Articles | Volume 3, issue 2
https://doi.org/10.5194/wcd-3-693-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-3-693-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Jul 2022
Research article |
| 01 Jul 2022
| 01 Jul 2022
The response of tropical cyclone intensity to changes in environmental temperature
James M. Done
CORRESPONDING AUTHOR
National Center for Atmospheric Research, 3090 Center Green Drive,
Boulder, Colorado 80301, USA
Willis Research Network, 51 Lime St, London, EC3M 7DQ, UK
Gary M. Lackmann
Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina 27607, USA
Andreas F. Prein
National Center for Atmospheric Research, 3090 Center Green Drive,
Boulder, Colorado 80301, USA
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Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
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Sara Bacer, Julien Beaumet, Martin Ménégoz, Hubert Gallée, Enzo Le Bouëdec, and Chantal Staquet
Weather Clim. Dynam., 5, 211–229, https://doi.org/10.5194/wcd-5-211-2024, https://doi.org/10.5194/wcd-5-211-2024, 2024
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Edgar Dolores-Tesillos and Stephan Pfahl
Weather Clim. Dynam., 5, 163–179, https://doi.org/10.5194/wcd-5-163-2024, https://doi.org/10.5194/wcd-5-163-2024, 2024
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Yuan-Bing Zhao, Nedjeljka Žagar, Frank Lunkeit, and Richard Blender
Weather Clim. Dynam., 4, 833–852, https://doi.org/10.5194/wcd-4-833-2023, https://doi.org/10.5194/wcd-4-833-2023, 2023
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Victoria A. Sinclair and Jennifer L. Catto
Weather Clim. Dynam., 4, 567–589, https://doi.org/10.5194/wcd-4-567-2023, https://doi.org/10.5194/wcd-4-567-2023, 2023
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Koffi Worou, Thierry Fichefet, and Hugues Goosse
Weather Clim. Dynam., 4, 511–530, https://doi.org/10.5194/wcd-4-511-2023, https://doi.org/10.5194/wcd-4-511-2023, 2023
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The Atlantic equatorial mode (AEM) of variability is partly responsible for the year-to-year rainfall variability over the Guinea coast. We used the current climate models to explore the present-day and future links between the AEM and the extreme rainfall indices over the Guinea coast. Under future global warming, the total variability of the extreme rainfall indices increases over the Guinea coast. However, the future impact of the AEM on extreme rainfall events decreases over the region.
Hanna Joos, Michael Sprenger, Hanin Binder, Urs Beyerle, and Heini Wernli
Weather Clim. Dynam., 4, 133–155, https://doi.org/10.5194/wcd-4-133-2023, https://doi.org/10.5194/wcd-4-133-2023, 2023
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Warm conveyor belts (WCBs) are strongly ascending, cloud- and precipitation-forming airstreams in extratropical cyclones. In this study we assess their representation in a climate simulation and their changes under global warming. They become moister, become more intense, and reach higher altitudes in a future climate, implying that they potentially have an increased impact on the mid-latitude flow.
Hanin Binder, Hanna Joos, Michael Sprenger, and Heini Wernli
Weather Clim. Dynam., 4, 19–37, https://doi.org/10.5194/wcd-4-19-2023, https://doi.org/10.5194/wcd-4-19-2023, 2023
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Warm conveyor belts (WCBs) are the main cloud- and precipitation-producing airstreams in extratropical cyclones. The latent heat release that occurs during cloud formation often contributes to the intensification of the associated cyclone. Based on the Community Earth System Model Large Ensemble coupled climate simulations, we show that WCBs and associated latent heating will become stronger in a future climate and be even more important for explosive cyclone intensification than in the present.
Davide Faranda, Stella Bourdin, Mireia Ginesta, Meriem Krouma, Robin Noyelle, Flavio Pons, Pascal Yiou, and Gabriele Messori
Weather Clim. Dynam., 3, 1311–1340, https://doi.org/10.5194/wcd-3-1311-2022, https://doi.org/10.5194/wcd-3-1311-2022, 2022
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We analyze the atmospheric circulation leading to impactful extreme events for the calendar year 2021 such as the Storm Filomena, Westphalia floods, Hurricane Ida and Medicane Apollo. For some of the events, we find that climate change has contributed to their occurrence or enhanced their intensity; for other events, we find that they are unprecedented. Our approach underscores the importance of considering changes in the atmospheric circulation when performing attribution studies.
Philipp Breul, Paulo Ceppi, and Theodore G. Shepherd
Weather Clim. Dynam., 3, 645–658, https://doi.org/10.5194/wcd-3-645-2022, https://doi.org/10.5194/wcd-3-645-2022, 2022
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Understanding how the mid-latitude jet stream will respond to a changing climate is highly important. Unfortunately, climate models predict a wide variety of possible responses. Theoretical frameworks can link an internal jet variability timescale to its response. However, we show that stratospheric influence approximately doubles the internal timescale, inflating predicted responses. We demonstrate an approach to account for the stratospheric influence and recover correct response predictions.
Sebastian Schemm, Lukas Papritz, and Gwendal Rivière
Weather Clim. Dynam., 3, 601–623, https://doi.org/10.5194/wcd-3-601-2022, https://doi.org/10.5194/wcd-3-601-2022, 2022
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Much of the change in our daily weather patterns is due to the development and intensification of extratropical cyclones. The response of these systems to climate change is an important topic of ongoing research. This study is the first to reproduce the changes in the North Atlantic circulation and extratropical cyclone characteristics found in fully coupled Earth system models under high-CO2 scenarios, but in an idealized, reduced-complexity simulation with uniform warming.
