Articles | Volume 21, issue 20
https://doi.org/10.5194/acp-21-15493-2021
© Author(s) 2021. 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-21-15493-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impacts of tropical cyclones on the thermodynamic conditions in the tropical tropopause layer observed by A-Train satellites
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Canada
Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Canada
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Cited articles
Anthes, R. A., Bernhardt, P. A., Chen, Y., Cucurull, L., Dymond, K. F., Ector, D., Healy, S. B., Ho, S.-P., Hunt, D. C., Kuo, Y.-H., Liu, H., Manning, K., McCormick, C., Meehan, T. K., Randel, W J., Rocken, C., Schreiner, W S., Sokolovskiy, S. V., Syndergaard, S., Thompson, D. C., Trenberth, K. E., Wee, T.-K., Yen, N. L., and Zeng, Z.: The COSMIC/FORMOSAT-3
mission: Early results, B. Am. Meteorol. Soc., 89,
313–334, 2008. a
Aumann, H. H. and Ruzmaikin, A.: Frequency of deep convective clouds in the tropical zone from 10 years of AIRS data, Atmos. Chem. Phys., 13, 10795–10806, https://doi.org/10.5194/acp-13-10795-2013, 2013. a, b, c
Aumann, H. H., DeSouza-Machado, S. G., and Behrangi, A.: Deep convective clouds at the tropopause, Atmos. Chem. Phys., 11, 1167–1176, https://doi.org/10.5194/acp-11-1167-2011, 2011. a, b
Austin, R. T., Heymsfield, A. J., and Stephens, G. L.: Retrieval of ice cloud
microphysical parameters using the CloudSat millimeter-wave radar and
temperature, J. Geophys. Res.-Atmos., 114, D00A23, https://doi.org/10.1029/2008JD010049, 2009. a
Avery, M. A., Davis, S. M., Rosenlof, K. H., Ye, H., and Dessler, A. E.: Large
anomalies in lower stratospheric water vapour and ice during the 2015–2016
El Niño, Nat. Geosci., 10, 405–409, 2017. a
Berk, A., Conforti, P., Kennett, R., Perkins, T., Hawes, F., and Van Den Bosch,
J.: MODTRAN® 6: A major upgrade of the
MODTRAN® radiative transfer code, in: 2014 6th Workshop on
Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
(WHISPERS), 24–27 June 2014, Lausanne, Switzerland, IEEE, pp. 1–4, https://doi.org/10.1109/WHISPERS.2014.8077573, 2014. a
Bernath, P. F., McElroy, C. T., Abrams, M. C., Boone, C. D., Butler, M., Camy-Peyret, C., Carleer, M., Clerbaux, C., Coheur, P.-F., Colin, R., DeCola, P., DeMazière, M., Drummond, J. R., Dufour, D., Evans, W. F. J., Fast, H., Fussen, D., Gilbert, K., Jennings, D. E., Llewellyn, E. J., Lowe, R. P., Mahieu, E., McConnell, J. C., McHugh, M., McLeod, S. D., Michaud, R., Midwinter, C., Nassar, R., Nichitiu, F., Nowlan, C., Rinsland, C. P., Rochon, Y. J., Rowlands, N., Semeniuk, K., Simon, P., Skelton, R., Sloan, J. J., Soucy, M.-A., Strong, K., Tremblay, P., Turnbull, D., Walker, K. A., Walkty, I., Wardle, D. A., Wehrle, V., Zander, R., and Zou, J.:
Atmospheric chemistry experiment (ACE): mission overview, Geophys.
Res. Lett., 32, L15S01, https://doi.org/10.1029/2005GL022386, 2005. a
Biondi, R., Ho, S.-P., Randel, W., Syndergaard, S., and Neubert, T.: Tropical
cyclone cloud-top height and vertical temperature structure detection using
GPS radio occultation measurements, J. Geophys. Res.-Atmos., 118, 5247–5259, 2013. a
Bowman, K. W., Rodgers, C. D., Kulawik, S. S., Worden, J., Sarkissian, E., Osterman, G., Steck, T., Ming Lou, Eldering, A., Shephard, M., Worden, H., Lampel, M., Clough, S., Brown, P., Rinsland, C., Gunson, M., and Beer, R.:
Tropospheric emission spectrometer: Retrieval method and error analysis, IEEE
T. Geosci. Remote, 44, 1297–1307, 2006. a
Brewer, A.: Evidence for a world circulation provided by the measurements of
helium and water vapour distribution in the stratosphere, Q. J.
