Articles | Volume 24, issue 16
https://doi.org/10.5194/acp-24-9435-2024
© 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/acp-24-9435-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Aug 2024
Research article |
| 28 Aug 2024
| 28 Aug 2024
Distinct structure, radiative effects, and precipitation characteristics of deep convection systems in the Tibetan Plateau compared to the tropical Indian Ocean
Yuxin Zhao
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
Jiming Li
CORRESPONDING AUTHOR
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
Deyu Wen
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
Yarong Li
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
Yuan Wang
Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou, China
Jianping Huang
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
Related authors
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
Short summary
Short summary
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.
Jiayi Li, Yang Wang, Jiming Li, Weiyuan Zhang, Lijie Zhang, and Yuan Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-3601, https://doi.org/10.5194/egusphere-2024-3601, 2024
Short summary
Short summary
A key challenge in climate projections is the uncertainty in cloud water response to anthropogenic aerosols, especially its time-dependence on diurnal microphysical-dynamic boundary layer feedback. Geostationary satellite shows neglecting the variations induces a compensation up to 45% of the initial cooling effect from increased cloud droplet concentration. The results provide new insights in aerosol-cloud interactions, verifying this is a significant yet often overlooked source of uncertainty.
Hongxiang Yu, Michael Lehning, Guang Li, Benjamin Walter, Jianping Huang, and Ning Huang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2458, https://doi.org/10.5194/egusphere-2024-2458, 2024
Short summary
Short summary
Cornices are overhanging snow accumulations that form on mountain crests. Previous studies focused on how cornices collapse, little is known about why they form in the first place, specifically how snow particles adhere together to form the front end of the cornice. This study looked at the movement of snow particles around a developing cornice to understand how they gather, the speed and angle at which the snow particles hit the cornice surface, and how this affects the shape of the cornice.
Ruixue Li, Bida Jian, Jiming Li, Deyu Wen, Lijie Zhang, Yang Wang, and Yuan Wang
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
Short summary
Short summary
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.
Honglin Pan, Jianping Huang, Jiming Li, Zhongwei Huang, Minzhong Wang, Ali Mamtimin, Wen Huo, Fan Yang, Tian Zhou, and Kanike Raghavendra Kumar
Earth Syst. Sci. Data, 16, 1185–1207, https://doi.org/10.5194/essd-16-1185-2024, https://doi.org/10.5194/essd-16-1185-2024, 2024
Short summary
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.
Naifu Shao, Chunsong Lu, Xingcan Jia, Yuan Wang, Yubin Li, Yan Yin, Bin Zhu, Tianliang Zhao, Duanyang Liu, Shengjie Niu, Shuxian Fan, Shuqi Yan, and Jingjing Lv
Atmos. Chem. Phys., 23, 9873–9890, https://doi.org/10.5194/acp-23-9873-2023, https://doi.org/10.5194/acp-23-9873-2023, 2023
Short summary
Short summary
Fog is an important meteorological phenomenon that affects visibility. Aerosols and the planetary boundary layer (PBL) play critical roles in the fog life cycle. In this study, aerosol-induced changes in fog properties become more remarkable in the second fog (Fog2) than in the first fog (Fog1). The reason is that aerosol–cloud interaction (ACI) delays Fog1 dissipation, leading to the PBL meteorological conditions being more conducive to Fog2 formation and to stronger ACI in Fog2.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Yuan Wang, Silvia Henning, Laurent Poulain, Chunsong Lu, Frank Stratmann, Yuying Wang, Shengjie Niu, Mira L. Pöhlker, Hartmut Herrmann, and Alfred Wiedensohler
Atmos. Chem. Phys., 22, 15943–15962, https://doi.org/10.5194/acp-22-15943-2022, https://doi.org/10.5194/acp-22-15943-2022, 2022
Short summary
Short summary
Aerosol particle activation affects cloud, precipitation, radiation, and thus the global climate. Its long-term measurements are important but still scarce. In this study, more than 4 years of measurements at a central European station were analyzed. The overall characteristics and seasonal changes of aerosol particle activation are summarized. The power-law fit between particle hygroscopicity factor and diameter was recommended for predicting cloud
condensation nuclei number concentration.
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
Short summary
Short summary
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
Atmos. Chem. Phys., 21, 9809–9828, https://doi.org/10.5194/acp-21-9809-2021, https://doi.org/10.5194/acp-21-9809-2021, 2021
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Ackerman, A. S., Toon, O. B., Stevens, D. E., Heymsfield, A. J., Ramanathan, V., and Welton, E. J.: Reduction of Tropical Cloudiness by Soot, Science, 288, 1042–1047, https://doi.org/10.1126/science.288.5468.1042, 2000.
Andreae, M. O., Rosenfeld, D., Artaxo, P., Costa, A. A., Frank, G. P., Longo, K. M., and Silva-Dias, M. A. F.: Smoking Rain Clouds over the Amazon, Science, 303, 1337–1342, https://doi.org/10.1126/science.1092779, 2004.
Bai, J. and Xu, X.: Atmospheric hydrological budget with its effects over Tibetan Plateau, J. Geogr. Sci., 14, 81–86, https://doi.org/10.1007/BF02873094, 2004.
Bowen, G. J.: A faster water cycle, Science, 332, 430–431, https://doi.org/10.1126/science.1205253, 2011.
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.
