Mountains are among the areas most vulnerable to global warming and intensifying climate variability (Beniston 2003). Mountains are also rich biodiversity repositories, and they are the world’s water towers (Viviroli et al. 2007; Immerzeel et al. 2020), which meet a substantial portion of the environmental and human water demands and ecosystem commodities relied upon by downstream communities (Immerzeel et al. 2010; Tse-ring et al. 2010). Home to some of the most iconic mountains, including the highest mountain on Earth, Mount Everest, the Tibetan Plateau (TP) is often referred to as the “roof of the world.” The TP possesses the greatest number of glaciers outside of polar regions (Yao et al. 2013) and hosts the sources of the continent’s largest life-supporting rivers (Qiu 2008; Immerzeel et al. 2010). The TP is recognized as having significant dynamic and thermal impacts on East Asian weather and climate patterns, and its effects can even spread globally (Ye and Gao 1979; Wu et al. 2007).
The Mount Everest region has experienced evident climate changes over the past few decades (Kang et al. 2022; Potocki et al. 2022). Significant warming (Kang et al. 2022; Han et al. 2021) and a substantial precipitation decrease have reshaped the local environment (Yang et al. 2014). Glaciers and snow have been rapidly retreating in this region (Salerno et al. 2015; Ye et al. 2015; Xu et al. 2016), permafrost melt has been accelerating (Ran et al. 2018), snowlines have been receding dramatically (Pelto et al. 2021), extreme weather has become more frequent and severe (Bookhagen 2010), previously reliable water sources have been disrupted (Immerzeel et al. 2010, 2020), and natural disasters (e.g., avalanches and glacier lake outbursts) have been instigated (Kääb et al. 2018; Miner et al. 2020). These changes impact atmospheric and hydrological cycles, and the consequences are likely to snowball in the future. The distinct natural environments of this iconic mountain region are intricately linked to the global climate system. The exposure of dark rock due to the rapid shrinking of glaciers and snow coverage as well as the upward shifting of snow lines (Potocki et al. 2022) contributes to changes in albedo and absorbed solar radiation, accelerating the warming rate due to snow-albedo feedback and exacerbating water scarcity in downstream areas during the summer growth season (Immerzeel et al. 2010). The changing thermal forcing in this critical area significantly affects local- and regional-scale atmospheric circulation (Yang et al. 2014) and the consequent transportation, exchange, and dispersion of pollutants (Kang et al. 2003; Cong et al. 2015). Permafrost thawing disrupts the hydrological cycle and greenhouse gas emissions, thereby aggravating the warming trend (Koven et al. 2015). Flash floods, landslides, and debris flows are becoming increasingly frequent and intense, posing significant risks to downstream livelihoods and the environment (Eriksson et al. 2009; Wester et al. 2019).
The sensitivity and vulnerability of this region to climate variability make it an ideal long-term platform for monitoring the ongoing climate changes and the unique land–atmosphere interactions over high mountains. Land–atmosphere interactions are important topic in climate change studies because they encompass a wide range of complicated processes and feedbacks, linking the land surface with the atmospheric boundary layer via several biophysical and biochemical processes. In particular, the exchange of heat, momentum, carbon, and water between the land and the overlying atmosphere occurs across spatial and temporal scales. These interactions are transdisciplinary and bidirectional, contributing considerably to weather forecasting and climate predictability (Berg et al. 2016; Seneviratne et al. 2006). Despite their importance, processes in land–atmosphere interactions are complicated in the Mount Everest region and may differ from other places due to the diverse topographic features (Ye and Gao 1979). As bridging the knowledge gap relies heavily on integrated observations, current numerical models generally include simple process representations with poorly constrained interactions (Santanello et al. 2018). The lack of a realistic depiction of land–atmosphere interactions in the Himalayas adds to the uncertainty in plateau land process simulations, high-impact weather forecasting, and climate modeling and prediction (Yang et al. 2009; Su et al. 2013; Xie et al. 2018, 2019; Nury et al. 2022). From this point of view, high-altitude observations are crucial for model physics evaluation and improvement (Bollasina and Benedict 2004). As a result, there is an urgent need to bridge the knowledge gap in the processes and feedback inherent in the land–atmosphere interactions in the Mount Everest region.
This region has the strongest land–atmosphere interactions in midlatitudes region (Xue et al. 2017) due to the elevated topography, complicated but distinctive and diverse geographical conditions as well as the intense radiation and thermodynamic effects. Its unique geographic and atmospheric conditions contribute to the formation of strong and distinguishable local weather system, climatic and environmental features (Ye and Gao 1979). It is an ideal natural platform for studying multispheric interactions among atmospheric, cryosphere, hydrological, geological, and environmental processes in the context of global environmental changes. More specifically, the continuous undulating surface disrupts the flow of winds, while winds transfer heat, moisture, and pollutants worldwide (Mayewski et al. 2020).
