Articles | Volume 25, issue 9
https://doi.org/10.5194/acp-25-5021-2025
© Author(s) 2025. 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-25-5021-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 May 2025
Research article |
| 15 May 2025
| 15 May 2025
Anvil–radiation diurnal interaction: shortwave radiative-heating destabilization driving the diurnal variation of convective anvil outflow and its modulation on the radiative cancellation
School of Atmospheric Sciences, Nanjing University, Nanjing, China
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Cited articles
Atlas, R. L., Bretherton, C. S., Sokol, A. B., Blossey, P. N., and Khairoutdinov, M. F.: Tropical Cirrus Are Highly Sensitive to Ice Microphysics Within a Nudged Global Storm-Resolving Model, Geophys. Res. Lett., 51, e2023GL105868, https://doi.org/10.1029/2023gl105868, 2024.
Berry, E. and Mace, G. G.: Cloud properties and radiative effects of the Asian summer monsoon derived from A-Train data, J. Geophys. Res.-Atmos., 119, 9492–9508, https://doi.org/10.1002/2014jd021458, 2014.
Bony, S., Stevens, B., Coppin, D., Becker, T., Reed, K. A., Voigt, A., and Medeiros, B.: Thermodynamic control of anvil cloud amount, P. Natl. Acad. Sci. USA, 113, 8927–8932, https://doi.org/10.1073/pnas.1601472113, 2016.
Bretherton, C. S.: Insights into low-latitude cloud feedbacks from high-resolution models, Philos. T. A, 373, 20140415, https://doi.org/10.1098/rsta.2014.0415, 2015.
Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M., and Bladé, I.: The Effective Number of Spatial Degrees of Freedom of a Time-Varying Field, J. Climate, 12, 1990–2009, https://doi.org/10.1175/1520-0442(1999)012<1990:Tenosd>2.0.Co;2, 1999.
Chen, G., Wang, W. C., Bao, Q., and Li, J.: Evaluation of Simulated Cloud Diurnal Variation in CMIP6 Climate Models, J. Geophys. Res.-Atmos., 127, e2021JD036422, https://doi.org/10.1029/2021jd036422, 2022.
Chen, S. S. and Houze, R. A.: Diurnal variation and life-cycle of deep convective systems over the tropical pacific warm pool, Q. J. Roy. Meteor. Soc., 123, 357–388, https://doi.org/10.1002/qj.49712353806, 1997.
Clement, A. C. and Soden, B.: The Sensitivity of the Tropical-Mean Radiation Budget, J. Climate, 18, 3189–3203, https://doi.org/10.1175/jcli3456.1, 2005.
Daniels, J., Bresky, W., Bailey, A., Allegrino, A., Velden, C. S., and Wanzong, S.: Chapter 8 – Winds from ABI on the GOES-R Series, in: The GOES-R Series, edited by: Goodman, S. J., Schmit, T. J., Daniels, J., and Redmon, R. J., Elsevier, 79–94, https://doi.org/10.1016/B978-0-12-814327-8.00008-1, 2020.
Deng, M., Mace, G. G., and Wang, Z.: Anvil Productivities of Tropical Deep Convective Clusters and Their Regional Differences, J. Atmos. Sci., 73, 3467–3487, https://doi.org/10.1175/jas-d-15-0239.1, 2016.
Dessler, A. E., Palm, S. P., and Spinhirne, J. D.: Tropical cloud-top height distributions revealed by the Ice, Cloud, and Land Elevation Satellite (ICESat)/Geoscience Laser Altimeter System (GLAS), J. Geophys. Res.-Atmos., 111, D12215, https://doi.org/10.1029/2005jd006705, 2006.
DeWitt, T. D. and Garrett, T. J.: Finite domains cause bias in measured and modeled distributions of cloud sizes, Atmos. Chem. Phys., 24, 8457–8472, https://doi.org/10.5194/acp-24-8457-2024, 2024.
DeWitt, T. D., Garrett, T. J., Rees, K. N., Bois, C., Krueger, S. K., and Ferlay, N.: Climatologically invariant scale invariance seen in distributions of cloud horizontal sizes, Atmos. Chem. Phys., 24, 109–122, https://doi.org/10.5194/acp-24-109-2024, 2024.
