temperatures, including the patterns associated with EDW in the Qinling-Daba Mountains. These findings are consistent with prior research on MODIS LST conducted by Kindstedt, Schild et al 73 and Zikan, Adolph et al 74 . During the period of 2001–2010, we observed a general "cooling" trend in the Qinling-Daba Mountains, as reflected by the MODIS LST data. This finding aligns with global climate change studies conducted from 1998 to 2012, a period during which the global mean surface temperature did not experience significant warming 75–77 . However, it is important to note that high altitudes in the Qinling-Daba Mountains exhibited an inverse warming trend and noticeable EDW. This suggests that climate warming did not stagnate or decelerate in the region from 2001–2010, particularly at high altitudes. This phenomenon is similar to what is observed in the Arctic, where it significantly contributes to the ongoing global warming trend 78 . The explanation for this phenomenon lies in the perspective of the global warming energy balance, as proposed by Duan and Xiao 79 . EDW, observed in this study, occurs only in the spring during the period of 2001–2010 in the Qinling-Daba Mountains, coinciding with a general climate cooling trend. Conversely, during the period of 2011–2021 in autumn and winter, a noticeable negative EDW was observed. This suggests that EDW or negative EDW events occur in specific spatial and temporal scales rather than universally. These findings are consistent with other studies conducted by Zeng, Chen et al 21 , Nigrelli and Chiarle 80 , and Li, Chen et al 81 . It is challenging to draw a general conclusion about whether the warming rate is higher in mountainous regions than that of flatlands, as it may vary across different mountain regions and scales. Therefore, it becomes necessary to narrow down the spatial and temporal scales to explore detailed patterns of EDW, This viewpoint is supported by other studies focusing on EDW in the Tibetan Plateau 58,59 . 4.2 Mechanisms causing EDW and negative EDW in the Qinling-Daba Mountains The findings of the study indicate that ALB has a negative effect on the temporal trend of LST during the spring of 2001–2010 and the winter of 2011–2021 at high altitudes (above 2500m). This negative effect of ALB on LST enhances the difference in the temporal variation of LST between the high altitudes and low altitudes, leading to the occurrence of EDW during the spring of 2001–2010 and negative EDW during the winter of 2011–2021. This relationship between ALB and EDW/ negative EDW is linked to the feedback of ALB to climate change. Climate warming is known to lead to a reduction in snow cover and an increase in vegetation cover, as suggested by previous researches 82,83 ,These changes in land cover conditions, specifically the enhancement of vegetation cover and the reduction of snow cover, contribute to a decrease in ALB 84 , Consequently, there is an increase in net surface radiation 85 , and an increase of solar short-wave radiation absorbed by the ground 86 ,which ultimately leads to an increase in LST. The feedback mechanism involving ALB and climate warming has been applied to explain EDW events over the Tibetan Plateau and the Colorado Rockies, as demonstrated in studies conducted by Kang, Xu et al 5 , You, Min et al 87 , and You, Zhang et al 88 . These studies highlight the role of ALB in influencing the spatiotemporal variations of LST and the occurrence of EDW in the Qinling-Daba Mountains. According to the study, at low altitudes (0-2500m). AOD has a negative effect on the temporal trend of LST during the spring of 2001–2010, as well as in the autumn and winter of 2011–2021. This negative effect of AOD further enhances the difference in the temporal variation of LST between the high altitudes and low altitudes, contributing to the occurrence of EDW and negative EDW events in the temporal variation of LST during the spring of 2001–2010, as well as in the autumn and winter of 2011–2021.The effect of AOD on the EDW can be attributed to the spatiotemporal distribution of AOD and its effect on solar radiation. Atmospheric aerosol pollutants tend to accumulate at relatively low altitudes (< 2500m), leading to a decrease in the shortwave radiative flux reaching lower altitudes 89 . This reduction in solar radiation at lower altitudes contributes to the variation in LST at lower altitudes. AOD produces both absorption and scattering effects on solar radiation due to variations in aerosol particles, with the scattering effect being greater than the absorption effect 90,91 . This causes a negative effect on LST 92,93 . The observed increases in AOD from 2001–2010 and decrease in AOD from 2011–2021 at low elevations in the study result in a cooling effect and warming effect on the LST at low altitudes, respectively, leading to significant EDW / negative EDW in different periods. These viewpoints are supported by other studies. Philipona 38 and Zeng 21 have attributed the occurrence of negative EDW to a decrease in atmospheric sources of pollution and a reduction in shortwave radiation, resulting in an increase in "solar brightening" at densely populated valleys in low altitudes 94,95 . In addition to the factors examined in this study, there are other contributors that may play a role in the phenomena associated with EDW / negative EDW. For instance, previous studies have suggested that the cloud-radiation feedback process in the eastern Tibetan Plateau is a primary factor contributing to the increase in warming amplitude of the plateau with increasing altitude during the CO 2 doubling experiment 96 . Furthermore, land use changes have potential to induce EDW 97–99 as they often result in variations in topographic gradients in the Qinling-Daba mountains 100 . The overall ecological quality of the Qinling-Daba mountains area exhibits a linear improvement trend with altitude below 3200m, while the rate of improvement tends to flatten out beyond that altitude 100 . This variation in ecological quality with altitude can have implications for local climate patterns and potentially contribute to the occurrence of EDW. Various factors such as different vegetation cover types, soil moisture, snow thickness, and surface albedo purity, can also influence changes in LST trends to varying degrees 101 . These factors can have an impact on the occurrence of EDW/negative EDW. 4.3 Effects of EDW pattern on ecosystem in the Qinling-Daba mountains As north-south transition zone in China, the Qinling-Daba mountains acts as a climatic barrier, influencing mountain altitudinal belts, biodiversity and water conservation, and in turn affecting ecosystem services throughout the central China 43 . Over the past few decades, there has been a significant upward shift in the transitional climatic zones in the Qinling-Daba mountains due to temporal variations in temperatures 42,102 . If these temperatures surpass temperature thresholds for vegetation sequestration capacity, it can lead to large-scale forest mortality in the forest line area of Bashan 103 . The EDW, as observed in the study, complicate the situations of the climate change and its impact on ecosystems. During the spring period of climate cooling from 2001 to 2010, climate warming was still observed in high altitudes, and EDW can mitigate the differences in vegetation between high and low altitudes. Conversely, during the winter and autumn period of climate warming from 2011 to 2021, negative EDW associated with a trend of climate cooling in high altitudes, thereby amplifying temperature differences between high altitudes and low altitudes. The pattern of EDW over the Qinling-Daba mountains plays a crucial role in determining the distribution of surface heat source and have far-reaching effects on the ecosystem in the Qinling-Daba mountains 9 . The inconsistent temperature variations caused by EDW between high and low altitudes can increase ecosystem instability, particularly in the context of vegetation shifting from low to higher altitudes due to climate warming 99 , given the influence of temperature changes on plant productivity, as well as the carbon and nitrogen cycle processes of the ecosystem 104 . 