The earliest meteorological observations on Mount Everest region date back to the manual observations of temperature and wind speed and direction conducted by Somervell (1926) at four different altitudes on the northern side. There are very few high-altitude, long-term, comprehensive meteorological observations in this region, although four integrated scientific expeditions were conducted in 1959/60, 1966–68, 1975, and 2005 (Shen 1975; Xie 1975; Gao 1980). The “Pyramid” program (Bollasina et al. 2002) was an ambitious effort that took place on the south slope of Everest to bridge the high-altitude observational gap. The program operated for more than 10 years but stopped in 2014 owing to a lack of funding. National Geographic and Rolex’s Perpetual Planet Everest Expedition installed a network of five automatic weather stations (AWSs) along the southern climbing route of Mount Everest in 2019, which greatly improved the monitoring of this iconic mountain (Matthews et al. 2020, 2022). However, the northern and southern slopes differ significantly because Mount Everest acts as a natural barrier, obstructing warm and wet winds northward into the TP (Sun et al. 2017). Previous efforts have provided valuable insights into the knowledge of meteorology and climate in this extraordinarily heterogeneous mountain topography. Nonetheless, reliable and long-term meteorological observations as well as multiscale and multidisciplinary data in land surface science research in high-altitude complex terrain areas are spatiotemporally scarce due to instrumental issues, extreme environmental conditions, the complexity of the topography, the difficulties in the management of high-altitude mountain stations, and the only recent understanding of their scientific importance. Therefore, it is necessary to accept the challenge to fill the scarcity data on the northern slope of Mount Everest by establishing a comprehensive observation network, despite the extreme topography and harsh climate conditions. Observations from such a network may aid in comprehensively improving the capability to monitor the climate system and provide data support for a more holistic understanding of the land–atmosphere interactions over this unrivaled region. Thus, promoting the development of numerical models and retrieval algorithms, facilitating the assessment of impacts of multiscale land–atmosphere interactions on weather patterns, atmospheric circulations, and climate, preventing weather disasters, mitigating potential environmental issues, and serving specialized scientific support for sustainable development.
Scientific objectives
We focus on regional and global climate change in relation to the “water–cryosphere–atmosphere–biology” multisphere interactions and the associated mechanisms. The construction of the Qomolangma Special Atmospheric Processes and Environmental Changes (QOMS) (a summary table of the notation introduced can be consulted in Table 1) observation network aims to address the following key scientific issues:
- 1)Establishing a symbolic observation system and a comprehensive data platform in the Third Pole region to ensure the availability of comprehensive, continuous, systematic, and standardized observations of multisphere interactions; improving the comprehensive observational capabilities of the climate system and providing data support for dealing with climate change; serving ecological civilization construction; and promoting Earth system science research and the sustainable development of the TP and its surrounding regions.
- 2)Understanding the multiscale variation characteristics of land–atmosphere interactions over the different underlying surfaces with complex terrains, such as energy and water exchanges, key land surface parameters, turbulence characteristics in steep terrain, fine-scale characteristics of wind fields, atmospheric vertical structures, local circulation characteristics, boundary layer parameters, and the impact of complex terrain on the local atmospheric circulation patterns, surface thermal and dynamic characteristics as well as climate, climate change, and climatic variability. All these factors are expected to provide a solid basis for evaluating numerical models and satellite retrieval products, to promote physical representation of land surface processes and refine retrieval algorithms for high mountain terrain, thereby facilitating the understanding of the effects of land–atmosphere interaction processes on weather and climate.
- 3)Revealing the influence of land–atmosphere interactions in this high mountain terrain on regional and global atmospheric circulation, water vapor and pollutant transport and associated feedback, as well as the mechanisms influencing regional and global energy and water cycles. These efforts facilitate natural disaster forecasting and early warnings, climate risk prevention, and climate change mitigation and adaptation.
Notation description.
Observation network layout
Site deployment and design principle.
A comprehensive observation network of land–atmosphere interactions has been gradually established since 2005 to improve the capability of retrieving comprehensive observations of the climate system, as well as distinctive weather and climate features over the northern slope of Mount Everest. The QOMS network consists of six observation stations ranging in altitude from 4,276 to 6,523 m at the Ruopula Col, with a variety of landscapes along the Rongbuk glacier and valley (Fig. 1). Glacier–desert–shrub–meadow landscapes are typical in this mountain region, providing good coverage for the broad monitoring of heterogeneous mountain environments and climate, and they will contribute to the evaluation and improvement of satellite retrieval algorithms and physical-based models.
Map and layout of the National Observation and Research Station of China for Qomolangma Special Atmospheric Processes and Environmental Changes (QOMS).
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Map and layout of the National Observation and Research Station of China for Qomolangma Special Atmospheric Processes and Environmental Changes (QOMS).
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Map and layout of the National Observation and Research Station of China for Qomolangma Special Atmospheric Processes and Environmental Changes (QOMS).
