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Slower decay of landfalling hurricanes in a warming world

An Author Correction to this article was published on 19 April 2021

This article has been updated


When a hurricane strikes land, the destruction of property and the environment and the loss of life are largely confined to a narrow coastal area. This is because hurricanes are fuelled by moisture from the ocean1,2,3, and so hurricane intensity decays rapidly after striking land4,5. In contrast to the effect of a warming climate on hurricane intensification, many aspects of which are fairly well understood6,7,8,9,10, little is known of its effect on hurricane decay. Here we analyse intensity data for North Atlantic landfalling hurricanes11 over the past 50 years and show that hurricane decay has slowed, and that the slowdown in the decay over time is in direct proportion to a contemporaneous rise in the sea surface temperature12. Thus, whereas in the late 1960s a typical hurricane lost about 75 per cent of its intensity in the first day past landfall, now the corresponding decay is only about 50 per cent. We also show, using computational simulations, that warmer sea surface temperatures induce a slower decay by increasing the stock of moisture that a hurricane carries as it hits land. This stored moisture constitutes a source of heat that is not considered in theoretical models of decay13,14,15. Additionally, we show that climate-modulated changes in hurricane tracks16,17 contribute to the increasingly slow decay. Our findings suggest that as the world continues to warm, the destructive power of hurricanes will extend progressively farther inland.

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Fig. 1: Effect of SST on the decay of North Atlantic landfalling hurricanes.
Fig. 2: Effect of SST on the decay of simulated landfalling hurricanes.
Fig. 3: Effect of hurricane motion on the decay of North Atlantic landfalling hurricanes.

Data availability

Hurricane intensity: the Atlantic HURDAT2 database is available at SST: the HadISST database is available at The data for the intensity and other parameters for the 71 landfall events of our study are included in the Supplementary Information. The data for the τ time series and the SST time series plotted in Fig. 1 are provided with the paper. Source data are provided with this paper.

Code availability

The Cloud Model 1 (CM1) source code is available at

Change history


  1. 1.

    Ooyama, K. Numerical simulation of the life cycle of tropical cyclones. J. Atmos. Sci. 26, 3–40 (1969).

    ADS  Google Scholar 

  2. 2.

    Emanuel, K. A. An air-sea interaction theory for tropical cyclones. Part I: Steady-state maintenance. J. Atmos. Sci. 43, 585–605 (1986).

    ADS  Google Scholar 

  3. 3.

    Emanuel, K. Tropical cyclones. Annu. Rev. Earth Planet. Sci. 31, 75–104 (2003).

    ADS  CAS  Google Scholar 

  4. 4.

    Kaplan, J. & DeMaria, M. A simple empirical model for predicting the decay of tropical cyclone winds after landfall. J. Appl. Meteorol. Climatol. 34, 2499–2512 (1995).

    ADS  Google Scholar 

  5. 5.

    Kaplan, J. & DeMaria, M. On the decay of tropical cyclone winds after landfall in the New England area. J. Appl. Meteorol. Climatol. 40, 280–286 (2001).

    ADS  Google Scholar 

  6. 6.

    Emanuel, K. A. The dependence of hurricane intensity on climate. Nature 326, 483–485 (1987).

    ADS  Google Scholar 

  7. 7.

    Emanuel, K. Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436, 686–688 (2005).

    ADS  CAS  Google Scholar 

  8. 8.

    Elsner, J. B., Kossin, J. P. & Jagger, T. H. The increasing intensity of the strongest tropical cyclones. Nature 455, 92–95 (2008).

    ADS  CAS  Google Scholar 

  9. 9.

    Knutson, T. et al. Tropical cyclones and climate change assessment: Part I. Detection and attribution. Bull. Am. Meteorol. Soc. 100, 1987–2007 (2019).

    ADS  Google Scholar 

  10. 10.

    Bhatia, K. T. et al. Recent increases in tropical cyclone intensification rates. Nat. Commun. 10, 3942 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Landsea, C. W. & Franklin, J. L. Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Weath. Rev. 141, 3576–3592 (2013).

    ADS  Google Scholar 

  12. 12.

    Rayner, N. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. Atmos. 108, 4407 (2003).

    ADS  Google Scholar 

  13. 13.

    Eliassen, A. On the Ekman layer in a circular vortex. J. Meteorol. Soc. Jpn. 49A, 784–789 (1971).