Chakraborty, S., Fu, R., Rosenfeld, D., and Massie, S. T.: The influence of aerosols and meteorological conditions on the total rain volume of the mesoscale convective systems over tropical continents, Geophys. Res. Lett., 45, 13099–13106, https://doi.org/10.1029/2018GL080371, 2018.
Chen, Y., Zhang, A., Zhang, Y., Cui, C., Wan, R., Wang, B., and Fu, Y.: A Heavy Precipitation Event in the Yangtze River Basin Led by an Eastward Moving Tibetan Plateau Cloud System in the Summer of 2016, J. Geophys. Res.-Atmos., 125, e2020JD032429, https://doi.org/10.1029/2020JD032429, 2020.
CloudSat DPC: 2B-CLDCLASS-LIDAR P1_R05, CloudSat Data Processing Center [data set], https://www.cloudsat.cira.colostate.edu/data-products/2b-cldclass-lidar, last access: 31 October 2022a.
CloudSat DPC: 2B-FLXHR-LIDAR P1_R05, CloudSat Data Processing Center [data set], https://www.cloudsat.cira.colostate.edu/data-products/2b-flxhr-lidar, last access: 31 October 2022b.
Dagan, G., Koren, I., Altaratz, O., and Heiblum, R. H.: Time-dependent, non-monotonic response of warm convective cloud fields to changes in aerosol loading, Atmos. Chem. Phys., 17, 7435–7444, https://doi.org/10.5194/acp-17-7435-2017, 2017.
Devasthale, A. and Fueglistaler, S.: A climatological perspective of deep convection penetrating the TTL during the Indian summer monsoon from the AVHRR and MODIS instruments, Atmos. Chem. Phys., 10, 4573–4582, https://doi.org/10.5194/acp-10-4573-2010, 2010.
Di Giuseppe, F. and Tompkins, A. M.: Generalizing Cloud Overlap Treatment to Include the Effect of Wind Shear, J. Atmos. Sci., 72, 2865–2876, https://doi.org/10.1175/jas-d-14-0277.1, 2015.
Dong, W., Lin, Y., Wright, J. S., Ming, Y., Xie, Y., Wang, B., Luo, Y., Huang, W., Huang, J., and Wang, L.: Summer rainfall over the southwestern Tibetan Plateau controlled by deep convection over the Indian subcontinent, Nat. Commun., 7, 1–9, https://doi.org/10.1038/ncomms10925, 2016.
Douglas, A. and L'Ecuyer, T.: Quantifying cloud adjustments and the radiative forcing due to aerosol-cloud interactions in satellite observations of warm marine clouds, Atmos. Chem. Phys., 20, 6225–6241, https://doi.org/10.5194/acp-20-6225-2020, 2020.
Duan, A. and Wu, G.: Change of cloud amount and the climate warming on the Tibetan Plateau, Geophys. Res. Lett., 33, L22704, https://doi.org/10.1029/2006GL027946, 2006.
Duan, A., Hu, D., Hu, W., and Zhang, P.: Precursor effect of the Tibetan Plateau heating anomaly on the seasonal march of the East Asian summer monsoon precipitation, J. Geophys. Res.-Atmos., 125, e2020JD032948, https://doi.org/10.1029/2020JD032948, 2020.
Ekman, A., Engström, A., and Wang, C.: The effect of aerosol composition and concentration on the development and anvil properties of a continental deep convective cloud, Q. J. R. Meteorol. Soc., 133, 1439–1452, https://doi.org/10.1002/QJ.108, 2007.
Fan, J., Yuan, T., Comstock, J. M., Ghan, S., Khain, A., Leung, L. R., Li, Z., Martins, V. J., and Ovchinnikov, M.: Dominant role by vertical wind shear in regulating aerosol effects on deep convective clouds, J. Geophys. Res.-Atmos., 114, D22206, https://doi.org/10.1029/2009JD012352, 2009.
Feng, Z., Dong, X., Xi, B., Schumacher, C., Minnis, P., and Khaiyer, M.: Top-of-atmosphere radiation budget of convective core/stratiform rain and anvil clouds from deep convective systems, J. Geophys. Res.-Atmos., 116, D23202, https://doi.org/10.1029/2011JD016451, 2011.
Forster, P. M. d. F. and Shine, K. P.: Assessing the climate impact of trends in stratospheric water vapor, Geophys. Res. Lett., 29, 1086, https://doi.org/10.1029/2001GL013909, 2002.
Fu, Y., Liu, Q., Zi, Y., Feng, S., Li, Y., and Liu, G.: Summer precipitation and latent heating over the Tibetan Plateau based on TRMM measurements, Plat. Mount. Meteorol. Res., 28, 8–18, https://doi.org/10.3969/j.issn.1674-2184.2008.01.002, 2008.
Fu, Y., Pan, X., Yang, Y., Chen, F., and Liu, P.: Climatological characteristics of summer precipitation over East Asia measured by TRMM PR: A review, J. Meteorol. Res., 31, 142–159, https://doi.org/10.1007/s13351-017-6156-9, 2017.
Fu, Y., Ma, Y., Zhong, L., Yang, Y., Guo, X., Wang, C., Xu, X., Yang, K., Xu, X., Liu, L., Fan, G., Li, Y., and Wang, D.: Land-surface processes and summer-cloud-precipitation characteristics in the Tibetan Plateau and their effects on downstream weather: a review and perspective, Natl. Sci. Rev., 7, 500–515, https://doi.org/10.1093/nsr/nwz226, 2020.