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
The QOMS_Main is the center site of this network, located downstream of the Rongbuk valley, at an elevation of 4,276 m above sea level. The valley around the QOMS_Main is flat, and the surface is covered by sand and gravel with sparse vegetation. QOMS_Wetland (4,460 m) and QOMS_Shrub (4,650 m) were specifically chosen for monitoring the climatic system throughout the wetland and shrub environments, respectively. Both locations are on flat terrain that enables observations representative of the respective land–atmosphere interactions under typical ecosystems. The QOMS_Camp (5,200 m) is located at the north base camp, absence of local anthropogenic influence as the farthest area of the north side of the Mount Everest accessible to tourists and local residents is 2 km away, and hence, it is an ideal place for monitoring natural climatic variability. QOMS_Moraine (5,820 m) was established on the terminal moraine of East Rongbuk glacier, whereas QOMS_Col (6,520 m) was located at Ruopula Col, the accumulation zone of East Rongbuk glacier (X. Yang et al. 2011), which has a relatively flat terrain. These two stations were chosen to track the critical interactions that contribute to the glacier mass balance.
Instrumentation.
During the early stages of the QOMS_Main, it was instrumented with a planetary boundary layer (PBL) observational system that measures air temperature, relative humidity, wind speed and direction, and air pressure at five levels; it also has a soil hydrothermal system, a surface radiation monitoring system, an eddy covariance system, a 1,290 MHz wind profiling radar (WPR), a radio acoustic sounding system, and an atmospheric environmental observation system (e.g., aerosols, precipitation chemical composition, and pollen); finally, it can obtain geophysical-related observations (e.g., seismic). We never stopped expanding comprehensive observation capabilities for environmental and climatic studies while maintaining the regular operation of these instruments. Subsequently, more sites with different land cover (including wetlands, shrubs, moraines, and glaciers) were continuously added to the network. Each station is equipped with routine meteorological measurements, radiation instruments, and soil temperature and soil water content probes. However, due to the differences in the orientation and the constraints of the observational conditions, the instruments were customized for each station. For example, multilayer meteorological measurements were made at all stations except the QOMS_Camp, and soil hydrothermal properties were recorded at varying depths (Table 2). Moreover, in order to measure high-frequency wind and scalar atmospheric data series, gas, energy, and momentum for estimating turbulent fluxes within atmospheric boundary layers, the eddy covariance system was installed at the QOMS_Main, QOMS_Shrub, and QOMS_Camp stations (Table 2). The turbulent fluxes are used extensively for verification and tuning of numerical models (e.g., global climate models, mesoscale and weather models, land surface models, and ecological models) and remote sensing estimates from satellites and aircraft. The QOMS network has matured into a multispherical observation platform capable of monitoring atmospheric physics, atmospheric environments, plant ecology, soil hydrothermal regimes, geophysical and hydrological processes after 17 years of hard effort (Fig. 2).
An overview of the core observation sensors for each of the six sites.
The main instruments on board the QOMS_Main.
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
The main instruments on board the QOMS_Main.
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
The main instruments on board the QOMS_Main.
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Operation and maintenance.
The QOMS network is manned throughout the year, and various inspections rotate through scenarios to ensure the continuity and accuracy of the observations. Except for the QOMS_Moraine and QOMS_Col, we execute monthly on-site inspection of the operational status of all observational equipment, and the data are retrieved as well. Onsite personnel are responsible for the inspections, maintenance, as well as the continuity and quality of data for each station. All data are transmitted to our data center for processing, analyzation, and storage. Furthermore, the remote transmit module is custom equipped for inspection and on-demand data transfer. Professional technical specialists perform full instrument maintenance (cleaning and calibration) twice a year to guarantee that any detected technical problems are properly and promptly resolved. Nonetheless, the QOMS_Moraine and QOMS_Col have suffered several serious solar power system failures, and the entire observation systems were eventually destroyed one year after the establishment because of the weather extremes. This further highlights the extremely challenging in monitoring the changing environments at this extreme altitude.
Research highlights
Surface meteorological characteristics.
The monthly average near-surface air temperature (Fig. 3a) at lower altitudes is generally higher than that at higher altitudes but with a larger diurnal variation. On the other hand, the diurnal temperature variation at the QOMS_Wetland is an exception, and its seasonal air temperature variation is smaller than the QOMS_Shrub, as the near-surface air temperature is related to surface land-cover features and soil moisture conditions. The monthly average near-surface air temperature drops below the freezing point from November to March, which is frequently accompanied by strong, windy weather (Fig. 3b). The diurnal temperature variation at the QOMS_Main and QOMS_Shrub stations frequently exceeds 30°C. The substantial dependency of spatial and seasonal variations in near-surface air temperature and wind speed on both elevation and surface land-cover properties highlights the necessity of establishing a monitoring network spanning different altitudes and land surface characteristics in this region. It is only in this way that can we capture the main features of energy and water exchange at the land–atmosphere interface in this heterogeneous region, as well as quantify the significance of its land–atmosphere interactions in the global energy and water cycle from a more comprehensive and systematic perspective. Previous studies have shown a significant warming trend over a wide area of this region (Han et al. 2021; Kang et al. 2022), and a minor recovery or restored surface wind speed has been reported (Zhu et al. 2017). However, based on the long-term in situ observations from the QOMS_Main, very small interannual variations in the near surface temperature and wind speed were found during the period from 2006 to 2021 (Figs. 3c,d). The surface wind speed here is strongly influenced by the subtropical jet (STJ) and local circulation systems, such as glacier winds and valley winds (Chen et al. 2012; Sun et al. 2017), as well as the interplay between topography and synoptic circulation (Lai et al. 2021). Zhang et al. (2018) concluded that the change in circulation from prevailing glacier winds to prevailing upslope winds and the oscillation of upslope winds due to cloud cover contribute to the decrease in wind speed at the QOMS_Main. The inconsistency in air temperature and wind speed trends across research emphasizes the importance of long-term field observations and the need for additional attribution analysis. The network provides vital information that can be used to clarify the actual climatic variability and, potentially, to explain disparities that exist in previous studies.
