Air Temperature Variability in High-Elevation Glacierized Regions: Observations from Six Catchments on the Tibetan Plateau

Wei Yang aState Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
bCAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China

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Meilin Zhu aState Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

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Xiaofeng Guo cState Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Huabiao Zhao aState Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
bCAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China
dNgari Station for Desert Environment Observation and Research, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Tibet, China

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Abstract

Near-surface air temperature variability and the reliability of temperature extrapolation within glacierized regions are important issues for hydrological and glaciological studies that remain elusive because of the scarcity of high-elevation observations. Based on air temperature data in 2019 collected from 12 automatic weather stations, 43 temperature loggers, and 6 national meteorological stations in 6 different catchments, this study presents air temperature variability in different glacierized and nonglacierized regions and assesses the robustness of different temperature extrapolations to reduce errors in melt estimation. The results show high spatial variability in temperature lapse rates (LRs) in different climatic contexts, with the steepest LRs located on the cold and dry northwestern Tibetan Plateau and the lowest LRs located on the warm and humid monsoonal-influenced southeastern Tibetan Plateau. Near-surface air temperatures in high-elevation glacierized regions of the western and central Tibetan Plateau are less influenced by katabatic winds and thus can be linearly extrapolated from off-glacier records. In contrast, the local katabatic winds prevailing on the temperate glaciers of the southeastern Tibetan Plateau exert pronounced cooling effects on the ambient air temperature, and thus, on-glacier air temperatures are significantly lower than that in elevation-equivalent nonglacierized regions. Consequently, linear temperature extrapolation from low-elevation nonglacierized stations may lead to as much as 40% overestimation of positive degree-days, particularly with respect to large glaciers with a long-flowline distances and significant cooling effects. These findings provide noteworthy evidence that the different LRs and relevant cooling effects on high-elevation glaciers under distinct climatic regimes should be carefully accounted for when estimating glacier melting on the Tibetan Plateau.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wei Yang, yangww@itpcas.ac.cn; Huabiao Zhao, zhaohb@itpcas.ac.cn

Abstract

Near-surface air temperature variability and the reliability of temperature extrapolation within glacierized regions are important issues for hydrological and glaciological studies that remain elusive because of the scarcity of high-elevation observations. Based on air temperature data in 2019 collected from 12 automatic weather stations, 43 temperature loggers, and 6 national meteorological stations in 6 different catchments, this study presents air temperature variability in different glacierized and nonglacierized regions and assesses the robustness of different temperature extrapolations to reduce errors in melt estimation. The results show high spatial variability in temperature lapse rates (LRs) in different climatic contexts, with the steepest LRs located on the cold and dry northwestern Tibetan Plateau and the lowest LRs located on the warm and humid monsoonal-influenced southeastern Tibetan Plateau. Near-surface air temperatures in high-elevation glacierized regions of the western and central Tibetan Plateau are less influenced by katabatic winds and thus can be linearly extrapolated from off-glacier records. In contrast, the local katabatic winds prevailing on the temperate glaciers of the southeastern Tibetan Plateau exert pronounced cooling effects on the ambient air temperature, and thus, on-glacier air temperatures are significantly lower than that in elevation-equivalent nonglacierized regions. Consequently, linear temperature extrapolation from low-elevation nonglacierized stations may lead to as much as 40% overestimation of positive degree-days, particularly with respect to large glaciers with a long-flowline distances and significant cooling effects. These findings provide noteworthy evidence that the different LRs and relevant cooling effects on high-elevation glaciers under distinct climatic regimes should be carefully accounted for when estimating glacier melting on the Tibetan Plateau.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wei Yang, yangww@itpcas.ac.cn; Huabiao Zhao, zhaohb@itpcas.ac.cn

1. Introduction

Near-surface air temperature is one of the most important meteorological variables for hydrological and glaciological models in glacierized regions. Turbulent exchange of energy and water vapor fluxes between the glacier surface and the atmosphere can be significantly affected by the surface air temperature (Ohmura 2001). In addition, surface air temperature is a modulating factor of precipitation phase changes (e.g., rain, snow, and sleet), which has remarkable effects on the surface albedo and consequently changes the surface solar radiation balance (Ding et al. 2014; Fujita and Ageta 2000; Mölg et al. 2012, 2014). Therefore, accurate air temperature estimations are an essential prerequisite for understanding glacier melting and future glacier responses under different climatic scenarios.

The Tibetan Plateau (TP) and surrounding regions contain the largest number of glaciers outside the polar regions, and glacier meltwater is critical for the local ecological system and downstream water resources (Immerzeel et al. 2010). Distributed hydrological and glaciological models are often forced using air temperature fields in glacierized regions that are either interpolated/extrapolated from modeled or reanalyzed climatology (Hofer et al. 2010; Kraaijenbrink et al. 2017) or extrapolated from station measurements mostly available in low-elevation nonglacierized regions (Gardner et al. 2009). The near-surface temperature lapse rate (LR) is an important parameter in glaciological and hydrological models but is often calculated in different empirical manners (Immerzeel et al. 2010; Ragettli et al. 2016; Rees and Collins 2006). Previous sensitivity studies have pointed out that glacier melting/mass balance models are highly sensitive to the practical designation of LRs (Gardner and Sharp 2009; Petersen and Pellicciotti 2011; Petersen et al. 2013; Shaw et al. 2017). A sensitivity of approximately 600-mm water equivalent cumulative June–August melt per 0.1°C (100 m)−1 change in the LR was found across a 500-m altitude range in Vestari-Hagafellsjökull, Iceland (Hodgkins et al. 2012). Although the LR is an important parameter in cryosphere models, previous studies have mainly focused on the spatial and temporal changes in nonglacierized LRs based on several lower-altitude, more accessible meteorological stations (Immerzeel et al. 2014; Kattel et al. 2015; Pratap et al. 2019) and LRs are rarely quantified by virtue of in situ determinations over mountainous glacierized regions (X. Yang et al. 2011).

