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Mount Everest (Qomolangma), the highest mountain on Earth, is an unrivaled natural research platform for understanding multispheric interactions over heterogeneous landscapes. The land–atmosphere interactions in this iconic mountain region have paramount importance for weather and climate predictions at both regional and global scales; however, observing and modeling these interactions is inherently challenging due to the extreme environment. The scarcity of multiscale observations hinders progress in this field. Thus, establishing a comprehensive network to systematically observe the land–atmosphere interactions across multiscales in this unrivaled region, is the basis for gaining a better understanding of weather, climate, and climate change. As one of the 69 national observation and research stations in China, the Qomolangma Special Atmospheric Processes and Environmental Changes (QOMS) observation network of land–atmosphere interactions has been established over the northern slope of Mount Everest since 2005. This network consists of six sites with different underlying surfaces, which significantly improves the observational capabilities for the climate system. These observations have promoted the understanding of land–atmosphere interactions and their impacts on multiscale weather patterns, atmospheric circulations, and climate and have provided data support for informing and guiding model development and remote sensing monitoring. Facing an unprecedented opportunity with enormous development possibilities, we emphasize the considerable potential of these observations for understanding and predicting weather and climate in the Himalayas and beyond. Additionally, we expect to extend the future focus to model–data fusion and to societally relevant applications, such as natural disaster prevention and climate change mitigation and adaptation.

Wind-driven snow transport has important implications for spatial–temporal heterogeneity of snow distribution and snowpack evolution in mountainous areas, such as the French Alps. Due to the paucity of near-surface observations, our knowledge on the spatiotemporal variability of blowing snow occurrences is rather limited. Based on multiyear in situ observations, the spatial–temporal variability in the occurrence of blowing snow events in the French Alps was presented to investigate potential links with ambient meteorological conditions. Statistical analysis of the observations demonstrates that blowing snow events are frequently observed with substantial spatiotemporal variability. Most stations experienced snow transport one out of every five days throughout winter, and the corresponding cumulative hours with blowing snow occurrence accounted for 8% of the month in winter. Blowing snow events generally last 4–8 h in winter and early spring. The likelihood of blowing snow occurrences increases with wind speed but with divergent patterns across snow types. The frequency of blowing snow occurrences with concurrent snowfall is substantially higher than that without concurrent snowfall, although high spatiotemporal variability was found. The considerable variation in snow transport frequency can be explained by contrasting meteorological conditions, local climate, snowpack properties, and topography (elevation and aspect). The temperature-based empirical scheme failed to recognize individual occurrence of blowing snow events because of the significantly overestimated threshold wind speeds, highlighting the importance of validation using in situ observations. Our results contribute to the understanding of spatiotemporal occurrence of blowing snow events and facilitate the development of blowing snow models.

Wind-driven snow transport has important implications for the spatial-temporal heterogeneity of snow distribution and snowpack evolution in mountainous areas, such as the European Alps. The climatological and hydrological significance of this region have been extensively investigated using satellite and numerical models. However, knowledge of the spatiotemporal variability of blowing snow is in its infancy because of inaccuracies in satellite-based blowing snow algorithms and the absence of quantitative assessments. Here, we present the spatiotemporal variability and magnitude of blowing snow events, and explore the potential links with ambient meteorological conditions using near surface blowing snow observations from the ISAW outdoor environmental monitoring network. Results show frequent occurrence of blowing snow, and contrasting seasonal variability in the French Alps. On average, monthly blowing snow days range from 5.0 to 14.3 days when using the snow flux threshold of 0.1 g m−2 s−1. The minimum and maximum frequencies of blowing snow days are observed in September and January, respectively, accounting for between 16.7 % and 46.1 % of the month. However, the frequency of monthly blowing snow days varies widely between stations, and this variability is more pronounced at lower threshold levels. Blowing snow events with relatively high magnitudes of snow mass flux (1.0 g m−2 s−1) occur more frequently than low-intensity events (snow mass flux ranges from 0.1 to 0.5 g m−2 s−1). By imposing a minimum duration of 4 h, the monthly cumulative hours with blowing snow occurrences can be up to 255 hours, but show significant seasonal and spatial variability. The considerable variability observed across this region can be explained by contrasting local climate (particularly wind speed and air temperature), snowpack properties, topography and vegetation. The snow-mass transported during relatively high magnitude blowing snow events accounts for about 90 % of all the transported snow mass, highlighting the importance of major events. Blowing snow events that occur with concurrent snowfall are generally associated with intense snow transport. Transport of wet snow and dry snow is mostly concentrated in the range of 0.1 to 0.5 g m−2 s−1 and 0.5 to 1.0 g m−2 s−1, respectively. Understanding the spatiotemporal variability of blowing snow occurrences and the potential links with ambient meteorological conditions is critical for constructing effective avalanche disaster warning systems, and for promoting quantitative evaluation and development of satellite retrieval algorithms and blowing snow models.