Doelling, D. R., Sun, M., Nguyen, L. T., Nordeen, M. L., Haney, C. O., Keyes, D. F., and Mlynczak, P. E.: Advances in Geostationary-Derived Longwave Fluxes for the CERES Synoptic (SYN1deg) Product, J. Atmos. Ocean. Tech., 33, 503–521, https://doi.org/10.1175/jtech-d-15-0147.1, 2016.
Fu, Q. and Liou, K. N.: Parameterization of the Radiative Properties of Cirrus Clouds, J. Atmos. Sci., 50, 2008–2025, https://doi.org/10.1175/1520-0469(1993)050<2008:Potrpo>2.0.Co;2, 1993.
Fu, Q., Liou, K. N., Cribb, M. C., Charlock, T. P., and Grossman, A.: Multiple Scattering Parameterization in Thermal Infrared Radiative Transfer, J. Atmos. Sci., 54, 2799–2812, https://doi.org/10.1175/1520-0469(1997)054<2799:Mspiti>2.0.Co;2, 1997.
Fu, R., Del Genio, A. D., and Rossow, W. B.: Behavior of Deep Convective Clouds in the Tropical Pacific Deduced from ISCCP Radiances, J. Climate, 3, 1129–1152, https://doi.org/10.1175/1520-0442(1990)003<1129:Bodcci>2.0.Co;2, 1990.
Futyan, J. M. and Del Genio, A. D.: Deep Convective System Evolution over Africa and the Tropical Atlantic, J. Climate, 20, 5041–5060, https://doi.org/10.1175/JCLI4297.1, 2007.
Gasparini, B., Rasch, P. J., Hartmann, D. L., Wall, C. J., and Dütsch, M.: A Lagrangian Perspective on Tropical Anvil Cloud Lifecycle in Present and Future Climate, J. Geophys. Res.-Atmos., 126, e2020JD033487, https://doi.org/10.1029/2020jd033487, 2021.
Gasparini, B., Sokol, A. B., Wall, C. J., Hartmann, D. L., and Blossey, P. N.: Diurnal Differences in Tropical Maritime Anvil Cloud Evolution, J. Climate, 35, 1655–1677, https://doi.org/10.1175/jcli-d-21-0211.1, 2022.
Gettelman, A. and Sherwood, S. C.: Processes Responsible for Cloud Feedback, Current Climate Change Reports, 2, 179–189, https://doi.org/10.1007/s40641-016-0052-8, 2016.
Gray, W. M. and Jacobson, R. W.: Diurnal Variation of Deep Cumulus Convection, Mon. Weather Rev., 105, 1171–1188, https://doi.org/10.1175/1520-0493(1977)105<1171:Dvodcc>2.0.Co;2, 1977.
Ham, S. H., Kato, S., Rose, F. G., Winker, D., L'Ecuyer, T., Mace, G. G., Painemal, D., Sun-Mack, S., Chen, Y., and Miller, W. F.: Cloud occurrences and cloud radiative effects (CREs) from CERES-CALIPSO-CloudSat-MODIS (CCCM) and CloudSat radar-lidar (RL) products, J. Geophys. Res.-Atmos., 122, 8852–8884, https://doi.org/10.1002/2017jd026725, 2017.
Ham, S.-H., Kato, S., Rose, F. G., Sun-Mack, S., Chen, Y., Miller, W. F., and Scott, R. C.: Combining Cloud Properties from CALIPSO, CloudSat, and MODIS for Top-of-Atmosphere (TOA) Shortwave Broadband Irradiance Computations: Impact of Cloud Vertical Profiles, J. Appl. Meteorol. Climatol., 61, 1449–1471, https://doi.org/10.1175/jamc-d-21-0260.1, 2022.
Hartmann, D. L.: Tropical anvil clouds and climate sensitivity, P. Natl. Acad. Sci. USA, 113, 8897–8899, https://doi.org/10.1073/pnas.1610455113, 2016.
Hartmann, D. L. and Berry, S. E.: The balanced radiative effect of tropical anvil clouds, J. Geophys. Res.-Atmos., 122, 5003–5020, https://doi.org/10.1002/2017jd026460, 2017.