Moreover, an increasing number of studies in alpine and polar glacierized regions have shown that the surface air temperature on melting glacier surfaces can differ substantially from that on nonglacierized regions (Ayala et al. 2015; Carturan et al. 2015; Greuell and Böhm 1998; Marshall et al. 2007; Shaw et al. 2021; Shea and Moore 2010; Troxler et al. 2020). Katabatic winds are commonly present over melting glaciers, hence affecting both the surface air temperature distribution and the turbulent fluxes across the glacier–atmosphere interface (Van Den Broeke 1997). The cooler, denser air flows down the glacier along the flow line and produces anomalously cooler temperatures than those measured at the same elevation outside the katabatic boundary layer, in turn representing a prominent mechanism that weakens the near-surface LRs (Marshall et al. 2007). A 1°C air temperature increase in the nonglacierized region generally corresponds to a less than 1°C increase at the corresponding elevation of the melting glacier surface, which is called the cooling effect or the sensitivity of near-surface air temperature to ambient temperature change (Greuell and Böhm 1998; Shaw et al. 2017; Shea and Moore 2010). A failure to account for such cooling effects being differentiated between glacierized and nonglacierized regions may result in large errors not only in downscaled/extrapolated temperature fields but also in the magnitude of modeled glacier melt (Gardner and Sharp 2009; Petersen et al. 2013).

The statistical model (Shea and Moore 2010) and the thermodynamic model (Ayala et al. 2015; Greuell and Böhm 1998) approach were therefore established to account for such prominent cooling effects on melting glaciers. Their performance has been previously tested for a wide range of glaciers in the Alps, polar regions and on the southeastern TP (Bravo et al. 2019; Carturan et al. 2015; Shaw et al. 2021; Troxler et al. 2020). These foregoing studies are implemented primarily for relatively low-altitude glacier termini ranging from 0 to 3000 m MSL or under a relatively warm and humid climatic background (see Table 2 in Shaw et al. 2021). It should be noted that the majority of glaciers on the TP are located at extremely high elevations with termini above 5000 m MSL and in relatively cold and dry environments (Shi and Liu 2000). Intriguing issues such as the influence of katabatic activity and the magnitude of the cooling effect involved with these glaciers remain to be addressed with care. Possible differences in the elevation dependence of near-surface air temperature between nonglacierized and glacierized regions under different climatic regimes remain to be examined by using in situ measurements. And the reliability of air temperature estimations for the glacierized regions from low-elevation records warrants close investigation. However, field experiments on the variability of distributed on-glacier air temperature remain rare because of practical constraints due to the harsh and remote environments, particularly with respect to the high-elevation western TP.

Thus, we were motivated to design a network of synchronous temperature measurements by selecting several glacierized regions in different geographical and climatic contexts across the entire TP. The primary objectives of this study are (i) to explore temperature variability and its elevation dependence (both off-glacier and on-glacier) for six catchments on the TP; (ii) to quantify the magnitude of glacier cooling effects under different climate regimes; and (iii) to discuss possible modeled biases introduced by extrapolating low-elevation meteorological records to high-elevation glacierized regions by employing constant LRs, which are commonly adopted in a wide range of glaciological/hydrological models. Observational studies of this type are considered to be beneficial for advancing the methodological approaches for estimating air temperature for different glaciological and hydrological models and, furthermore, for improving the understanding of glacier responses to climate change on the TP.

2. Study regions and methods

a. Study regions and station locations

The six selected glacierized regions, which are referred to as Guliya, Aru, Naimona’nyi, Gagze, Dunde, and Parlung, have a wide range of geographical and climatic characteristics (Fig. 1 and Table 1). Both Guliya Glacier and Aru Glacier represent the westerly influenced regions on the northwestern TP. Naimona’nyi Glacier is located in the western Himalayas, while the Parlung Glaciers (including Parlung 4 Glacier, Parlung 94 Glacier, and Parlung 390 Glacier) are located in a typical glacierized region in the monsoon-influenced eastern Himalayas. Dagze Glacier and Dunde Glacier are both located on the inner and northeastern TP, area less influenced by both the Indian summer monsoon and the midlatitude westerlies. A total of 12 automatic weather stations (AWSs) and 43 temperature loggers (T loggers) were deployed either within or in close proximity to these six glacierized regions (Fig. 2). To facilitate the evaluation of an empirical extrapolation approach based on temperature records taken from low-elevation regions, the daily temperature records available at six adjacent low-elevation meteorological stations operated by the China Meteorological Administration (CMA) were also employed in this study. The spatial distribution of these stations in each glacierized region is shown in Figs. 1 and 2. A list of all temperature sensors in the six glacierized regions, station IDs, latitude and longitude coordinates, elevations, on- and off-glacier statuses, and flowline distances is provided in Table 2.