5 Conclusion In this study, we investigated the EDW patterns of LST across different seasons in the Qinling-Daba mountains using MODIS LST data, and explored the relationships between the EDW patterns of LST and ALB as well as AOD. Based on our analysis, the following conclusions can be drawn: ( 1 ) A strong correlation was observed between MODIS LST and air temperatures in the Qinling-Daba Mountains. The coefficient of determination (R²) between air temperatures and LST at all meteorological stations exceeded 0.9, suggesting the feasibility of using MODIS LST to predict the temperature trends in the Qinling-Daba mountains. ( 2 ) During the period from 2001 to 2010, an EDW trend was observed, primarily in the spring season. The Correlation coefficient between elevation and the rate of temporal variations of LST was found to be 0.844 in the 1000-5000m region, indicating higher altitudes experienced a greater rate of warming. However, in contrast, a significant negative EDW phenomenon occurred in autumn and winter from 2011 to 2021. The Correlation coefficient between elevation and the rate of temporal variations of LST was − 0.86 in autumn, while in winter, it was − 0.888. It indicates a trend of climate cooling in high altitudes during autumn and winter from 2011 to 2021. ( 3 ) The EDW of LST is influenced by the combined effects of ALB and AOD. During the spring period from 2001 to 2010 and the winter period from 2011 to 2021, ALB had a negative impact on the temporal change of LST above 2500m. In contrast, the trends of AOD exhibited a negative correlation with LST trends, primarily in lower altitudes (0-2500 m) during the spring of 2001–2010, as well as in the autumn and winter of 2011–2021. By considering the combined effects of ALB and AOD, we can gain a comprehensive understanding of the patterns of EDW and integrate the different ecological environments from high and low altitudes in the Qinling-Daba Mountains. Declarations Conflicts of Interest: All authors declare that there are not any personal or financial conflicts of interest. Author Contribution Conceptualization, Lian, Y. and Zhang, Y.; methodology, data curation and visualization, Lian, Y. and Zhao, F.; writing-original draft preparation, Lian, Y. and Zhao, F.; writing-review and editing, Tang, J., Zhang, Y., Yu, H., Zheng, Z., and Wang, Y.; All authors have read and agreed to the published version of the manuscript. Acknowledgments: This research is funded by the Natural Science Foundation of Henan (Grant No.232300420165), the Science and Technology Tackling Program of Henan Province (Grant No. 242102210003) and the Key Scientific Research Project of Higher Education Institutions in Henan Province (Grant No. 24A630004). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Data Availability The data that support the findings of this study are available from the authors upon reasonable request. References Lee, H. et al. AR6 Synthesis Report: Climate Change 2023. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4399888","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":310532743,"identity":"c3ce5b47-d1a0-4050-833d-62d237027fd2","order_by":0,"name":"Yuanyuan Lian","email":"","orcid":"","institution":"Henan University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Lian","suffix":""},{"id":310532744,"identity":"3713fd93-3128-4e88-8c7c-5de4e96d2d1d","order_by":1,"name":"Jiale Tang","email":"","orcid":"","institution":"Henan University","correspondingAuthor":false,"prefix":"","firstName":"Jiale","middleName":"","lastName":"Tang","suffix":""},{"id":310532745,"identity":"224d4429-9f6e-4ac6-ad24-c5f4ecf3b33a","order_by":2,"name":"Yanli Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYDACCSjNxwwibRjk2NibDxCnhQ2sJY3BmI/nWAKRWhggWhLnSeQo4NUhP7v52cOvbXZ5bOwMzJ95EmzS2xhyGBh+VGzDqYVxzjFzY9m25GKgw9ikeRLSctsYzh5g7DlzG6cWZokEM2nJNubENqAWZt4fh3PbGPsSmBnbcGthk0j/BtRSD9ICctj/dDZmHgO8WngkcswkP7YdBmlhADrsQAIbGwEtEhI5ZdIM546DHSY5JyHZsI2HLeEgPr/Iz0jfJvmjrDqxn/8A84c3CXby8vMfH3zwowK3FnAQ8IIjhf8DXOQAXvVAwPjjDyElo2AUjIJRMKIBAGsGSubk7vRjAAAAAElFTkSuQmCC","orcid":"","institution":"Henan Finance University","correspondingAuthor":true,"prefix":"","firstName":"Yanli","middleName":"","lastName":"Zhang","suffix":""},{"id":310532746,"identity":"28430e28-cc05-4719-aeb9-190f39de7103","order_by":3,"name":"Fang Zhao","email":"","orcid":"","institution":"Henan University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Zhao","suffix":""},{"id":310532747,"identity":"04e9ff65-81eb-47ec-adf3-2465c10a0cf3","order_by":4,"name":"Haifang Yu","email":"","orcid":"","institution":"Chaoyang Teachers College","correspondingAuthor":false,"prefix":"","firstName":"Haifang","middleName":"","lastName":"Yu","suffix":""},{"id":310532748,"identity":"e6815a96-cdd5-4688-9ae8-690ae789fed0","order_by":5,"name":"Zhixian Zheng","email":"","orcid":"","institution":"Henan University","correspondingAuthor":false,"prefix":"","firstName":"Zhixian","middleName":"","lastName":"Zheng","suffix":""},{"id":310532749,"identity":"a1008adf-a482-4af0-af99-5faf796f4f2c","order_by":6,"name":"Yumeng Wang","email":"","orcid":"","institution":"Henan University","correspondingAuthor":false,"prefix":"","firstName":"Yumeng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-05-10 09:55:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4399888/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4399888/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-75835-x","type":"published","date":"2024-11-04T15:57:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57759543,"identity":"9758180d-2860-4b3a-8357-41c6b294fb87","added_by":"auto","created_at":"2024-06-05 09:08:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1085262,"visible":true,"origin":"","legend":"\u003cp\u003eTopography of the Qinling-Daba Mountains and locations of meteorological stations\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4399888/v1/db0acf86bc36d9a414ac57b1.png"},{"id":57759542,"identity":"1794717d-f1c7-4a1e-9136-f7c0aafb355e","added_by":"auto","created_at":"2024-06-05 09:08:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":333287,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of temporal variations between air temperature and surface temperature in the Qinling-Daba Mountains from 2001 to 2021\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4399888/v1/130c3e16e8ffcf04014a85ec.png"},{"id":57759544,"identity":"51bd4b24-514f-4df1-be72-475c0cacbafb","added_by":"auto","created_at":"2024-06-05 09:08:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":276880,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of land surface temperature trends in the Qinling-Daba Mountains from 2001 to 2021; Trend line of land surface temperature changes in the annual mean (b) and maximum temperatures (c) for the years 2001-2010 and 2011-2021.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4399888/v1/9c9cff0b9a88d7ffe66431cc.png"},{"id":57760561,"identity":"94b92130-fab6-47bb-9281-c277dfcf31ba","added_by":"auto","created_at":"2024-06-05 09:24:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":459931,"visible":true,"origin":"","legend":"\u003cp\u003eKernel density plots of MODIS LST interannual trends (°C/a) with elevation for the\u003c/p\u003e\n\u003cp\u003efour seasons of 2001-2010 and 2011-2021 (90% (p≤0.1) significance was obtained by using Sen's method (the horizontal and vertical coordinates represent the temperature and elevation, respectively, and the median is used as the horizontal and vertical axes to divide the planar right-angled coordinate system into four quadrants, The first, second, third and fourth quadrants represent high altitude warming, high altitude cooling, low altitude cooling and low altitude warming. The colors of the scattered dots represent the density of the dots).