    Google Scholar 

  14. 14.

    Eliassen, A. & Lystad, M. The Ekman layer of a circular vortex—a numerical and theoretical study. Geophys. Norv. 31, 1–16 (1977).

    ADS  Google Scholar 

  15. 15.

    Montgomery, M. T., Snell, H. D. & Yang, Z. Axisymmetric spindown dynamics of hurricane-like vortices. J. Atmos. Sci. 58, 421–435 (2001).

    ADS  Google Scholar 

  16. 16.

    Murakami, H. & Wang, B. Future change of North Atlantic tropical cyclone tracks: projection by a 20-km-mesh global atmospheric model. J. Clim. 23, 2699–2721 (2010).

    ADS  Google Scholar 

  17. 17.

    Colbert, A. J., Soden, B. J., Vecchi, G. A. & Kirtman, B. P. The impact of anthropogenic climate change on North Atlantic tropical cyclone tracks. J. Clim. 26, 4088–4095 (2013).

    ADS  Google Scholar 

  18. 18.

    Wallace, J. M. & Hobbs, P. V. Atmospheric Science: An Introductory Survey Vol. 92 (Elsevier, 2006).

  19. 19.

    Tuleya, R. E. & Kurihara, Y. A numerical simulation of the landfall of tropical cyclones. J. Atmos. Sci. 35, 242–257 (1978).

    ADS  Google Scholar 

  20. 20.

    Tuleya, R. E. Tropical storm development and decay: sensitivity to surface boundary conditions. Mon. Weath. Rev. 122, 291–304 (1994).

    ADS  Google Scholar 

  21. 21.

    Simpson, R. H. & Riehl, H. The Hurricane And Its Impact (Louisiana State Univ. Press, 1981).

  22. 22.

    Bloemer, M. S. Climatology and Analysis of the Decay of Tropical Cyclones Making Landfall in the US from the Atlantic Basin. Master’s thesis, Florida State Univ. (2009).

  23. 23.

    Chen, J. & Chavas, D. R. The transient responses of an axisymmetric tropical cyclone to instantaneous surface roughening and drying. J. Atmos. Sci. 77, 2807–2834 (2020).

    ADS  Google Scholar 

  24. 24.

    Smith, S. W. The Scientist And Engineer’s Guide To Digital Signal Processing Ch. 15 (California Technical Pub., 1997).

  25. 25.

    Bryan, G. H. & Fritsch, J. M. A benchmark simulation for moist nonhydrostatic numerical models. Mon. Weath. Rev. 130, 2917–2928 (2002).

    ADS  Google Scholar 

  26. 26.

    Bryan, G. H. & Rotunno, R. The maximum intensity of tropical cyclones in axisymmetric numerical model simulations. Mon. Weath. Rev. 137, 1770–1789 (2009).

    ADS  Google Scholar 

  27. 27.

    Bryan, G. H. Effects of surface exchange coefficients and turbulence length scales on the intensity and structure of numerically simulated hurricanes. Mon. Weath. Rev. 140, 1125–1143 (2012).

    ADS  Google Scholar 

  28. 28.

    Emanuel, K. Assessing the present and future probability of hurricane Harvey’s rainfall. Proc. Natl Acad. Sci. USA 114, 12681–12684 (2017).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Keellings, D. & Hernández Ayala, J. J. Extreme rainfall associated with hurricane Maria over Puerto Rico and its connections to climate variability and change. Geophys. Res. Lett. 46, 2964–2973 (2019).

    ADS  Google Scholar 

  30. 30.

    Kossin, J. P. A global slowdown of tropical-cyclone translation speed. Nature 558, 104–107 (2018).

    ADS  CAS  Google Scholar 

  31. 31.

    Zhang, G., Murakami, H., Knutson, T. R., Mizuta, R. & Yoshida, K. Tropical cyclone motion in a changing climate. Sci. Adv. 6, eaaz7610 (2020).

    ADS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Elsner, J. B. Tracking hurricanes. Bull. Am. Meteorol. Soc. 84, 353–356 (2003).

    ADS  Google Scholar 

  33. 33.

    Kossin, J. P., Camargo, S. J. & Sitkowski, M. Climate modulation of North Atlantic hurricane tracks. J. Clim. 23, 3057–3076 (2010).