Fueglistaler, S. and Fu, Q.: Impact of clouds on radiative heating rates in the tropical lower stratosphere, J. Geophys. Res.-Atmos., 111, D23202, https://doi.org/10.1029/2006JD007273, 2006.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Clim., 30, 5419–5454, https://doi.org/10.1175/jcli-d-16-0758.1, 2017.
Global Modeling and Assimilation Office (GMAO): MERRA-2 inst3_3d_aer_Nv: 3d, 3-Hourly, Instantaneous, Model-Level, Assimilation, Aerosol Mixing Ratio V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/LTVB4GPCOTK2, 2023.
Gong, J., Zeng, X., Wu, D. L., Munchak, S. J., Li, X., Kneifel, S., Ori, D., Liao, L., and Barahona, D.: Linkage among ice crystal microphysics, mesoscale dynamics, and cloud and precipitation structures revealed by collocated microwave radiometer and multifrequency radar observations, Atmos. Chem. Phys., 20, 12633–12653, https://doi.org/10.5194/acp-20-12633-2020, 2020.
Han, X., Zhao, B., Lin, Y., Chen, Q., Shi, H., Jiang, Z., Fan, X., Wang, J., Liou, K.-N., and Gu, Y.: Type-Dependent Impact of Aerosols on Precipitation Associated With Deep Convective Cloud Over East Asia, J. Geophys. Res.-Atmos., 127, e2021JD036127, https://doi.org/10.1029/2021JD036127, 2022.
Hartmann, D. L., Holton, J. R., and Fu, Q.: The heat balance of the tropical tropopause, cirrus, and stratospheric dehydration, Geophys. Res. Lett., 28, 1969–1972, https://doi.org/10.1029/2000GL012833, 2001.
Haynes, J. M., Jakob, C., Rossow, W. B., Tselioudis, G., and Brown, J.: Major characteristics of Southern Ocean cloud regimes and their effects on the energy budget, J. Climate., 24, 5061–5080, https://doi.org/10.1175/2011JCLI4052.1, 2011.
Haynes, J. M., Vonder Haar, T. H., L'Ecuyer, T., and Henderson, D.: Radiative heating characteristics of Earth's cloudy atmosphere from vertically resolved active sensors, Geophys. Res. Lett., 40, 624–630, https://doi.org/10.1002/grl.50145, 2013.
Henderson, D. and L'Ecuyer, T.: level 2B fluxes and heating rates with lidar [2B-FLXHR-LIDAR] process description and interface control document, CloudSat Project Rep., https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/2b-flxhr-lidar/2B-FLXHR-LIDAR_PDICD.P1_R05.rev0.pdf (last access: 16 February 2024), 2020.
Henderson, D. S., L'Ecuyer, T., Stephens, G., Partain, P., and Sekiguchi, M.: A Multisensor Perspective on the Radiative Impacts of Clouds and Aerosols, J. Appl. Meteorol. Clim., 52, 853–871, https://doi.org/10.1175/JAMC-D-12-025.1, 2013.
Hersbach, H., Bell W, P. B., Horányi, A., J., M.-S., J., N., Radu, R., Schepers, D., Simmons, A., Soci, C., and Dee, D.: Global reanalysis: goodbye ERA-Interim, hello ERA5, ECMWF Newsletter, 159, 17–24, https://doi.org/10.21957/vf291hehd7, 2019.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. R. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, last access: 8 May 2023.
Heymsfield, A. J., Miloshevich, L. M., Schmitt, C., Bansemer, A., Twohy, C., Poellot, M. R., Fridlind, A., and Gerber, H.: Homogeneous Ice Nucleation in Subtropical and Tropical Convection and Its Influence on Cirrus Anvil Microphysics, J. Atmos. Sci., 62, 41–64, https://doi.org/10.1175/JAS-3360.1, 2005.
Hoffmann, L., Günther, G., Li, D., Stein, O., Wu, X., Griessbach, S., Heng, Y., Konopka, P., Müller, R., Vogel, B., and Wright, J. S.: From ERA-Interim to ERA5: the considerable impact of ECMWF's next-generation reanalysis on Lagrangian transport simulations, Atmos. Chem. Phys., 19, 3097–3124, https://doi.org/10.5194/acp-19-3097-2019, 2019.
Hu, L., Deng, D., Gao, S., and Xu, X.: The seasonal variation of Tibetan Convective Systems: Satellite observation, J. Geophys. Res.-Atmos., 121, 5512–5525, https://doi.org/10.1002/2015JD024390, 2016.
Hu, L., Deng, D., Xu, X., and Zhao, P.: The regional differences of Tibetan convective systems in boreal summer, J. Geophys. Res.-Atmos., 122, 7289–7299, https://doi.org/10.1002/2017JD026681, 2017.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E. J., Sorooshian, S., Tan, J., and Xie, P.: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) Algorithm Theoretical Basis Document (ATBD) Version 06, https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_ATBD_V06.pdf (last access: 13 February 2023), 2019.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Jackson, T.: GPM IMERG Final Precipitation L3 Half Hourly 0.1° × 0.1° V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/IMERG/3B-HH/06, last access: 13 December 2023.
Jensen, E. J., Ackerman, A. S., and Smith, J. A.: Can overshooting convection dehydrate the tropical tropopause layer, J. Geophys. Res.-Atmos., 112, D11209, https://doi.org/10.1029/2006JD007943, 2007.
