Articles | Volume 16, issue 3
https://doi.org/10.5194/essd-16-1185-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-1185-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The Tibetan Plateau space-based tropospheric aerosol climatology: 2007–2020
Honglin Pan
Collaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China
Institute of Desert Meteorology, China Meteorological Administration, Urumqi, 830002, China
National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi, 830002, China
Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi, 830002, China
Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi, 830002, China
Key Laboratory of Tree-ring Physical and Chemical Research, China Meteorological Administration, Urumqi, 830002, China
Collaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China
Jiming Li
Collaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China
Zhongwei Huang
Collaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China
Minzhong Wang
Institute of Desert Meteorology, China Meteorological Administration, Urumqi, 830002, China
National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi, 830002, China
Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi, 830002, China
Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi, 830002, China
Key Laboratory of Tree-ring Physical and Chemical Research, China Meteorological Administration, Urumqi, 830002, China
Ali Mamtimin
Institute of Desert Meteorology, China Meteorological Administration, Urumqi, 830002, China
National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi, 830002, China
Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi, 830002, China
Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi, 830002, China
Key Laboratory of Tree-ring Physical and Chemical Research, China Meteorological Administration, Urumqi, 830002, China
Institute of Desert Meteorology, China Meteorological Administration, Urumqi, 830002, China
National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi, 830002, China
Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi, 830002, China
Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi, 830002, China
Key Laboratory of Tree-ring Physical and Chemical Research, China Meteorological Administration, Urumqi, 830002, China
Fan Yang
Institute of Desert Meteorology, China Meteorological Administration, Urumqi, 830002, China
National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang, Urumqi, 830002, China
Taklimakan Desert Meteorology Field Experiment Station of China Meteorological Administration, Urumqi, 830002, China
Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi, 830002, China
Key Laboratory of Tree-ring Physical and Chemical Research, China Meteorological Administration, Urumqi, 830002, China
Tian Zhou
Collaborative Innovation Center for Western Ecological Safety, College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China
Kanike Raghavendra Kumar
Department of Engineering Physics, College of Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur 522302, Andhra Pradesh, India
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EGUsphere, https://doi.org/10.5194/egusphere-2024-2458, https://doi.org/10.5194/egusphere-2024-2458, 2024
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Atmos. Chem. Phys., 24, 9777–9803, https://doi.org/10.5194/acp-24-9777-2024, https://doi.org/10.5194/acp-24-9777-2024, 2024
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Hemispheric or interannual averages of reflected solar radiation (RSR) can mask signals from seasonally active or region-specific mechanisms. We examine RSR characteristics from latitude and month perspectives, revealing decreased trends observed by CERES in both hemispheres driven by clear-sky atmospheric and cloud components at 30–50° N and cloud components at 0–50° S. AVHRR achieves symmetry criteria within uncertainty and is suitable for the long-term analysis of hemispheric RSR symmetry.
Yuxin Zhao, Jiming Li, Deyu Wen, Yarong Li, Yuan Wang, and Jianping Huang
Atmos. Chem. Phys., 24, 9435–9457, https://doi.org/10.5194/acp-24-9435-2024, https://doi.org/10.5194/acp-24-9435-2024, 2024
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Guangyao Dai, Songhua Wu, Wenrui Long, Jiqiao Liu, Yuan Xie, Kangwen Sun, Fanqian Meng, Xiaoquan Song, Zhongwei Huang, and Weibiao Chen
Atmos. Meas. Tech., 17, 1879–1890, https://doi.org/10.5194/amt-17-1879-2024, https://doi.org/10.5194/amt-17-1879-2024, 2024
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Qiantao Liu, Zhongwei Huang, Jiqiao Liu, Weibiao Chen, Qingqing Dong, Songhua Wu, Guangyao Dai, Meishi Li, Wuren Li, Ze Li, Xiaodong Song, and Yuan Xie
Atmos. Meas. Tech., 17, 1403–1417, https://doi.org/10.5194/amt-17-1403-2024, https://doi.org/10.5194/amt-17-1403-2024, 2024
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The achieved results revealed that the ACDL observations were in good agreement with the ground-based lidar measurements during dust events. The heights of cloud top and bottom from these two measurements were well matched and comparable. This study proves that the ACDL provides reliable observations of aerosol and cloud in the presence of various climatic conditions, which helps to further evaluate the impacts of aerosol on climate and the environment, as well as on the ecosystem in the future.