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4399888/v1/2a45d5400083ee502cecf438.png"},{"id":57759547,"identity":"be113031-2288-4c1a-b2d1-48b140fec252","added_by":"auto","created_at":"2024-06-05 09:08:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":268806,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation coefficients between different seasonal interannual trends in MODIS ground temperature and elevation in the Qinling-Daba Mountains, 2001-2010, 2011-2021\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4399888/v1/e32a2175c0e6171caebf0117.png"},{"id":57760196,"identity":"5949541b-4256-4d2b-82c2-24ce2fb609ed","added_by":"auto","created_at":"2024-06-05 09:16:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":130023,"visible":true,"origin":"","legend":"\u003cp\u003eTrend values of mean changes in surface temperature (LST) and surface albedo (ALB) for spring (a), autumn (b), and winter (c), 2001-2010, 2011-2021, in 500 m wide altitude bands, with the horizontal values representing the different altitude zones.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4399888/v1/172a53aaf63f541dbd486959.png"},{"id":57760194,"identity":"aa36239f-61bd-4faf-af0e-8fd2f39895e9","added_by":"auto","created_at":"2024-06-05 09:16:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":115852,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation coefficients between surface albedo (ALB) trends and surface temperature trends for spring 2001-2010 (Fig. a), autumn 2011-2021 (Fig. b), and winter (Fig. c) above 2500m (**、*indicate significant Spearman correlations with p＜0.01 and p＜0.05, respectively)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4399888/v1/dbf15709cca4fe910ef6bdce.png"},{"id":57759550,"identity":"68918376-d3f6-450b-9a92-0d218f9909d4","added_by":"auto","created_at":"2024-06-05 09:08:38","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":134064,"visible":true,"origin":"","legend":"\u003cp\u003eTrend values of surface temperature (LST) and aerosol optical depth (AOD), averaged over a 500 m wide altitude band, for spring (a), autumn (b), and winter (c) in elevation from 0 to 2500 m, 2001-2010, 2011-2021, with the horizontal values representing the different altitudinal zones.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4399888/v1/20b1155c36c3aa9ec5072628.png"},{"id":57759548,"identity":"286f0cf0-3f24-43c7-bd3e-39f6de15d3b3","added_by":"auto","created_at":"2024-06-05 09:08:38","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":130846,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation coefficients between aerosol optical depth trends and surface temperature trends for spring 2001-2010 (Fig. a), autumn 2011-2021 (Fig. b), and winter (Fig. c) from 0 to 2500 m. (**、*indicate significant Spearman correlations with p＜0.01 and p＜0.05, respectively)\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4399888/v1/1018c61f7c0af8d792e37df1.png"},{"id":68750069,"identity":"e5bdbb5d-fa3c-4ba6-be94-f0930bb04236","added_by":"auto","created_at":"2024-11-11 16:09:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3453845,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4399888/v1/ad489a1a-5f3e-4aef-9181-f137c8680a6f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of the elevation-dependent warming of land surface temperatures in the Qinling-Daba Mountains and its relationship with land surface albedo and aerosol optical depth from 2001 to 2021","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe rise in global surface temperatures, amounts to a 1.1\u0026deg;C in the period 2011\u0026ndash;2020 compared to 1850\u0026ndash;1900, and climate patterns indicate an anticipated escalation to 1.5\u0026deg;C, indicating the continuous global warming trend from 2011 to 2040 \u003csup\u003e1\u003c/sup\u003e. Climate warming is influenced by various factors, including cloudiness, water vapor feedbacks, surface albedo, aerosols, soil moisture, and others, exhibiting complex seasonal variations \u003csup\u003e2,3\u003c/sup\u003e. Altitude is suggested to impact the seasonal variations of surface temperatures and contribute to the complexity of temperature change \u003csup\u003e4\u0026ndash;6\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eElevation-dependent warming (EDW) is a well-documented phenomenon characterized by temperature trends rising faster in high altitude mountains \u003csup\u003e7,8\u003c/sup\u003e. This phenomenon is supported and validated in numerous major mountain ranges, such as the Tibetan Plateau \u003csup\u003e9\u0026ndash;12\u003c/sup\u003e, Rocky Mountains of North America \u003csup\u003e13,14\u003c/sup\u003e, Yunnan-Kweichow Plateau \u003csup\u003e15\u003c/sup\u003e, Tropical Andes mountain range \u003csup\u003e16\u003c/sup\u003e. However, it should be noted that the occurrence of the phenomenon of AEDW depends on the season, the study period and mountainous areas \u003csup\u003e17\u0026ndash;20\u003c/sup\u003e. Negative EDW was observed in central Europe and eastern North America through the monitoring of global weather station sites, with a higher warming trend identified at lower altitudes than at higher altitudes \u003csup\u003e21\u003c/sup\u003e. Hence, some studies suggested that there was no clear relationship between elevation and the degree of warming \u003csup\u003e17,22\u0026ndash;24\u003c/sup\u003e. It is simplistic to dismiss the link between elevation and climate warming, since the complex variations in EDW contribute to the complexity in climate warming in mountainous regions, including seasonal and interannual variations \u003csup\u003e25\u003c/sup\u003e. This complexity of EDW is particularly pronounced in ecological transition zones, where there is a horizontal zonation of climate and vegetation, coupled with vertical zonation corresponding to elevation changes \u003csup\u003e26\u003c/sup\u003e. Unfortunately, the occurrence of EDW and its seasonal difference have not been confirmed in the transition mountain ranges between the north and south of China, although it is crucial for a comprehensive understanding of climate warming trends of China.\u003c/p\u003e \u003cp\u003eSurface albedo (ALB) and aerosol optical depth (AOD) are considered as significant drivers of elevation-dependent warming \u003csup\u003e27\u0026ndash;29\u003c/sup\u003e. ALB constitutes a key variable influencing the Earth's climate system and plays a crucial role in numerical climate models and surface energy balance equations \u003csup\u003e30\u003c/sup\u003e. Variations in ALB have been linked to anomalous warming at high altitudes during the spring and winter seasons \u003csup\u003e31\u003c/sup\u003e. ALB emerges as primary contributors to heightened warming at high altitudes like the Tibetan Plateau, with warming associated with ALB ranging from about 0.26 to 0.50 K in winter and from 0.27 to 0.77 K in spring \u003csup\u003e32\u003c/sup\u003e. Under warming conditions, receding snowpack on the ground leads to decreased ALB, increased absorption of solar radiation by the surface, and enhanced warming at high altitudes \u003csup\u003e33,34\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAOD is an important parameter of aerosol optical properties. It signifies the integration of aerosol extinction coefficients in the vertical direction under clear and cloudless weather, depicting the absorption and scattering of atmospheric radiation by aerosols \u003csup\u003e35,36\u003c/sup\u003e. Aerosol particles influence the atmospheric radiation balance through both direct and indirect radiative forcing mechanisms \u003csup\u003e37\u003c/sup\u003e. Aerosol absorption and scattering contribute to the reduction of solar radiation over the ground, subsequently impacting the surface thermal environment \u003csup\u003e28\u003c/sup\u003e, and consequently, the EDW phenomenon \u003csup\u003e20,21,38\u003c/sup\u003e. In contrast to ALB, the impact of AOD on EDW is predominant at low altitudes\u003csup\u003e37\u003c/sup\u003e. Prior studies have predominantly focused on the influence of surface albedo on changes in EDW at high altitudes \u003csup\u003e27,39\u003c/sup\u003e or on the impact of aerosols on changes in EDW at low altitudes \u003csup\u003e40,41\u003c/sup\u003e. In fact, the emergence of EDW pattern in a huge mountain systems is a result of the combined effects of spatial and temporal variations in LST at low altitude and high altitude, However, the influence of AOD and ALB on EDW is different in season \u003csup\u003e31\u003c/sup\u003e. The purpose of this paper is to investigate the existence of the EDW phenomenon in an ecologically transitional zone characterized by complex climate and terrain, specifically analyzing its presence in different seasons and altitudes. Additionally, the study aims to identify the underlying causes of the observed EDW patterns in these varied conditions.