Hartmann, D. L. and Larson, K.: An important constraint on tropical cloud – climate feedback, Geophys. Res. Lett., 29, 1951, https://doi.org/10.1029/2002gl015835, 2002.
Hartmann, D. L., Gasparini, B., Berry, S. E., and Blossey, P. N.: The Life Cycle and Net Radiative Effect of Tropical Anvil Clouds, J. Adv. Model. Earth Sy., 10, 3012–3029, https://doi.org/10.1029/2018ms001484, 2018.
Hendon, H. H. and Woodberry, K.: The diurnal cycle of tropical convection, J. Geophys. Res., 98, 16623–16637, https://doi.org/10.1029/93jd00525, 1993.
Houze, R. A.: Mesoscale convective systems, Rev. Geophys., 42, RG4003, https://doi.org/10.1029/2004rg000150, 2004.
Hu, X., Ge, J., Li, W., Du, J., Li, Q., and Mu, Q.: Vertical Structure of Tropical Deep Convective Systems at Different Life Stages From CloudSat Observations, J. Geophys. Res.-Atmos., 126, e2021JD035115, https://doi.org/10.1029/2021JD035115, 2021.
Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G., Nelkin, E. J., Bowman, K. P., Hong, Y., Stocker, E. F., and Wolff, D. B.: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/jhm560.1, 2007.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/IMERG/3B-HH/07, 2023.
Igel, M. R., Drager, A. J., and van den Heever, S. C.: A CloudSat cloud object partitioning technique and assessment and integration of deep convective anvil sensitivities to sea surface temperature, J. Geophys. Res.-Atmos., 119, 10515–10535, https://doi.org/10.1002/2014jd021717, 2014.
Kato, S., Sun-Mack, S., Miller, W. F., Rose, F. G., Chen, Y., Minnis, P., and Wielicki, B. A.: Relationships among cloud occurrence frequency, overlap, and effective thickness derived from CALIPSO and CloudSat merged cloud vertical profiles, J. Geophys. Res.-Atmos., 115, D00H28, https://doi.org/10.1029/2009jd012277, 2010.
Kato, S., Rose, F. G., Sun-Mack, S., Miller, W. F., Chen, Y., Rutan, D. A., Stephens, G. L., Loeb, N. G., Minnis, P., Wielicki, B. A., Winker, D. M., Charlock, T. P., Stackhouse Jr., P. W., Xu, K.-M., and Collins, W. D.: Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties, J. Geophys. Res.-Atmos., 116, D19209, https://doi.org/10.1029/2011JD016050, 2011.
Kiehl, J. T.: On the Observed Near Cancellation between Longwave and Shortwave Cloud Forcing in Tropical Regions, J. Climate, 7, 559–565, https://doi.org/10.1175/1520-0442(1994)007<0559:Otoncb>2.0.Co;2, 1994.
L'Ecuyer, T. S. and Jiang, J. H.: Touring the atmosphere aboard the A-Train, Phys. Today, 63, 36–41, https://doi.org/10.1063/1.3463626, 2010.
Lilly, D. K.: Cirrus Outflow Dynamics, J. Atmos. Sci., 45, 1594–1605, https://doi.org/10.1175/1520-0469(1988)045<1594:Cod>2.0.Co;2, 1988.
Matsui, T., Zeng, X., Tao, W.-K., Masunaga, H., Olson, W. S., and Lang, S.: Evaluation of Long-Term Cloud-Resolving Model Simulations Using Satellite Radiance Observations and Multifrequency Satellite Simulators, J. Atmos. Ocean. Tech., 26, 1261–1274, https://doi.org/10.1175/2008jtecha1168.1, 2009.
McKim, B., Bony, S., and Dufresne, J.-L.: Weak anvil cloud area feedback suggested by physical and observational constraints, Nat. Geosci., 17, 392–397, https://doi.org/10.1038/s41561-024-01414-4, 2024.
Minnis, P., Trepte, Q. Z., Sun-Mack, S., Chen, Y., Doelling, D. R., Young, D. F., Spangenberg, D. A., Miller, W. F., Wielicki, B. A., Brown, R. R., Gibson, S. C., and Geier, E. B.: Cloud Detection in Nonpolar Regions for CERES Using TRMM VIRS and Terra and Aqua MODIS Data, IEEE T. Geosci. Remote, 46, 3857–3884, https://doi.org/10.1109/tgrs.2008.2001351, 2008.