Roy. Meteor. Soc., 75, 351–363, 1949. a
Chahine, M. T., Pagano, T. S., Aumann, H. H., Atlas, R., Barnet, C., Blaisdell, J., Chen, L., Divakarla, M., Fetzer, E. J., Goldberg, M., Gautier, C., Granger, S., Hannon, S., Irion, F. W., Kakar, R., Kalnay, E., Lambrigtsen, B. H., Lee, S., Le Marshall, J., Mcmillan, W. W., Mcmillin, L., Olsen, E. T., Revercomb, H., Rosenkranz, P., Smith, W. L., Staelin, D., Strow, L. L., Susskind, J., Tobin, D., Wolf, W., and Zhou, L.: AIRS:
Improving weather forecasting and providing new data on greenhouse gases,
B. Am. Meteorol. Soc., 87, 911–926, https://doi.org/10.1175/BAMS-87-7-911, 2006. a
Corti, T., Luo, B. P., Fu, Q., Vömel, H., and Peter, T.: The impact of cirrus clouds on tropical troposphere-to-stratosphere transport, Atmos. Chem. Phys., 6, 2539–2547, https://doi.org/10.5194/acp-6-2539-2006, 2006. a, b
Delanoë, J. and Hogan, R. J.: Combined CloudSat-CALIPSO-MODIS retrievals of
the properties of ice clouds, J. Geophys. Res.-Atmos.,
115, D00H29, https://doi.org/10.1029/2009JD012346, 2010. a, b
Deng, M., Mace, G. G., Wang, Z., and Okamoto, H.: Tropical Composition, Cloud
and Climate Coupling Experiment validation for cirrus cloud profiling
retrieval using CloudSat radar and CALIPSO lidar, J. Geophys.
Res.-Atmos., 115, D00J15, https://doi.org/10.1029/2009JD013104, 2010. a
Dessler, A., Hintsa, E., Weinstock, E., Anderson, J., and Chan, K.: Mechanisms controlling water vapor in the lower stratosphere: A tale of two stratospheres, J. Geophys. Res.-Atmos., 100,
23167–23172, 1995. a
Dessler, A., Schoeberl, M., Wang, T., Davis, S., and Rosenlof, K.:
Stratospheric water vapor feedback, P. Natl. Acad.
Sci. USA, 110, 18087–18091, 2013. a
Feng, J.: A-Train observation of thermodynamic conditions above tropical cyclones, Mendeley Data, V1 [data set], https://doi.org/10.17632/fy3gg7ch42.1, 2021. a
Feng, J., Huang, Y., and Qu, Z.: A simulation-experiment-based assessment of retrievals of above-cloud temperature and water vapor using a hyperspectral infrared sounder, Atmos. Meas. Tech., 14, 5717–5734, https://doi.org/10.5194/amt-14-5717-2021, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w
Fetzer, E. J., Read, W. G., Waliser, D., Kahn, B. H., Tian, B., Vömel, H.,
Irion, F. W., Su, H., Eldering, A., de la Torre Juarez, M., Jiang, J., and
Dang, V.: Comparison of upper tropospheric water vapor observations from the
Microwave Limb Sounder and Atmospheric Infrared Sounder, J.
Geophys. Res.-Atmos,, 113, D22110, https://doi.org/10.1029/2008JD010000, 2008. a
Gettelman, A., Randel, W., Wu, F., and Massie, S.: Transport of water vapor in
the tropical tropopause layer, Geophys. Res. Lett., 29, 9-1–9-4, https://doi.org/10.1029/2001GL013818, 2002. a
Gettelman, A., Weinstock, E. M., Fetzer, E. J., Irion, F. W., Eldering, A.,
Richard, E. C., Rosenlof, K. H., Thompson, T. L., Pittman, J. V., Webster,
C. R., and Herman, R. L.: Validation of Aqua satellite data in the upper
troposphere and lower stratosphere with in situ aircraft instruments,
Geophys. Res. Lett., 31, L22107, https://doi.org/10.1029/2004GL020730, 2004. a
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. Climatol., 52, 853–871, 2013. a
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, Robin J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, Fr., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy.