    ADS  Google Scholar 

  34. 34.

    Rogers, R. E. & Davis, R. E. The effect of coastline curvature on the weakening of Atlantic tropical cyclones. Int. J. Climatol. 13, 287–299 (1993).

    Google Scholar 

  35. 35.

    Kossin, J. P., Emanuel, K. A. & Vecchi, G. A. The poleward migration of the location of tropical cyclone maximum intensity. Nature 509, 349–352 (2014).

    ADS  CAS  Google Scholar 

  36. 36.

    Ho, F. P., Su, J. C., Hanevich, K. L., Smith, R. J. & Richards, F. P. Hurricane climatology for the Atlantic and Gulf coasts of the United States. NOAA Technical Report NWS 38, (1987).

  37. 37.

    Weinkle, J., Maue, R. & Pielke, R., Jr. Historical global tropical cyclone landfalls. J. Clim. 25, 4729–4735 (2012).

    ADS  Google Scholar 

  38. 38.

    Klotzbach, P. J., Bowen, S. G., Pielke, R., Jr & Bell, M. Continental US hurricane landfall frequency and associated damage: observations and future risks. Bull. Am. Meteorol. Soc. 99, 1359–1376 (2018).

    ADS  Google Scholar 

  39. 39.

    Neumann, C. An update to the National Hurricane Center “Track Book”. In Minutes of the 48th Interdepartmental Conference A-47–A-53 (Office of Fed. Coord. for Meteor. Services and Supporting Research, NOAA, 1994).

  40. 40.

    Chavas, D. land_or_ocean.m. MATLAB Central File Exchange (2020).

  41. 41.

    Schreck, C. J. III, Knapp, K. R. & Kossin, J. P. The impact of best track discrepancies on global tropical cyclone climatologies using IBTrACS. Mon. Weath. Rev. 142, 3881–3899 (2014).

    ADS  Google Scholar 

  42. 42.

    Nolan, D. S., Zhang, J. A. & Uhlhorn, E. W. On the limits of estimating the maximum wind speeds in hurricanes. Mon. Weath. Rev. 142, 2814–2837 (2014).

    ADS  Google Scholar 

  43. 43.

    Jin, F.-F., Boucharel, J. & Lin, I.-I. Eastern Pacific tropical cyclones intensified by El Niño delivery of subsurface ocean heat. Nature 516, 82–85 (2014).

    ADS  CAS  Google Scholar 

  44. 44.

    Dunion, J. P. Rewriting the climatology of the tropical North Atlantic and Caribbean Sea atmosphere. J. Clim. 24, 893–908 (2011).

    ADS  Google Scholar 

  45. 45.

    Miyamoto, Y. & Takemi, T. An effective radius of the sea surface enthalpy flux for the maintenance of a tropical cyclone. Atmos. Sci. Lett. 11, 278–282 (2010).

    ADS  Google Scholar 

  46. 46.

    Yuan, S., Zhong, Z., Yao, H., Yuan, W. & Xiaodan, W. The dynamic and thermodynamic effects of relative and absolute sea surface temperature on tropical cyclone intensity. J. Meteor. Res. 27, 40–49 (2013).

    Google Scholar 

  47. 47.

    Riehl, H. Tropical Meteorology (McGraw-Hill, 1954).

  48. 48.

    Holland, G. J., Belanger, J. I. & Fritz, A. A revised model for radial profiles of hurricane winds. Mon. Weath. Rev. 138, 4393–4401 (2010).

    ADS  Google Scholar 

  49. 49.

    Khairoutdinov, M. & Emanuel, K. Rotating radiative-convective equilibrium simulated by a cloud-resolving model. J. Adv. Model. Earth Syst. 5, 816–825 (2013).

    ADS  Google Scholar 

  50. 50.

    Chavas, D. R. & Emanuel, K. Equilibrium tropical cyclone size in an idealized state of axisymmetric radiative–convective equilibrium. J. Atmos. Sci. 71, 1663–1680 (2014).

    ADS  Google Scholar 

  51. 51.

    Chavas, D. R., Lin, N., Dong, W. & Lin, Y. Observed tropical cyclone size revisited. J. Clim. 29, 2923–2939 (2016).

    ADS  Google Scholar 

  52. 52.