Minnis, P., Sun-Mack, S., Young, D. F., Heck, P. W., Garber, D. P., Chen, Y., Spangenberg, D. A., Arduini, R. F., Trepte, Q. Z., Smith, W. L., Ayers, J. K., Gibson, S. C., Miller, W. F., Hong, G., Chakrapani, V., Takano, Y., Liou, K.-N., Xie, Y., and Yang, P.: CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data – Part I: Algorithms, IEEE T. Geosci. Remote, 49, 4374–4400, https://doi.org/10.1109/tgrs.2011.2144601, 2011.
NASA/LARC/SD/ASDC: CERES A-Train Integrated CALIPSO, CloudSat, CERES, and MODIS (CCCM) Merged Release B1, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/AQUA/CERES/NEWS_CCCM-FM3-MODIS-CAL-CS_L2.RELB1, 2011.
NASA/LARC/SD/ASDC: SatCORPS CERES GEO Edition 4 MTSAT-1R Southern Hemisphere Version 1.0, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/MTS01/CERES/GEO_ED4_SH_L2.V01, 2016a.
NASA/LARC/SD/ASDC: SatCORPS CERES GEO Edition 4 MTSAT-1R Northern Hemisphere Version 1.0, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/MTS01/CERES/GEO_ED4_NH_L2.V01, 2016b.
Nesbitt, S. W. and Zipser, E. J.: The Diurnal Cycle of Rainfall and Convective Intensity according to Three Years of TRMM Measurements, J. Climate, 16, 1456–1475, https://doi.org/10.1175/1520-0442(2003)016<1456:TDCORA>2.0.CO;2, 2003.
Nicholls, M. E.: An investigation of how radiation may cause accelerated rates of tropical cyclogenesis and diurnal cycles of convective activity, Atmos. Chem. Phys., 15, 9003–9029, https://doi.org/10.5194/acp-15-9003-2015, 2015.
Nowicki, S. M. J. and Merchant, C. J.: Observations of diurnal and spatial variability of radiative forcing by equatorial deep convective clouds, J. Geophys. Res.-Atmos., 109, D11202, https://doi.org/10.1029/2003JD004176, 2004.
Powell, S. W., Houze, R. A., Kumar, A., and McFarlane, S. A.: Comparison of Simulated and Observed Continental Tropical Anvil Clouds and Their Radiative Heating Profiles, J. Atmos. Sci., 69, 2662–2681, https://doi.org/10.1175/jas-d-11-0251.1, 2012.
Raghuraman, S. P., Medeiros, B., and Gettelman, A.: Observational Quantification of Tropical High Cloud Changes and Feedbacks, J. Geophys. Res.-Atmos., 129, e2023JD039364, https://doi.org/10.1029/2023JD039364, 2024.
Roca, R., Fiolleau, T., and Bouniol, D.: A Simple Model of the Life Cycle of Mesoscale Convective Systems Cloud Shield in the Tropics, J. Climate, 30, 4283–4298, https://doi.org/10.1175/JCLI-D-16-0556.1, 2017.
Rossow, W. B. and Schiffer, R. A.: ISCCP Cloud Data Products, B. Am. Meteorol. Soc., 72, 2–20, https://doi.org/10.1175/1520-0477(1991)072<0002:ICDP>2.0.CO;2, 1991.
Ruppert, J. H. and Hohenegger, C.: Diurnal Circulation Adjustment and Organized Deep Convection, J. Climate, 31, 4899–4916, https://doi.org/10.1175/jcli-d-17-0693.1, 2018.
Santek, D., Dworak, R., Nebuda, S., Wanzong, S., Borde, R., Genkova, I., García-Pereda, J., Galante Negri, R., Carranza, M., Nonaka, K., Shimoji, K., Oh, S. M., Lee, B.-I., Chung, S.-R., Daniels, J., and Bresky, W.: 2018 Atmospheric Motion Vector (AMV) Intercomparison Study, Remote Sens., 11, 2240, https://doi.org/10.3390/rs11192240, 2019.