This study investigates the long-term variations of local land atmosphere coupling (LoCo) over Tibetan Plateau (TP) by applying a mixing diagram to the observational data at six stations over TP and ERA5 and the possible influence of Southern Asian monsoon. The result indicates that the monthly-mean daily variation in T2m, q2m, Hsfc, and LEsfc at Nyingchi, Nagqu, Nam Co, Qomolangma, Ngari, and Muztagata in ERA5 are close to those in observational data. Comparison of mixing diagram analysis using the monthly-mean variables of ERA5 and the observational data indicates ERA5 could provide reliable information of LoCo at six stations. The relationships between Hsfc and daytime PBLH, and the variations of LCL deficit at six stations are different due to the differences in the soil states. The long-term variations in the PBL energy budgets, mean daytime PBLH, and LCL deficits at 31◦N and 90◦E show clear annual variations and have a close relationship between Southern Asian monsoon. The possible influence of the Southern Asian monsoon is also discussed in terms of the relationship between the Webster-Yang index and the PBL energy budgets, mean PBLH and mean LCL over TP.

This study aimed to investigate the relationship between the boreal summer Silk Road Pattern (SRP) and the atmospheric heat () over the Tibetan Plateau (TP) region, using 5 reanalysis datasets over the period 1979–2019. Our results indicate an interdecadal change of boreal summer SRP over the Eurasian region, with a regime shift in the spatial structure at around 1997. Meanwhile, the summer anomaly also shows a clear interdecadal increasing trend over the TP region, which is highly correlated with the interdecadal variation of the SRP. The impact of the SRP on the summer was also investigated. The regime shift of the SRP would have generated circulation anomalies over the Lake Baikal region in 500 hPa, which would have inhibited moisture transport across the eastern boundary of the TP. Meanwhile, Indian Summer Monsoon (ISM) would also transport water vapor through the southern boundary of the TP and increased the contents of water vapor in the TP. Associated with this increase in moisture, the change of vertical motion would result in large plenty of precipitation, which released latent heat and enhanced in summer. Thus, the regime shift in summer SRP was an important factor contributing to changes in summer over the TP in recent decades.