Characteristics of surface air temperature (2 m) and wind speed (10 m) in the Mount Everest region. (a) Seasonal variation in surface air temperature. (b) Seasonal variation in wind speed. (c) Temporal evolution of monthly average surface air temperature and wind speed during 2006–21 at the QOMS_Main. (d) Temporal evolution of the annual mean surface air temperature and wind speed during 2006–21 at the QOMS_Main. The lower (upper) whisker in the boxplot in (a) and (b) represents the minimum (maximum) value, the lower (upper) boundary of the box denotes the lower (higher) quartile, while the line in the box denotes the mean of corresponding dataset.
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Characteristics of surface air temperature (2 m) and wind speed (10 m) in the Mount Everest region. (a) Seasonal variation in surface air temperature. (b) Seasonal variation in wind speed. (c) Temporal evolution of monthly average surface air temperature and wind speed during 2006–21 at the QOMS_Main. (d) Temporal evolution of the annual mean surface air temperature and wind speed during 2006–21 at the QOMS_Main. The lower (upper) whisker in the boxplot in (a) and (b) represents the minimum (maximum) value, the lower (upper) boundary of the box denotes the lower (higher) quartile, while the line in the box denotes the mean of corresponding dataset.
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Characteristics of surface air temperature (2 m) and wind speed (10 m) in the Mount Everest region. (a) Seasonal variation in surface air temperature. (b) Seasonal variation in wind speed. (c) Temporal evolution of monthly average surface air temperature and wind speed during 2006–21 at the QOMS_Main. (d) Temporal evolution of the annual mean surface air temperature and wind speed during 2006–21 at the QOMS_Main. The lower (upper) whisker in the boxplot in (a) and (b) represents the minimum (maximum) value, the lower (upper) boundary of the box denotes the lower (higher) quartile, while the line in the box denotes the mean of corresponding dataset.
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Surface radiation and energy exchange characteristics.
Apparent seasonal variations are observed for the shortwave and longwave radiation (Figs. 4a,b), and they are closely related to monsoon activity. In the sidebar “Computation of the turbulent heat fluxes and roughness lengths” we detail the procedure for calculating the turbulent heat fluxes and roughness lengths (aerodynamic roughness length and effective aerodynamic roughness length are described the following section). The decline in downward shortwave radiation and sensible heat flux (H) and increase in latent heat flux (LE) in June (Fig. 4c) indicate the start of the monsoon season (Khadka et al. 2021). Sensible heat flux dominates during the premonsoon period. It reaches its maximum in May and declines with the break in the monsoon and increases at the end of the monsoon, as high water vapor content is observed and heavy rains occur at night during the monsoon period (Chen et al. 2012). According to Wang et al. (2013), the summer monsoon contributes 95% of the annual rainfall. The dominant component of the TP heat source in the monsoon season is LE, which is released in association with monsoon precipitation. The LE and H at the QOMS_Shrub are both higher than those at the other stations, indicating that surface energy partitioning is subject to land surface features. An obvious seasonal variation in surface albedo is observed at these stations, as the variation is closely related to the solar elevation angle, the occurrence of rainfall and snowfall, and surface characteristics, such as soil moisture, vegetation coverage, and roughness (Chen et al. 2012). The surface albedo at the QOMS_Wetland remains low throughout the year, which is much lower than that of other stations, highlighting the impacts of underlying surface type on the surface albedo and subsequent surface radiation budget. A 0.08% soil moisture anomaly leads to a 0.0008 variance in surface albedo (Chen et al. 2012). Earlier studies based on either reanalysis product or AWS observations have reported a declining H (Zhu et al. 2017; Wang et al. 2019) with a rapid rebound after 2000 (Wang et al. 2019; Duan et al. 2022). Nonetheless, K. Yang et al. (2011) and Han et al. (2019) observed a declining trend in the H. Earlier research mostly relied on reanalysis products or calculations from AWSs using the bulk transfer method, and thus, uncertainty is an inevitable challenge that should be explicitly considered. The spatial-scale mismatch between point observations and coarse mesoscale reanalysis might also contribute to this discrepancy. According to a footprint analysis, 90% of the turbulent fluxes measured at the QOMS_Main are contributed by the area lies between 100 and 160 m upwind from the observation point. Besides, the impacts of inhomogeneous alpine environment and land-cover type over this region cannot be neglected as well. Long-term direct observations of surface energy flux components are rare, but the observations enable constraint of the strength and trend of surface energy flux over the TP. Because of the critical implications of the TP’s surface heat flux on the Asian summer monsoon, the contributions of local meteorological conditions to the evolution of surface heat fluxes should be clarified.