\u003c/p\u003e \u003cp\u003eThe Qinling-Daba Mountains, located in the ecological transition zones between the northern subtropical zone and the warm-temperate zone of China \u003csup\u003e42,43\u003c/sup\u003e, is considered a potentially vulnerable area in terms of ecology \u003csup\u003e44\u0026ndash;46\u003c/sup\u003e. Between 1969 and 2017, the mean annual temperature in the Qinling-Daba Mountains increased by about 1.2\u0026deg;C \u003csup\u003e47\u003c/sup\u003e, potentially causing a northward shift in its temperature zone \u003csup\u003e48\u003c/sup\u003e. However, climate warming in the Qinling-Daba Mountains varies significantly between high and low altitudes, due to the complex topography and Underlying surface \u003csup\u003e49\u003c/sup\u003e. Consequently, this region serves as an ideal location for studying EDW of the temporal variations of LST in the ecological transition zone. However, the scarcity of meteorological stations in mountainous areas presents a challenge in exploring temperature variations, which hinders the study of EDW changes and their influencing mechanisms in the Qinling-Daba Mountains. The Moderate Resolution Imaging Spectroradiometer (MODIS) product compensates for the lack of mountain sites and has been widely used to estimate changes in quantitative mountain air temperatures \u003csup\u003e50\u0026ndash;52\u003c/sup\u003e. It has been applied to address the challenges in studying EDW (Palazzi, Mortarini et al., 2019). In this study, we assessed the characteristics of surface temperature trends in the Qinling-Daba Mountains from 2001 to 2021 based on MODIS LST, and analyzed the relationship between LST trends and elevation in different years, aiming to explore the roles of ALB and AOD in the EDW of LST in the mountains on seasonal and annual scales.\u003c/p\u003e"},{"header":"2 Material and method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe Qinling-Daba Mountains are located between 30 \u0026deg; N and 36 \u0026deg; N latitude and 101 \u0026deg; E and 114 \u0026deg; E longitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mountain range consists of the Qinling Mountain Range and the Daba Mountain Range, with the Han Shui Valley lying between them. The topography of the Qinling-Daba mountains gradually decreases from west to east. The western part connected to the Tibetan Plateau, representing the highest point of the Qinling-Daba mountain range, reaching an altitude of 5528 m. In contrast, the eastern part connects to the plains and has altitudes ranging from 1000 to 1500 m. The Qinling-Daba Mountains serve as the boundary between the warm-temperate zone and the northern subtropical zone in China \u003csup\u003e53\u003c/sup\u003e. This region is characterized by transitions from a deciduous broadleaved forest belt to an evergreen broad-leaved forest belt. On the north of the Qinling Mountain Range, the vegetation is primarily a warm-temperate deciduous broadleaved forest zone, while on the south of the Qinling Mountain Range, it transitions to a mixed evergreen and deciduous broad-leaved forest belt. Moving further south to the southern slope of the Daba Mountain Range, the vegetation predominantly consists of subtropical evergreen broadleaved forests.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Land Surface Temperature\u003c/h2\u003e \u003cp\u003eIn this study, the MODIS LST product (MOD11A1) was acquired from the NASA website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ladweb.nascom.nasa.gov\u003c/span\u003e\u003cspan address=\"https://ladweb.nascom.nasa.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The MOD11A1 product provides daily per-pixel surface temperature and emissivity at a 1km spatial resolution, deriving from various parameters including MODIS geographic information data, emissivity, cloud mask, atmospheric temperature, water content, snow cover, and surface cover parameters \u003csup\u003e54\u003c/sup\u003e. The product also offers pertinent quality control assessments, observation time, apparent zenith angle, and clear-sky coverage.\u003c/p\u003e \u003cp\u003eData preprocessing comprises two main components: data filtering and data merging. Initially, outliers were addressed based on the product's quality assurance of the MOD11A1 product, followed by merging the qualified daily data for four seasons\u0026mdash;spring, summer, autumn, and winter\u0026mdash;to calculate their mean values. Subsequently, the seasonal mean value at a 1km resolution for the years 2001\u0026ndash;2021 was determined. In contrast to previous studies on EDW conducted at an annual scale \u003csup\u003e55\u0026ndash;57\u003c/sup\u003e, focusing on seasonal scales enables a more nuanced understanding of seasonal differences in EDW, facilitating the identification of significant EDW patterns \u003csup\u003e58,59\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Surface Albedo\u003c/h2\u003e \u003cp\u003eMODIS surface reflectance products, specifically the MOD09A1 dataset, provide estimates of surface spectral reflectance, which were obtained from USGS website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The MOD09A1 data provides estimates of surface spectral reflectance in an 8-day gridded Level 3 product in a sinusoidal projection at 500 m resolution. The scientific dataset within the MOD09A1 product consists of reflectance values in bands 1\u0026ndash;7, quality assurance assessments and pixels for a given day of the year. It also includes solar angle, angle of view and zenith angle, for the period from January 1, to December 31, 2021.\u003c/p\u003e \u003cp\u003eTo process the MODIS data for analysis, a mosaic projection transformation of the MODIS data was performed using the MRT software. This transformation converted the projected coordinates to WGS-84 coordinate system. Anomalies in the data were processed, and the individual datasets were summed and averaged to create seasonal averages. Furthermore, all the data was resampled to a 1-kilometer resolution to ensure spatial consistency. To calculate the Albedo (ALB) values, the method developed by Shunlin Liang \u003csup\u003e60\u003c/sup\u003e, as Equation (A.1), was used to compute the ALBs based on MOD09A1. The Equation (A.1) is as follows:\u003c/p\u003e \u003cp\u003eALB\u0026thinsp;=\u0026thinsp;0.160a1\u0026thinsp;+\u0026thinsp;0.291a2\u0026thinsp;+\u0026thinsp;0.243a3\u0026thinsp;+\u0026thinsp;0.116a4\u0026thinsp;+\u0026thinsp;0.112a5\u0026thinsp;+\u0026thinsp;0.081a7-0.0015 (A.1)\u003c/p\u003e \u003cp\u003eWhere: a1, a2, a3, a4, a5, and a7 represent the reflectance values in band 1, 2, 3, 4, 5, and 7, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Aerosol Optical Depth\u003c/h2\u003e \u003cp\u003eMODIS is an important instrument for detecting atmospheric aerosols, and various aerosol satellite inversion methods are grounded in different principles tailored to diverse surface types and aerosol compositions. Within the MODIS AOD algorithm for land, two independent algorithms are employed: the dark image element algorithm for dark backgrounds like vegetation-covered land \u003csup\u003e61\u003c/sup\u003e and the dark blue algorithm for bright backgrounds \u003csup\u003e62,63\u003c/sup\u003e. In 2018, NASA introduced the Multi-Angle Atmospheric Correction Algorithm for Atmospheric AOD (MAIAC) product, known as, MCD19A2. MAIAC is a popular algorithm that incorporates time-series analyses with pixel- and image-based processing to enhance the accuracy of cloud detection, aerosol inversion, and atmospheric corrections \u003csup\u003e64\u003c/sup\u003e. MCD19A2 is derived from the direct fusion of MODIS observations from two satellites, offering high temporal and spatial resolutions and generating daily AOD data at spatial resolutions of 5km x 5km and 1km x 1km. In this paper, we acquired a product with a wavelength of 550 nm from the MCD19A2 data version C6, covering the period from January 1, 2001, to December 31, 2021 from NASA's website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://search.earthdata.nasa.gov\u003c/span\u003e\u003cspan address=\"https://search.earthdata.nasa.