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., von der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020.
Sobel, A. H., Nilsson, J., and Polvani, L. M.: The Weak Temperature Gradient Approximation and Balanced Tropical Moisture Waves, J. Atmos. Sci., 58, 3650–3665, https://doi.org/10.1175/1520-0469(2001)058<3650:TWTGAA>2.0.CO;2, 2001.
Sokol, A. B. and Hartmann, D. L.: Tropical Anvil Clouds: Radiative Driving Toward a Preferred State, J. Geophys. Res.-Atmos., 125, e2020JD033107, https://doi.org/10.1029/2020jd033107, 2020.
Sokol, A. B., Wall, C. J., and Hartmann, D. L.: Greater climate sensitivity implied by anvil cloud thinning, Nat. Geosci., 17, 398–403, https://doi.org/10.1038/s41561-024-01420-6, 2024.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., and Mitrescu, C.: The Cloudsat Mission and the a-Train, B. Am. Meteorol. Soc., 83, 1771–1790, https://doi.org/10.1175/bams-83-12-1771, 2002.
Suzuki, K., Golaz, J. C., and Stephens, G. L.: Evaluating cloud tuning in a climate model with satellite observations, Geophys. Res. Lett., 40, 4464–4468, https://doi.org/10.1002/grl.50874, 2013.
Takahashi, H. and Luo, Z.: Where is the level of neutral buoyancy for deep convection?, Geophys. Res. Lett., 39, L15809, https://doi.org/10.1029/2012gl052638, 2012.
Thompson, D. W. J., Bony, S., and Li, Y.: Thermodynamic constraint on the depth of the global tropospheric circulation, P. Natl. Acad. Sci. USA, 114, 8181–8186, https://doi.org/10.1073/pnas.1620493114, 2017.
Tian, Y. D., Peters-Lidard, C. D., Eylander, J. B., Joyce, R. J., Huffman, G. J., Adler, R. F., Hsu, K. L., Turk, F. J., Garcia, M., and Zeng, J.: Component analysis of errors in satellite-based precipitation estimates, J. Geophys. Res.-Atmos., 114, D24101, https://doi.org/10.1029/2009JD011949, 2009.
Wall, C. J., Hartmann, D. L., Thieman, M. M., Smith, W. L., and Minnis, P.: The Life Cycle of Anvil Clouds and the Top-of-Atmosphere Radiation Balance over the Tropical West Pacific, J. Climate, 31, 10059–10080, https://doi.org/10.1175/jcli-d-18-0154.1, 2018.
Wall, C. J., Norris, J. R., Gasparini, B., Smith, W. L., Thieman, M. M., and Sourdeval, O.: Observational Evidence that Radiative Heating Modifies the Life Cycle of Tropical Anvil Clouds, J. Climate, 33, 8621–8640, https://doi.org/10.1175/jcli-d-20-0204.1, 2020.
Wang, Z. and Yuan, J.: Observing convective activities in complex convective organizations and their contributions to precipitation and anvil cloud amounts, Atmos. Chem. Phys., 24, 13811–13831, https://doi.org/10.5194/acp-24-13811-2024, 2024.
Wang, Z., Ge, J., Yan, J., Li, W., Yang, X., Wang, M., and Hu, X.: Interannual shift of tropical high cloud diurnal cycle under global warming, Clim. Dynam., 59, 3391–3400, https://doi.org/10.1007/s00382-022-06273-6, 2022.
Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., Lee, R. B., Smith, G. L., and Cooper, J. E.: Clouds and the Earth's Radiant Energy System (CERES): An Earth Observing System Experiment, B. Am. Meteorol. Soc., 77, 853–868, https://doi.org/10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2, 1996.
Williams, M. and Houze, R. A.: Satellite-Observed Characteristics of Winter Monsoon Cloud Clusters, Mon. Weather Rev., 115, 505–519, https://doi.org/10.1175/1520-0493(1987)115<0505:Socowm>2.0.Co;2, 1987.
Williamson, M. S., Thackeray, C. W., Cox, P. M., Hall, A., Huntingford, C., and Nijsse, F. J. M. M.: Emergent constraints on climate sensitivities, Rev. Modern Phys., 93, 025004, https://doi.org/10.1103/RevModPhys.93.025004, 2021.