Characteristics of surface energy exchange. (a) Seasonal variation in downward and upward shortwave radiation. (b) Seasonal variation in downward and upward longwave radiation. (c) Seasonal variation in sensible heat flux and latent heat flux. (d) Seasonal variation in surface albedo. (e) Temporal evolution of annual mean downward and upward shortwave radiationand downward and upward longwave radiation, and (f) sensible heat flux and latent heat during 2006–21 at the QOMS_Main.
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Characteristics of surface energy exchange. (a) Seasonal variation in downward and upward shortwave radiation. (b) Seasonal variation in downward and upward longwave radiation. (c) Seasonal variation in sensible heat flux and latent heat flux. (d) Seasonal variation in surface albedo. (e) Temporal evolution of annual mean downward and upward shortwave radiationand downward and upward longwave radiation, and (f) sensible heat flux and latent heat during 2006–21 at the QOMS_Main.
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Characteristics of surface energy exchange. (a) Seasonal variation in downward and upward shortwave radiation. (b) Seasonal variation in downward and upward longwave radiation. (c) Seasonal variation in sensible heat flux and latent heat flux. (d) Seasonal variation in surface albedo. (e) Temporal evolution of annual mean downward and upward shortwave radiationand downward and upward longwave radiation, and (f) sensible heat flux and latent heat during 2006–21 at the QOMS_Main.
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Computation of the turbulent heat fluxes and roughness lengths
Raw, high-frequency data (sampled at 10 Hz) were converted to 30-min turbulent fluxes using the EddyPro 7 Software (an open-source eddy covariance postfield processing software). First, raw data pass through several quality control filters [sonic anemometer diagnostic value, infrared gas analyzers (IRGA) diagnostic value and high-frequency spike detection] before covariances and fluxes are calculated. Wind components are rotated and time lag (induced because of the travel time of an air particle between the sonic and IRGA) is corrected after initial high-frequency data quality control. Additional corrections (e.g., compensate density fluctuations) are applied to 30-min fluxes and the turbulent fluxes are finally calculated with quality control flag provided (1–9). The z0m was estimated using a statistical method proposed by Yang et al. (2008), in which the optimal value of z0m corresponded to the peak frequency in the histogram of ln(z0m). The atmospheric stability, frication velocity, and wind speed are necessary input for the determination of z0m. The roughness features of land surface within can be characterized by the height and density of the roughness obstacles (estimated based on high-resolution digital elevation model) in each grid. Han et al. (2015) summarized the estimation of
Soil hydrothermal characteristics.
The difference between soil temperatures at various depths were minimal in this region during spring and autumn, while a more evident vertical soil temperature gradient was observed at the QOMS_Main during summer and winter (Figs. 5a,c,e,g). Soil freezing depth at the QOMS_Main (about 40 cm) was much shallower than other stations. In contrast, the frozen depth reached 80 cm (or more due to observation depth limitation) at the QOMS_Camp and the QOMS_Wetland and 160 cm (or more) at the QOMS_Shrub. Except for the QOMS_Wetland, a low soil moisture content with obvious seasonal variation was observed at the three stations due to the dry climate with scarce rainfall and the influence of the soil freezing process in winter. During the early freezing stage and thawing period, a sharp fluctuation in the soil moisture content was noticed in the shallow layer. Soil moisture is crucial in regulating the energy and water exchange processes at the soil–atmosphere interface. Chen et al. (2012) demonstrated that the ratio between H and net radiation could reach 0.49 when the soil is dry, and it decreases to 0.14 when the soil is moist. Soil properties (e.g., soil type, porosity, and organic content) and land surface characteristics contribute to the spatial variability in the vertical gradient of soil temperature and soil moisture content.
Annual variation in vertical (a),(c),(e),(g) soil temperature and (b),(d),(f),(h) soil moisture profile at the QOMS_Main, QOMS_Shrubland, QOMS_Camp, and QOMS_Wetland (averaged based on available observations).
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Annual variation in vertical (a),(c),(e),(g) soil temperature and (b),(d),(f),(h) soil moisture profile at the QOMS_Main, QOMS_Shrubland, QOMS_Camp, and QOMS_Wetland (averaged based on available observations).