Jiang, J. H., Su, H., Huang, L., Wang, Y., Massie, S., Zhao, B., Omar, A., and Wang, Z.: Contrasting effects on deep convective clouds by different types of aerosols, Nat. Commun., 9, 3874, https://doi.org/10.1038/S41467-018-06280-4, 2018.
Jiang, X., Li, Y., Yang, S., Yang, K., and Chen, J.: Interannual Variation of Summer Atmospheric Heat Source over the Tibetan Plateau and the Role of Convection around the Western Maritime Continent, J. Clim., 29, 121–138, https://doi.org/10.1175/JCLI-D-15-0181.1, 2016.
Khain, A., Rosenfeld, D., and Pokrovsky, A.: Aerosol impact on the dynamics and microphysics of deep convective clouds, Q. J. R. Meteorol. Soc., 131, 2639–2663, https://doi.org/10.1256/QJ.04.62, 2005.
Khain, A. P., Phillips, V., Benmoshe, N., and Pokrovsky, A.: The Role of Small Soluble Aerosols in the Microphysics of Deep Maritime Clouds, J. Atmos. Sci., 69, 2787–2807, https://doi.org/10.1175/2011JAS3649.1, 2012.
Kirk-Davidoff, D. B., Hintsa, E. J., Anderson, J. G., and Keith, D. W.: The effect of climate change on ozone depletion through changes in stratospheric water vapour, Nature, 402, 399–401, https://doi.org/10.1038/46521, 1999.
Kottayil, A., Satheesan, K., John, V. O., and Antony, R.: Diurnal variation of deep convective clouds over Indian monsoon region and its association with rainfall, Atmos. Res., 255, 105540, https://doi.org/10.1016/j.atmosres.2021.105540, 2021.
Koren, I., Kaufman, Y. J., Rosenfeld, D., Remer, L. A., and Rudich, Y.: Aerosol invigoration and restructuring of Atlantic convective clouds, Geophys. Res. Lett., 32, L14828, https://doi.org/10.1029/2005gl023187, 2005.
Koren, I., Vanderlei Martins, J., Remer, L. A., and Afargan, H.: Smoke invigoration versus inhibition of clouds over the amazon, Science, 321, 946–949, https://doi.org/10.1126/science.1159185, 2008.
Koren, I., Feingold, G., and Remer, L. A.: The invigoration of deep convective clouds over the Atlantic: aerosol effect, meteorology or retrieval artifact?, Atmos. Chem. Phys., 10, 8855–8872, https://doi.org/10.5194/acp-10-8855-2010, 2010.
Koren, I., Dagan, G., and Altaratz, O.: From aerosol-limited to invigoration of warm convective clouds, Science, 344, 1143–1146, https://doi.org/10.1126/science.1252595, 2014.
L'Ecuyer, T. S., Hang, Y., Matus, A. V., and Wang, Z.: Reassessing the Effect of Cloud Type on Earth's Energy Balance in the Age of Active Spaceborne Observations. Part I: Top of Atmosphere and Surface, J. Clim., 32, 6197–6217, https://doi.org/10.1175/JCLI-D-18-0753.1, 2019.
Li, J., Mao, J., and Wang, F.: Comparative study of five current reanalyses in characterizing total cloud fraction and top of the atmosphere cloud radiative effects over the Asian monsoon region, Int. J. Climatol., 37, 5047–5067, https://doi.org/10.1002/joc.5143, 2017.
Li, J., Lv, Q., Jian, B., Zhang, M., Zhao, C., Fu, Q., Kawamoto, K., and Zhang, H.: The impact of atmospheric stability and wind shear on vertical cloud overlap over the Tibetan Plateau, Atmos. Chem. Phys., 18, 7329–7343, https://doi.org/10.5194/acp-18-7329-2018, 2018.
Li, R. and Q.-L. Min.: Impacts of mineral dust on the vertical structure of precipitation, J. Geophys. Res.-Atmos., 115, D09203, https://doi.org/10.1029/2009JD011925, 2010.
Li, R., Dong, X., Guo, J., Fu, Y., Zhao, C., Wang, Y., and Min, Q.: The implications of dust ice nuclei effect on cloud top temperature in a complex mesoscale convective system, Sci. Rep., 7, 13826, https://doi.org/10.1038/s41598-017-12681-0, 2017.
Li, W., Schumacher, C., and McFarlane, S. A.: Radiative heating of the ISCCP upper level cloud regimes and its impact on the large-scale tropical circulation, J. Geophys. Res.-Atmos., 118, 592–604, https://doi.org/10.1002/jgrd.50114, 2013.
Li, Z., Niu, F., Fan, J., Liu, Y., Rosenfeld, D., and Ding, Y.: Long-term impacts of aerosols on the vertical development of clouds and precipitation, Nat. Geosci., 4, 888–894, https://doi.org/10.1038/ngeo1313, 2011.
Lin, B., Wielicki, B. A., Minnis, P., Chambers, L., Xu, K.-M., Hu, Y., and Fan, A.: The Effect of Environmental Conditions on Tropical Deep Convective Systems Observed from the TRMM Satellite, J. Clim., 19, 5745–5761, https://doi.org/10.1175/JCLI3940.1, 2006.