The Tibetan Plateau (TP) plays a critical role in influencing regional and global climate, via both thermal and dynamical mechanisms. Meanwhile, as the largest high-elevation part of the cryosphere outside the polar regions, with vast areas of mountain glaciers, permafrost and seasonally frozen ground, the TP is characterized as an area sensitive to global climate change. However, meteorological stations are sparely and biased distributed over the TP, owing to the harsh environmental conditions, high elevations, complex topography, and heterogeneous surfaces. Moreover, due to the weak representative of the stations, atmospheric conditions and the local land-atmosphere coupled system over the TP as well as its effects on surrounding regions are poorly quantified. This paper presents a long-term (2005–2016) dataset of hourly land-atmosphere interaction observations from an integrated high-elevation, cold region observation network, which is composed of six field observation and research platforms on the TP. In-situ observations, at the hourly resolution, consisting of measurements of micrometeorology, surface radiation, eddy covariance (EC), and soil temperature and soil water content profiles. Meteorological data were monitored by automatic weather station (AWS) or a planetary boundary layer (PBL) observation system composed of multiple meteorological element instruments. Multilayer soil hydrothermal data were recorded to capture vertical variations in soil temperature and water content and to study the freeze-thaw processes. In addition, to capture the high-frequency vertical exchanges of energy, momentum, water vapor and carbon dioxide within the atmospheric boundary layer, an EC system consisting of an ultrasonic anemometer and an infrared gas analyzer was installed at each station. The release of these continuous and long-term datasets with hourly time resolution represents a leap forward in scientific data sharing over the TP, and it has been partially used in the past to assist in understanding key land surface processes. This dataset is described here comprehensively for facilitating a broader multidisciplinary community by enabling the evaluation and development of existing or new remote sensing algorithms as well as geophysical models for climate research and forecasting. The whole datasets are freely available at Science Data Bank (http://www.dx.doi.org/10.11922/sciencedb.00103, Ma et al., 2020) and, additionally at the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/en/data/b9ab35b2-81fb-4330-925f-4d9860ac47c3/).

The Local land atmosphere coupling (LoCo) focuses on the interactions between soil conditions, surface fluxes, planetary boundary layer (PBL) growth, and the formations of convective clouds and precipitations. Study of LoCo over the Tibetan Plateau (TP) is of great significance for understanding TP's role in the Asian Water Tower. A series of real-case simulations using the Weather Research and Forecasting Model (WRF) with different combinations of land surface models (LSM) schemes and PBL schemes has been carried out to investigate the LoCo characteristics over a typical underlying surface in the central TP in rainy season. The LoCo characteristics in the study area are analyzed by applying a mixing diagram to the simulation results. The analysis indicates that the WRF simulations using the Noah with BouLac, MYNN, and YSU produce closer results to the observation in terms of curves of Cp*θ and Lv*q, surface fluxes (Hsfc and LEsfc), entrainment fluxes (Hent and LEent) at site BJ/Nagqu than those using the CLM with BouLac, MYNN, and YSU. The frequency distributions of Hsfc, LEsfc, Hent, and LEent in the study area confirm this result. The spatial distributions of simulated Hsfc, LEsfc, Hent, and LEent using WRF with Noah and BouLac suggest that the spatial distributions of Hsfc and LEsfc in the study area are consistent with that of soil moisture, but the spatial distributions of Hent and LEent are quite different from that of soil moisture. A close examination of the relationship between entrainment fluxes and cloud water content (QCloud) reveals that the grids with small Hent and large LEent tend to have high QCloud and Hsfc, suggesting that high Hsfc is conductive to convective cloud formation, which leads to small Hent and large LEent. Sensitivity analysis of LoCo to the soil moisture at site BJ/Nagqu indicates that on a sunny day, an increase in soil moisture leads to an increase in LEsfc but decreases in Hsfc, Hent, and LEent. The sensitivity of the relationship between simulated maximum daytime PBL height (PBLH) and mean daytime evapotranspiration (EF) in the study area to soil moisture indicates that the rate at which the maximum daytime PBLH decreases with the mean EF increase as the initial soil moisture goes up. The analysis of simulated Hsfc, LEsfc, Hent, and LEent under different soil moisture conditions reveals that the frequency of Hent ranging from 80 to 240 W/m2 and the frequency of LEent ranging from −240 to −90 W/m2 both increase as the initial soil moisture increases. Coupled with the changes in QCloud, the changes in Hent and LEent as the initial soil moisture increases indicate that the rise in soil moisture leads to an increase in the cloud amount but a decrease in QCloud.