Edgar Dolores-Tesillos, Franziska Teubler, and Stephan Pfahl
Weather Clim. Dynam., 3, 429–448, https://doi.org/10.5194/wcd-3-429-2022, https://doi.org/10.5194/wcd-3-429-2022, 2022
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Strong winds caused by extratropical cyclones represent a costly hazard for European countries. Here, based on CESM-LENS coupled climate simulations, we show that future changes of such strong winds are characterized by an increased magnitude and extended footprint southeast of the cyclone center. This intensification is related to a combination of increased diabatic heating and changes in upper-level wave dynamics.
Sara Bacer, Fatima Jomaa, Julien Beaumet, Hubert Gallée, Enzo Le Bouëdec, Martin Ménégoz, and Chantal Staquet
Weather Clim. Dynam., 3, 377–389, https://doi.org/10.5194/wcd-3-377-2022, https://doi.org/10.5194/wcd-3-377-2022, 2022
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We study the impact of climate change on wintertime atmospheric blocking over Europe. We focus on the frequency, duration, and size of blocking events. The blocking events are identified via the weather type decomposition methodology. We find that blocking frequency, duration, and size are mostly stationary over the 21st century. Additionally, we compare the blocking size results with the size of the blocking events identified via a different approach using a blocking index.
Ioana Ivanciu, Katja Matthes, Arne Biastoch, Sebastian Wahl, and Jan Harlaß
Weather Clim. Dynam., 3, 139–171, https://doi.org/10.5194/wcd-3-139-2022, https://doi.org/10.5194/wcd-3-139-2022, 2022
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Greenhouse gas concentrations continue to increase, while the Antarctic ozone hole is expected to recover during the twenty-first century. We separate the effects of ozone recovery and of greenhouse gases on the Southern Hemisphere atmospheric and oceanic circulation, and we find that ozone recovery is generally reducing the impact of greenhouse gases, with the exception of certain regions of the stratosphere during spring, where the two effects reinforce each other.
Roman Brogli, Silje Lund Sørland, Nico Kröner, and Christoph Schär
Weather Clim. Dynam., 2, 1093–1110, https://doi.org/10.5194/wcd-2-1093-2021, https://doi.org/10.5194/wcd-2-1093-2021, 2021
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In a warmer future climate, climate simulations predict that some land areas will experience excessive warming during summer. We show that the excessive summer warming is related to the vertical distribution of warming within the atmosphere. In regions characterized by excessive warming, much of the warming occurs close to the surface. In other regions, most of the warming is redistributed to higher levels in the atmosphere, which weakens the surface warming.
Gustav Strandberg and Petter Lind
Weather Clim. Dynam., 2, 181–204, https://doi.org/10.5194/wcd-2-181-2021, https://doi.org/10.5194/wcd-2-181-2021, 2021
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Precipitation is a key climate variable with a large impact on society but also difficult to simulate as it depends largely on temporal and spatial scales. We look here at the effect of model resolution on precipitation in Europe, from coarse-scale global model to small-scale regional models. Higher resolution improves simulated precipitation generally, but individual models may over- or underestimate precipitation even at higher resolution.
Dor Sandler and Nili Harnik
Weather Clim. Dynam., 1, 427–443, https://doi.org/10.5194/wcd-1-427-2020, https://doi.org/10.5194/wcd-1-427-2020, 2020
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The circumglobal teleconnection pattern (CTP) is a wavy pattern of wintertime midlatitude subseasonal flow. It is also linked to various extreme weather events. The CTP is predicted to play a prominent role in future climate. We find that for future projections, most CMIP5 models predict that the CTP will develop a
preferredphase. Our work establishes that the CTP-like climate change signature is in fact comprised of several regional effects, partly due to shifts in CTP phase distributions.
Andreas Chrysanthou, Amanda C. Maycock, and Martyn P. Chipperfield
Weather Clim. Dynam., 1, 155–174, https://doi.org/10.5194/wcd-1-155-2020, https://doi.org/10.5194/wcd-1-155-2020, 2020
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We perform 50-year-long time-slice experiments using the Met Office HadGEM3 global climate model in order to decompose the Brewer–Dobson circulation (BDC) response to an abrupt quadrupling of CO2 in three distinct components, (a) the rapid adjustment, associated with CO2 radiative effects; (b) a global uniform sea surface temperature warming; and (c) sea surface temperature patterns. This demonstrates a potential for fast and slow timescales of the response of the BDC to greenhouse gas forcing.
Matthias Röthlisberger, Michael Sprenger, Emmanouil Flaounas, Urs Beyerle, and Heini Wernli
Weather Clim. Dynam., 1, 45–62, https://doi.org/10.5194/wcd-1-45-2020, https://doi.org/10.5194/wcd-1-45-2020, 2020
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In this study we quantify how much the coldest, middle and hottest third of all days during extremely hot summers contribute to their respective seasonal mean anomaly. This
extreme-summer substructurevaries substantially across the Northern Hemisphere and is directly related to the local physical drivers of extreme summers. Furthermore, comparing re-analysis (i.e. measurement-based) and climate model extreme-summer substructures reveals a remarkable level of agreement.