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The data has a daily temporal resolution and a spatial resolution of 1 km x 1 km. The daily data was processed to calculate the seasonal mean values of AOD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Meteorological stations\u003c/h2\u003e \u003cp\u003eThe meteorological stations utilized in this study were sourced from the monthly dataset of surface climate data in China provided by the National Meteorological Information Centre (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://data.cma.cn/\u003c/span\u003e\u003cspan address=\"http://data.cma.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). There are 100 stations, located within the study area, providing the daily air temperature, spanning from 2001 to 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These stations were primarily located in six provinces and municipalities, namely, Gansu, Shaanxi, Henan, Sichuan, Hubei, and Chongqing. We processed the temperature anomalies and aggregated the daily data into monthly average data using the monthly averaging method to assess the accuracy of MODIS LST.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 DEM\u003c/h2\u003e \u003cp\u003eThe digital elevation model (DEM) data utilized in this paper were downloaded from the USGS website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://eaethexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://eaethexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with a spatial resolution of 30 m. The DEM is stable, digitally processed, and maintains a constant accuracy. It undergoes real-time updates, making it easy to automate and providing real-time information. It serves as the fundamental data for national geographic information, widely used \u003csup\u003e65\u0026ndash;67\u003c/sup\u003e. To obtain the DEM data for the Qinling-Daba Mountains, we extracted the data based on the vector boundary (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and reprojected it to the WGS-84 coordinate system. The data was resampled to a 1-kilometer resolution to ensure spatial consistency with the MODIS data we utilized.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Methods\u003c/h2\u003e \u003cp\u003eIn this study, LST was compared with air temperatures recorded at meteorological stations in different months from 2001 to 2021 to assess the accuracy of MODIS LST in the Qinling-Daba mountains. The evaluation criteria consisted of R\u003csup\u003e2\u003c/sup\u003e (coefficient of determination), root mean square error (RMSE), and ratio of standard deviations (RSD). To examine the surface temperature trends in the Qinling-Daba Mountains over the study period, we employed the Mann-Kendall test and Theil-Sen Median method in the Qinling-Daba Mountain from 2001 to 2021. The Mann-Kendall test is a nonparametric test that does not assume a specific probability distribution for the series under examination \u003csup\u003e68\u003c/sup\u003e. It is commonly used for long term trends in temperature, precipitation, runoff, etc \u003csup\u003e69\u003c/sup\u003e. The test evaluates the correlation between data points in the time series to determine if there is a significant trend change \u003csup\u003e70,71\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Theil-Sen Median method, also known as Sen\u0026rsquo;s slope estimation, is a robust non-parametric statistical approach used for trend analysis. The method is computationally efficient, insensitive to measurement error and outlier data, and commonly applied in the analysis of long time series data \u003csup\u003e72\u003c/sup\u003e. Furthermore, we conducted a Pearson correlation analysis to examine the relationship between altitude and climate warming, determining the EDW. Additionally, we explored the relationship between LST and surface albedo and aerosol optical depth in the mountains, Finally, we discussed the mechanisms underlying the variation of the EDW at low and high altitudes in the Qinling-Daba Mountains.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Assessment of the accuracy of MODIS LST in the Qinling-Daba Mountains\u003c/h2\u003e\n\u003cp\u003eThe time series analysis of MODIS LST and air temperature from 2001 to 2021 reveals that the monthly average LST follows a similar variations trend to the air temperature in the Qinling-Daba Mountains (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Both LST and air temperature reach their highest average values in July and the lowest values in January and December. However, the LST is slightly higher than the air temperature across all months. The average LST in the Qinling-Daba Mountains from 2001 to 2021 is 17.03\u0026deg;C, while the average air temperature is 13.56\u0026deg;C. The correlation between air temperature and surface temperature remains strong throughout the 21-year period with R\u0026sup2; above 0.9 at all stations in the Qinling-Daba Mountains. Specifically, 47% of the stations had R\u0026sup2; between 0.9 and 0.95, and 53% had R\u0026sup2; values above 0.95. The root means square error (RMSE) of the air temperature and LST was 3.83, and the ratio of standard deviation (RSD) is 1.31, These results suggest that MODIS LST effectively captures the trend changes in air temperature in the Qinling-Daba Mountains.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Spatiotemporal variations of LST in the Qinling-Daba Mountains from 2001 to 2021\u003c/h2\u003e\n\u003cp\u003eThe spatiotemporal variations in LST trends from 2001 to 2021 indicate a decline in LST across most regions, except for certain areas in the eastern fringe, which show an increasing trend in LST (Figure 3a). However, there is a notable distinction between the rates of change in mean and maximum LST for the periods 2001-2010 and 2011-2021, suggesting a cooling trend in LST during the first decade and a significant warming trend in the subsequent period (Figures 3b and c). To further validate the disparity in LST between the two study periods, we calculated Sen\u0026rsquo;s slope estimates of LST for 2001-2010 and 2011-2021. The results demonstrate that the LST trends in the two study periods exhibit contrasting changes in the rate of the LST variations during winter compared to other seasons (bottom right of Fig. 3a). In winter, the Qinling-Daba Mountains experienced a noticeable warming trend from 2001 to 2010, while in spring, summer and autumn, the Sen slopes were -0.06 ℃/a, -0.19 ℃/a and -0.0685 ℃/a, respectively, showing a cooling trend. However, in the winter of 2011-2021, the temperature tended to decrease (-0.036 ℃/a). On the other hand, the Sen slope of LST in other seasons was positive, with spring, summer, and autumn exhibiting climatic tendency rates of 0.0057 \u0026deg;C/a, 0.029 \u0026deg;C/a, 0.031 \u0026deg;C/a, and 0.031 \u0026deg;C/a, respectively. These\u0026nbsp;findings\u0026nbsp;highlight significant changes in the climate propensity rate of LST during\u0026nbsp;the\u0026nbsp;two distinct time periods, 2001-2010 and 2011-2021. The relationships between spatiotemporal variations in LST trends and altitudes were explored based on these two time periods with opposite LST trends.