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Annual variation in vertical (a),(c),(e),(g) soil temperature and (b),(d),(f),(h) soil moisture profile at the QOMS_Main, QOMS_Shrubland, QOMS_Camp, and QOMS_Wetland (averaged based on available observations).
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Turbulent flux parameters.
The aerodynamic roughness length (z0m) at the QOMS_Shrub (Fig. 6a) is high in July and August (0.026 and 0.027 m, respectively) as vegetation thrives, in contrast to the QOMS_Main and QOMS_Camp, where only sparse vegetation exists with weak seasonal variation. Han et al. (2015) reported 0.025 m of z0m, which is within the range of values derived using multiyear eddy covariance measurements (0.015–0.026 m). These three stations have a similar diurnal variation in monthly mean excessive resistance for heat transfer [kB−1 = ln(z0m/z0h), where k is the von Kármán constant and B−1 is a nondimensional bulk parameter, z0h is the thermal roughness length], with lower values at night and higher values during the day, particularly in the afternoon (Ma et al. 2011). According to Yang et al. (2008), using a constant value of kB−1 may result in serious bias in estimating surface heat flux. Given the apparent diurnal variation and spatial heterogeneity in kB−1, as well as its critical role in turbulent flux computation, it is preferable for the kB−1 scheme to give not only the spatial-dependent mean value but also the diurnal variations. Wang and Ma (2011) reported evident diurnal and seasonal variations in the bulk transfer coefficients at the QOMS_Main, with the averages of heat transfer coefficient (Cd) and momentum transfer coefficient (Ch) being 0.73 × 10−3 and 1.0 × 10−3 in October, respectively. The values of the average Cd are larger than those of the average Ch. The transfer coefficients are atmospheric stability-dependent land surface parameters. They vary with thermodynamical states, underlying surface conditions and surrounding topography. Because of the differences in methods and observation technologies employed, the results obtained or used in different studies span several times or several orders of magnitude, even in the same region (Li et al. 2001). Given the substantial spatial and temporal variations in the aerodynamic and thermodynamic parameters, it is inappropriate to regard them as constants in numerical models. In this regard, the in situ observations can provide sufficient information (e.g., the “true” state of local land surface characteristics) to validate, constrain, and improve models. The Surface Energy Balance System (SEBS) with a revised roughness height parameterization, for example, performed better in the surface turbulent flux simulation than the original model when evaluated with observations from the QOMS_Main (Chen et al. 2013a). These results highlight that a better paradigm for bringing together observations and models would target sources of model error, guide model developments, and improve model capabilities in simulating the land–atmosphere interactions by advancing model process representations.
Characteristics of the land surface turbulent flux parameters (z0m and kB−1 were calculated based on available eddy covariance observations). (a) Seasonal variation in aerodynamic roughness length at the QOMS_Main, QOMS_Shrub, and QOMS_Camp. (b) Diurnal variation in monthly mean excessive resistance for heat transfer at the QOMS_Main, QOMS_Shrub, and QOMS_Camp. (c) Variation in effective aerodynamic roughness length (
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Characteristics of the land surface turbulent flux parameters (z0m and kB−1 were calculated based on available eddy covariance observations). (a) Seasonal variation in aerodynamic roughness length at the QOMS_Main, QOMS_Shrub, and QOMS_Camp. (b) Diurnal variation in monthly mean excessive resistance for heat transfer at the QOMS_Main, QOMS_Shrub, and QOMS_Camp. (c) Variation in effective aerodynamic roughness length (
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Characteristics of the land surface turbulent flux parameters (z0m and kB−1 were calculated based on available eddy covariance observations). (a) Seasonal variation in aerodynamic roughness length at the QOMS_Main, QOMS_Shrub, and QOMS_Camp. (b) Diurnal variation in monthly mean excessive resistance for heat transfer at the QOMS_Main, QOMS_Shrub, and QOMS_Camp. (c) Variation in effective aerodynamic roughness length (
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
In rugged mountainous areas, shear stress resulting from the canopy drag and form drag caused by topography should be simultaneously considered in turbulent flux parameterization. Han et al. (2015) used wind profile data from a wind profiler and GPS radiosondes at the QOMS_Main to quantify the effective aerodynamic roughness length (
Local atmospheric circulation patterns.