Cited articles
Alland, J. J., Tang, B H., Corbosiero, K. L., and Bryan, G. H.: Synergistic
effects of midlevel dry air and vertical wind shear on tropical cyclone
development. Part I: Downdraft ventilation, J. Atmos. Sci., 78, 763–782,
https://doi.org/10.1175/JAS-D-20-0054.1, 2021a
Alland, J. J., Tang, B. H., Corbosiero, K. L., and Bryan, G. H.: Combined
effects of midlevel dry air and vertical wind shear on tropical cyclone
development. Part II: Radial ventilation, J. Atmos. Sci., 78, 783–796,
https://doi.org/10.1175/JAS-D-20-0055.1, 2021b.
Allen, M. R. and Ingram, W. J.: Constraints on future changes in climate
and the hydrologic cycle, Nature, 419, 224–232, https://doi.org/10.1038/nature01092, 2002.
Alvey III, G. R., Zipser, E., and Zawislak, J.: How does Hurricane Edouard (2014) evolve toward symmetry before rapid intensification? A high-resolution
ensemble study, J. Atmos. Sci., 77, 1329–1351, https://doi.org/10.1175/JAS-D-18-0355.1, 2020.
Amrhein, V., Greenland, S., and McShane, B.: Scientists rise up against
statistical significance, Nature, 567, 305–307,
https://doi.org/10.1038/d41586-019-00857-9, 2019.
Bister, M. and Emanuel, K. A.: Dissipative heating and hurricane intensity,
Meteorol. Atmos. Phys., 65, 233–240, https://doi.org/10.1007/BF01030791, 1998.
Bryan, G. H.: Effects of surface exchange coefficients and turbulence length
scales on the intensity and structure of numerically simulated hurricanes,
Mon. Weather Rev., 140, 1125–1143, https://doi.org/10.1175/MWR-D-11-00231.1,
2012.
Bryan, G. H. and Fritsch, J. M.: A benchmark simulation for moist
nonhydrostatic numerical models, Mon. Weather Rev., 130, 2917–2928,
https://doi.org/10.1175/1520-0493(2002)130<2917:ABSFMN>2.0.CO;2, 2002 (code available at: https://www2.mmm.ucar.edu/people/bryan/cm1/, last access: 22 June 2022).
Bryan, G. H. and Rotunno, R.: The maximum intensity of tropical cyclones in
axisymmetric numerical model simulations, Mon. Weather Rev., 137, 1770–1789,
https://doi.org/10.1175/2008MWR2709.1, 2009a.
Bryan, G. H. and Rotunno, R.: Evaluation of an analytical model for the
maximum intensity of tropical cyclones, J. Atmos. Sci., 66, 3042–3060,
https://doi.org/10.1175/2009JAS3038.1, 2009b.
Butchart, N.: The Brewer-Dobson circulation, Rev. Geophys., 52, 157–184,
https://doi.org/10.1002/2013RG000448, 2014.
Cordero, E. C. and Forster, P. M. D. F.: Stratospheric variability and trends in models used for the IPCC AR4, Atmos. Chem. Phys., 6, 5369–5380, https://doi.org/10.5194/acp-6-5369-2006, 2006.
Dai, A.: Recent climatology, variability, and trends in global surface
humidity, J. Climate, 19, 2589–3606, https://doi.org/10.1175/JCLI3816.1, 2006.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, D. P., and
Bechtold, P.: The ERA-Interim reanalysis: Configuration and performance of
the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597,
https://doi.org/10.1002/qj.828, 2011.
Deser, C., Knutti, R., Solomon, S., and Phillips, A. S.: Communication of the
role of natural variability in future North American climate, Nat. Clim.
Change, 2, 775–779, https://doi.org/10.1038/nclimate1562, 2012.
Dunion, J. P.: Rewriting the climatology of the tropical North Atlantic and
Caribbean Sea atmosphere, J. Climate, 24, 893–908,
https://doi.org/10.1175/2010JCLI3496.1, 2011.
Durre, I., Vose, R. S., and Wuertz, D. B.: Overview of the integrated global
radiosonde archive, J. Climate, 19, 53–68, https://doi.org/10.1175/JCLI3594.1, 2006.
Elsner, J. B. and Jagger, T. H.: Hurricane climatology: a modern statistical
guide using R, Oxford University Press, https://doi.org/10.1093/oso/9780199827633.001.0001, 2013.
Elsner, J. B., Kossin, J. P., and Jagger, T. H.: The increasing intensity of
the strongest tropical cyclones, Nature, 455, 92–95, https://doi.org/10.1038/nature07234, 2008.
Emanuel, K. A.: An air-sea interaction theory for tropical cyclones. Part
I: Steady-state maintenance, J. Atmos. Sci., 43, 585–604,
https://doi.org/10.1175/1520-0469(1986)043<0585:AASITF>2.0.CO;2, 1986.
Emanuel, K. A.: The dependence of hurricane intensity on climate, Nature,
326, 483–485, https://doi.org/10.1038/326483a0, 1987.
Emanuel, K. A.: The maximum intensity of hurricanes, J. Atmos. Sci., 45,
1143–1155, https://doi.org/10.1175/1520-0469(1988)045<1143:TMIOH>2.0.CO;2, 1988.