Meteor. Soc., 146, 1999–2049, 2020. a
Holton, J. R. and Gettelman, A.: Horizontal transport and the dehydration of
the stratosphere, Geophys. Res. Lett., 28, 2799–2802, 2001. a
Huang, Y., Ramaswamy, V., Huang, X., Fu, Q., and Bardeen, C.: A strict test in
climate modeling with spectrally resolved radiances: GCM simulation versus
AIRS observations, Geophys. Res. Lett., 34, L24707, https://doi.org/10.1029/2007GL031409, 2007. a
Huang, Y., Zhang, M., Xia, Y., Hu, Y., and Son, S.-W.: Is there a stratospheric
radiative feedback in global warming simulations?, Clim. Dynam., 46,
177–186, 2016. a
Iacono, M. J., Mlawer, E. J., Clough, S. A., and Morcrette, J.-J.: Impact of an
improved longwave radiation model, RRTM, on the energy budget and
thermodynamic properties of the NCAR community climate model, CCM3, J. Geophys. Res.-Atmos., 105, 14873–14890, 2000. a
Jensen, E. J., Pfister, L., Jordan, D. E., Fahey, D. W., Newman, P. A.,
Thornberry, T., Rollins, A., Diskin, G., Bui, T. P., McGill, M., Hlavka D., Lawson, P. R., Gao, R., Pilewskie, P., Elkins, J., Hintsa, E., Moore, F., Mahoney, M. J., Atlas, E., Stutz, J., Pfeilsticker, K., Wofsy, S. C., Evan, S., and Rosenlof, K. H.: The
NASA Airborne Tropical TRopopause EXperiment (ATTREX), SPARC Newsletter, 41,
15–24, 2013. a, b
Jiang, J. H., Su, H., Zhai, C., Perun, V. S., Del Genio, A., Nazarenko, L. S., Donner, L. J., Horowitz, L., Seman, C., Cole, J., Gettelman, A., Ringer, M. A., Rotstayn, L., Jeffrey, S., Wu, T., Brient, F., Dufresne, JL., Kawai, H., Koshiro, T., Watanabe, M., Lécuyer, T. S., Volodin, E. M., Iversen, T., Drange, H., Mesquita, M. D. S., Read, W. G., Waters, J. W., Tian, B., Teixeira, J., and Stephens, G. L.: Evaluation of cloud
and water vapor simulations in CMIP5 climate models using NASA “A-Train”
satellite observations, J. Geophys. Res.-Atmos., 117, D14105, https://doi.org/10.1029/2011JD017237, 2012. a
Jiang, J. H., Su, H., Zhai, C., Wu, L., Minschwaner, K., Molod, A. M., and
Tompkins, A. M.: An assessment of upper troposphere and lower stratosphere
water vapor in MERRA, MERRA2, and ECMWF reanalyses using Aura MLS
observations, J. Geophys. Res.-Atmos., 120, 11468–11485, https://doi.org/10.1002/2015JD023752,
2015. a, b
L'Ecuyer, T. S. and Jiang, J. H.: Touring the atmosphere aboard the A-Train,
in: AIP Conference Proceedings, American Institute
of Physics, 1401, 245–256, https://doi.org/10.1063/1.3653856, 2011. a
Lee, K.-O., Dauhut, T., Chaboureau, J.-P., Khaykin, S., Krämer, M., and Rolf, C.: Convective hydration in the tropical tropopause layer during the StratoClim aircraft campaign: pathway of an observed hydration patch, Atmos. Chem. Phys., 19, 11803–11820, https://doi.org/10.5194/acp-19-11803-2019, 2019. a
Livesey, N., Read, W., Wagner, P., Froidevaux, L., Lambert, A., Manney, G.,
Millán Valle, L., Pumphrey, H., Santee, M., Schwartz, M., Wang, S., Fuller, R., Jarnot, R., and Knosp, B.: Version
4.2 x Level 2 data quality and description document, JPL D-33509 Rev. C, available at: https://mls.jpl.nasa.gov/data/v4-2_data_quality_document.pdf (last access: 11 October 2021), 2017. a, b, c, d, e
Mace, G. G., Zhang, 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. a
McErlich, C., McDonald, A., Schuddeboom, A., and Silber, I.: Comparing
Satellite-and Ground-Based Observations of Cloud Occurrence Over High
Southern Latitudes, J. Geophys. Res.-Atmos., 126,
e2020JD033607, https://doi.org/10.1029/2020JD033607, 2021. a
Okamoto, H., Sato, K., and Hagihara, Y.: Global analysis of ice microphysics