Due to the unique location of the QOMS_Main and the surrounding topography, the local lower atmospheric circulation at the QOMS_Main is significantly influenced by the nearby valleys and glacier environment, and by the synoptic circulations in the central Himalayan region, such as the STJ and South Asian summer monsoon. These features have been confirmed by in situ observations at the QOMS_Main from AWS, GPS radiosondes, and wind profiler (Chen et al. 2007, 2012; Sun et al. 2007, 2017). The most pronounced meteorological phenomenon at the QOMS_Main is the strong afternoon wind occurring throughout the year with a distinct seasonal variation. It blows southwesterly during the nonmonsoon season and southeasterly during the monsoon season (Fig. 7). The mechanisms of these strong afternoon winds are studied based on both in situ observations and numerical simulations. The southwesterly wind during nonmonsoon seasons was driven by downward momentum transport from the strong westerly winds of the STJ aloft (Sun et al. 2017, 2018). However, there is no consensus yet on the strong afternoon southeasterly wind during the monsoon period. Chen et al. (2012) demonstrated that it was a synthetic result of the East Asia monsoon and persistent glacier wind, while Sun et al. (2017, 2018) argued that the southeasterly wind was likely an extension of the wind in the Arun Valley (east of Mount Everest), which was driven by a strong horizontal thermal gradient from the valley to the QOMS_Main; additionally, the heat low over the TP also plays an important role in enhancing the winds in the Arun Valley and the QOMS_Main as a sequence. The mechanisms controlling the strong daytime winds in the southern and northern Himalayan valleys are noticeably distinct, indicating that the local circulations are different (Sun et al. 2017, 2018). These results help to illustrate the pollutant monitoring results in this region and confirm that the QOMS_Main is an ideal location to investigate pollutant transportation across the Himalayas.
Diurnal variation in near-surface wind at the QOMS_Main observed using AWS observations averaged during (a) monsoon (from 24 May to 21 Jun 2006) and (b) nonmonsoon (from 29 Mar to 6 Apr 2007) seasons. Wind barbs of the average wind velocity obtained by WPR during (c) monsoon and (d) nonmonsoon seasons. A half barb is 2.5 m s−1, and a full barb is 5 m s−1 (Sun et al. 2018).
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Diurnal variation in near-surface wind at the QOMS_Main observed using AWS observations averaged during (a) monsoon (from 24 May to 21 Jun 2006) and (b) nonmonsoon (from 29 Mar to 6 Apr 2007) seasons. Wind barbs of the average wind velocity obtained by WPR during (c) monsoon and (d) nonmonsoon seasons. A half barb is 2.5 m s−1, and a full barb is 5 m s−1 (Sun et al. 2018).
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Diurnal variation in near-surface wind at the QOMS_Main observed using AWS observations averaged during (a) monsoon (from 24 May to 21 Jun 2006) and (b) nonmonsoon (from 29 Mar to 6 Apr 2007) seasons. Wind barbs of the average wind velocity obtained by WPR during (c) monsoon and (d) nonmonsoon seasons. A half barb is 2.5 m s−1, and a full barb is 5 m s−1 (Sun et al. 2018).
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
The synergistic effects of the large-scale atmospheric circulation and local topography on the development of PBL at the QOMS_Main were investigated using radiosonde and other field observations on two sunny days (Lai et al. 2021). Westerlies prevailed above the top of the stable boundary layer on both days (Figs. 8b,d) but with different wind speeds and directions at the height of the mountain ridge parallel to or intersecting with the axis of the Rongbuk valley (Figs. 8g,f). The strong surface wind was attributed to downward momentum transfer, one possible cause for the strong near-surface winds during winter (Sun et al. 2017, 2018), according to a clear consistency between the near-surface wind and the wind at 500 hPa. The downward transmission of the westerlies to the valley floor was strong (weak) and associated with large (small) H and deep (low) PBL. These results demonstrate the important role of the interaction between local topography and large-scale synoptic winds in the development of the PBL.
Vertical profiles of (a),(c) wind speed and (b),(d) wind direction at the QOMS_Main from radiosonde observations on two sunny days with distinct synoptic conditions (23 and 28 Nov 2014). The horizontal dashed lines represent the corresponding tops of the convective boundary layer. The gray shading indicates the heights of the surrounding ridges. Diurnal variations in near-surface wind speed and direction on (e) 23 and (f) 28 Nov. Variations in wind speed and wind direction at 500 hPa (retrieved from ERA5) and the ground surface (10 m) on (e) 23 and (f) 28 Nov. Sounding times are represented by dashed black vertical lines (Lai et al. 2021).
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Vertical profiles of (a),(c) wind speed and (b),(d) wind direction at the QOMS_Main from radiosonde observations on two sunny days with distinct synoptic conditions (23 and 28 Nov 2014). The horizontal dashed lines represent the corresponding tops of the convective boundary layer. The gray shading indicates the heights of the surrounding ridges. Diurnal variations in near-surface wind speed and direction on (e) 23 and (f) 28 Nov. Variations in wind speed and wind direction at 500 hPa (retrieved from ERA5) and the ground surface (10 m) on (e) 23 and (f) 28 Nov. Sounding times are represented by dashed black vertical lines (Lai et al. 2021).
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Vertical profiles of (a),(c) wind speed and (b),(d) wind direction at the QOMS_Main from radiosonde observations on two sunny days with distinct synoptic conditions (23 and 28 Nov 2014). The horizontal dashed lines represent the corresponding tops of the convective boundary layer. The gray shading indicates the heights of the surrounding ridges. Diurnal variations in near-surface wind speed and direction on (e) 23 and (f) 28 Nov. Variations in wind speed and wind direction at 500 hPa (retrieved from ERA5) and the ground surface (10 m) on (e) 23 and (f) 28 Nov. Sounding times are represented by dashed black vertical lines (Lai et al. 2021).