Emanuel, K. A.: The theory of hurricanes, Annu. Rev. Fluid Mech., 23,
179–196, https://doi.org/10.1146/annurev.fl.23.010191.001143, 1991.
Emanuel, K. A.: Hurricanes: Tempests in a greenhouse, Phys. Today, 59,
74–75, https://doi.org/10.1063/1.2349743, 2006.
Emanuel, K. A.: Atlantic tropical cyclones downscaled from climate
reanalyses show increasing activity over past 150 years, Nat. Commun., 12,
1–8, https://doi.org/10.1038/s41467-021-27364-8, 2021.
Emanuel, K. A., Solomon, S., Folini, D., Davis, S., and Cagnazzo, C.:
Influence of tropical tropopause layer cooling on Atlantic hurricane
activity, J. Climate, 26, 2288–2301, https://doi.org/10.1175/JCLI-D-12-00242.1, 2013.
European Centre for Medium-Range Weather Forecasts: ERA-Interim Project. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], Boulder, CO, https://doi.org/10.5065/D6CR5RD9, 2009 (data available at: https://apps.ecmwf.int/datasets/, last access: 22 June 2022).
European Centre for Medium-Range Weather Forecasts: ERA5 Reanalysis
(0.25 Degree Latitude-Longitude Grid). Research Data Archive at the National
Center for Atmospheric Research, Computational and Information Systems
Laboratory [data set], Boulder, CO, https://doi.org/10.5065/BH6N-5N20, 2019 (data available at: https://apps.ecmwf.int/datasets/, last access: 22 June 2022).
European Centre for Medium-Range Weather Forecasts: ERA5.1:
Corrections to ERA5 Stratospheric Temperature 2000-2006. Research Data
Archive at the National Center for Atmospheric Research, Computational and
Information Systems Laboratory [data set], Boulder, CO, https://doi.org/10.5065/CBTN-V814, 2020 (data available at: https://apps.ecmwf.int/datasets/, last access: 22 June 2022).
Ferrara, M., Groff, F., Moon, Z., Keshavamurthy, K., Robeson, S. M., and Kieu, C.: Large-scale control of the lower stratosphere on variability of tropical cyclone intensity, Geophys. Res. Lett., 44, 4313–4323,
https://doi.org/10.1002/2017GL073327, 2017.
Fujiwara, M., Hibino, T., Mehta, S. K., Gray, L., Mitchell, D., and Anstey, J.: Global temperature response to the major volcanic eruptions in multiple reanalysis data sets, Atmos. Chem. Phys., 15, 13507–13518, https://doi.org/10.5194/acp-15-13507-2015, 2015.
Gentry, M. S. and Lackmann, G. M.: Sensitivity of simulated tropical cyclone
structure and intensity to horizontal resolution, Mon. Weather Rev., 138,
688–704, https://doi.org/10.1175/2009MWR2976.1, 2010.
Gettelman, A., Hegglin, M. I., Son, S.-W., Kim, J., Fujiwara, M., Birner, T., Kremser, S., Rex, M., Añel, J. A., Akiyoshi, H., Austin, J., Bekki, S., Braesike, P., Brühl, C., Butchart, N., Chipperfield, M., Dameris, M., Dhomse, S., Garny, H., Hardiman, S. C., Jöckel, P., Kinnison, D. E., Lamarque, J. F., Mancini, E., Marchand, M., Michou, M., Morgenstern, O., Pawson, S., Pitari, G., Plummer, D., Pyle, J. A., Rozanov, E., Scinocca, J., Shepherd, T. G., Shibata, K., Smale, D., Teyssèdre, H., and Tian, W.: Multimodel assessment of the upper troposphere
and lower stratosphere: Tropics and global trends, J. Geophys. Res., 115,
D00M08, https://doi.org/10.1029/2009JD013638, 2010.
Gilford, D.: dgilford/pyPI: pyPI v1.3 (initial package release), Version v1.3, Zenodo [code], https://doi.org/10.5281/zenodo.3985975, 2020.
Gilford, D. M.: pyPI (v1.3): Tropical Cyclone Potential Intensity Calculations in Python, Geosci. Model Dev., 14, 2351–2369, https://doi.org/10.5194/gmd-14-2351-2021, 2021.
Gilford, E. M., Solomon, S., and Emanuel, K. A.: On the seasonal cycles of
tropical cyclone potential intensity, J. Climate, 30, 6085–6096, 2017.
Gutmann, E. D., Rasmussen, R. M., Liu, C., Ikeda, K., Bruyere, C. L., Done,
J. M., Garrè, L., Friis-Hansen, P., and Veldore, V.: Changes in
hurricanes from a 13-yr convection-permitting pseudo–global warming
simulation, J. Climate, 31, 3643–3657, https://doi.org/10.1175/JCLI-D-17-0391.1, 2018.
Haimberger, L.: Homogenization of radiosonde temperature time series using
innovation statistics, J. Climate, 20, 1377–1403, https://doi.org/10.1175/JCLI4050.1, 2007 (data available at: https://www.univie.ac.at/theoret-met/research/raobcore/, last access: 22 June 2022).
Haimberger, L., Tavolato, C., and Sperka, S.: Toward elimination of the warm
bias in historic radiosonde temperature records – Some new results from a
comprehensive intercomparison of upper-air data, J. Climate, 21, 4587–4606,
https://doi.org/10.1175/2008JCLI1929.1, 2008.