Shikuan Jin, Yingying Ma, Zhongwei Huang, Jianping Huang, Wei Gong, Boming Liu, Weiyan Wang, Ruonan Fan, and Hui Li
Atmos. Chem. Phys., 23, 8187–8210, https://doi.org/10.5194/acp-23-8187-2023, https://doi.org/10.5194/acp-23-8187-2023, 2023
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To better understand the Asian aerosol environment, we studied distributions and trends of aerosol with different sizes and types. Over the past 2 decades, dust, sulfate, and sea salt aerosol decreased by 5.51 %, 3.07 %, and 9.80 %, whereas organic carbon and black carbon aerosol increased by 17.09 % and 6.23 %, respectively. The increase in carbonaceous aerosols was a feature of Asia. An exception is found in East Asia, where the carbonaceous aerosols reduced, owing largely to China's efforts.
Yaozhi Jiang, Kun Yang, Youcun Qi, Xu Zhou, Jie He, Hui Lu, Xin Li, Yingying Chen, Xiaodong Li, Bingrong Zhou, Ali Mamtimin, Changkun Shao, Xiaogang Ma, Jiaxin Tian, and Jianhong Zhou
Earth Syst. Sci. Data, 15, 621–638, https://doi.org/10.5194/essd-15-621-2023, https://doi.org/10.5194/essd-15-621-2023, 2023
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Our work produces a long-term (1979–2020) high-resolution (1/30°, daily) precipitation dataset for the Third Pole (TP) region by merging an advanced atmospheric simulation with high-density rain gauge (more than 9000) observations. Validation shows that the produced dataset performs better than the currently widely used precipitation datasets in the TP. This dataset can be used for hydrological, meteorological and ecological studies in the TP.
Yuxin Zhao, Jiming Li, Lijie Zhang, Cong Deng, Yarong Li, Bida Jian, and Jianping Huang
Atmos. Chem. Phys., 23, 743–769, https://doi.org/10.5194/acp-23-743-2023, https://doi.org/10.5194/acp-23-743-2023, 2023
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Diurnal variations of clouds play an important role in the radiative budget and precipitation. Based on satellite observations, reanalysis, and CMIP6 outputs, the diurnal variations in total cloud cover and cloud vertical distribution over the Tibetan Plateau are explored. The diurnal cycle of cirrus is a key focus and found to have different characteristics from those found in the tropics. The relationship between the diurnal cycle of cirrus and meteorological factors is also discussed.
Chenglong Zhou, Yuzhi Liu, Qingzhe Zhu, Qing He, Tianliang Zhao, Fan Yang, Wen Huo, Xinghua Yang, and Ali Mamtimin
Atmos. Chem. Phys., 22, 5195–5207, https://doi.org/10.5194/acp-22-5195-2022, https://doi.org/10.5194/acp-22-5195-2022, 2022
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Based on the radiosonde observations, an anomalously warm layer is measured at altitudes between 500 and 300 hPa over the Tarim Basin (TB) with an average intensity of 2.53 and 1.39 K in the spring and summer, respectively. The heat contributions of dust to this anomalously warm atmospheric layer in spring and summer were 13.77 and 10.25 %, respectively. Topographically, the TB is adjacent to the Tibetan Plateau; we propose the concept of the Tibetan heat source’s northward extension.
Jingyu Yao, Zhongming Gao, Jianping Huang, Heping Liu, and Guoyin Wang
Atmos. Chem. Phys., 21, 15589–15603, https://doi.org/10.5194/acp-21-15589-2021, https://doi.org/10.5194/acp-21-15589-2021, 2021
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Gap-filling usually accounts for a large source of uncertainties in the annual CO2 fluxes, though gap-filling CO2 fluxes is challenging at dryland sites due to small fluxes. Using data collected from a semiarid site, four machine learning methods are evaluated with different lengths of artificial gaps. The artificial neural network and random forest methods outperform the other methods. With these methods, uncertainties in the annual CO2 flux at this site are estimated to be within 16 g C m−2.