Fig. 1.
Fig. 1.

Location of the six selected regions (Guliya, Aru, Naimona’nyi, Gagze, Dunde, and Parlung) on the TP and the spatial distribution of automatic weather stations (AWSs) and nearby meteorological stations of the CMA. Gray shading represents the areas with an elevation above 2500 m.

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0122.1

Fig. 2.
Fig. 2.

Zoomed-in spatial distribution of the T loggers, AWSs, and CMA stations in each glacierized region on the TP. Please refer to Tables 1 and 2 for detailed information on the individual glaciers under investigation. The background satellite images were taken from ESRI DigitalGlobe (Redlands, California).

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0122.1

Table 1

Glacial locations; maximum (Zmax), minimum (Zmin), and mean (Zmean) elevations; aspect; slope; area; and mean annual air temperature and total precipitation from the corresponding ERA5 grids during the period from 1979 to 2020.

Table 1
Table 2

Overview of stations, locations, elevations, and sensors within the six regions. The long dash represents no data.

Table 2

1) Guliya Glacier

Guliya Glacier is an outlet glacier flowing from an ice cap located in the western Kunlun Mountains. The total glacierized area is approximately 111.3 km2 with an elevation ranging from 5487 to 6649 m MSL (Arendt et al. 2017). Recent geodetic studies have shown that the average glacier mass balance in this region was close to zero or slightly positive from 2000 to the 2010s (Brun et al. 2017; Shean et al. 2020). Due to the harsh remote environment, very few in situ measurements have been performed in this region. In September 2015, two AWSs were installed at 5496 m MSL on the terminus moraine and at 6005 m MSL on the glacial surface. The surface mass/energy balance was investigated by using these AWSs and stake measurements on glacial surfaces (Li et al. 2019). In October 2018, a total of five additional HOBO MX2301 T loggers were installed on the glacial surface from 5695 to 6078 m MSL (Fig. 2a). To facilitate the evaluation of an empirical extrapolation approach based on low-elevation temperature records, two AWSs at Ali Station (4256 m MSL, 250 km southwest) and at Aru Lake (4978 m MSL, 170 km southeast) were also exploited for an approximate derivation of the LRs (Figs. 1 and 2b and Table 2). The elevations of these nine stations range from a minimum of 4256 m MSL at Ali Station to a maximum of 6078 m MSL (Fig. 3).

Fig. 3.
Fig. 3.

Elevation ranges of all stations used in each glacierized region on the TP; black and blue triangles represent the stations in off-glacier and on-glacier areas, respectively.

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0122.1

2) Aru Glacier

Aru Glacier is a typical valley glacier that is approximately 180 km southeast of Guliya Glacier. The total glacierized area is approximately 7.0 km2 with elevations ranging from 5417 to 6207 m MSL (Arendt et al. 2017). Two massive ice collapses occurred 15 km north of Aru Glacier in 2017 (Kääb et al. 2018). An AWS was installed in 2017 near the collapse fan (Fig. 2b). In October 2018, a total of seven HOBO MX2301 T loggers were deployed on Aru Glacier, with one T logger located in front of the terminus moraines and the six other T loggers distributed on the glacier surface from 5357 to 6013 m MSL. The Ali Station, which is approximately 250 km from Aru Glacier, was also used to investigate the temperature–elevation relationship from a large-scale perspective. The elevation range of these eight temperature records extends from a minimum of 4256 m at Ali Station to 6027 m MSL at the glacier summit (Fig. 3)

3) Naimona’nyi Glacier

Naimona’nyi Glacier is located over the north slope of the western Himalayas. This valley glacier has a total area of 7.4 km2 and runs northward from 7261 m MSL at the summit to 5545 m MSL at the glacial terminus (Arendt et al. 2017). Naimona’nyi Glacier has experienced substantial mass loss, and the equilibrium line altitude has been elevated to above 6000 m MSL due to recent air temperature increases and substantial precipitation decreases (Zhao et al. 2016). An AWS was installed at 5538 m MSL on the lateral moraine in 2012. In October 2018, five HOBO MX2301 T loggers were deployed: one in the nonglacierized region at 5477 m MSL and four from 5827 to 6075 m MSL (Fig. 2c). In addition, the Pulan meteorological station (3900 m MSL) was located in the downstream valley approximately 25 km southwest of Naimona’nyi Glacier. The elevation range of these seven stations extends from the lowest elevation of 3900 m MSL at Pulan Station to a maximum of 6075 m (Fig. 3).

4) Dagze region

The Dagze region is located on the inner TP, which hosts many endorheic lakes, such as Dagze Co and Selin Co. A few glaciers have developed around several mountain summits. In July 2015, three AWSs (4480, 5000, and 5880 m MSL) were installed to investigate the altitudinal gradient of meteorological variables (Yang et al. 2018). The AWS at 5880 m MSL is near Mugagangqiong Glacier, which is a small flat glacier (1.9 km2) covering mountain summits with glacierized elevations ranging from 5741 to 6115 m MSL (Arendt et al. 2017). Due to temperature sensor malfunctioning of the AWS at 4480 m MSL in 2019, we use only the data from the other two AWSs (Fig. 2d). In addition, nearby CMA records from Gaize Station (250 km west) were adopted to derive the LRs from a regional perspective (Fig. 1). These stations span an altitudinal range of 1400 m from 4500 m MSL at Gaize Station to 5880 m MSL at AWS5880 near Mugagangqiong Glacier (Fig. 3).