Haimberger, L., Tavolato, C., and Sperka, S.: Homogenization of the global
radiosonde temperature dataset through combined comparison with reanalysis
background series and neighboring stations, J. Climate, 25, 8108–8131,
https://doi.org/10.1175/JCLI-D-11-00668.1, 2012.
Hakim, G. J.: The mean state of axisymmetric hurricanes in statistical
equilibrium, J. Atmos. Sci., 68, 1364–1376, https://doi.org/10.1175/2010JAS3644.1, 2011.
Hardiman, S. C., Butchart, N., and Calvo, N.: The morphology of the
Brewer–Dobson circulation and its response to climate change in CMIP5
simulations, Q. J. Roy. Meteor. Soc., 140, 1958–1965, https://doi.org/10.1002/qj.2258, 2014.
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.
Hazeleger, W., van den Hurk, B. J., Min, E., van Oldenborgh, G. J.,
Petersen, A. C., Stainforth, D. A., Vasileiadou, E., and Smith, L. A.: Tales of future weather, Nat. Clim. Change, 5, 107–113, 2015.
Held, I. M. and Soden, B. J.: Robust responses of the hydrological cycle to
global warming, J. Climate, 19, 5686–5699, https://doi.org/10.1175/JCLI3990.1, 2006.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., and
Simmons, A.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hill, K. A. and Lackmann, G. M.: The impact of future climate change on TC
intensity and structure: A downscaling approach, J. Climate, 24, 4644–4661,
https://doi.org/10.1175/2011JCLI3761.1, 2011.
Holland, G. and Bruyère, C. L.: Recent intense hurricane response to
global climate change, Clim. Dynam., 42, 617–627, https://doi.org/10.1007/s00382-013-1713-0, 2014.
Holland, G. J.: The maximum potential intensity of tropical cyclones, J.
Atmos. Sci., 54, 2519–2541,
https://doi.org/10.1175/1520-0469(1997)054<2519:TMPIOT>2.0.CO;2, 1997.
Jewson, S. and Lewis, N.: Statistical decomposition of the recent increase
in the intensity of tropical storms, Oceans, 1, 311–325,
https://doi.org/10.3390/oceans1040021, 2020.
Jung, C. and Lackmann, G. M.: Extratropical transition of Hurricane Irene
(2011) in a changing climate, J. Climate, 32, 4847–4871,
https://doi.org/10.1175/JCLI-D-18-0558.1, 2019.
Khairoutdinov, M. and Emanuel, K.: Rotating radiative-convective equilibrium
simulated by a cloud-resolving model, J. Adv. Model. Earth Sy., 5,
816–825, https://doi.org/10.1002/2013MS000253, 2013.
Kieu, C. and Zhang, D. L.: The control of environmental stratification on
the hurricane maximum potential intensity, Geophys. Res. Lett., 45,
6272–6280, https://doi.org/10.1029/2018GL078070, 2018.
Klotzbach, P. and Landsea, C.: Extremely intense hurricanes: Revisiting
Webster et al. (2005) after 10 years, J. Climate, 28, 7621–7629,
https://doi.org/10.1175/JCLI-D-15-0188.1, 2015.
Knapp, K. R. and Kruk, M. C.: Quantifying interagency differences in
tropical cyclone best-track wind speed estimates, Mon. Weather Rev., 138,
1459–1473, https://doi.org/10.1175/2009MWR3123.1, 2010.
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C.
J.: The international best track archive for climate stewardship (IBTrACS)
unifying tropical cyclone data, B. Am. Meteorol. Soc., 91, 363–376,
https://doi.org/10.1175/2009BAMS2755.1, 2010 (data available at: https://www.ncdc.noaa.gov/ibtracs/, last access: 22 June 2022).
Knutson, T., Camargo, S. J., Chan, J. C., Emanuel, K., Ho, C. H., Kossin, J.,
Mohapatra, M., Satoh, M., Sugi, M., Walsh, K., and Wu, L.: Tropical cyclones
and climate change assessment: Part I: Detection and attribution, B. Am. Meteorol. Soc., 100, 1987–2007, https://doi.org/10.1175/BAMS-D-18-0189.1, 2019.
Knutson, T., Camargo, S. J., Chan, J. C., Emanuel, K., Ho, C. H., Kossin, J.,
Mohapatra, M., Satoh, M., Sugi, M., Walsh, K., and Wu, L.: Tropical cyclones
and climate change assessment: Part II: Projected response to anthropogenic
warming, B. Am. Meteorol. Soc., 101, E303–E322,
https://doi.org/10.1175/BAMS-D-18-0194.1, 2020.
Knutson, T. R., McBride, J. L., Chan, J., Emanuel, K., Holland, G., Landsea,
C., Held, I., Kossin, J. P., Srivastava, A. K., and Sugi, M.: Tropical
cyclones and climate change, Nat. Geosci., 3, 157–163, https://doi.org/10.1038/ngeo779, 2010.
Kossin, J. P.: Validating atmospheric reanalysis data using tropical
cyclones as thermometers, B. Am. Meteoro. Soc, 96, 1089-1096,
https://doi.org/10.1175/BAMS-D-14-00180.1, 2015.
Kossin, J. P., Olander, T. L., and Knapp, K. R.: Trend analysis with a new
global record of tropical cyclone intensity, J. Climate, 26, 9960–9976,
https://doi.org/10.1175/JCLI-D-13-00262.1, 2013.