The Local land atmosphere coupling (LoCo) focuses on the interactions between soil conditions, surface fluxes, PBL growth, and the formations of convective clouds and precipitations, and a study of LoCo over the Tibetan Plateau (TP) is of great significance to understand its role of “Asian Water Tower”. This study investigates the LoCo characteristics over a typical underlying surface in central TP in the rainy season based on a series of real case simulations using Weather Research and Forecasting Model (WRF) with different combinations of land surface model (LSM) schemes and planetary boundary layer (PBL) schemes based on in-situ measurements. Then the LoCo characteristics over a typical underlying surface in central TP are analyzed using a mixing diagram. The simulations results indicates that WRF simulations using Noah with BouLac, MYNN, and YSU produce much better results in terms of curves of Cp*theta and Lv*q, surface fluxes (Hsfc and LEsfc), entrainment fluxes (Hent and LEent) at site BJ/Nagqu that those using CLM with BouLac, MYNN, and YSU do. The frequency distributions of Hsfc, LEsfc, Hent, and LEent in the study area confirmed this result. The spatial distributions of simulated Hsfc, LEsfc, Hent, and LEent using WRF with Noah and BouLac suggest that the spatial distributions of Hsfc and LEsfc in the study area show a good consistent with that of soil moisture, but the spatial distributions of Hent and LEent are quite different from that of soil moisture. A close examination of the relationship between entrainment fluxes and cloud water contents (QCloud) reveals that the grids with small Hent and large LEent are likely to have high QCloud and Hsfc. This means that high Hsfc is conductive to convective cloud formations, which lead to small Hent and large LEent. Sensitivity analysis of LoCo to the soil moisture at site BJ/Nagqu indicates that in a sunny day, an increase in soil moisture leads to an increase in LEsfc but a decrease in Hsfc, Hent, and LEent. The sensitivity of the relationship between simulated max daytime PBLH and mean daytime EF in the study area to soil moistures indicates that the rate at which the max daytime PBLH decrease with the mean EF increases as the initial soil moisture goes up. The analysis of simulated Hsfc, LEsfc, Hent, and LEent under different soil moisture conditions reveals that the frequencies of Hent ranging from 80 W/m2 and over 240 W/m2 and frequency of LEent ranging from -240 W/m2 to -90 W/m2 increase as the initial soil moisture increases. Coupled with the changes in QCloud, the changes in Hent and LEent as the initial soil moisture increases indicate that the increase in soil moisture lead to an increase in cloud amounts but a decrease in QCloud.

Accurately estimating the surface fluxes of over the heterogeneous land surface in Tibetan Plateau will be helpful to advance the understanding of its influence on regional climate and hydrology. This paper presents a study on the spatial heterogeneity of land surface parameters in terms of the spatial variability and spatial structure of land surface parameters and the influence on surface fluxes over a typical land surface in Tibetan Plateau. The results suggest that the sensible heat fluxes (H) and latent heat fluxes (LE) in the study area in the rain and dry seasons show apparent spatial variabilities due to the spatial heterogeneity in the leaf area index (LAI) and land surface undulations. The relative frequency distribution of H and LE at the spatial resolution of 30 m suggests that the spatial variability of surface fluxes has a close relationship with the spatial heterogeneity of land surface temperature (LST) and LAI. The variogram analyses of LST, LAI, H, and LE in the study area in rain season indicate that the spatial structures of LST and LAI are different and the spatial structures of H and LE are strongly influenced by the spatial structures of LST and LAI in both rain and dry seasons. The optimal pixel sizes for LST, LAI, H, and LE in the study area are 506, 156, 500, and 225 m in the rain season. The optimal pixel sizes for LST, H, and LE in the study area are 165, 165, and 162 m in the dry season. An analysis of the relative frequency distributions (RFDs) of the LST, LAI, H, and LE at different spatial resolutions in the rain and dry seasons reveals that their values at the maximum relative frequency keep stable although their spatial variabilities become weak as the spatial resolution decreases. The averages of LST, LAI, H, and LE of different spatial resolutions of the study area in rain and dry seasons vary within small ranges, suggesting that the influence of spatial resolution on the averaged land surface parameters and surface fluxes in the study area is small. This work will be helpful for the accurate estimation of the surface fluxes over a large heterogeneous land surface for regional climate modeling in Tibetan Plateau.