from CloudSat and CALIPSO: Incorporation of specular reflection in lidar
signals, J. Geophys. Res.-Atmos., 115, D22209, https://doi.org/10.1029/2009JD013383, 2010. a
Olsen, E., Fetzer, E., Hulley, G., Manning, E., Blaisdell, J., Iredell, L.,
Susskind, J., Warner, J., Wei, Z., Blackwell, W., and Maddy, E.: AIRS/AMSU/HSB version 6 level 2 product user guide, Jet Propulsion Laboratory, Version, 1, available at: https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProductDocumentation/3.3.4_ProductGenerationAlgorithms/V6_L2_Product_User_Guide.pdf (last access: 11 October 2021), 2013. a
Parkinson, C. L.: Aqua: An Earth-observing satellite mission to examine water
and other climate variables, IEEE T. Geosci. Remote, 41, 173–183, 2003. a
Partain, P.: Cloudsat ECMWF-AUX auxiliary data process description and
interface control document, Cooperative Institute for Research in the
Atmosphere Rep, available at: ftp://ftp.cira.colostate.edu/ftp/CloudSat/Docs/ECMWF-AUX_PDICD.P_R01.20040730.pdf (last access: 11 October 2021), 2004. a
Plumb, R. A.: Stratospheric transport, J. Meteorol. Soc.
Jpn. Ser. II, 80, 793–809, 2002. a
Qu, Z., Huang, Y., Vaillancourt, P. A., Cole, J. N. S., Milbrandt, J. A., Yau, M.-K., Walker, K., and de Grandpré, J.: Simulation of convective moistening of the extratropical lower stratosphere using a numerical weather prediction model, Atmos. Chem. Phys., 20, 2143–2159, https://doi.org/10.5194/acp-20-2143-2020, 2020. a
Randel, W. and Park, M.: Diagnosing observed stratospheric water vapor
relationships to the cold point tropical tropopause, J. Geophys.
Res.-Atmos., 124, 7018–7033, 2019. a
Read, W. G., Lambert, A., Bacmeister, J., Cofield, R. E., Christensen, L. E.,
Cuddy, D. T., Daffer, W. H., Drouin, B. J., Fetzer, E., Froidevaux, L.,
Fuller, R., Herman, R., Jarnot, R. F., Jiang, J. H., Jiang, Y. B., Kelly, K.,
Knosp, B. W., Kovalenko, L. J., Livesey, N. J., Liu, H.-C., Manney, G. L.,
Pickett, H. M., Pumphrey, H. C., Rosenlof, K. H., Sabounchi, X., Santee,
M. L., Schwartz, M. J., Snyder, W. V., Stek, P. C., Su, H., Takacs, L. L.,
Thurstans, R. P., Vömel, H., Wagner, P. A., Waters, J. W., Webster, C. R.,
Weinstock, E. M., and Wu, D. L.: Aura Microwave Limb Sounder upper
tropospheric and lower stratospheric H2O and relative humidity with respect to ice validation, J. Geophys. Res.-Atmos., 112,
D24S35, https://doi.org/10.1029/2007JD008752, 2007. a
Rivoire, L., Birner, T., and Knaff, J. A.: Evolution of the upper-level thermal
structure in tropical cyclones, Geophys. Res. Lett., 43, 10–530,
2016. a
Rivoire, L., Birner, T., Knaff, J. A., and Tourville, N.: Quantifying the
radiative impact of clouds on tropopause layer cooling in tropical cyclones, J. Climate, 33, 6361–6376, https://doi.org/10.1175/JCLI-D-19-0813.1, 2020. a, b, c, d
Robinson, F. and Sherwood, S.: Modeling the impact of convective entrainment on
the tropical tropopause, J. Atmos. Sci., 63, 1013–1027,
2006. a
Rodgers, C. D.: Inverse methods for atmospheric sounding: theory and practice, vol. 2, World scientific, https://doi.org/10.1142/3171, 2000. a, b
Romps, D. M. and Kuang, Z.: Overshooting convection in tropical cyclones,
Geophys. Res. Lett., 36, L09804, https://doi.org/10.1029/2009GL037396, 2009. a
Saito, M., Iwabuchi, H., Yang, P., Tang, G., King, M. D., and Sekiguchi, M.:
Ice particle morphology and microphysical properties of cirrus clouds
inferred from combined CALIOP-IIR measurements, J. Geophys.