Kossin, J. P., Knapp, K. R., Olander, T. L., and Velden, C. S.: Global
increase in major tropical cyclone exceedance probability over the past four
decades, P. Natl. Acad. Sci. USA, 117, 11975–11980, https://doi.org/10.1073/pnas.1920849117, 2020.
Kuang, Z. and Hartmann, D. L.: Testing the fixed anvil temperature
hypothesis in a cloud-resolving model, J. Climate, 20, 2051–2057,
https://doi.org/10.1175/JCLI4124.1, 2007.
Lackmann, G. M.: Hurricane Sandy before 1900 and after 2100, B. Am. Meteorol. Soc., 96, 547–560, https://doi.org/10.1175/BAMS-D-14-00123.1, 2015.
Landsea, C. W., Harper, B. A., Hoarau, K., and Knaff, J. A.: Can we detect
trends in extreme tropical cyclones?, Science, 313, 452–454,
https://doi.org/10.1126/science.1128448, 2006.
Lee, C. Y., Tippett, M., Sobel, A., and Camargo, S. J.: Rapid intensification and the bimodal distribution of tropical cyclone intensity, Nat. Commun., 7, 10625, https://doi.org/10.1038/ncomms10625, 2016.
Meehl, G. A., Washington, W. M., Ammann, C. M., Arblaster, J. M., Wigley, T.
M. L., and Tebaldi, C.: Combinations of natural and anthropogenic forcings in
twentieth-century climate, J. Climate, 17, 3721–3727,
https://doi.org/10.1175/1520-0442(2004)017<3721:CONAAF>2.0.CO;2, 2004.
Meehl, G. A., Washington, W. M., Arblaster, J. M., Hu A., Teng, H., Tebaldi, C., Sanderson, B., Lamarque, J. F., Conley, A., Strand, W. G., and White III, J. B.: Climate system response to external forcings and climate change projections in CCSM4, J. Climate, 25, 3661–3683.
https://doi.org/10.1175/JCLI-D-11-00240.1, 2012.
Mitchell, D. M., Thorne, P. W., Stott, P. A., and Gray, L. J.: Revisiting the
controversial issue of tropical tropospheric temperature trends, Geophys.
Res. Lett., 40, 2801–2806, https://doi.org/10.1002/grl.50465, 2013.
O'Gorman, P. A. and Singh, M. S.: Vertical structure of warming consistent
with an upward shift in the middle and upper troposphere, Geophys. Res.
Lett., 40, 1838–1842, https://doi.org/10.1002/grl.50328, 2013.
Pall, P., Allen, M. R., and Stone, D. A.: Testing the Clausius–Clapeyron
constraint on changes in extreme precipitation under CO2 warming, Clim. Dynam., 28, 351–363, https://doi.org/10.1007/s00382-006-0180-2, 2007.
Pauluis, O. M. and Zhang, F.: Reconstruction of thermodynamic cycles in a
high-resolution simulation of a hurricane, J. Atmos. Sci., 74, 3367–3381,
https://doi.org/10.1175/JAS-D-16-0353.1, 2017.
Persing, J., Montgomery, M. T., McWilliams, J. C., and Smith, R. K.: Asymmetric and axisymmetric dynamics of tropical cyclones, Atmos. Chem. Phys., 13, 12299–12341, https://doi.org/10.5194/acp-13-12299-2013, 2013.
Philipona, R., Mears, C., Fujiwara, M., Jeannet, P., Thorne, P., Bodeker,
G., Haimberger, L., Hervo, M., Popp, C., Romanens, G., and Steinbrecht, W.:
Radiosondes show that after decades of cooling, the lower stratosphere is
now warming, J. Geophys. Res.-Atmos., 123, 12509–12522, https://doi.org/10.1029/2018JD028901, 2018.
Po-Chedley, S. and Fu, Q.: Discrepancies in tropical upper tropospheric
warming between atmospheric circulation models and satellites, Environ. Res.
Lett., 7, 044018, https://doi.org/10.1088/1748-9326/7/4/044018, 2012.
Prein, A. F. and Heymsfield, A. J.: Increased melting level height impacts
surface precipitation phase and intensity, Nat. Clim. Change, 10, 771–776,
https://doi.org/10.1038/s41558-020-0825-x, 2020.
Prein, A. F., Liu, C., Ikeda, K., Trier, S. B., Rasmussen, R. M., Holland,
G. J., and Clark, M. P.: Increased rainfall volume from future convective
storms in the US, Nat. Clim. Change, 7, 880–884, https://doi.org/10.1038/s41558-017-0007-7, 2017.
Rahmstorf, S., Foster, G., and Cahill, N.: Global temperature analysis:
Recent trends and some pitfalls, Environ Res. Lett., 12, 054001,
https://doi.org/10.1088/1748-9326/aa6825, 2017.
Ramaswamy, V., Schwarzkopf, M. D., Randel, W. J., Santer, B. D., Soden, B.
J., and Stenchikov, G. L.: Anthropogenic and natural influences in the
evolution of lower stratospheric cooling, Science, 311, 1138–1141,
https://doi.org/10.1126/science.1122587, 2006.
