The Universal Thermal Climate Index (UTCI) is believed to be a very powerful tool for providing information on human thermal perception in the domain of public health, but the solar radiation as an input variable is difficult to access. Thus, this study aimed to explore the optimal strategy on estimation of solar radiation to increase the accuracy in UTCI calculation, and to identify the spatial and temporal variation in UTCI over China. With daily meteorological data collected in 35 tourism cities in China from 1961 to 2020, two sunshine-based Angstrom and Ogelman models, and two temperature-based Bristow and Hargreaves models, together with neural network and support vector machine-learning methods, were tested against radiation measurements. The results indicated that temperature-based models performed the worst with the lowest NSE and highest RMSE. The machine-learning methods performed better in calibration, but the predictive ability decreased significantly in validation due to big data requirements. In contrast, the sunshine-based Angstrom model performed best with high NSE (Nash–Sutcliffe Efficiency) of 0.84 and low RMSE (Root Mean Square Error) of 35.4 J/m² s in validation, which resulted in a small RMSE of about 1.2 °C in UTCI calculation. Thus, Angstrom model was selected as the optimal strategy on radiation estimation for UTCI calculation over China. The spatial distribution of UTCI showed that days under no thermal stress were high in tourism cities in central China within a range from 135 to 225 days, while the largest values occurred in Kunming and Lijiang in southwest China. In addition, days under no thermal stress during a year have decreased in most tourism cities of China, which could be attributed to the asymmetric changes in significant decrease in frost days and slightly increase in hot days. However, days under no thermal stress in summer time have indeed decreased, accompanying with increasing days under strong stress, especially in the developed regions such as Yangze River Delta and Zhujiang River Delta. Based on the study, we conclude that UTCI can successfully depict the overall spatial distribution and temporal change of the thermal environments in the tourism cities over China, and can be recommend as an efficient index in the operational services for assessing and predicting thermal perception for public health. However, extreme cold and heat stress in the tourism cities of China were not revealed by UTCI due to mismatch of the daily UTCI with category at hourly scale, which makes it an urgent task to redefine category at daily scale in the next research work.

For estimating the annual mean of daily solar irradiation in plateau mountainous regions, observed data from 15 radiation stations were used to validate different empirical estimation methods over the Tibetan Plateau. Calibration indicates that sunshine-based site-dependent models perform better than temperature-based ones. Then, the highly rated sunshine-based Ångström model and temperature-based Bristow model were selected for regional application. The geographical models perform much better than the average models, but still not ideally. To achieve better performance, the Ångström-type model was improved using altitude and water vapor pressure as the leading factors. The improved model can accurately predict the coefficients at all the stations, and performs the best among all models with an average Nash-Sutcliffe Efficiency value of 0.856. Spatial distribution of the annual mean of daily solar irradiation was then estimated with the improved model. It is indicated that there is an increasing trend of radiation from east to west, with a great center of the annual mean of daily solar irradiation on southwest Tibetan Plateau ranging from 20 to 24 MJ·m⁻². The improved model should be further validated against observations before its applications in other plateau mountainous regions.

Ground-based zenith scattered light differential optical absorption spectroscopy (DOAS) measurements were performed in summer and autumn (27 May–30 November) 2020 at Golmud (94°54′ E, 36°25′ N; 2807.6 m altitude) to investigate the abundances and temporal variations of ozone (O₃) and its depleting substances over the northern Tibetan Plateau (TP). The differential slant column densities (dSCDs) of O₃, nitrogen dioxide (NO₂), bromine monoxide (BrO), and chlorine dioxide (OClO) were simultaneously retrieved from scattered solar spectra in the zenith direction during the twilight period. The O₃ vertical column densities (VCDs) were derived by applying the Langley plot method, for which we investigated the sensitivities to the chosen wavelength, the a-priori O₃ profile and the aerosol extinction profile used in O₃ air mass factor (AMF) simulation as well as the selected solar zenith angle (SZA) range. The mean O₃ VCDs from June to November 2020 are 7.21 × 10¹⁸ molec·cm⁻² and 7.18 × 10¹⁸ molec·cm⁻² at sunrise and sunset, respectively. The derived monthly variations of the O₃ VCDs, ranging from a minimum of 6.9 × 10¹⁸ molec·cm⁻² in October to 7.5 × 10¹⁸ molec·cm⁻² in November, well matched the OMI satellite product, with a correlation coefficient R = 0.98. The NO₂ VCDs at SZA = 90°, calculated by a modified Langley plot method, were systematically larger at sunset than at sunrise as expected with a pm/am ratio of ~1.56. The maximum of the monthly NO₂ VCDs, averaged between sunrise and sunset, was 3.40 × 10¹⁵ molec·cm⁻² in July. The overall trends of the NO₂ VCDs were gradually decreasing with the time and similarly observed by the ground-based zenith DOAS and OMI. The average level of the BrO dSCD_90°–80° (i.e., dSCD between 90° and 80° SZA) was 2.06 × 10¹⁴ molec·cm⁻² during the period of June–November 2020. The monthly BrO dSCD_90°–80° presented peaks in August and July for sunrise and sunset, respectively, and slowly increased after October. During the whole campaign period, the OClO abundance was lower than the detection limit of the instrument. This was to be expected because during that season the stratospheric temperatures were above the formation temperature of polar stratospheric clouds. Nevertheless, this finding is still of importance, because it indicates that the OClO analysis works well and is ready to be used during periods when enhanced OClO abundances can be expected. As a whole, ground-based zenith DOAS observations can serve as an effective way to measure the columns of O₃ and its depleting substances over the TP. The aforementioned results are helpful in investigating stratospheric O₃ chemistry over the third pole of the world.