Res.-Atmos., 122, 4440–4462, 2017. a
Sampson, C. R. and Schrader, A. J.: The automated tropical cyclone forecasting
system (version 3.2), B. Am. Meteorol. Soc., 81,
1231–1240, 2000. a
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. a
Schoeberl, M., Jensen, E., Pfister, L., Ueyama, R., Wang, T., Selkirk, H.,
Avery, M., Thornberry, T., and Dessler, A.: Water vapor, clouds, and
saturation in the tropical tropopause layer, J. Geophys. Res.-Atmos., 124, 3984–4003, 2019. a
Schoeberl, M. R., Dessler, A. E., and Wang, T.: Simulation of stratospheric water vapor and trends using three reanalyses, Atmos. Chem. Phys., 12, 6475–6487, https://doi.org/10.5194/acp-12-6475-2012, 2012. a
Schubert, W. H. and McNoldy, B. D.: Application of the concepts of Rossby
length and Rossby depth to tropical cyclone dynamics, J. Adv.
Model. Earth Sy., 2, 7, https://doi.org/10.3894/JAMES.2010.2.7, 2010. a
Schwartz, M. J., Lambert, A., Manney, G. L., Read, W. G., Livesey, N. J., Froidevaux, L., Ao, C. O., Bernath, P. F., Boone, C. D., Cofield, R. E., Daffer, W. H., Drouin, B. J., Fetzer, E. J., Fuller, R. A., Jarnot, R. F., Jiang, J. H., Jiang, Y. B., Knosp, B. W., Krüger, K., Li, J.-L. F., Mlynczak, M. G., Pawson, S., Russell III, J. M., Santee, M. L., Snyder, W. V., Stek, P. C., Thurstans, R. P., Tompkins, A. M., Wagner, P. A., Walker, K. A., Waters, J. W., and Wu, D. L.: Validation of the Aura
Microwave Limb Sounder temperature and geopotential height measurements,
J. Geophys. Res.-Atmos., 113, D15S11, https://doi.org/10.1029/2007JD008783, 2008. a, b, c
Schwartz, M. J., Read, W. G., Santee, M. L., Livesey, N. J., Froidevaux, L.,
Lambert, A., and Manney, G. L.: Convectively injected water vapor in the
North American summer lowermost stratosphere, Geophys. Res. Lett.,
40, 2316–2321, 2013. a
Solomon, S., Rosenlof, K. H., Portmann, R. W., Daniel, J. S., Davis, S. M.,
Sanford, T. J., and Plattner, G.-K.: Contributions of stratospheric water
vapor to decadal changes in the rate of global warming, Science, 327,
1219–1223, 2010. a
Stephens, G. L., Vane, D. G., Tanelli, S., Im, E., Durden, S., Rokey, M.,
Reinke, D., Partain, P., Mace, G. G., Austin, R., L'Ecuyer, T., Haynes, J., Lebsock, M., Suzuki, K., Waliser, D., Wu, D., Kay, J., Gettelman, A., Wang, Z., and Marchand, R.: CloudSat mission:
Performance and early science after the first year of operation, J.