Liu, C., Zipser, E. J., and Nesbitt, S. W.: Global distribution of tropical deep convection: Different perspectives from TRMM infrared and radar data, J. Climate., 20, 489–503, https://doi.org/10.1175/JCLI4023.1, 2007.
Liu, H., Guo, J., Koren, I., Altaratz, O., Dagan, G., Wang, Y., Jiang, J. H., Zhai, P., and Yung, Y. L.: Non-Monotonic Aerosol Effect on Precipitation in Convective Clouds over Tropical Oceans, Sci. Rep., 9, 7809, https://doi.org/10.1038/s41598-019-44284-2, 2019.
Liu, X. and Chen, B.: Climatic warming in the Tibetan Plateau during recent decades, Int. J. Climatol., 20, 1729–1742, https://doi.org/10.1002/1097-0088(20001130)20:14<1729::AID-JOC556>3.0.CO;2-Y, 2000.
Liu, Y., Zhu, Q., Huang, J., Hua, S., and Jia, R.: Impact of dust-polluted convective clouds over the Tibetan Plateau on downstream precipitation, Atmos. Environ., 209, 67–77, https://doi.org/10.1016/J.ATMOSENV.2019.04.001, 2019.
Lv, Q., Li, J., Wang, T., and Huang, J.: Cloud radiative forcing induced by layered clouds and associated impact on the atmospheric heating rate, J. Meteorol. Res., 29, 779–792, https://doi.org/10.1007/s13351-015-5078-7, 2015.
Luo, Y., Zhang, R., Qian, W., Luo, Z., and Hu, X.: Intercomparison of Deep Convection over the Tibetan Plateau–Asian Monsoon Region and Subtropical North America in Boreal Summer Using CloudSat/CALIPSO Data, J. Clim., 24, 2164–2177, https://doi.org/10.1175/2010JCLI4032.1, 2011.
Luo, Z. J., Jeyaratnam, J., Iwasaki, S., Takahashi, H., and Anderson, R.: Convective vertical velocity and cloud internal vertical structure: An A-Train perspective, Geophys. Res. Lett., 41, 723–729, https://doi.org/10.1002/2013GL058922, 2014.
Mace, G. G., Zhang, Q. Q., Vaughan, M., Marchand, R., Stephens, G., Trepte, C., and Winker, D.: A description of hydrometeor layer occurrence statistics derived from the first year of merged Cloudsat and CALIPSO data, J. Geophys. Res.-Atmos., 114, D00A26, https://doi.org/10.1029/2007JD009755, 2009.
Mace, G. G.: Cloud properties and radiative forcing over the maritime storm tracks of the Southern Ocean and North Atlantic derived from A-Train, J. Geophys. Res., 115, D10201, https://doi.org/10.1029/2009JD012517, 2010.
McDonnell, K. A. and Holbrook, N. J.: A Poisson regression model approach to predicting tropical cyclogenesis in the Australian/southwest Pacific Ocean region using the SOI and saturated equivalent potential temperature gradient as predictors, Geophys. Res. Lett., 31, L20110, https://doi.org/10.1029/2004GL020843, 2004.
McFarlane, S. A., Mather, J. H., and Ackerman, T. P.: Analysis of tropical radiative heating profiles: A comparison of models and observations, J. Geophys. Res.-Atmos., 112, D14218, https://doi.org/10.1029/2006JD008290, 2007.
Mieslinger, T., Horváth, Á., Buehler, S. A., and Sakradzija, M.: The Dependence of Shallow Cumulus Macrophysical Properties on Large-Scale Meteorology as Observed in ASTER Imagery, J. Geophys. Res.-Atmos., 124, 11477–11505, https://doi.org/10.1029/2019JD030768, 2019.
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015.
Myers, T. A. and J. R. Norris.: Observational Evidence That Enhanced Subsidence Reduces Subtropical Marine Boundary Layer Cloudiness, J. Clim., 26, 7507–7524, https://doi.org/10.1175/JCLI-D-12-00736.1, 2013.
Naud, C. M., Del Genio, A., Mace, G. G., Benson, S., Clothiaux, E. E., and Kollias, P.: Impact of Dynamics and Atmospheric State on Cloud Vertical Overlap, J. Clim., 21, 1758–1770, https://doi.org10.1175/2007JCLI1828.1, 2008.
Neggers, R. A. J., Jonker, H. J. J., and Siebesma, A. P.: Size Statistics of Cumulus Cloud Populations in Large-Eddy Simulations, J. Atmos. Sci., 60, 1060–1074, https://doi.org/10.1175/1520-0469(2003)60<1060:SSOCCP>2.0.CO;2, 2003.
Pan, Z., Mao, F., Lu, X., Gong, W., Shen, H., and Mao, Q.: Enhancement of vertical cloud-induced radiative heating in East Asian monsoon circulation derived from CloudSat-CALIPSO observations, Int. J. Remote. Sens., 41, 595–614, https://doi.org/10.1080/01431161.2019.1646935, 2020.
Pan, Z., Rosenfeld, D., Zhu, Y., Mao, F., Gong, W., Zang, L., and Lu, X.: Observational Quantification of Aerosol Invigoration for Deep Convective Cloud Lifecycle Properties Based on Geostationary Satellite, J. Geophys. Res.-Atmos., 126, e2020JD034275, https://doi.org/10.1029/2020JD034275, 2021.
Peng, J., Zhang, H., and Li, Z.: Temporal and spatial variations of global deep cloud systems based on CloudSat and CALIPSO satellite observations, Adv. Atmos. Sci., 31, 593–603, https://doi.org/10.1007/S00376-013-3055-6, 2014.