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 EDW and its seasonal differentiations in the spatiotemporal variations of LST in the Qinling-Daba Mountains from 2001to 2021\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between altitude and the climate propensity rate of LST reveals a significant EDW pattern during the spring of the period 2001\u0026ndash;2010 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The results indicate that pixels reflecting the temporal variations in LST are primarily located in quadrant Ⅲ (\u0026lt;0 ℃), presenting a cooling trend, in the low elevation region, particularly at 500-1500m, during the spring. The primary rate of change in LST in this region is approximately \u0026minus;\u0026thinsp;0.2 ℃/a. Conversely, there is a significant warming rate at higher altitudes, with pixels reflecting temporal variations in LST distributed in quadrant Ⅰ (\u0026gt;0 ℃), above 2500m, during the spring. In these high-altitude areas, the range of LST change mainly between 0.2\u0026ndash;0.4 ℃/a. Furthermore, Pearson correlation analyses of surface temperature trends and elevation for the periods 2001\u0026ndash;2010 across different seasons reveal that the warming trend of LST becomes more apparent, with a correlation coefficient of 0.742 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), between overall elevation and LST change trend during the spring in the Qinling-Daba Mountains (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe relationship between altitude and the climate propensity rate of LST reveals a significant negative EDW pattern during the autumn and winter of the period 2011\u0026ndash;2021 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The pixels reflecting temporal variations in LST are primarily located in the quadrant Ⅳ (\u0026gt;0 ℃) within the altitude range of 0-2500 m, indicating a pronounced warming phenomenon. The primary rate of change in LST is mainly concentrated in the range of 0.1\u0026ndash;0.25 ℃/a across the four seasons. However, in spring and summer, the pixels reflecting the temporal variations in LST are discretely distributed at different altitudes. On the other hand, a wider range of cooling trends occurs at high altitudes during autumn and winter (LST change trend values mainly falling in quadrant Ⅱ), with the main trend values ranging from \u0026minus;\u0026thinsp;0.15 to -0.25\u0026deg;C/a. Additionally, Pearson correlation analyses of surface temperature trends and elevation for the periods 2011\u0026ndash;2021 across different seasons reveal that the cooling trend of LST becomes more apparent with the increase in altitude during autumn and winter. The correlation coefficient reaches \u0026minus;\u0026thinsp;0.860 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in autumn and \u0026minus;\u0026thinsp;0.888 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in winter (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Effects of ALB and AOD on the EDW of LST in the Qinling-Daba Mountains\u003c/h2\u003e\n\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n\u003ch2\u003e3.4.1 Relationship between ALB and EDW of LST in the Qinling-Daba Mountains\u003c/h2\u003e\n\u003cp\u003eTo systematically elucidate the mechanism causing the occurrence of EDW in LST, we selected the spring of 2001\u0026ndash;2010 and the autumn and winter of 2011\u0026ndash;2021, which were marked by pronounced EDW and negative EDW trends, and explored the effects of ALB on the EDW of LST in the Qinling-Daba Mountains. Initially, we employed Theil-Sen Median trend analysis to analyze the trends in ALB during the selected time periods. The results indicated a downward trend with increasing altitude in the spring from 2001 to 2010, especially, a more pronounced decline trend in ALB was observed at higher altitudes (Figure. 6a). Concurrently, the temporal variations of LST exhibited an increasing trend, particularly noticeable above 2500 m. This trend in LST was opposite to the variations observed in ALB. Correlation analysis between the temporal trend of LST and ALB revealed a significant negative correlation, reaching \u0026minus;\u0026thinsp;0.32 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Figure. 7a). These findings suggest that ALB had a negative influence on the occurrence of EDW of LST in regions above 2500 m.\u003c/p\u003e\n\u003cp\u003eThe results indicate that there is no significant correlation between the change rates of LST and ALB (P\u0026gt;0.05) during the autumn of 2011-2021 (Figure. 6b and Figure. 7b). However, in the winter of 2011-2021, the temporal trend of LST exhibited a significant negative correlation with ALB (Figure. 7c), with a correlation coefficient reaching to -0.346 (P \u0026lt; 0.01). Notably, during the period of 2011-2021, a distinct \"mirror\" relationship was observed between the temporal trends of LST and ALB at altitudes of 2500-5000min the winter (Figure. 6c). Specifically, the temporal trend of LST demonstrated a decreasing pattern with increasing altitude between 2500 m and 5000 m. At its lowest value at 4500 m, the trend of LST reversed and started increasing. On the other hand, during the same period, ALB initially showed an increasing pattern and reached its highest point at 4500 m before decreasing with increasing altitude. Overall, whether it is positive or negative in EDW, ALB has a negative effect on the temporal trend of LST during\u0026nbsp;the spring of 2001-2010 and the winter of 2011-2021 at high altitudes (above 2500 m)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n\u003ch2\u003e3.4.2 Relationship between AOD and EDW of LST in the Qinling-Daba Mountains\u003c/h2\u003e\n\u003cp\u003eDuring the spring from 2001 to 2010, when significant EDW events were observed, the temporal trend of LST exhibited an inverse relationship with AOD as the elevation increased from 0 to 2500 m (Fig.\u0026nbsp;8). Specifically, the trend of LST showed an increasing pattern, ranging from while the trend of AOD decreased, as the elevation increased from 0 to 2500 m. Furthermore, correlation analysis demonstrated a significant negative correlation between AOD and LST trends during spring in the period of 2001\u0026ndash;2010 with correlation coefficients reaching to -0.28 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn the autumn and winter from 2011 to 2021, during which negative EDW events were observed, the temporal trend of LST exhibited an inverse relationship with AOD as the elevation increased from 0 to 2500 m (Fig.\u0026nbsp;8). In autumn, the trend of LST decreased from 0.04 /a to -0.0038 /a, while the trend of AOD increased from \u0026minus;\u0026thinsp;0.023 /a to -0.001 /a as the elevation increased from 0 to 2500 m. Similarly, in winter, the trend of LST decreased from 0.024 /a to -0.001 /a, while the trend of AOD increased from \u0026minus;\u0026thinsp;0.03 /a to -0.0039 /a with the increase in elevation from 0 to 2500 m. Furthermore, correlation analysis demonstrated significant negative correlations between AOD and LST trends during the autumn and winter from 2011 to 2021. The correlation coefficients reached notable of -0.504 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u0026minus;\u0026thinsp;0.421 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), respectively, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e. Overall, regardless of whether it is positive or negative in EDW, AOD has a negative effect on the temporal trend of LST during the spring of 2001\u0026ndash;2010, as well as in the autumn and winter of 2011\u0026ndash;2021, particularly at low altitudes (0-2500 m).