Bida Jian, Jiming Li, Guoyin Wang, Yuxin Zhao, Yarong Li, Jing Wang, Min Zhang, and Jianping Huang
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We evaluate the performance of the AMIP6 model in simulating cloud albedo over marine subtropical regions and the impacts of different aerosol types and meteorological factors on the cloud albedo based on multiple satellite datasets and reanalysis data. The results show that AMIP6 demonstrates moderate improvement over AMIP5 in simulating the monthly variation in cloud albedo, and changes in different aerosol types and meteorological factors can explain ~65 % of the changes in the cloud albedo.
Xiaoyu Hu, Jinming Ge, Jiajing Du, Qinghao Li, Jianping Huang, and Qiang Fu
Atmos. Meas. Tech., 14, 1743–1759, https://doi.org/10.5194/amt-14-1743-2021, https://doi.org/10.5194/amt-14-1743-2021, 2021
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Cloud radars are powerful instruments that can probe detailed cloud structures. However, radar echoes in the lower atmosphere are always contaminated by clutter. We proposed a multi-dimensional probability distribution function that can effectively discriminate low-level clouds from clutter by considering their different features in several variables. We applied this method to the radar observations at the SACOL site and found the results have good agreement with lidar detection.
Yueming Cheng, Tie Dai, Jiming Li, and Guangyu Shi
Atmos. Chem. Phys., 20, 15307–15322, https://doi.org/10.5194/acp-20-15307-2020, https://doi.org/10.5194/acp-20-15307-2020, 2020
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In this paper we present the analysis of the aerosol vertical features observed by CATS collected from 2015 to 2017 over three selected regions (North China, the Tibetan Plateau, and the Tarim Basin) over different timescales. This comprehensive information provides insights into the seasonal variations and diurnal cycles of the aerosol vertical features across East Asia.
Cited articles
Boos, W. and Kuang, Z.: Dominant control of the South Asian monsoon by orographic insulation versus plateau heating, Nature, 436, 218–222, 2010.
Bucci, S., Cagnazzo, C., Cairo, F., Di Liberto, L., and Fierli, F.: Aerosol variability and atmospheric transport in the Himalayan region from CALIOP 2007–2010 observations, Atmos. Chem. Phys., 14, 4369–4381, https://doi.org/10.5194/acp-14-4369-2014, 2014.
Buchard, V., da Silva, A. M., Colarco, P. R., Darmenov, A., Randles, C. A., Govindaraju, R., Torres, O., Campbell, J., and Spurr, R.: Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis, Atmos. Chem. Phys., 15, 5743–5760, https://doi.org/10.5194/acp-15-5743-2015, 2015.
Carrió, G. G., van Den Heever, S. C., and Cotton, W. R.: Impacts of nucleating aerosol on anvil-cirrus clouds: A modeling study, Atmos. Res., 84, 111–131, 2007.
Chen, S., Huang, J., Zhao, C., Qian, Y., Leung, L. R., and Yang, B.: Modeling the transport and radiative forcing of Taklimakan dust over the Tibetan Plateau: A case study in the summer of 2006, J. Geophys. Res.-Atmos., 118, 797–812, 2013.
Chen, S., Zhang, R., Mao, R., Zhang, Y., Chen, Y., Ji, Z., Gong, Y., and Guan, Y.: Sources, characteristics and climate impact of light-absorbing aerosols over the Tibetan Plateau, Earth-Sci. Rev., 232, 104111, https://doi.org/10.1016/j.earscirev.2022.104111, 2022.
Chen, X., Zuo, H., Zhang, Z., Cao, X., Duan, J., Zhu, C., Zhang, Z., and Wang, J.: Full-coverage 250 m monthly aerosol optical depth dataset (2000–2019) amended with environmental covariates by an ensemble machine learning model over arid and semi-arid areas, NW China, Earth Syst. Sci. Data, 14, 5233–5252, https://doi.org/10.5194/essd-14-5233-2022, 2022.
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements, Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019.
Goldberg, D. L., Gupta, P., Wang, K., Jena, C., Zhang, Y., Lu, Z., and Streets, D. G.: Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States, Atmos. Environ., 199, 443–452, https://doi.org/10.1016/j.atmosenv.2018.11.049, 2019.