5) Dunde Glacier

Dunde Glacier is located on the northeastern TP, which is near the Qilian Mountains (Fig. 1). This glacier has a total area of 20 km2, with an elevation ranging from 4782 to 5325 m MSL (Arendt et al. 2017). In October 2018, a total of five T loggers, including one over the terminus moraine and four on the glacier surface, were deployed (Fig. 2e). The daily air temperatures recorded from the nearby national weather station (Xiaozhaohuo Station: 2767 m MSL) were also solicited to provide the lowest-elevation temperature records in this region (Fig. 1). The elevation difference between the lowest and highest stations exceeded 2500 m (Fig. 3).

6) Parlung Glaciers

The Parlung catchment is located in the upper stream area of the Parlung Zangbu River basin on the southeastern TP (Fig. 1). The Parlung catchment is influenced by the Indian summer monsoon, and a large number of temperate glaciers in this region are characterized by high accumulation and high ablation (Xu et al. 2009; Yang et al. 2020). The glacio-meteorological and hydrological characteristics were previously investigated in several studies (Guo et al. 2011; W. Yang et al. 2011; Li et al. 2016; Yang et al. 2013, 2016). In this catchment, three glaciers with different areas (referred to as Parlung 4, Parlung 94, and Parlung 390) have been monitored for air temperature since July 2018 (Shaw et al. 2021). The areas are 11.7 km2 for Parlung 4 Glacier, 2.0 km2 for Parlung 94 Glacier, and 0.5 km2 for Parlung 390 Glacier (Fig. 2f). A total of 21 T loggers were deployed near/on these three glaciers, with eight sensors in the nonglacierized regions and 13 sensors on the glacier surface. In addition, hourly temperature data from five AWSs ranging in altitude from 3924 to 5380 m MSL were available in this catchment (Fig. 2f). Of these five AWSs, one was located on Parlung 4 Glacier and four were located in nonglacierized regions. In addition, three CMA records from Bomi (2737 m MSL), Zayu (2327 m MSL), and Zuogong (3780 m MSL) were used for characterization of the temperature–elevation dependency (Fig. 1). The resulting 29 stations spanned an elevational range of about 3000 m (Fig. 3).

b. Data and methods

1) Air temperature measurements

Air temperatures were measured by using Vaisala HMP155 and HOBO MX2301/U23 Pro v2 T loggers (Table 3). All temperature sensors were installed in solar radiation shields mounted on metal stakes or a tripod to achieve a nominal measurement height of approximately 2 m above the surface (Fig. S1). Temperatures at both the AWSs and the T loggers were recorded as 1-h or 30-min averages. Each dataset obtained from these stations covered an overlapping observation period from January to October 2019. To test the performance of the T loggers in the glacierized regions, we compared the hourly divergence of two naturally ventilated air temperature observations between the T logger of P19_4 and Vaisala HMP155 at AWS4800 that were collocated within a few meters horizontally on Parlung 4 Glacier (Table 2 and Fig. S2). The absolute differences between the two stations showed a mean of 0.32°C, with a standard deviation of 0.64°C. All air temperatures were measured in combination with a naturally ventilated radiation shield, which could lead to high-biased air temperature readings during periods with low wind speed and high shortwave radiation because of potentially insufficient ventilation. Due to the absence of an artificially ventilated measurement as a reference, a true uncertainty value cannot be determined precisely for the observations of our study and can only be assumed to be ±0.5°C based upon previous literature (Carturan et al. 2015; Shaw et al. 2021; Troxler et al. 2020). ERA5 reanalysis data (0.25°, ∼31 km) from the fifth generation of the European Centre for Medium-Range Weather Forecasts (Hersbach et al. 2020) were adopted specifically for knowledge of the climatic backgrounds of the glacierized regions (Table 1).

Table 3

Temperature sensors and their technical specifications.

Table 3

2) Calculation of the LR

In our investigation, the LR is used to refer to the change in near-surface temperature with elevation rather than the free-atmosphere temperature lapse rate in this study. A strongly negative (steep) LR indicates that temperature decreases rapidly with an increasing elevation, whereas the decrease is slower for a less negative (shallower) LR. LRs were calculated by using a simple linear regression over all available air temperatures in each of the six glacierized regions, which was followed by calculation of the statistical significance of the derived relationship and minimization of the uncertainties (Kattel et al. 2018; Petersen et al. 2013). The statistical significance of the altitudinal dependence is provided by the coefficient of determination for linear regression (R2).

3) Examination of glacier cooling effects

The cooling effects were examined by comparing the in situ on-glacier observations and the hourly estimates of the extrapolated air temperature, which were estimated from the off-glacier records by using the hourly LRs and the corresponding elevation of each temperature station under examination (Shaw et al. 2021; Shea and Moore 2010). The hourly LRs were derived by comparing the off-glacier records and the highest-elevation temperature logger for each glacier, which were assumed to be unaffected or only slightly affected by the presence of a glacier boundary layer. Moreover, the data under the warmest weather conditions were pooled together to interpret causal factors that indicate the distinct temperature behavior for individual glaciers. Following the methodology of previous studies (Ayala et al. 2015; Shaw et al. 2021; Troxler et al. 2020), we organized our observations by 95th percentiles (the warmest 5%) of off-glacier temperature (G-AWS1 for Guliya, A1 for Aru, N1 for Naimona’nyi, D1 for Dunde, AWS4600 for Parlung). We evaluated how robust the overall linear relationship can be found between near-surface air temperature and the elevation for these extremely warm weather conditions that typically produce the largest cooling effects. To discuss the relationship between the cooling effect and the flowline distance, we also calculated the flowline distance for each glacier by using the TopoToolbox function in MATLAB (Schwanghart and Kuhn 2010) and elevation information was derived from the Shuttle Radar Topography Mission DEM with a native 30-m resolution.