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Spatiotemporal variations of EDW in the Qinling-Daba Mountains from 2001 to 2021\u003c/h2\u003e \u003cp\u003eThe results indicate that MODIS LST effectively captures the spatiotemporal variations of temperatures, including the patterns associated with EDW in the Qinling-Daba Mountains. These findings are consistent with prior research on MODIS LST conducted by Kindstedt, Schild et al \u003csup\u003e73\u003c/sup\u003e and Zikan, Adolph et al \u003csup\u003e74\u003c/sup\u003e. During the period of 2001\u0026ndash;2010, we observed a general \"cooling\" trend in the Qinling-Daba Mountains, as reflected by the MODIS LST data. This finding aligns with global climate change studies conducted from 1998 to 2012, a period during which the global mean surface temperature did not experience significant warming \u003csup\u003e75\u0026ndash;77\u003c/sup\u003e. However, it is important to note that high altitudes in the Qinling-Daba Mountains exhibited an inverse warming trend and noticeable EDW. This suggests that climate warming did not stagnate or decelerate in the region from 2001\u0026ndash;2010, particularly at high altitudes. This phenomenon is similar to what is observed in the Arctic, where it significantly contributes to the ongoing global warming trend \u003csup\u003e78\u003c/sup\u003e. The explanation for this phenomenon lies in the perspective of the global warming energy balance, as proposed by Duan and Xiao \u003csup\u003e79\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEDW, observed in this study, occurs only in the spring during the period of 2001\u0026ndash;2010 in the Qinling-Daba Mountains, coinciding with a general climate cooling trend. Conversely, during the period of 2011\u0026ndash;2021 in autumn and winter, a noticeable negative EDW was observed. This suggests that EDW or negative EDW events occur in specific spatial and temporal scales rather than universally. These findings are consistent with other studies conducted by Zeng, Chen et al \u003csup\u003e21\u003c/sup\u003e, Nigrelli and Chiarle \u003csup\u003e80\u003c/sup\u003e, and Li, Chen et al \u003csup\u003e81\u003c/sup\u003e. It is challenging to draw a general conclusion about whether the warming rate is higher in mountainous regions than that of flatlands, as it may vary across different mountain regions and scales. Therefore, it becomes necessary to narrow down the spatial and temporal scales to explore detailed patterns of EDW, This viewpoint is supported by other studies focusing on EDW in the Tibetan Plateau \u003csup\u003e58,59\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Mechanisms causing EDW and negative EDW in the Qinling-Daba Mountains\u003c/h2\u003e \u003cp\u003eThe findings of the study indicate that ALB has a negative effect on the temporal trend of LST during the spring of 2001\u0026ndash;2010 and the winter of 2011\u0026ndash;2021 at high altitudes (above 2500m). This negative effect of ALB on LST enhances the difference in the temporal variation of LST between the high altitudes and low altitudes, leading to the occurrence of EDW during the spring of 2001\u0026ndash;2010 and negative EDW during the winter of 2011\u0026ndash;2021. This relationship between ALB and EDW/ negative EDW is linked to the feedback of ALB to climate change. Climate warming is known to lead to a reduction in snow cover and an increase in vegetation cover, as suggested by previous researches \u003csup\u003e82,83\u003c/sup\u003e,These changes in land cover conditions, specifically the enhancement of vegetation cover and the reduction of snow cover, contribute to a decrease in ALB \u003csup\u003e84\u003c/sup\u003e, Consequently, there is an increase in net surface radiation \u003csup\u003e85\u003c/sup\u003e, and an increase of solar short-wave radiation absorbed by the ground \u003csup\u003e86\u003c/sup\u003e,which ultimately leads to an increase in LST. The feedback mechanism involving ALB and climate warming has been applied to explain EDW events over the Tibetan Plateau and the Colorado Rockies, as demonstrated in studies conducted by Kang, Xu et al \u003csup\u003e5\u003c/sup\u003e, You, Min et al \u003csup\u003e87\u003c/sup\u003e, and You, Zhang et al \u003csup\u003e88\u003c/sup\u003e. These studies highlight the role of ALB in influencing the spatiotemporal variations of LST and the occurrence of EDW in the Qinling-Daba Mountains.\u003c/p\u003e \u003cp\u003eAccording to the study, at low altitudes (0-2500m). AOD has a negative effect on the temporal trend of LST during the spring of 2001\u0026ndash;2010, as well as in the autumn and winter of 2011\u0026ndash;2021. This negative effect of AOD further enhances the difference in the temporal variation of LST between the high altitudes and low altitudes, contributing to the occurrence of EDW and negative EDW events in the temporal variation of LST during the spring of 2001\u0026ndash;2010, as well as in the autumn and winter of 2011\u0026ndash;2021.The effect of AOD on the EDW can be attributed to the spatiotemporal distribution of AOD and its effect on solar radiation. Atmospheric aerosol pollutants tend to accumulate at relatively low altitudes (\u0026lt;\u0026thinsp;2500m), leading to a decrease in the shortwave radiative flux reaching lower altitudes \u003csup\u003e89\u003c/sup\u003e. This reduction in solar radiation at lower altitudes contributes to the variation in LST at lower altitudes. AOD produces both absorption and scattering effects on solar radiation due to variations in aerosol particles, with the scattering effect being greater than the absorption effect \u003csup\u003e90,91\u003c/sup\u003e. This causes a negative effect on LST \u003csup\u003e92,93\u003c/sup\u003e. The observed increases in AOD from 2001\u0026ndash;2010 and decrease in AOD from 2011\u0026ndash;2021 at low elevations in the study result in a cooling effect and warming effect on the LST at low altitudes, respectively, leading to significant EDW / negative EDW in different periods. These viewpoints are supported by other studies. Philipona \u003csup\u003e38\u003c/sup\u003e and Zeng \u003csup\u003e21\u003c/sup\u003e have attributed the occurrence of negative EDW to a decrease in atmospheric sources of pollution and a reduction in shortwave radiation, resulting in an increase in \"solar brightening\" at densely populated valleys in low altitudes \u003csup\u003e94,95\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to the factors examined in this study, there are other contributors that may play a role in the phenomena associated with EDW / negative EDW. For instance, previous studies have suggested that the cloud-radiation feedback process in the eastern Tibetan Plateau is a primary factor contributing to the increase in warming amplitude of the plateau with increasing altitude during the CO\u003csub\u003e2\u003c/sub\u003e doubling experiment \u003csup\u003e96\u003c/sup\u003e. Furthermore, land use changes have potential to induce EDW \u003csup\u003e97\u0026ndash;99\u003c/sup\u003e as they often result in variations in topographic gradients in the Qinling-Daba mountains \u003csup\u003e100\u003c/sup\u003e. The overall ecological quality of the Qinling-Daba mountains area exhibits a linear improvement trend with altitude below 3200m, while the rate of improvement tends to flatten out beyond that altitude \u003csup\u003e100\u003c/sup\u003e. This variation in ecological quality with altitude can have implications for local climate patterns and potentially contribute to the occurrence of EDW. Various factors such as different vegetation cover types, soil moisture, snow thickness, and surface albedo purity, can also influence changes in LST trends to varying degrees \u003csup\u003e101\u003c/sup\u003e. These factors can have an impact on the occurrence of EDW/negative EDW.