Geophys. Res.-Atmos., 113, D00A18, https://doi.org/10.1029/2008JD009982, 2008. a
Sun, Y. and Huang, Y.: An examination of convective moistening of the lower
stratosphere using satellite data, Earth and Space Science, 2, 320–330,
2015. a
Trepte, Q. Z., Minnis, P., Trepte, C., Sun-Mack, S., and Brown, R.: Improved
cloud detection in CERES edition 3 algorithm and comparison with the CALIPSO
vertical feature mask, in: Proc. 13th Conf. on Atmospheric Radiation and
Cloud Physics, 28 June–2 July 2010, Portland, Oregon, USA, 2010. a
Tseng, H.-H. and Fu, Q.: Temperature control of the variability of tropical
tropopause layer cirrus clouds, J. Geophys. Res.-Atmos.,
122, 11062–11075, https://doi.org/10.1002/2017JD027093, 2017. a
Wang, Z., Vane, D., Stephens, G., and Reinke, D.: Level 2 combined radar and
lidar cloud scenario classification product process description and interface
control document, JPL Rep, California Institute of Technology, Jet Propulsion Laboratory Doc., 22, available at: https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/2b-cldclass-lidar/2B-CLDCLASS-LIDAR_PDICD.P_R04.20120522.pdf (last access: 11 October 2021), 2012.
a, b, c
Waters, J. W., Froidevaux, L., Harwood, R. S., Jarnot, R. F., Pickett, H. M., Read, W. G., Siegel, P. H., Cofield, R. E., Filipiak, M. J., Flower, D. A., Holden, J. R., Lau, G. K., Livesey, N. J., Manney, G. L., Pumphrey, H. C., Santee, M. L., Wu, D. L., Cuddy, D. T., Lay, R. R., Loo, M. S., Perun, V. S., Schwartz, M. J., Stek, P. C., Thurstans, R. P., Boyles, M. A., Chandra, K. M., Chavez, M. C., Chen, G.-S., Chudasama, B. V., Dodge, R., Fuller, R. A., Girard, M. A., Jiang, J. H., Jiang, Y., Knosp, B. W., LaBelle, R. C., Lam, J. C., Lee, K. A., Miller, D., Oswald, J. E., Patel, N. C., Pukala, D. M., Quintero, O., Scaff, D. M., Van Snyder, W., Tope, M. C., Wagner, P. A., and Walch, M. J.: The earth observing system microwave limb sounder (EOS MLS) on the Aura satellite, IEEE T. Geosci. Remote, 44,
1075–1092, 2006. a, b
Wetherald, R. and Manabe, S.: Cloud feedback processes in a general circulation
model, J. Atmos. Sci., 45, 1397–1416, 1988. a
Winker, D. M., Pelon, J., Coakley Jr., 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, 2010. a
World Meteorological Organisation (WMO): Meteorology – a three-dimensional science, WMO Bull., 6, 134–138, available at: https://library.wmo.int/doc_num.php?explnum_id=6960 (last access: 11 October 2021), 1957. a
Wright, J. S. and Fueglistaler, S.: Large differences in reanalyses of diabatic heating in the tropical upper troposphere and lower stratosphere, Atmos. Chem. Phys., 13, 9565–9576, https://doi.org/10.5194/acp-13-9565-2013, 2013. a
Wright, J. S., Sun, X., Konopka, P., Krüger, K., Legras, B., Molod, A. M., Tegtmeier, S., Zhang, G. J., and Zhao, X.: Differences in tropical high clouds among reanalyses: origins and radiative impacts, Atmos. Chem. Phys., 20, 8989–9030, https://doi.org/10.5194/acp-20-8989-2020, 2020. a, b, c, d
Yang, Q., Fu, Q., and Hu, Y.: Radiative impacts of clouds in the tropical
tropopause layer, J. Geophys. Res.-Atmos., 115, D00H12, https://doi.org/10.1029/2009JD012393, 2010. a, b
Young, A. H., Bates, J. J., and Curry, J. A.: Application of cloud vertical
structure from CloudSat to investigate MODIS-derived cloud properties of
cirriform, anvil, and deep convective clouds, J. Geophys.
Res.-Atmos., 118, 4689–4699, 2013. a
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This study conducts a comprehensive analysis of thermodynamic fields above tropical cyclones. Using a synergistic retrieval method, we develop the first infrared hyperspectra-based dataset of collocated temperature and water vapor profiles above deep convective clouds. It discloses the unique impacts of convective overshoots on the tropical tropopause layer (TTL). Challenging conventional views, our study suggests that convective hydration may be limited by the radiative balance above cyclones.
This study conducts a comprehensive analysis of thermodynamic fields above tropical cyclones....
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