4) Calculation of the positive degree-day sum

The positive degree-day sum (PDD) was calculated from the arithmetic total of daily average temperatures above 0°C (T+) in a given time period (°C day). Accurate knowledge of the PDD is critically important for the temperature index method, which assumes a positive relationship between glacial ablation and air temperature and is popularly used in many glaciological and hydrological models (Hock 2003; Gardner et al. 2009; Hodgkins et al. 2012; Kraaijenbrink et al. 2017). The mean differences in the PDD during the period from January to October 2019 were used to evaluate the performance of temperature extrapolation from low-elevation records.

3. Results

a. Air temperature characteristics in different glacierized regions

A multitude of factors, including the latitude, surface cover and regional topographic conditions, may contribute to differences in near-surface air temperature even at the same elevation. Three stations in glacierized regions near an altitude of 6000 m were studied: Guliya (6005 m MSL), Aru (6013 m MSL), and Naimona’nyi Glacier (5985 m MSL) (Table 1). A comparison of air temperature recorded at these high elevations reveals that the mean air temperature at both the annual (January–October) and summer (June–August) scales is highest at Naimona’nyi Glacier and displays a gradual decreasing trend from low latitudes to high latitudes (Figs. 4a,b). The summer/annual air temperature at ∼6000 m MSL on Naimona’nyi Glacier (30.3°N) in the western Himalayas was approximately 3°C higher than that on Guliya Glacier (35.2°N) in the western Kunlun Mountains.

Fig. 4.
Fig. 4.

(a),(c) Daily air temperature traces near elevations of ∼6000 and ∼5000 m MSL and (b),(d) their mean values during the summer season (June–August) and at the annual scale (January–October) in different regions.

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0122.1

At comparable elevations of approximately 5000 m MSL, four stations are intercompared across Aru (4978 m MSL at AWS_aru), Dagze (5000 m MSL at AWS5000), Parlung (4992 m MSL at P8_94), and Dunde (4947 m MSL at D1) (see Figs. 4c,d). The coldest air temperature is found in the Dunde region at higher latitudes (38.1°N) on the inner TP, with temperatures below 0°C during the summer season (June–August) and −10°C during the period between January and October. However, both annual and summer air temperatures at ∼5000 m MSL in the westerlies-dominated Aru region (33.8°N) on the western Kunlun Mountain and in the monsoon-dominated Parlung region (29.5°N) on the southeastern TP display similar values, despite the ∼4.3° difference in latitude. The maximum air temperature near 5000 m MSL is observed in the Dagze region on the central TP, where summer air temperature is approximately 2.5°–2.7°C higher than that in the Aru and Parlung regimes at similar elevations.

Figure 5 shows the hourly mean summer temperature of all 34 on-glacier stations for the five basins (Guliya, Aru, Naimona’nyi, Dunde, and Parlung). Hourly air temperatures for all on-glacier stations (ranging from 4768 to 5377 m MSL) on the Parlung Glaciers are above the melting point throughout the summer season (Fig. 5a). In contrast, the mean hourly air temperatures on the other glaciers are found to be higher than 0°C for only the afternoon hours in the ablation zone. Radiative cooling at night and warming during the daytime lead to enlarged diurnal amplitudes in the high-elevation glacierized regions. The diurnal amplitude of air temperatures for the glaciers on the western and northwestern TP (ranging from ∼6° to ∼9°C) is obviously larger than that for temperate glaciers, which are mostly constrained within an interval of approximately 3°C on the Parlung Glaciers (Fig. 5b).

Fig. 5.
Fig. 5.

(a) The mean hourly air temperature on the glacier surface of five glacierized regions and (b) their mean diurnal amplitude during the summer season (June–August).

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0122.1

b. Spatial and temporal variability of LRs in different glacierized regions

Figure 6 shows the mean temperatures during the summer (June–August) and the study period (January–October) recorded at each station as a function of the corresponding elevations. Except in the Parlung Glacier region, robust correlations between mean summer/annual temperature and elevation, regardless of whether the region is glacierized or nonglacierized, are observed. All LRs are highly linear, with R2 values greater than 0.95. Spatially, the LRs show marked differences among these six regions in different geographical locations. The steepest LRs are found on Guliya Glacier on the northwestern TP [−0.97°C (100 m)−1 in the summer season and −1.03°C (100 m)−1 annually]. These LRs are similar to the dry adiabatic LR [−0.98°C (100 m)−1]. The LRs at Aru are slightly shallower than those in the Guliya region. In the Naimona’nyi, Gagze, and Dunde regions, both annual and summer LRs range from −0.7° to −0.8°C (100 m)−1. Such values agree with the mean values of −0.72°C (100 m)−1 reported on the norther slope of the glacierized Mount Everest (X. Yang et al. 2011). The minimum LR occurs on the monsoon-influenced southeastern TP, with summer values of −0.45°C (100 m)−1 (on-glacier) and −0.55°C (100 m)−1 (off-glacier) and an annual value of −0.64°C (100 m)−1.