The local land atmosphere coupling (LoCo) focuses on the interactions between the soil states, surface fluxes, planetary boundary-layer (PBL) growth, and entrainments, offering a unique opportunity to understanding the interactions between the land and atmosphere over Tibetan Plateau (TP). This study investigates the LoCo characteristics over TP in the dry and rainy seasons by applying a mixing diagram method to the observational data at Nyingchi, Nam Co, Qomolangma, and Ngari in 2014 and the reanalysis data ERA5 from 2011 to 2014. Comparisons between the observed rainfall, T2m, q2m, surface sensible and latent heat fluxes (Hsfc and LEsfc) and PBLH and these variables in ERA5 indicates that these variables of ERA5 are relatively reliable despite the wet and dry bias in ERA5. Comparisons of the mixing diagram based on the observation and ERA5 indicate that the ERA5 can produce relatively reliable information of the LoCo characteristics at Nam Co and Qomolangma in the June, August, and November. Analysis of the temporal variation in PBL energy budgets from 2011 to 2014 indicates that PBL energy budgets at the four stations show clear seasonal variations, indicating the influenced of the Southern Asian monsoon and the westerly jet stream on the LoCo characteristics. Analysis of LoCo characteristics over TP indicates that the spatial distributions of monthly mean PBL energy budgets over TP are strongly influenced by the snow cover in the dry season and by the soil moisture in the rainy season. The result also suggests that the relationships between Hsfc and PBLH are strongly influenced by the snow cover in the dry season and by the soil moisture in the rainy season.

An analysis of the local atmospheric circulation in a northern Himalayan valley in the region of Mount Everest is presented. Data were collected using an automatic weather station over a one-year period in 2014. A ground-based wind profiler radar (WPR) and an in situ GPS radiosonde (RS) were also employed. This study focuses on the characteristics of afternoon strong wind events in the downstream of Rongbuk Valley. We found that: (1) The occurrence of the southwesterly wind during non-monsoon was in good consistency with high values of westerly wind at high levels over this region and confirmed to be driven by the strong westerly jet aloft. (2) The strong afternoon wind in monsoon season has a persistent southeasterly direction, which differs from the prevailing direction of the strong wind in non-monsoon. This flow was found to be independent of the wind aloft and was strongly seasonal, developing at Qomolangma Station (QOMS) when the subtropical jet stream had moved northward and was most stabl...

In this paper, the reliability of the wind speed, temperature, humidity, pressure, and precipitation values of three surface meteorological forcing products [China Meteorological Administration Land Data Assimilation System, version 2 (CLDAS-2); China Meteorological Forcing Dataset (CMFD); and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2)] in the Tibetan Plateau (TP) region was investigated from 2008 to 2014. Compared with the China Meteorological Administration (CMA) observations, CLDAS-2 exhibited the highest correlation coefficient for wind speed, CMFD displayed the best coefficients for temperature and specific humidity, and MERRA-2 best reflected pressure variations. Based on the biases, CLDAS-2 exhibited the best overall performance for temperature, specific humidity, and pressure, while CMFD displayed the best performance for wind speed. The high overall accuracy and false alarm ratio of precipitation based on MERRA-2 both stem from its continuous overestimation of the precipitation frequency. Both CLDAS-2 and CMFD overestimated the nonprecipitation frequency in comparisons with CMA observations, and a significant positive bias exists in MERRA-2 based on the analysis of daily precipitation. The results obtained from the comparisons with field observations over the TP and CMA observations are similar, except for the temperature and humidity biases of CLDAS-2. The meteorological effects on the coupled land–blowing snow modeling discussed in this paper suggest that the occurrence of blowing snow and snowdrift sublimation are projected to be reduced by CLDAS-2 due to the underestimation of wind speed, continual lack of snowfall events, and the positive biases in low temperatures and humidity, while simulations of blowing processes by MERRA-2 are likely to be much more severe than they actually are. These results may contribute to identifying deficiencies associated with the development of land surface models coupled with a blowing snow model.