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Effects of EDW pattern on ecosystem in the Qinling-Daba mountains\u003c/h2\u003e \u003cp\u003eAs north-south transition zone in China, the Qinling-Daba mountains acts as a climatic barrier, influencing mountain altitudinal belts, biodiversity and water conservation, and in turn affecting ecosystem services throughout the central China \u003csup\u003e43\u003c/sup\u003e. Over the past few decades, there has been a significant upward shift in the transitional climatic zones in the Qinling-Daba mountains due to temporal variations in temperatures \u003csup\u003e42,102\u003c/sup\u003e. If these temperatures surpass temperature thresholds for vegetation sequestration capacity, it can lead to large-scale forest mortality in the forest line area of Bashan \u003csup\u003e103\u003c/sup\u003e. The EDW, as observed in the study, complicate the situations of the climate change and its impact on ecosystems. During the spring period of climate cooling from 2001 to 2010, climate warming was still observed in high altitudes, and EDW can mitigate the differences in vegetation between high and low altitudes. Conversely, during the winter and autumn period of climate warming from 2011 to 2021, negative EDW associated with a trend of climate cooling in high altitudes, thereby amplifying temperature differences between high altitudes and low altitudes. The pattern of EDW over the Qinling-Daba mountains plays a crucial role in determining the distribution of surface heat source and have far-reaching effects on the ecosystem in the Qinling-Daba mountains \u003csup\u003e9\u003c/sup\u003e. The inconsistent temperature variations caused by EDW between high and low altitudes can increase ecosystem instability, particularly in the context of vegetation shifting from low to higher altitudes due to climate warming \u003csup\u003e99\u003c/sup\u003e, given the influence of temperature changes on plant productivity, as well as the carbon and nitrogen cycle processes of the ecosystem \u003csup\u003e104\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn this study, we investigated the EDW patterns of LST across different seasons in the Qinling-Daba mountains using MODIS LST data, and explored the relationships between the EDW patterns of LST and ALB as well as AOD. Based on our analysis, the following conclusions can be drawn:\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) A strong correlation was observed between MODIS LST and air temperatures in the Qinling-Daba Mountains. The coefficient of determination (R\u0026sup2;) between air temperatures and LST at all meteorological stations exceeded 0.9, suggesting the feasibility of using MODIS LST to predict the temperature trends in the Qinling-Daba mountains.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) During the period from 2001 to 2010, an EDW trend was observed, primarily in the spring season. The Correlation coefficient between elevation and the rate of temporal variations of LST was found to be 0.844 in the 1000-5000m region, indicating higher altitudes experienced a greater rate of warming. However, in contrast, a significant negative EDW phenomenon occurred in autumn and winter from 2011 to 2021. The Correlation coefficient between elevation and the rate of temporal variations of LST was \u0026minus;\u0026thinsp;0.86 in autumn, while in winter, it was \u0026minus;\u0026thinsp;0.888. It indicates a trend of climate cooling in high altitudes during autumn and winter from 2011 to 2021.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The EDW of LST is influenced by the combined effects of ALB and AOD. During the spring period from 2001 to 2010 and the winter period from 2011 to 2021, ALB had a negative impact on the temporal change of LST above 2500m. In contrast, the trends of AOD exhibited a negative correlation with LST trends, primarily in lower altitudes (0-2500 m) during the spring of 2001\u0026ndash;2010, as well as in the autumn and winter of 2011\u0026ndash;2021. By considering the combined effects of ALB and AOD, we can gain a comprehensive understanding of the patterns of EDW and integrate the different ecological environments from high and low altitudes in the Qinling-Daba Mountains.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eAll authors declare that there are not any personal or financial conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Lian, Y. and Zhang, Y.; methodology, data curation and visualization, Lian, Y. and Zhao, F.; writing-original draft preparation, Lian, Y. and Zhao, F.; writing-review and editing, Tang, J., Zhang, Y., Yu, H., Zheng, Z., and Wang, Y.; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eThis research is funded by the Natural Science Foundation of Henan (Grant No.232300420165), the Science and Technology Tackling Program of Henan Province (Grant No. 242102210003) and the Key Scientific Research Project of Higher Education Institutions in Henan Province (Grant No. 24A630004). 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Global environmental change effects on ecosystems: the importance of land-use legacies. Global Change Biology 22, 1361\u0026ndash;1371, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gcb.13146\u003c/span\u003e\u003cspan address=\"10.1111/gcb.13146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1992).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"The Qinling-Daba Mountains, MODIS LST, Elevation-dependent warming, Land surface albedo, Aerosol optical depth","lastPublishedDoi":"10.21203/rs.3.rs-4399888/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4399888/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, we examined the elevation-dependent warming (EDW) patterns of MODIS LST across different seasons in the Qinling-Daba Mountains, further investigate the connections between the EDW patterns of LST and ALB as well as AOD. The key findings include: 1) Our study reveals a robust correlation between LST and air temperature in the Qinling-Daba Mountains, suggesting the feasibility of using MODIS LST to predict the temperature trends 2) During the period from 2001 to 2010, MODIS LST shows a significant EDW trend, primarily in the spring season. In contrast, a negative EDW is observed in the period during 2011\u0026ndash;2021, which is contrary to the earlier decade, particularly during the autumn and winter seasons. 3) EDW of MODIS LST is affected by the combination of ALB and AOD. The former has a negative influence on the change of LST, particularly above 2500 m in elevation. However, the latter is negatively correlated with the trend of MODIS LST, primarily at lower and middle altitudes (0-2500 m). This study gives a comprehensive explanation for the EDW of the temporal variations of LST in the Qinling-Daba Mountains to improve our understanding of the complex interactions and potential future climate scenarios in the region.\u003c/p\u003e","manuscriptTitle":"Assessment of the elevation-dependent warming of land surface temperatures in the Qinling-Daba Mountains and its relationship with land surface albedo and aerosol optical depth from 2001 to 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-05 09:08:33","doi":"10.21203/rs.3.rs-4399888/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-20T09:58:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-06T09:29:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-06T03:59:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259534735351349417953099657067822317897","date":"2024-07-28T08:59:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8488390870509647824588623547226742128","date":"2024-07-25T00:53:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-04T19:25:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-04T19:24:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-27T05:44:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-24T02:49:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-10T09:53:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"edd696fd-f645-45b3-bf10-8dd723eea1f1","owner":[],"postedDate":"June 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32816561,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science"},{"id":32816562,"name":"Earth and environmental sciences/Climate sciences/Climate change"}],"tags":[],"updatedAt":"2024-11-11T16:04:22+00:00","versionOfRecord":{"articleIdentity":"rs-4399888","link":"https://doi.org/10.1038/s41598-024-75835-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-04 15:57:18","publishedOnDateReadable":"November 4th, 2024"},"versionCreatedAt":"2024-06-05 09:08:33","video":"","vorDoi":"10.1038/s41598-024-75835-x","vorDoiUrl":"https://doi.org/10.1038/s41598-024-75835-x","workflowStages":[]},"version":"v1","identity":"rs-4399888","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4399888","identity":"rs-4399888","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}