Fig. 6.
Fig. 6.

Elevation dependence of air temperature during the summer season (June–August) represented by dots and the study period (January–October) represented by triangles for the six different regions. Note that the blue and black dots represent the glacierized and nonglacierized records, respectively, and the dashed and solid lines represent the summer and annual linear regressions, respectively.

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0122.1

Temporally, there is a marked seasonal cycle of LRs for all the regions. Figure 7 shows their monthly LRs, including the on-glacier LRs in the Parlung region, with a high R2 in most months. The LRs in the summer season are generally shallower than those in the winter-spring season. It should be noted that the shallow on-glacier LRs with low coefficients during the spring season (April–May) in the Parlung region may reflect the possible influence of spring snowfall accumulation, because precipitation in this region displayed a two-peak pattern, with peaks identified in spring and summer seasons (Maussion et al. 2014; Yang et al. 2013). Snowfall and snow accumulation will affect the nominal 2-m height of air temperature measurements by the T loggers during this period (Fig. 7b). Less confidence can be gained in the on-glacier temperature measurements for the spring season because of the unknown distance of T loggers to the underlying snow surface or the fact that some T loggers may be buried in a snowpack.

Fig. 7.
Fig. 7.

Mean monthly cycle of temperature LRs for the six glacierized regions on the TP, with the different values in the nonglacierized (Parlung) and glacierized (Parlung_glacier) regions, with their (a) coefficient of determination and (b) standard deviations (error bars).

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0122.1

c. Cooling effects on different glaciers

The differences between the observed on-glacier temperature and estimated ambient temperature for three selected points with different flowline distances on each glacier are shown in Fig. 8. The deviation of estimated and observed air temperatures theoretically begins at a critical temperature threshold that suggests the katabatic onset, and thus cooling effects occur on the melting glacier surface (Shea and Moore 2010). Such comparisons help confirm the presence of katabatic winds and the magnitude of the cooling effect along the flowline distances (Shaw et al. 2021; Shea and Moore 2010). For the Guliya, Aru, and Naimona’nyi Glaciers on the high-elevation western TP, differences between estimated ambient air temperature and observed on-glacier temperatures are less significant. However, for the terminus of Dunde Glacier and Parlung Glacier, deviation was detected between the estimated and observed air temperatures, which suggests that the katabatic boundary layer is well developed along the glacier flowline distance and that the cooling effect on these glaciers can greatly alter the magnitude and spatial distribution of on-glacier temperatures, particularly for Parlung 4 Glacier with a long flowline distance and a large spatial extent (Shaw et al. 2021).

Fig. 8.
Fig. 8.

Observed vs estimated ambient near-surface temperatures at different temperature loggers on different glaciers, showing different cooling effects along the flowline (f). The dashed lines are 1:1 lines.

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0122.1

The variation in mean air temperatures (the warmest 5% of off-glacier temperatures) as a function of the elevation further demonstrates a weaker glacier cooling effect on the western TP but a significant effect on the southeastern TP (Fig. 9). Both on-glacier and off-glacier air temperatures on the Guliya, Aru, and Naimona’nyi Glaciers decreased with the elevation at LRs in exceedance of the dry adiabatic lapse rates [−1.06°C (100 m)−1 for Guliya; −0.96°C (100 m)−1 for Aru, and −1.24°C (100 m)−1 for Naimona’nyi]. However, the elevation dependence of air temperature displays a clear contrasting pattern between on-glacier and off-glacier areas in the Parlung region due to the significant cooling effect along the flowline. The difference between the estimated and observed temperatures reaches 5.6°C at an elevation of 4650 m with a flowline distance in exceeding 7000 m. The mean on-glacier LR [−0.29°C (100 m)−1] is significantly shallower than the off-glacier LR [−0.85°C (100 m)−1].

Fig. 9.
Fig. 9.

Elevation dependence of near-surface temperature under the 95th percentiles (the warmest 5%) of off-glacier temperature. Note that the temperature loggers at the Parlung Glaciers are divided into off-glacier and on-glacier groups due to significantly different patterns, and the three proglacier temperature loggers (P15_4, P16_4, P17_4) are removed due to mixed influences of both katabatic wind and valley wind near the terminus of Parlung 4 Glacier (inside the circle).

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0122.1

d. Modeled biases introduced by extrapolating low-elevation off-glacier records to glacierized regions

For regional-scale hydrological and glaciological models, constant LRs are routinely employed in many studies (Kraaijenbrink et al. 2017; Ragettli et al. 2016). The technical viability of temperature extrapolation from low-elevation nonglacierized regions and the reanalysis climatology should be quantitatively evaluated before forcing these hydrological and glaciological models. The performance of temperature extrapolation from the low-elevation nonglacierized stations was examined using constant regional summer LRs and a routinely used environmental lapse rate (ELR) of −0.65°C (100 m)−1 (Figs. 10 and 11). The mean differences in summer air temperature and annual total PDD were averaged across all available on-glacier stations at each glacier. Clearly, extrapolation from the nearby low-elevation CMA temperature records (Tstation) by applying the distinct summer mean LRs in each region shows satisfactory performance on the western and northeastern TP (Guliya, Aru, Naimona’nyi, Gagze, Dunde Glacier). The mean difference is constrained to range from −0.2° to 0.6°C for summer air temperature and from −0.4°C day to 14.3°C day for the PDD. In contrast, the air temperature on the Parlung Glaciers on the southeastern TP was greatly overestimated, with mean differences of ∼+1.5°C for summer air temperature and ∼+171°C day for the PDD. The ice melting factor on Parlung 4 Glacier was estimated to be 11.7 mm °C−1 day−1 (W. Yang et al. 2011). Consequently, a possible overestimate of approximately 2000 mm water equivalent cumulative melt is expected in the Parlung region if the low-elevation nonglacierized air records and the regional nonglacierized LR are applied to force the glaciological/hydrological model.