Ramsay, H. A.: The effects of imposed stratospheric cooling on the maximum
intensity of tropical cyclones in axisymmetric radiative–convective
equilibrium, J. Climate, 26, 9977–9985, https://doi.org/10.1175/JCLI-D-13-00195.1, 2013.
Riemer, M., Montgomery, M. T., and Nicholls, M. E.: A new paradigm for intensity modification of tropical cyclones: thermodynamic impact of vertical wind shear on the inflow layer, Atmos. Chem. Phys., 10, 3163–3188, https://doi.org/10.5194/acp-10-3163-2010, 2010.
Rogers, R. F., Reasor, P. D., and Lorsolo, S.: Airborne Doppler observations
of the inner-core structural differences between intensifying and
steady-state tropical cyclones, Mon. Weather Rev., 141, 2970–2991,
https://doi.org/10.1175/MWR-D-12-00357.1, 2013.
Rotunno, R. and Emanuel, K. A.: An air–sea interaction theory for tropical
cyclones. Part II: Evolutionary study using a nonhydrostatic axisymmetric
numerical model, J. Atmos. Sci., 44, 542–561,
https://doi.org/10.1175/1520-0469(1987)044<0542:AAITFT>2.0.CO;2, 1987.
Rousseau-Rizzi, R. and Emanuel, K.: An evaluation of hurricane
superintensity in axisymmetric numerical models, J. Atmos. Sci., 76,
1697–1708, https://doi.org/10.1175/JAS-D-18-0238.1, 2019.
Rousseau-Rizzi, R. and Emanuel, K.: A weak temperature gradient framework to
quantify the causes of potential intensity variability in the tropics, J.
Climate, 34, 8669–8682, https://doi.org/10.1175/JCLI-D-21-0139.1, 2021.
Rousseau-Rizzi, R., Rotunno, R., and Bryan, G.: A Thermodynamic Perspective
on Steady-State Tropical Cyclones, J. Atmos. Sci., 78, 583–593,
https://doi.org/10.1175/JAS-D-20-0140.1, 2021.
Rousseau-Rizzi, R., Merlis, T. M., and Jeevanjee, N.: The connection between
Carnot and CAPE formulations of TC potential intensity, J. Climate, 35,
941–954, https://doi.org/10.1175/JCLI-D-21-0360.1, 2022.
Santer, B. D., Wigley, T. M., Mears, C., Wentz, F. J., Klein, S. A., Seidel,
D. J., Taylor, K. E., Thorne, P. W., Wehner, M. F., Gleckler, P. J., and
Boyle, J. S.: Amplification of surface temperature trends and variability in
the tropical atmosphere, Science, 309, 1551–1556, https://doi.org/10.1126/science.1114867, 2005.
Santer, B. D., Thorne, P. W., Haimberger, L., Taylor, K. E., Wigley, T. M. L., Lanzante, J. R., Solomon, S., Free, M., Gleckler, P. J., Jones, P. D., Karl, T. R., Klein, S. A., Mears, C., Nychka, D., Schmidt, G. A., Sherwood, S. C., and Wentz, F. J.: Consistency of modelled and observed temperature trends in the tropical troposphere, Int. J. Climatol., 28, 1703–1722, https://doi.org/10.1002/joc.1756, 2008.
Schreck III, C. J., Knapp, K. R., and Kossin, J. P.: The impact of best track
discrepancies on global tropical cyclone climatologies using IBTrACS, Mon.
Weather Rev. 142, 3881–3899, https://doi.org/10.1175/MWR-D-14-00021.1, 2014.
Shen, W., Tuleya, R. E., and Ginis, I.: A sensitivity study of the
thermodynamic environment on GFDL model hurricane intensity: Implications
for global warming, J. Climate, 13, 109–121,
https://doi.org/10.1175/1520-0442(2000)013<0109:ASSOTT>2.0.CO;2, 2000.
Shepherd, T. G.: Storyline approach to the construction of regional climate
change information, P. Roy. Soc. A-Math. Phy., 475, 20190013, https://doi.org/10.1098/rspa.2019.0013, 2019.
Sherwood, S. C., Lanzante, J. R., and Meyer, C. L.: Radiosonde daytime biases
and late-20th century warming, Science, 309, 1556–1559, https://doi.org/10.1126/science.1115640, 2005.
Simmons, A. J., Poli, P., Dee, D. P., Berrisford, P., Hersbach, H.,
Kobayashi, S., and Peubey, C.: Estimating low-frequency variability and
trends in atmospheric temperature using ERA-Interim, Q. J. Roy. Meteor. Soc., 140, 329–353, https://doi.org/10.1002/qj.2317, 2014.
Simmons, A. J., Soci, C., Nicolas, J., Bell, B., Berrisford, P., Dragani,
R., Flemming, J., Haimberger, L., Healey, S. B., Hersbach, H., Horányi,
A., Inness, A., Muñoz-Sabater, J., Radu, R., and Schepers, D.: Global
stratospheric temperature bias and other stratospheric aspects of ERA5 and
ERA5.1, Technical Memorandum 859, ECMWF, Reading, UK, https://doi.org/10.21957/rcxqfmg0, 2020.
Smith, R. K., Montgomery, M. T., and Nguyen, S. V.: Axisymmetric dynamics of
tropical cyclone intensification in a three dimensional model, Q. J. Roy. Meteor. Soc., 134, 337–351, https://doi.org/10.1175/JAS-D-17-0179.1, 2008.