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, https://doi.org/10.1175/2009JTECHA1281.1, 2009.
Winker, D. M., Pelon, J., Coakley, J. A., Ackerman, S. A., Charlson, R. J., Colarco, P. R., Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T. L., Le Treut, H., Mccormick, M. P., Mégie, G., Poole, L., Powell, K., Trepte, C., Vaughan, M. A., and Wielicki, B. A.: The CALIPSO Mission: A Global 3D View of Aerosols and Clouds, B. Am. Meteorol. Soc., 91, 1211–1230, https://doi.org/10.1175/2010BAMS3009.1, 2010.
Yang, Y., Zhao, C., Sun, Y., Chi, Y., and Fan, H.: Convective cloud detection and tracking using the new-generation geostationary satellite over South China, IEEE T. Geosci. Remote, 61, 1–12, https://doi.org/10.1109/tgrs.2023.3298976, 2023.
Yang, Y., Zhao, C., Wang, Y., Sun, Y., Fan, H., Zhao, X., and Zhou, Y.: Evolution Characteristics of Convective Clouds With Relatively Small Scales Over South China, J. Geophys. Res.-Atmos., 129, e2024JD040780, https://doi.org/10.1029/2024JD040780, 2024.
Yin, J. and Porporato, A.: Diurnal cloud cycle biases in climate models, Nat. Commun., 8, 2269, https://doi.org/10.1038/s41467-017-02369-4, 2017.
Yin, J. and Porporato, A.: Radiative effects of daily cycle of cloud frequency in past and future climates, Clim. Dynam., 54, 1625–1637, https://doi.org/10.1007/s00382-019-05077-5, 2019.
Yuan, J. and Houze, R. A.: Global Variability of Mesoscale Convective System Anvil Structure from A-Train Satellite Data, J. Climate, 23, 5864–5888, https://doi.org/10.1175/2010jcli3671.1, 2010.
Yuan, J. and Houze, R. A.: Deep Convective Systems Observed by A-Train in the Tropical Indo-Pacific Region Affected by the MJO, J. Atmos. Sci., 70, 465–486, https://doi.org/10.1175/jas-d-12-057.1, 2013.
Yuan, J., Houze, R. A., and Heymsfield, A. J.: Vertical Structures of Anvil Clouds of Tropical Mesoscale Convective Systems Observed by CloudSat, J. Atmos. Sci., 68, 1653–1674, https://doi.org/10.1175/2011jas3687.1, 2011.
Zelinka, M. D. and Hartmann, D. L.: Why is longwave cloud feedback positive?, J. Geophys. Res., 115, D16117, https://doi.org/10.1029/2010jd013817, 2010.
Zeng, X., Tao, W.-K., Powell, S. W., Houze, R. A., Ciesielski, P., Guy, N., Pierce, H., and Matsui, T.: A Comparison of the Water Budgets between Clouds from AMMA and TWP-ICE, J. Atmos. Sci., 70, 487–503, https://doi.org/10.1175/jas-d-12-050.1, 2013.
Zhao, M.: An Investigation of the Connections among Convection, Clouds, and Climate Sensitivity in a Global Climate Model, J. Climate, 27, 1845–1862, https://doi.org/10.1175/jcli-d-13-00145.1, 2014.
Zhao, M., Golaz, J. C., Held, I. M., Ramaswamy, V., Lin, S. J., Ming, Y., Ginoux, P., Wyman, B., Donner, L. J., Paynter, D., and Guo, H.: Uncertainty in Model Climate Sensitivity Traced to Representations of Cumulus Precipitation Microphysics, J. Climate, 29, 543–560, https://doi.org/10.1175/jcli-d-15-0191.1, 2016.
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.
Short summary
Convective anvil outflow directly driven by the shortwave radiative-heating destabilization is strong during the daytime, whereas the outflow contributed by the longwave radiative cooling through radiative destabilization and circulation is weak. This leads to the diurnal variation in the convection-producing anvil clouds, which in turn can influence the radiative energy budget.
Convective anvil outflow directly driven by the shortwave radiative-heating destabilization is...
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