Guan, H., Chatfield, R. B., Freitas, S. R., Bergstrom, R. W., and Longo, K. M.: Modeling the effect of plume-rise on the transport of carbon monoxide over Africa with NCAR CAM, Atmos. Chem. Phys., 8, 6801–6812, https://doi.org/10.5194/acp-8-6801-2008, 2008.
Guan, H., Esswein, R., Lopez, J., Bergstrom, R., Warnock, A., Follette-Cook, M., Fromm, M., and Iraci, L. T.: A multi-decadal history of biomass burning plume heights identified using aerosol index measurements, Atmos. Chem. Phys., 10, 6461–6469, https://doi.org/10.5194/acp-10-6461-2010, 2010.
Hammer, M. S., Martin, R. V., Li, C., Torres, O., Manning, M., and Boys, B. L.: Insight into global trends in aerosol composition from 2005 to 2015 inferred from the OMI Ultraviolet Aerosol Index, Atmos. Chem. Phys., 18, 8097–8112, https://doi.org/10.5194/acp-18-8097-2018, 2018.
Hsu, N. C., Si-Chee, T., King, M. D., and Herman, J. R.: Aerosol properties over bright-reflecting source regions, IEEE T. Geosci. Remote, 42, 557–569, 2004.
Hu, Q., Wang, H., Goloub, P., Li, Z., Veselovskii, I., Podvin, T., Li, K., and Korenskiy, M.: The characterization of Taklamakan dust properties using a multiwavelength Raman polarization lidar in Kashi, China, Atmos. Chem. Phys., 20, 13817–13834, https://doi.org/10.5194/acp-20-13817-2020, 2020.
Hu, Y., Rodier, S., Xu, K., Sun, W., Huang, J., Lin, B., Zhai, P., and Josset, D.: Occurrence,liquid water content, and fraction of supercooled water clouds from combined CALIOP/IIR/MODIS measurements, J. Geophys. Res., 115, D00H34, https://doi.org/10.1029/2009JD012384, 2010.
Huang, J.: Dataset of characteristic parameters of tropospheric aerosols over the Qinghai Tibet Plateau (2007–2020), National Tibetan Plateau / Third Pole Environment Data Center [data set], https://doi.org/10.11888/Atmos.tpdc.300614, 2023.
Huang, J., Minnis, P., Chen, B., Huang, Z., Liu, Z., Zhao, Q., Yi, Y., and Ayers, J. K.: Long-range transport and vertical structure of Asian dust from CALIPSO and surface measurements during PACDEX, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2008JD010620, 2008.
Huang, L., Jiang, J. H., Tackett, J. L., Su, H., and Fu, R.: Seasonal and diurnal variations of aerosol extinction profile and type distribution from CALIPSO 5-year observations, J. Geophys. Res.-Atmos., 118, 4572–4596, 2013.
Immerzeel, W., Van Beek, L., and Bierkens, M.: Climate change will affect the Asian water towers, Science, 328, 1382–1385, 2010.
Iglewicz, B. and Hoaglin, D.: How to Detect and Handle Outliers, ASQC basic references in quality control, ASQC Quality Press, Milwaukee, 1993.
IPCC: Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp., 2013.
IPCC: Climate Change 2021: The Physical Science Basis, Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, in press, https://doi.org/10.1017/9781009157896, 2021.
Jeong, M.-J. and Hsu, N. C.: Retrievals of aerosol single-scattering albedo and effective aerosol layer height for biomass-burning smoke: Synergy derived from “A-Train” sensors, Geophys. Res. Lett., 35, L24801, https://doi.org/10.1029/2008GL036279, 2008.
Kahn, R. A., Gaitley, B. J., Garay, M. J., Diner, D. J., Eck, T. F., Smirnov, A., and Holben, B. N.: Multiangle Imaging SpectroRadiometer global aerosol product assessment by comparison with the Aerosol Robotic Network, J. Geophys. Res.-Atmos., 115, D23209, https://doi.org/10.1029/2010jd014601, 2010.