Sobel, A. H., Camargo, S. J., Hall, T. M., Lee, C. Y., Tippett, M. K., and Wing, A. A.: Human influence on tropical cyclone intensity, Science, 353, 242–246, https://doi.org/10.1126/science.aaf6574, 2016.
Strazzo, S. E., Elsner, J. B. and LaRow, T. E.: Quantifying the sensitivity
of maximum, limiting, and potential tropical cyclone intensity to SST:
Observations versus the FSU/COAPS global climate model, J. Adv. Model. Earth
Sy., 7, 586–599, https://doi.org/10.1002/2015MS000432, 2015.
Tao, D., Rotunno, R., and Bell, M.: Lilly's Model for Steady-State Tropical
Cyclone Intensity and Structure, J. Atmos. Sci., 77, 3701–3720,
https://doi.org/10.1175/JAS-D-20-0057.1, 2020.
Thompson, D. W. J., Seidel, D. J., Randel, W. J., Zou, C. Z., Butler, A. H.,
Mears, C., Osso, A., Long, C., and Lin, R.: The mystery of recent
stratospheric temperature trends, Nature, 491, 692–697,
https://doi.org/10.1038/nature11579, 2012.
Thorne, P. W., Lanzante, J. R., Peterson, T. C., Seidel, D. J., and Shine, K.
P.: Tropospheric temperature trends: History of an ongoing controversy,
WIREs Clim. Change, 2, 66–88, https://doi.org/10.1002/wcc.80, 2011.
Ting, M., Kossin, J. P., Camargo, S. J., and Li, C.: Past and future
hurricane intensity change along the US east coast, Scientific Reports, 9,
7765, https://doi.org/10.1038/s41598-019-44252-w, 2019.
Tuleya, R. E., Bender, M. A., Knutson, T. R., Sirutis, J. J., Thomas, B.,
and Ginis, I.: Impact of upper tropospheric temperature anomalies and
vertical wind shear on tropical cyclone evolution using an idealized version
of the operational GFDL hurricane model, J. Atmos. Sci., 73, 3803–3820,
https://doi.org/10.1175/JAS-D-16-0045.1, 2016.
Uppala, S. M., Kållberg, P. W., Simmons, A. J., Andrae, U., Bechtold, V.
D. C., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A.,
and Li, X.: The ERA-40 re-analysis, Q. J. Roy. Meteor. Soc., 131, 2961–3012, https://doi.org/10.1256/qj.04.176, 2005.
Vecchi, G. A., Fueglistaler, S., Held, I. M., Knutson, T. R., and Zhao, M.:
Impacts of atmospheric temperature changes on tropical cyclone activity, J.
Climate, 26, 3877–3891, https://doi.org/10.1175/JCLI-D-12-00503.1, 2013.
Wadler, J. B., Zhang, J. A., Jaimes, B. and Shay, L. K.: The Rapid
Intensification of Hurricane Michael (2018): Storm Structure and the
Relationship to Environmental and Air-Sea Interactions. Mon. Weather Rev., 149, 245–267, https://doi.org/10.1175/MWR-D-20-0145.1, 2021.
Wang, Y.: Vortex Rossby waves in a numerically simulated tropical cyclone.
Part I: Overall structure, potential vorticity, and kinetic energy budgets,
J. Atmos. Sci., 59, 1213–1238,
https://doi.org/10.1175/1520-0469(2002)059<1213:VRWIAN>2.0.CO;2, 2002.
Wasserstein, R. L., Schirm, A. L., and Lazar, N. A.: Moving to a world
beyond “p<0.05”, Am. Stat., 73, 1537–2731, https://doi.org/10.1080/00031305.2019.1583913, 2019.
Wilcoxon, F.: Individual comparisons by ranking methods, Biometrics Bull., 1,
80–83, https://doi.org/10.2307/3001968, 1945.
Willett, K. M., Gillett, N. P, Jones, P. D., and Thorne, P. W.: Attribution
of observed surface humidity changes to human influence, Nature, 449,
710–712, https://doi.org/10.1038/nature06207, 2007.
Xu, K. M., Wong, T., Wielicki, B. A., Parker, L., Lin, B., Eitzen, Z. A. and
Branson, M.: Statistical analyses of satellite cloud object data from CERES.
Part II: Tropical convective cloud objects during 1998 El Niño and
evidence for supporting the fixed anvil temperature hypothesis, J. Climate,
20, 819–842, https://doi.org/10.1175/JCLI4069.1, 2007.
Zawislak, J., Jiang, H., Alvey III, G. R., Zipser, E. J., Rogers, R. F.,
Zhang, J. A., and Stevenson, S. N.: Observations of the structure and
evolution of Hurricane Edouard (2014) during intensity change. Part I:
Relationship between the thermodynamic structure and precipitation, Mon. Weather Rev., 144, 3333–3354, https://doi.org/10.1175/MWR-D-16-0018.1, 2016.
Short summary
We know that warm oceans generally favour tropical cyclones (TCs). Less is known about the role of air temperature above the oceans extending into the lower stratosphere. Our global analysis of historical records and computer simulations suggests that TCs strengthen in response to historical temperature change while also being influenced by other environmental factors. Ocean warming drives much of the strengthening, with relatively small contributions from temperature changes aloft.
We know that warm oceans generally favour tropical cyclones (TCs). Less is known about the role...