Fig. 10.
Fig. 10.

The measured and modeled PDD obtained by applying different temperature extrapolation methods, including extrapolation from the lowest nonglacierized stations by constant regional summer LRs in each regime and the commonly used ELR of −0.65°C (100 m)−1.

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0122.1

Fig. 11.
Fig. 11.

PDD differences and their percentages between measured and modeled daily air temperatures extrapolated from (a) the Bomi Station at 2737 m MSL and (b) AWS4600 at 4588 m MSL. The blue circle represents the on-glacier region and orange is the nonglacierized region, with a plus sign for overestimation and a minus sign for underestimation.

Citation: Journal of Applied Meteorology and Climatology 61, 3; 10.1175/JAMC-D-21-0122.1

Figure 11 further displays the spatial absolute and relative modeled biases introduced by applying the extrapolation from the CMA Bomi records at 2737 m MSL and the AWS4600 records at 4588 m MSL. For the extrapolation from daily records on Bomi (2737 m MSL), the absolute difference in PDDs on glacier surfaces generally ranges between +100° and +250°C day, which is in contrast to the lower overestimation in the nonglacierized region. The maximum difference occurs in the ablation zone with elevations between 4600 and 4900 m MSL and a flowline distance greater than 6700 m. The relative difference between the measured and modeled PDDs is less than ∼+10% in the nonglacierized region but approaches +60% in the glacierized region. The relative PDD difference displays an increasing pattern with ∼+40% at 4800 m MSL to more than ∼+80% at 5400 m for the glacierized regions. A similar overestimation pattern was also found when the nearby AWS4600 records were used to extrapolate the on-glacier air temperature (Fig. 11b). Such evidence reveals viable spatial air temperature estimates for nonglacierized regions but significantly large overestimates in glacierized regions if a regional nonglacierized LR is applied.

4. Discussion

Our synchronous observations provide evidence of steeper LRs in the high-elevation glacierized regions on the western TP but shallower LRs in the southeastern TP. Furthermore, the summer mean LRs in five of the six selected regions (except the Parlung region on the southeastern TP) are systematically steeper than the ELR of −0.65°C (100 m)−1, previously adopted for extrapolations of low-elevation temperatures to higher altitudes (Kraaijenbrink et al. 2017). Such spatial differences can be partly explained by different climatic backgrounds. LRs are generally shallow in humid environments where moist adiabatic cooling leads to intense latent heating (Kattel et al. 2018). A mean value of −0.4°C (100 m)−1 was also found in the Langtang Valley in the monsoon-influenced Nepalese Himalayas (Immerzeel et al. 2014). The mean LR in the monsoonal season was approximately −0.55°C (100 m)−1 on the southern slope of Mount Everest (Matthews et al. 2020). Similar to the Nepalese Himalayas, the southeastern TP is typically warm and humid due to the influence of Indian summer monsoons along the Brahmaputra River (Maussion et al. 2014; Yang et al. 2013) and thus has the shallow LRs (Figs. 6 and 7). In contrast, the relatively arid continental climate on the western and inner TP (Li et al. 2019; Maussion et al. 2014), where the average elevation is greater than 4500 m, contributes to the steep LRs with values close to the dry adiabatic lapse rate (Fig. 6). Temporally, the seasonal variation in LRs for our six study regions (Fig. 7) also agreed with those other studies that reported shallow values in the summer but steep values in the winter (Blandford et al. 2008; Kattel and Yao. 2018; X. Yang et al. 2011). The latent heating over the higher elevations and reduced solar heating over the lower elevation could decrease the LRs in the summer season that coincides with the seasonal progression of moisture increase. The temporal variation in LRs in the Guliya region is more evident than that on other glaciers. For the Parlung catchment, the seasonal fluctuation in on-glacier LRs displays greater variability than that in nonglacierized regions. Suitable regional LRs with different climatic backgrounds should therefore be adopted for temperature extrapolations when applying region-scale hydrological and glaciological models.