Kim, M.-H., Omar, A. H., Tackett, J. L., Vaughan, M. A., Winker, D. M., Trepte, C. R., Hu, Y., Liu, Z., Poole, L. R., Pitts, M. C., Kar, J., and Magill, B. E.: The CALIPSO version 4 automated aerosol classification and lidar ratio selection algorithm, Atmos. Meas. Tech., 11, 6107–6135, https://doi.org/10.5194/amt-11-6107-2018, 2018.
Kojima, T., Buseck, P. R., Wilson, J. C., Reeves, J. M., and Mahoney, M. J.: Aerosol particles from tropical convective systems: Cloud tops and cirrus anvils, J. Geophys. Res.-Atmos., 109, https://doi.org/10.1029/2003JD004504, 2004.
Kovilakam, M., Thomason, L. W., Ernest, N., Rieger, L., Bourassa, A., and Millán, L.: The Global Space-based Stratospheric Aerosol Climatology (version 2.0): 1979–2018, Earth Syst. Sci. Data, 12, 2607–2634, https://doi.org/10.5194/essd-12-2607-2020, 2020.
Liou, K.: Influence of cirrus clouds on weather and climate processes: a global perspective, Mon. Weather Rev., 114, 1167–1199, 1986.
Liu, D., Wang, Z., Liu, Z., Winker, D., and Trepte, C.: A height resolved global view of dust aerosols from the first year CALIPSO lidar measurements, J. Geophys. Res., 113, D16214, https://doi.org/10.1029/2007JD009776, 2008.
Liu, P., Zhao, C., Liu, P., Deng, Z., Huang, M., Ma, X., and Tie, X.: Aircraft study of aerosol vertical distributions over Beijing and their optical properties, Tellus B, 61, 756–767, https://doi.org/10.1111/j.1600-0889.2009.00440.x, 2009.
Liu, Y., Hua, S., Jia, R., and Huang, J.: Effect of aerosols on the ice cloud properties over the Tibetan Plateau, J. Geophys. Res.-Atmos., 124, 9594–9608, 2019.
Liu, Y., Li, Y., Huang, J., Zhu, Q., and Wang, S.: Attribution of the Tibetan Plateau to northern drought, Nat. Sci. Rev., 7, 489–492, 2020.
Liu, Y., Huang, J., Wang, T., Li, J., Yan, H., and He, Y.: Aerosol-cloud interactions over the Tibetan Plateau: An overview, Earth-Sci. Rev., 234, 104216, https://doi.org/10.1016/j.earscirev.2022.104216, 2022.
Liu, Z., Liu, D., Huang, J., Vaughan, M., Uno, I., Sugimoto, N., Kittaka, C., Trepte, C., Wang, Z., Hostetler, C., and Winker, D.: Airborne dust distributions over the Tibetan Plateau and surrounding areas derived from the first year of CALIPSO lidar observations, Atmos. Chem. Phys., 8, 5045–5060, https://doi.org/10.5194/acp-8-5045-2008, 2008.
Liu, Z., Kar, J., Zeng, S., Tackett, J., Vaughan, M., Avery, M., Pelon, J., Getzewich, B., Lee, K.-P., Magill, B., Omar, A., Lucker, P., Trepte, C., and Winker, D.: Discriminating between clouds and aerosols in the CALIOP version 4.1 data products, Atmos. Meas. Tech., 12, 703–734, https://doi.org/10.5194/amt-12-703-2019, 2019.
Luo, H. and Yanai, M.: The large-scale circulation and heat sources over the Tibetan Plateau and surrounding areas during the early summer of 1979, Part II: Heat and moisture budgets, Mon. Weather Rev., 112, 966–989, 1984.
Matsuki, A., Iwasaka, Y., Osada, K., Matsunaga, K., Kido, M., Inomata, Y., Trochkine, D., Nishita, C., Nezuka, T., Sakai,T., Zhang,D., and Kwon, S. A.: Seasonal dependence of the long-range transport and vertical distribution of free tropospheric aerosols over east Asia: On the basis of aircraft and lidar measurements and isentropic trajectory analysis, J. Geophys. Res.-Atmos., 108, https://doi.org/10.1029/2002JD003266, 2003.
Molnar, P., Boos, W., and Battisti, D.: Orographic controls on climate and paleoclimate of Asia: thermal and mechanical roles for the Tibetan Plateau, Annu. Rev. Earth Planet. Sc., 38, 77–102, 2010.