Peng, J., Li, Z., Zhang, H., Liu, J., and Cribb, M.: Systematic Changes in Cloud Radiative Forcing with Aerosol Loading for Deep Clouds in the Tropics, J. Atmos. Sci., 73, 231–249, https://doi.org/10.1175/JAS-D-15-0080.1, 2016.
Randel, W. J., Park, M., Emmons, L., Kinnison, D., Bernath, P., Walker, K. A., Boone, C., and Pumphrey, H.: Asian Monsoon Transport of Pollution to the Stratosphere, Science, 328, 611–613, https://doi.org/10.1126/science.1182274, 2010.
Randles, C. A., Silva, A. M. d., Buchard, V., Darmenov, A., Colarco, P. R., Aquila, V., Bian, H., P, E., Nowottnick, Pan, X., Smirnov, A., Y, H., and Govindaraju, R.: The MERRA-2 Aerosol Assimilation, Technical Report Series on Global Modeling and Data Assimilation, Vol. 45, NASA Tech. Rep., https://gmao.gsfc.nasa.gov/pubs/docs/Randles887.pdf (last access: 16 February 2024), 2016.
Reichler, T., Dameris, M., and Sausen, R.: Determining the Tropopause Height from Gridded Data, Geophys. Res. Lett., 30, 2042, https://doi.org/10.1029/2003GL018240, 2003.
Robe, F. R. and Emanuel, K. A.: The Effect of Vertical Wind Shear on Radiative–Convective Equilibrium States, J. Atmos. Sci., 58, 1427–1445, https://doi.org/10.1175/1520-0469(2001)058<1427:TEOVWS>2.0.CO;2, 2001.
Rosenfeld, D.: TRMM observed first direct evidence of smoke from forest fires inhibiting rainfall, Geophys. Res. Lett., 26, 3105–3108, https://doi.org/10.1029/1999GL006066, 1999.
Rosenfeld, D., Lohmann, U., Raga, G. B., O'Dowd, C. D., Kulmala, M., Fuzzi, S., Reissell, A., and Andreae, M. O.: Flood or Drought: How Do Aerosols Affect Precipitation?, Science, 321, 1309–1313, https://doi.org/10.1126/science.1160606, 2008.
Sassen, K., Wang, Z., and Liu, D.: Cirrus clouds and deep convection in the tropics: Insights from CALIPSO and CloudSat, J. Geophys. Res.-Atmos., 114, D00H06, https://doi.org/10.1029/2009JD011916, 2009.
Savtchenko, A.: Deep convection and upper-tropospheric humidity: A look from the A-Train, Geophys. Res. Lett., 36, L06814, https://doi.org/10.1029/2009GL037508, 2009.
Sherwood, S. C. and Wahrlich, R.: Observed Evolution of Tropical Deep Convective Events and Their Environment, Mon. Weather Rev., 127, 1777–1795, https://doi.org/10.1175/1520-0493(1999)127<1777:OEOTDC>2.0.CO;2, 1999.
Slingo, A. and Slingo, J. M.: The response of a general circulation model to cloud longwave radiative forcing, I: Introduction and initial experiments, Q. J. R. Meteorol. Soc., 114, 1027–1062, https://doi.org/10.1002/qj.49711448209, 1988.
Slingo, J. M. and Slingo, A.: The response of a general circulation model to cloud longwave radiative forcing. II: Further studies, Q. J. R. Meteorol. Soc., 117, 333–364, https://doi.org/10.1002/qj.49711749805, 1991.
Squires, P. and Twomey, S.: The Relation Between Cloud Droplet Spectra and the Spectrum of Cloud Nuclei, in: Physics of Precipitation: Proceedings of the Cloud Physics Conference, Woods Hole, Massachusetts, 3–5 June, 1959, 211–219, https://doi.org/10.1029/GM005p0211, 1960.
Stephens, G. L., Li, J., Wild, M., Clayson, C. A., Loeb, N., Kato, S., L'Ecuyer, T., Stackhouse, P. W., Lebsock, M., and Andrews, T.: An update on Earth's energy balance in light of the latest global observations, Nat. Geosci., 5, 691–696, https://doi.org/10.1038/ngeo1580, 2012.
Sun, N., Fu, Y., Zhong, L., Zhao, C., and Li, R.: The Impact of Convective Overshooting on the Thermal Structure over the Tibetan Plateau in Summer Based on TRMM, COSMIC, Radiosonde, and Reanalysis Data, J. Clim., 34, 8047–8063, https://doi.org/10.1175/jcli-d-20-0849.1, 2021.
Sun, Y., Wang, Y., Zhao, C., Zhou, Y., Yang, Y., Yang, X., Fan, H., Zhao, X., and Yang, J.: Vertical Dependency of Aerosol Impacts on Local Scale Convective Precipitation, J. Geophys. Res.-Atmos., 50, e2022GL102186, https://doi.org/10.1029/2022GL102186, 2023.
Tao, W.-K., Li, X., Khain, A., Matsui, T., Lang, S., and Simpson, J.: Role of atmospheric aerosol concentration on deep convective precipitation: Cloud-resolving model simulations, J. Geophys. Res.-Atmos., 112, D24S18, https://doi.org/10.1029/2007JD008728, 2007.