Citation: Bulletin of the American Meteorological Society 104, 3; 10.1175/BAMS-D-22-0084.1
Summary and outlook
The roof of the world is no longer unreachable and untouchable, although humans have felt distanced both physically and psychologically for a long time. We have now overcome the major obstacles and paved the way for observations of “water–cryosphere–atmosphere–biology” multisphere interactions in harsh environmental conditions. The integrated network has served as a scientific research platform for mountain meteorology, alpine hydrology, the cryosphere, and environmental sciences thanks to observational advances and more than 17 years of effort. The network has been used for a variety of scientific purposes, and the use of field observations for meteorological and other applications has proven to be a success, significantly improving our understanding of multisphere interactions for heterogeneous landscapes, as well as hydrosphere, cryosphere, and ecosystem responses to regional climate change. In view of the diverse and unique natural conditions, fragile and sensitive ecology and environment of the Mount Everest region, as well as its unique characteristics and importance in the water and energy changes in the global climate system, the QOMS integrated network has been approved as one of 69 national observation and research stations of China, namely, the National Observation and Research Station of China for Qomolangma Special Atmospheric Processes and Environmental Changes. This accreditation offers an unprecedented opportunity with enormous development possibilities. Field observations will undoubtedly be an indispensable part of the future of plateau mountain meteorology for both research and operations.
However, there are challenges to meeting the scientific, operational, and technological needs of long-term sustainable development, adaptation and mitigation in the context of global climate change. The future focus of objectives extends not only to research and operations but also to applications that directly impact local human society. Ensuring continuous and high-quality observations, extending the applications of multisphere integrated observations, and exploring the potential possibility of providing meteorological services for the construction and protection of the local ecological environment and socioeconomic development could be a possible way forward.
The network has amassed a large amount of data, but most of the work thus far has focused on analyzing land–atmosphere interactions based on the measurements from one or several stations. Further studies based on long-term and high-quality observations will be more diverse and thorough. Satellite remote sensing techniques and regional or global climate models are becoming increasingly important for the understanding large-scale climate system but require models constrained by observations at various levels. Current models still struggle to reflect the peculiarities of the land–atmosphere energy and water exchange processes in this complex terrain. CMIP models also span a broad range of uncertainty regarding model parameters and model process representations, with potentially different regional climate responses. As a result, there are many possibilities to extend the research by supporting the evaluation and refinement of numerical models and remote sensing retrieval techniques, as well as assessing climate and environmental impacts and making efforts to prevent potential natural risks and assure sustainable development. Action has already been taken in this regard. For example, observations have been used to evaluate the feasibility and applicability of the SEBS for using high-resolution satellite datasets to generate surface turbulent heat flux maps considering the effects of rugged topography (Chen et al. 2013a,b; Han et al. 2017). There is a significant resolution gap among the site scale, remote sensing pixel scale, and grid scale. Synergistic measurements among scales from tens of meters to a few kilometers and even tens of kilometers will be particularly useful in bridging the gap, accompanied by the up-scaling process. There has been extensive collaboration with the local authority to promote more accurate weather prediction and disaster warnings for the Mount Qomolangma National Nature Reserve. Furthermore, the network served as a scientific support platform for scientific groups and public welfare organizations concerned about the impacts and responses of climate and environmental change in the Mount Everest region, the TP, and even the Third Pole and pan–Third Pole regions. Eight meteorological stations have recently been set up on the north side of the Mount Everest supported by the Second Tibetan Plateau Scientific Expedition and Research Program, with the highest one located at an altitude of 8,830 m, replacing the one at an altitude of 8,430 m installed on the south side of the mountain in 2019 (Matthews et al. 2020, 2022), to be the world’s highest of its kind. The establishment of these meteorological monitoring stations will undoubtedly strengthen the monitoring capability of this landmark peak accompanying with the construction of a three-dimensional comprehensive observation system and several additional planned AWSs that will be spread over this region.
Acknowledgments.
Yaoming Ma and Zhipeng Xie contributed equally to this work. This work is jointly supported by the second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0103), the National Natural Science Foundation of China (91837208, 41830650, 41905012), the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20060101), and the China Postdoctoral Science Foundation (2018M641489). We acknowledge Professor Jeff Dozier at University of California, Santa Barbara, and other two anonymous reviewers for the constructive suggestions and comments, which improved the quality of this manuscript.
Data availability statement.
The integrated land–atmospheric interaction observations from the QOMS observation network are available from the corresponding author upon reasonable request. This dataset will become publicly available for research purpose after checking compliance with the requirement demanded by local regulations. Hourly data from the QOMS_Main station spanning from 2005 to 2016 are openly available and can be freely accessed via https://data.tpdc.ac.cn/en/data/b9ab35b2-81fb-4330-925f-4d9860ac47c3/. For further documentation about the data, please refer to Ma et al. (2020).
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