In addition, the magnitudes of katabatic activities on different types of glaciers across the TP also greatly affected the performance of extrapolation from low-elevation records. The high-elevation residence of glacierized regions on the western TP (e.g., Guliya, Aru, and Naimona’nyi Glaciers) provides topographic conditions (terminal elevation above 5400 m MSL) for low near-surface air temperatures. As shown in Fig. 5, surface melting generally occurred in the afternoon in the ablation zone for the Guliya, Aru, and Naimona’nyi Glaciers. The katabatic activity was therefore weakly developed on the terminus of these high-elevation glaciers and thus produced an insignificant cooling effect (Fig. 9). Compared with the dominance of synoptic winds, the magnitude of down-glacier katabatic wind would be very weak. Near-surface air temperatures in both glacierized and nonglacierized regions are mainly influenced by synoptic winds in high-elevation regions; thus, the near-surface air temperature on a glacier surfaces could be linearly extrapolated from off-glacier regions with acceptable performance. However, due to the strong surface melting in the ablation season (near-surface air temperature generally above 0°C during the ablation season) on the monsoon-influenced low-elevation southeastern TP (Fig. 5), katabatic winds are better developed on the terminus of temperate glaciers (Guo et al. 2011; W. Yang et al. 2011; Shaw et al. 2021). A glacier boundary layer is formed under katabatic forcing and thus the dampening effect greatly reduces the diurnal amplitude of the near-surface air temperature (Gardner et al. 2009; Greuell and Böhm 1998; Shea and Moore 2010). The mean diurnal amplitude of all on-glacier air temperatures is constrained within approximately 3°C on the Parlung Glaciers. The cooling effects became significant along the flowline. Linear extrapolation using nonglacierized LRs, which is suitable for the high-elevation glacierized region on the western TP, would significantly overestimate air temperature for the Parlung Glaciers (Fig. 11).

Katabatic wind has been recognized to exert one of the major influences on near-surface temperatures for many melting glaciers characterized by positive air temperatures during summer worldwide (Greuell and Böhm 1998; Shea and Moore 2010). Model approaches, including the statistical model (Shea and Moore 2010) and thermodynamic model (Ayala et al. 2015; Greuell and Böhm 1998) approaches, were therefore proposed to account for such cooling effects under the katabatic boundary layer. The performance of both the statistical model (Shea and Moore 2010) and thermodynamic model (Ayala et al. 2015; Greuell and Böhm 1998) were tested in Alaska (e.g., Troxler et al. 2020), the Alps (e.g., Carturan et al. 2015), the South Patagonia Icefield (e.g., Bravo et al. 2019), and the southeastern TP (e.g., Shaw et al. 2021). It should be noted that all these tested glaciers feature relatively low-elevation termini, which contributes to continuous surface melting and thus the occurrence of katabatic activities. The statistical model proposed by Shea and Moore (2010) and the optimized parameters were suggested to be a possible feasible way to correct the cooling effect along the flowline for estimating on-glacier air temperatures on the southeastern TP (Shaw et al. 2021). Indeed, our synchronous observations further showed the contrasting magnitudes of katabatic winds on different glaciers under different climatic backgrounds on the TP. There exist heterogeneous climatic regimes on the TP (Maussion et al. 2014). The influence of katabatic wind on near-surface air temperature should be carefully examined on different types of glaciers across the TP. Even on the monsoon-influenced southeast TP, a wide range of temperate glaciers flow from ∼6000 m MSL to elevations as low as ∼2000 m MSL (Guo et al. 2015; Wu et al. 2018) and are developed under different thermal conditions and surface characteristics, such as debris-free and debris-covered conditions (Yang et al. 2017). The transferability of the abovementioned models, particularly the onset/magnitude of katabatic wind and its parameters, needs to be further evaluated with other large-size temperate glaciers widely present on the low-elevation southeastern TP and in the Himalayas. It is still warranted to develop a holistic understanding of temperature variability under different katabatic boundary layers in lower-elevation warm and humid glacierized regions.

5. Conclusions

In this study, air temperature records from a total of 61 stations (including 38 stations above 5000 m MSL) were collected in six catchments on the TP to analyze the air temperature distribution with elevation and to explore the reliability of temperature extrapolation in the glacierized zone. Our results showed high spatial variability in LRs in different climatic contexts, with the steepest LR on the cold and dry northwestern TP and the lowest LR on the warm and humid monsoon-influenced southeastern TP. The LR on the northwestern TP is close to the dry adiabatic value and is almost twice its counterpart found on the southeastern TP where moist adiabatic cooling leads to intense latent heating. Except in the glacierized region on the southeastern TP, on-glacier and nonglacierized surface air temperature can be reliably extrapolated from air temperature records at low elevations by using regional LRs. In contrast, on-glacier air temperature extrapolation from nonglacierized stations using nonglacierized LRs is not feasible on the glacierized southeastern TP because of significant cooling effects at above-zero atmospheric temperatures in the ablation season. Simple linear extrapolation from low-elevation nonglacierized temperature records would lead to at least 40% overestimation of PDD on the Parlung Glaciers. Therefore, special attention should be given to the regional LRs and the dataset used for extrapolations in glacierized regions when applying region-scale hydrological and glaciological models in different climatic regions. A wider network of ground-based measurements from different glaciers, critical evaluation of temperature-correction models, new air temperature parameterization, and the reanalysis data capability for different glacierized regions are considered necessary for the glacierized TP.

Acknowledgments.

We thank the two reviewers for their valuable comments. This research has been supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA2006020102), NSFC (project 41961134035, 41988101, 41971092), National Key Research and Development Project (2019YFC1509102, 2017YFA0603101), and a Royal Society Newton Advanced Fellowship (NA170325). We thank the National Climate Center, China Meteorological Administration, and European Centre for Medium-Range Weather Forecasts (ECMWF) for providing temperature data for this study.

Data availability statement.

All temperature data recorded by AWSs and T loggers are accessed through National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn/en/data) or by request to the corresponding author. The CMA data are available from Chinese Meteorological Data Service Center (http://data.cma.cn/).

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Supplementary Materials

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