Time series of MODIS land surface temperature (T s) and normalized difference vegetation index (NDVI) products, combined with digital elevation model (DEM) and meteorological data from 2001 to 2012, were used to map the spatial distribution of monthly mean air temperature over the Northern Tibetan Plateau (NTP). A time series analysis and a regression analysis of monthly mean land surface temperature (T s) and air temperature (T a) were conducted using ordinary linear regression (OLR) and geographical weighted regression (GWR). The analyses showed that GWR, which considers MODIS T s, NDVI and elevation as independent variables, yielded much better results [R2 Adj > 0.79; root-mean-square error (RMSE) = 0.51°C–1.12°C] associated with estimating T a compared to those from OLR (R 2 Adj = 0.40−0.78; RMSE = 1.60°C–4.38°C). In addition, some characteristics of the spatial distribution of monthly T a and the difference between the surface and air temperature (T d) are as follows. According to the analysis of the 0°C and 10°C isothermals, T a values over the NTP at elevations of 4000–5000 m were greater than 10°C in the summer (from May to October), and T a values at an elevation of 3200 m dropped below 0°C in the winter (from November to April). T a exhibited an increasing trend from northwest to southeast. Except in the southeastern area of the NTP, T d values in other areas were all larger than 0°C in the winter.

This paper presents the impact of two snow cover schemes (NY07 and SL12) in the Community Land Model version 4.5 (CLM4.5) on the snow distribution and surface energy budget over the Tibetan Plateau. The simulated snow cover fraction (SCF), snow depth, and snow cover days were evaluated against in situ snow depth observations and a satellite-based snow cover product and snow depth dataset. The results show that the SL12 scheme, which considers snow accumulation and snowmelt processes separately, has a higher overall accuracy (81.8%) than the NY07 (75.8%). The newer scheme performs better in the prediction of overall accuracy compared with the NY07; however, SL12 yields a 15.1% underestimation rate while NY07 overestimated the SCF with a 15.2% overestimation rate. Both two schemes capture the distribution of the maximum snow depth well but show large positive biases in the average value through all periods (3.37, 3.15, and 1.48 cm for NY07; 3.91, 3.52, and 1.17 cm for SL12) and overestimate snow cover days compared with the satellite-based product and in situ observations. Higher altitudes show larger root-mean-square errors (RMSEs) in the simulations of snow depth and snow cover days during the snow-free period. Moreover, the surface energy flux estimations from the SL12 scheme are generally superior to the simulation from NY07 when evaluated against ground-based observations, in particular for net radiation and sensible heat flux. This study has great implications for further improvement of the subgrid-scale snow variations over the Tibetan Plateau.