Nakajima, T., Higurashi, A., Kawamoto, K., and Penner, J. E.: A possible correlation between satellite-derived cloud and aerosol microphysical parameters, Geophys. Res. Lett., 28, 1171–1174, https://doi.org/10.1029/2000GL012186, 2001.
Nieberding, F., Wille, C., Fratini, G., Asmussen, M. O., Wang, Y., Ma, Y., and Sachs, T.: A long-term (2005–2019) eddy covariance data set of CO2 and H2O fluxes from the Tibetan alpine steppe, Earth Syst. Sci. Data, 12, 2705–2724, https://doi.org/10.5194/essd-12-2705-2020, 2020.
Pan, H., Huo, W., Wang, M., Zhang, J., Meng, L., Kumar, K. R., and Devi, N. L.: Insight into the climatology of different sand-dust aerosol types over the Taklimakan Desert based on the observations from radiosonde and A-train satellites, Atmos. Environ., 238, 117705, https://doi.org/10.1016/j.atmosenv.2020.117705, 2020.
Qiu, J.: China: The third pole, Nature, 454, 393–396, https://doi.org/10.1038/454393a, 2008.
Rieger, L. A., Bourassa, A. E., and Degenstein, D. A.: Merging the OSIRIS and SAGE II stratospheric aerosol records, J. Geophys. Res.-Atmos., 120, 8890–8904, https://doi.org/10.1002/2015JD023133, 2015.
Rieger, L. A., Zawada, D. J., Bourassa, A. E., and Degenstein, D. A.: A Multiwavelength Retrieval Approach for Improved OSIRIS Aerosol Extinction Retrievals, J. Geophys. Res.-Atmos., 124, 7286–7307, https://doi.org/10.1029/2018JD029897, 2019.
Rossow, W. and Schiffer, R.: Advances in understanding clouds from ISCCP, B. Am. Meteorol. Soc., 80, 2261–2287, 1999.
Sasano, Y.: Tropospheric aerosol extinction coefficient profiles derived from scanning lidar measurements over Tsukuba, Japan from 1990 to 1993, Appl. Optics, 35, 4941–4952, 1996.
Seifert, P., Ansmann, A., Müller, D., Wandinger, U., Althausen, D., Heymsfield, A. J., Massie, S. T., and Schmitt, C.: Cirrus optical properties observed with lidar, radiosonde, and satellite over the tropical Indian Ocean during the aerosol-polluted northeast and clean maritime southwest monsoon, J. Geophys. Res.-Atmos., 112, https://doi.org/10.1029/2006JD008352, 2007.
Thomason, L. W. and Vernier, J.-P.: Improved SAGE II cloud/aerosol categorization and observations of the Asian tropopause aerosol layer: 1989–2005, Atmos. Chem. Phys., 13, 4605–4616, https://doi.org/10.5194/acp-13-4605-2013, 2013.
Torres, O., Bhartia, P. K., Herman, J. R., Ahmad, Z., and Gleason, J.: Derivation of aerosol properties from satellite measurements of backscattered ultraviolet radiation: Theoretical basis, J. Geophys. Res., 103, 17099–17110, https://doi.org/10.1029/98JD00900, 1998.
Torres, O., Tanskanen, A., Veihelmann, B., Ahn, C., Braak, R., Bhartia, P. K., Veefkind, P., and Levelt, P.: Aerosols and surface UV products from Ozone Monitoring Instrument observations: An overview, J. Geophys. Res., 112, D24S47, https://doi.org/10.1029/2007JD008809, 2007.
Twomey, S. A.: The influence of pollution on the shortwave albedo of clouds, J. Atmos. Sci., 34, 1149–1154, 1977.
Vernier, J. P., Fairlie, T. D., Natarajan, M., Wienhold, F. G., Bian, J., Martinsson, B. G., Crumeyrolle, S., Thomason, L. W., and Bedka, K. M.: Increase in upper tropospheric and lower stratospheric aerosol levels and its potential connection with Asian pollution, J. Geophys. Res.-Atmos., 120, 1608–1619, 2015.
Wan, B., Gao, Z., Chen, F., and Lu, C.: Impact of Tibetan Plateau surface heating on persistent extreme precipitation events in Southeastern China, Mon. Weather Rev., 145, 3485–3505, 2017.