Wang, Q., Li, Z., Guo, J., Zhao, C., and Cribb, M.: The climate impact of aerosols on the lightning flash rate: Is it detectable from long-term measurements?, Atmos. Chem. Phys., 18, 12797–12816, https://doi.org/10.5194/acp-18-12797-2018, 2018.
Wang, R., Xian, T., Wang, M. X., Chen, F. J., Yang, Y. J., Zhang, X. D., Li, R., Zhong, L., Zhao, C., and Fu, Y. F.: Relationship between Extreme Precipitation and Temperature in Two Different Regions: The Tibetan Plateau and Middle-East China, J. Meteorol. Res., 33, 870–884, https://doi.org/10.1007/s13351-019-8181-3, 2019.
Wang, Z.: CloudSat 2B-CLDCLASS-LIDAR product process description and interface control document, Process Description and Interface Control Document (PDICD) P1_R05, CloudSat Project Rep., https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/2b-cldclass-lidar/2B-CLDCLASS-LIDAR_PDICD.P1_R05.rev0_.pdf (last access: 18 February 2024), 2019.
Wang, Z., Zhong, R., Lai, C., and Chen, J.: Evaluation of the GPM IMERG satellite-based precipitation products and the hydrological utility, Atmos. Res., 196, 151–163, https://doi.org/10.1016/j.atmosres.2017.06.020, 2017.
Warner, J.: A Reduction in Rainfall Associated with Smoke from Sugar-Cane Fires – An Inadvertent Weather Modification?, J. Appl. Meteorol. Clim., 7, 247–251, https://doi.org/10.1175/1520-0450(1968)007<0247:ARIRAW>2.0.CO;2, 1968.
Warner, J. and Twomey, S.: The Production of Cloud Nuclei by Cane Fires and the Effect on Cloud Droplet Concentration, J. Atmos. Sci., 24, 704–706, https://doi.org/10.1175/1520-0469(1967)024<0704:TPOCNB>2.0.CO;2, 1967.
Weisman, M. L. and Klemp, J. B.: The Dependence of Numerically Simulated Convective Storms on Vertical Wind Shear and Buoyancy, Mon. Weather Rev., 110, 504–520, https://doi.org/10.1175/1520-0493(1982)110<0504:TDONSC>2.0.CO;2, 1982.
Witkowski, M. M., Vane, D., and Livermore, T.: CloudSat-Life in Daylight Only Operations (DO-Op), 2018 SpaceOps Conference, Marseille, France, 1 June 2018, https://arc.aiaa.org/doi/pdf/10.2514/6.2018-2562 (last access: 26 August 2024), 2018.
WMO: Meteorology-a three-dimensional science: Second session for the commisstion for aerology, WMO Bulletin, 6, 134–138, https://library.wmo.int/idurl/4/42003 (last access: 23 August 2024), 1957.
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, https://doi.org/10.1175/1520-0493(1998)126<0913:TPFATT>2.0.CO;2, 1998.
Wu, G., Liu, Y., He, B., Bao, Q., Duan, A., and Jin, F.-F.: Thermal Controls on the Asian Summer Monsoon, Sci. Rep., 2, 404, https://doi.org/10.1038/srep00404, 2012.
Wu, G., He, B., Duan, A., Liu, Y., and Yu, W.: Formation and variation of the atmospheric heat source over the Tibetan Plateau and its climate effects, Adv. Atmos. Sci., 34, 1169–1184, https://doi.org/10.1007/s00376-017-7014-5, 2017.
Xian, T. and Homeyer, C. R.: Global tropopause altitudes in radiosondes and reanalyses, Atmos. Chem. Phys., 19, 5661–5678, https://doi.org/10.5194/acp-19-5661-2019, 2019.
Xiao, Z., Yu, Y., Miao, Y., Zhu, S., He, H., Wang, Y., and Che, H.: Impact of Aerosols on Convective System Over the North China Plain: A Numerical Case Study in Autumn, J. Geophys. Res.-Atmos., 128, e2022JD037465, https://doi.org/10.1029/2022JD037465, 2023.
Xu, W. and Zipser, E. J.: Diurnal variations of precipitation, deep convection, and lightning over and east of the eastern Tibetan Plateau, J. Climate. , 24, 448–465, https://doi.org/10.1175/2010JCLI3719.1, 2011.
Xu, X., Miao, Q., Wang, J., and Zhang, X.: The water vapor transport model at the regional boundary during the Meiyu period, Adv. Atmos. Sci., 20, 333–342, https://doi.org/10.1007/BF02690791, 2003.
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.
Xu, X., Wu, H., Yang, X., and Xie, L.: Distribution and transport characteristics of dust aerosol over Tibetan Plateau and Taklimakan Desert in China using MERRA-2 and CALIPSO data, Atmos. Environ., 237, 117670, https://doi.org/10.1016/j.atmosenv.2020.117670, 2020.
Xue, H. and Feingold, G.: Large-Eddy Simulations of Trade Wind Cumuli: Investigation of Aerosol Indirect Effects, J. Atmos. Sci., 63, 1605–1622, https://doi.org/10.1175/JAS3706.1, 2006.
Yan, H., Li, Z., Huang, J., Cribb, M., and Liu, J.: Long-term aerosol-mediated changes in cloud radiative forcing of deep clouds at the top and bottom of the atmosphere over the Southern Great Plains, Atmos. Chem. Phys., 14, 7113–7124, https://doi.org/10.5194/acp-14-7113-2014, 2014.