The northern slopes of Himalaya (NSH) have the highest average elevation in the world. It is difficult to assess how climate change has affected this region because only a few observations are available from the high terrain and harsh environment. This study investigates the long-term characteristics of temperature and precipitation in the NSH. Further, the association of these variations with atmospheric circulation patterns is also investigated. Our results indicated that the warming trend in this region is almost 1.5 times that of the TP region, 2 times that of China, and 3.5 times that of the world. Additionally, the warming rate of the NSH is more obvious than other regions in the Himalayas, which shows that different regions of the Himalayas have different sensitivity to climate change. Although the warming trend in the NSH region does not show obvious seasonal differences like the TP, the temperature increase rate in autumn and winter is still higher than that in spring and summer. The abrupt change point for the temperature increase in summer was about 5 years later than that in other seasons, indicating that the NSH region is more sensitive to climate warming in cooler seasons, which is similar to the western and northwestern Himalaya. Furthermore, the Southern Oscillation Index (SOI) displays significant relationships with the temperature in the NSH, meanwhile, the North Atlantic Oscillation index (NAO) and Western Pacific Subtropical High Intensity Index (WPI) also exist some correlations with seasonal temperature change. This indicating that the atmospheric circulation would also have affected the temperature increase in this region, especially in summer and winter. The changes in precipitation are only affected by the SOI during the monsoon season (June to September), indicating that ENSO influences precipitation changes through water vapor transmission. In contrast, the precipitation in the TP is correlated with NAO, SOI and WPI, which indicating the precipitation of the TP might be affected by multiple circulation systems.

The aerodynamic roughness length (z 0m ) is a crucial parameter in quantifying momentum, sensible and latent heat fluxes between land surface and atmosphere, and it depends greatly on spatial scales. This paper presents a tentative study on the upscaling analysis of z 0m in the north Tibetan Plateau based on in situ data from eddy covariance (EC) and large aperture scintillometer (LAS) and leaf area index (LAI) of MODerate-resolution Imaging Spectroradiometer (MODIS) with 250 m and 2 km spatial resolutions. The comparison of z 0m calculated from EC (z 0m_EC ) and LAS (z 0m _LAS ) data indicates that z 0m at both scales has apparent seasonal variations and is in good agreement with the LAI result. However, z 0m_LAS is higher than z 0m_EC , which is attributed to the differences in roughness elements in their footprints. An upscaling relationship for z 0m is developed with z 0m_EC , z 0m _LAS and LAI with 250 m spatial resolution of MODIS. In addition, an altitude correction factor is introduced into the vegetation height estimation equation because the cold environment in the north Tibetan Plateau, which is due to its high altitude, has a strong influence on vegetation height. The z 0m retrieval with 250 m spatial resolution in the rain season is validated with z 0m_EC at sites Nagqu/Amdo, Nagqu/MS3478 and Nagqu/NewD66, and the agreement is acceptable. The spatial distribution of z 0m retrievals at small spatial scale in the north Tibetan Plateau from June to September 2012 shows that the z 0m values are less than 0.015 m in most areas, with the exception of the area in the southeast part, where z 0m reaches 0.025 m owing to low altitudes. The z 0m retrievals at large spatial scale in the north Tibetan Plateau from June to September 2012 range from 0.015 to 0.065 m, and high values appear in the area with low altitudes. The spatial distribution and frequency statistics of z 0m retrievals at both spatial scales reveal the influence of altitude and LAI on the z 0m in the north Tibetan Plateau, suggesting the uniqueness of the Tibetan Plateau.

The influence of spatial scales on surface fluxes is an interesting but not fully investigated question. This paper presents an analysis on the influence of spatial scales on surface fluxes in the north Tibetan Plateau based on eddy covariance (EC) and large aperture scintillometer (LAS) data at site Nagqu/BJ, combined with the land surface temperature (LST) and normalized difference vegetation index (NDVI) of moderate-resolution imaging spectroradiometer (MODIS). The analysis shows that sensible heat fluxes calculated with LAS data (H_LAS) agree reasonably well with sensible heat fluxes calculated with EC data (H_EC) in the rain and dry seasons. The difference in their footprints due to the wind direction is an important reason for the differences in H_EC and H_LAS. The H_LAS are statistically more consistent with H_EC when their footprints overlap than when their footprints do not. A detailed analysis on H_EC and H_LAS changes with net radiation and wind direction in rain and dry season indicates that the spatial heterogeneity in net radiation created by clouds contributes greatly to the differences in H_EC and H_LAS in short-term variations. A significant relationship between the difference in footprint-weighted averages of LST and difference in H_EC and H_LAS suggests that the spatial heterogeneity in LST at two spatial scales is a reason for the differences in H_EC and H_LAS and that LST has a positive correlation with the differences in H_EC and H_LAS. A significant relationship between the footprint-weighted averages of NDVI and the ratio of sensible heat fluxes at two spatial scales to net radiation (H/Rn) in the rain season supports the analysis that the spatial heterogeneity in canopy at two spatial scales is another reason for differences in H_EC and H_LAS and that canopy has a negative correlation with (H/Rn). An analysis on the influence of the difference in aerodynamic roughness lengths at two spatial scales on sensible heat fluxes shows that the influence is greater in the dry season and smaller in the rain season because the ratio of z0m_LAS to z0m_EC is big in the dry season and is close to 1.0 in the rain season. This study on spatial scales on surface fluxes in the Tibetan Plateau will be helpful in analyzing and understanding its influence on climate.