Wei, X., Bai, K., Chang, N.-B., and Gao, W.: Multi-source hierarchical data fusion for high-resolution AOD mapping in a forest fire event, Int. J. Appl. Earth Obs., 102, 102366, https://doi.org/10.1016/j.jag.2021.102366, 2021.
Winker, D. M., Hunt, W. H., and McGill, M. J.: Initial performance assessment of CALIOP, Geophys. Res. Lett., 34, L19803, https://doi.org/10.1029/2007GL030135, 2007.
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt, W. H., and Young, S. A.: Overview of the CALIPSO mission and CALIOP data processing algorithms, J. Atmos. Ocean. Tech., 26, 2310–2323, 2009.
Wu, G. and Zhang, Y.: Tibetan Plateau forcing and the timing of the monsoon onset over South Asia and the South China Sea, Mon. Weather Rev., 126, 913–927, 1998.
Wu, G., Liu, Y., Zhang, Q., Duan, A., Wang, T., Wan, R., Liu, X., Li, W., Wang, Z., and Liang, X.: The influence of mechanical and thermal forcing by the Tibetan Plateau on Asian climate, J. Hydro. Meteor. Spec. Sect., 8, 770–789, 2007.
Wu, G., Liu, Y., Dong, B., Liang, X., Duan, A., Bao, Q., and Yu, J.: Revisiting Asian monsoon formation and change associated with Tibetan Plateau forcing: I. Formation, Clim. Dynam., 39, 1169–1181, 2012.
Wu, G., Duan, A., Liu, Y., Mao, J., Ren, R., Bao, Q., He, B., Liu, B., and Hu, W.: Tibetan Plateau climate dynamics: recent research progress and outlook, Natl. Sci. Rev., 2, 100–116, 2015.
Xu, C., Ma, Y. M., You, C., and Zhu, Z. K.: The regional distribution characteristics of aerosol optical depth over the Tibetan Plateau, Atmos. Chem. Phys., 15, 12065–12078, https://doi.org/10.5194/acp-15-12065-2015, 2015.
Xu, X., Lu, C., Shi, X., and Gao, S.: World water tower: an atmospheric perspective, Geophys. Res. Lett., 35, L20815, https://doi.org/10.1029/2008gl035867, 2008.
Yanai, M., Li, C., and Song, Z.: Seasonal heating of the Tibetan Plateau and its effects on the evolution of the Asian summer monsoon, J. Meteor. Soc. Japan, 70, 319–351, 1992.
Zhang, S., Huang, Z., Li, M., Shen, X., Wang, Y., Dong, Q., Bi, J., Zhang, J., Li, W., Li, Z., and Song, X.: Vertical structure of dust aerosols observed by a ground-based raman lidar with polarization capabilities in the center of the Taklimakan desert, Remote Sens., 14, 2461, https://doi.org/10.3390/rs14102461, 2022.
Zhang, S., Huang, Z., Alam, K., Li, M., Dong, Q., Wang, Y., Shen, X., Bi, J., Zhang, J., Li, W., Li, Z., Wang, W., Cui, Z., and Song, X.: Derived Profiles of CCN and INP Number Concentrations in the Taklimakan Desert via Combined Polarization Lidar, Sun-Photometer, and Radiosonde Observations, Remote Sens., 15, 1216, https://doi.org/10.3390/rs15051216, 2023.
Zhou, J., Yu, G., Jin, C., Qi, F., Liu, D., Hu, H., Gong, Z., Shi, G., Nakajima, T., and Takamura, T.: Lidar observations of Asian dust over Hefei, China in Spring 2000, J. Geophys. Res., 107, 4252, https://doi.org/10.1029/2001JD000802, 2002.
Short summary
We applied several correction procedures and rigorously checked for data quality constraints during the long observation period spanning almost 14 years (2007–2020). Nevertheless, some uncertainties remain, mainly due to technical constraints and limited documentation of the measurements. Even though not completely accurate, this strategy is expected to at least reduce the inaccuracy of the computed characteristic value of aerosol optical parameters.
We applied several correction procedures and rigorously checked for data quality constraints...
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