In order to investigate the abundance of and temporal variation in nitrogen dioxide (NO₂) in the troposphere and validate the corresponding satellite products during a normal year and the lockdown period of coronavirus disease 2019 (COVID-19) in Lhasa, a city on the Tibetan Plateau (TP), ground-based remote-sensing measurements captured by applying multi-axis differential optical absorption spectroscopy (MAX-DOAS) were recorded from August 2021 to March 2023 at the Lhasa site (91.14°E, 29.66°N; 3552.5 m altitude). The NO₂ differential slant column densities (dSCDs) were retrieved from the spectra of scattered solar light at different elevation angles. Then, the tropospheric NO₂ vertical column densities (VCDs) were calculated with the geometric approximation method. Based on the retrieved tropospheric NO₂ VCDs, we found that the pattern of monthly variation in tropospheric NO₂ VCDs in Lhasa presented two peaks, one in winter and one around May. According to the monthly means of tropospheric NO₂ VCDs during the COVID-19 lockdown, the NO₂ background level in Lhasa was estimated to be 0.53 × 10¹⁵ molecules·cm⁻². For diurnal variations in tropospheric NO₂ VCDs, the morning and evening peaks disappeared during the COVID-19 lockdown period. The east–west direction (i.e., along the river valley) was the main path of NO₂ transport and dispersion in Lhasa, but the tropospheric NO₂ VCDs were little dependent on the wind direction or wind speed during the COVID-19 lockdown. The correlation coefficient of tropospheric NO₂ VCDs was R = 0.33 (R = 0.43), with the averaged relative deviation of −28% (99%) for the TROPOMI (GEMS) relative to ground-based MAX-DOAS. The monthly deviations of tropospheric NO₂ VCDs between ground-based MAX-DOAS and the satellite showed a dependence on NO₂ abundance, with the maxima of the monthly positive deviations during the COVID-19 lockdown period. The GEMS could not capture the strong and systematic diurnal variation in tropospheric NO₂ VCDs in the “normal” year well. During the COVID-19 lockdown, the GEMS (>2 × 10¹⁵ molecules·cm⁻²) overestimated the hourly levels measured by ground-based MAX-DOAS (<1.6 × 10¹⁵ molecules·cm⁻²). As a whole, these results are beneficial to understanding the influences of anthropogenic activities on NO₂ background levels in Lhasa and to learning the accuracy of satellite products over the TP, with its high altitude and complex terrain.

As the coefficients of the Ångström–Prescott model is site-dependent, the sparsity of radiation stations in regions like the Tibetan Plateau (TP) presents challenges for model calibration. Due to the unique climate and the clean air conditions over the TP, it might be feasible to calibrate the Ångström–Prescott model with short-term observations from scientific expeditions. To test this hypothesis, we used various datasets with different lengths at four stations, together with 435 daily radiations measured during a scientific expedition at Banga in the central TP from 2014 to 2015, to calibrate the Ångström–Prescott model. We found that calibration with a 1-year data length resulted in model performances comparable to those with a 20-year data length. Analysis of the expedition observations showed that the monthly average daily radiation ranged from 15.2 MJ/m²d in December 2014 to 26.5 MJ/m²d in July 2015, with an average value of 20.6 MJ/m²d. When this set of expedition data was used for calibration, the Ångström–Prescott model performed well with an NSE (Nash–Sutcliffe efficiency) of 0.820. If no data were available for calibration, the coefficients of the Ångström–Prescott model could also be directly calculated by parameterization methods established with calibrations at the other radiation stations. In this situation, the LiuJD method performed the best with the highest NSE of 0.792, followed by the LiuXY method with an NSE of 0.764. The FAO method performed poorly with an NSE of 0.578, while the Gopinathan method performed the worst with the lowest NSE of 0.218. Thus, the best strategy to calibrate the Ångström–Prescott model in the Tibetan Plateau is to use data from local observations, even if collected over short periods. When these are not available, the coefficients of the Ångström–Prescott model should be calculated using the parameterization method established with calibrations over the Tibetan Plateau.

Glacial lake outburst floods (GLOF) hazards in alpine areas are increasing. The effects of climate change on GLOF hazards are unclear. This study examined 37 glacial lakes and climate data from 15 meteorological stations and explored the correlation between climate variations at different temporal scales. The results indicate that 19 GLOFs hazards occurred in El Niño (warm) years, 8 GLOFs hazards occurred in La Niña (cold) years, 3 GLOFs hazards occurred in cold/warm or warm/cold transition years, and 7 GLOFs hazards occurred in normal years. The higher the fluctuations, the higher the probability of GLOF hazards. Climatic conditions can be divided into three categories: extreme temperature and precipitation, as represented by the Guangxie Co GLOF; extreme precipitation, as represented by the Poge Co GLOF; and extreme temperature, as represented by the Tsho Ga GLOF.