Yan, Y. and Liu, Y.: Vertical Structures of Convective and Stratiform Clouds in Boreal Summer over the Tibetan Plateau and Its Neighboring Regions, Adv. Atmos. Sci., 36, 1089–1102, https://doi.org/10.1007/s00376-019-8229-4, 2019.
Yan, Y., Liu, Y., and Lu, J.: Cloud vertical structure, precipitation, and cloud radiative effects over Tibetan Plateau and its neighboring regions, J. Geophys. Res.-Atmos., 121, 5864–5877, https://doi.org/10.1002/2015JD024591, 2016.
Yang, K., He, J., Tang, W., Qin, J., and Cheng, C. C. K.: On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau, Agr. Forest Meteorol., 150, 38–46, https://doi.org/10.1016/j.agrformet.2009.08.004, 2010.
Yang, K., Ding, B., Qin, J., Tang, W., Lu, N., and Lin, C.: Can aerosol loading explain the solar dimming over the Tibetan Plateau, Geophys. Res. Lett., 39, L20710, https://doi.org/10.1029/2012GL053733, 2012.
Yao, T., Thompson, L., Yang, W., Yu, W., Gao, Y., Guo, X., Yang, X., Duan, K., Zhao, H., Xu, B., Pu, J., Lu, A., Xiang, Y., Kattel, D. B., and Joswiak, D.: Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings, Nat. Clim. Change, 2, 663–667, https://doi.org/10.1038/NCLIMATE1580, 2012.
Yao, T., Masson Delmotte, V., Gao, J., Yu, W., Yang, X., Risi, C., Sturm, C., Werner, M., Zhao, H., and He, Y.: A review of climatic controls on δ18O in precipitation over the Tibetan Plateau: Observations and simulations, Rev. Geophys., 51, 525–548, 2013.
Zang, L., Rosenfeld, D., Pan, Z., Mao, F., Zhu, Y., Lu, X., and Gong, W.: Observing aerosol primary convective invigoration and its meteorological feedback, Geophys. Res. Lett., 50, e2023GL104151, https://doi.org/10.1029/2023GL104151, 2023.
Zhang, C., Tang, Q., and Chen, D.: Recent Changes in the Moisture Source of Precipitation over the Tibetan Plateau, J. Clim., 30, 1807–1819, https://doi.org/10.1175/JCLI-D-15-0842.1, 2017.
Zhang, F., Yu, Q. R., Mao, J. L., Dan, C., Wang, Y., He, Q., Cheng, T., Chen, C., Liu, D., and Gao, Y.: Possible mechanisms of summer cirrus clouds over the Tibetan Plateau, Atmos. Chem. Phys., 20, 11799–11808, https://doi.org/10.5194/acp-20-11799-2020, 2020.
Zhang, J., Zhao, B., Liu, X., Lin, G., Jiang, Z., Wu, C., and Zhao, X.: Effects of Different Types of Aerosols on Deep Convective Clouds and Anvil Cirrus, Geophys. Res. Lett., 49, e2022GL099478, https://doi.org/10.1029/2022GL099478, 2022.
Zhang, Y., Sperber, K. R., and Boyle, J. S.: Climatology and Interannual Variation of the East Asian Winter Monsoon: Results from the 1979–95 NCEP/NCAR Reanalysis, Mon. Weather Rev., 125, 2605–2619, https://doi.org/10.1175/1520-0493(1997)125<2605:caivot>2.0.co;2, 1997.
Zhao, X., Zhao, C., Yang, Y., Sun, Y., and Xia, Y.: Dust aerosol impacts on the time of cloud formation in the Badain Jaran Desert area, J. Geophys. Res.-Atmos., 127, e2022JD037019, https://doi.org/10.1029/2022JD037019, 2022.
Zhao, Y., Li, J., Zhang, L., Deng, C., Li, Y., Jian, B., and Huang, J.: Diurnal cycles of cloud cover and its vertical distribution over the Tibetan Plateau revealed by satellite observations, reanalysis datasets, and CMIP6 outputs, Atmos. Chem. Phys., 23, 743–769, https://doi.org/10.5194/acp-23-743-2023, 2023.
Zhao, Y., Li, J., Wang, Y., Zhang, W., and Wen, D.: Warming climate-induced changes in cloud vertical distribution possibly exacerbate intra-atmospheric heating over the Tibetan Plateau, Geophys. Res. Lett., 51, e2023GL107713, https://doi.org/10.1029/2023GL107713, 2024.
Zhou, S., Wu, P., Wang, C., and Han, J.: Spatial distribution of atmospheric water vapor and its relationship with precipitation in summer over the Tibetan Plateau, J. Geogr. Sci., 22, 795–809, https://doi.org/10.1007/s11442-012-0964-8, 2012.
Short summary
This study identifies deep convection systems (DCSs), including deep convection cores and anvils, over the Tibetan Plateau (TP) and tropical Indian Ocean (TO). The DCSs over the TP are less frequent, showing narrower and thinner cores and anvils compared to those over the TO. TP DCSs show a stronger longwave cloud radiative effect at the surface and in the low-level atmosphere. Distinct aerosol–cloud–precipitation interaction is found in TP DCSs, probably due to the cold cloud bases.
This study identifies deep convection systems (DCSs), including deep convection cores and...
Altmetrics
Final-revised paper
Preprint