This study investigates the local land-atmosphere coupling (LoCo) in the rainy season by applying a mixing diagram method to the field measurements and ERA5 data over a typical underlying surface in Tibetan Plateau (TP). Comparison of frequencies of meteorological variables in June, July, August, and September (JJAS) in 2011 in the two datasets suggests that the ranges of meteorological variables are similar although the ERA5 shows a cold and wet bias. The mixing diagram analysis on 12 July, and 7–8 August 2011 calculated based on the two datasets reveals that the ERA5 could provide similar curves of the mixing diagram in the sunny days, although it tends to overestimate surface sensible and latent heat fluxes (Hsfc, LEsfc), and entrainment sensible heat fluxes (Hent), underestimate entrainment latent heat fluxes (LEent) in sunny days. The mixing diagram analysis under four different weather conditions based on ERA5 shows that LoCo behaves differently under different weather conditions and that as cloud amount increases, less heat and more water vapor are transported into the PBL from the land surface. Study of the monthly mean mixing diagram in JJAS from 2011 to 2014 based on the two datasets indicates the curves of the monthly-mean Cp*θ and Lv*q and PBL energy budgets exhibit a monthly variation, which relates to Southern Asian monsoon (ASM). Discussion of the relationship between mean Hsfc and max PBLH in the daytime reveals that the role of Hsfc in PBL growth at the daily scale is weakest in July due to the impact of ASM.

Evapotranspiration (ET) is an important component of the surface energy balance and water cycle, especially in arid and semiarid regions. The characteristics of the actual evapotranspiration (ETa), which was calculated using the eddy covariance method, and the reference evapotranspiration (ET0), which was estimated using the Food and Agriculture Organisation (FAO) Penman–Monteith method, were analysed. This work focussed on the seasonal variations in evapotranspiration and crop coefficient (Kc) above the heterogeneous canopy of an arid oasis ecosystem in a cornfield of the Zhangye oasis in northwestern China. The results showed that in 2008, the total net radiation (Rn) was 2457.73 MJ∙m−2 and that the rainfall was 117 mm. The average wind velocity, air temperature, and specific humidity, which were observed 2 m above the ground surface, were 1.23 m∙s−1, 7.07 °C, and 3.66 g∙kg−1, respectively. The total ETa and ET0 were 654.69 mm and 1039.92 mm, respectively; thus, the ET0 was higher than the ETa. The difference between the ET0 and ETa was high in summer and autumn, and low in winter and spring. The ETa was greatly influenced by irrigation events, whereas the ET0 was not influenced by irrigation. The ETa and ET0 were both greatly influenced by meteorological elements. The Kc values were less than 0.5 outside of the maize-growing stage and greater than 0.5 during the entire maize-growing stage (from 20 April to 22 September 2008). The Kc values were 0.63, 0.75, 0.78, 0.76, 0.61 and 0.71 at the seedling, shooting, heading, filling, and maturity stages and the entire growth stage, respectively.