    Lanzante, J. R. Uncertainties in tropical-cyclone translation speed. Nature 570, E6–E15 (2019).

    ADS  CAS  Google Scholar 

  53. 53.

    Yule, U. & Kendall, M. An Introduction To The Theory Of Statistics Ch. 12 (Griffin and Company, 1950).

  54. 54.

    Evans, C. et al. The extratropical transition of tropical cyclones. Part I: Cyclone evolution and direct impacts. Mon. Weath. Rev. 145, 4317–4344 (2017).

    ADS  Google Scholar 

  55. 55.

    Lee, S. H., Williams, P. D. & Frame, T. H. Increased shear in the North Atlantic upper-level jet stream over the past four decades. Nature 572, 639–642 (2019).

    ADS  CAS  Google Scholar 

  56. 56.

    Fairall, C., Bradley, E. F., Hare, J., Grachev, A. & Edson, J. Bulk parameterization of air-sea fluxes: updates and verification for the COARE algorithm. J. Clim. 16, 571–591 (2003).

    ADS  Google Scholar 

  57. 57.

    Donelan, M. et al. On the limiting aerodynamic roughness of the ocean in very strong winds. Geophys. Res. Lett. 31, L18306 (2004).

    ADS  Google Scholar 

  58. 58.

    Drennan, W. M., Zhang, J. A., French, J. R., McCormick, C. & Black, P. G. Turbulent fluxes in the hurricane boundary layer. Part II: Latent heat flux. J. Atmos. Sci. 64, 1103–1115 (2007).

    ADS  Google Scholar 

  59. 59.

    Rotunno, R. & 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 (1987).

    ADS  Google Scholar 

  60. 60.

    Goldenberg, S. B. & Shapiro, L. J. Physical mechanisms for the association of El Niño and West African rainfall with Atlantic major hurricane activity. J. Clim. 9, 1169–1187 (1996).

    ADS  Google Scholar 

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This work was supported by the Okinawa Institute of Science and Technology (OIST) Graduate University. We thank C. Landsea and F. Marks for help with Atlantic HURDAT2, P. Shah for help with statistical analysis, T. Sabuwala for help with computational analysis, the Scientific Computing and Data Analysis section at OIST for computational support, and G. Bryan for developing Cloud Model 1 and making it freely available.

Author information




L.L. and P.C. conceived the study; L.L. analysed the field data; L.L. conducted the computational simulations and analysed the data; L.L. and P.C. discussed the results; P.C. wrote the manuscript with input from L.L.; P.C. supervised the research.

Corresponding author

Correspondence to Pinaki Chakraborty.

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The authors declare no competing interests.

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Peer review information Nature thanks Daniel Chavas and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Analysis of North Atlantic landfalling hurricanes.

a, An illustrative example of computing τ using the slope of the best-fit line (solid line) for ln(V(t)/V(t1)) versus tt1 for hurricane Camille (1969). b, Histogram of the adjusted r2 to quantify the goodness of fit between V(t) and the exponential model. The data are from the 71 landfall events (Number) of our study. For each event, we compute the adjusted r2 by comparing the four data points of the measured ln(V(t)/V(t1)) with the corresponding points from the best-linear-fit line (see panel a). c, SST from the pre-landfall region (red dotted box; this region is the same as in the main text), the Main Development Region60 (MDR, yellow dotted box), and the Main Genesis Region7 (MGR, pink dotted box). (The map is from the MATLAB function worldmap.) d, The three plots show the same time series of τ (grey line) but the SST time series (blue line) corresponds to the three regions of panel c. (Each year, we average the SST in these regions over the hurricane season, June–November.) The SST time series are computed using the same procedure as employed for the τ time series in the main text. The correlation, r, between the τ time series and SST time series is most pronounced for the SST from the pre-landfall region. The τ–SST relationship is significant (at >93% CI) in all cases (Extended Data Table 2). e, An illustrative example of calculating θ for hurricane Dolly (1996). The red plus symbols show the track of the hurricane. Inset, a zoomed-in view of the landfall. We compute the incident direction using the first two overland track locations (solid red line). To compute the direction of the inland normal to the coastline, we first locate the coastline point nearest to the first overland track location. (We obtain the coastline data using the MATLAB database coastlines.) Then, to the coastline points (black dots) within 200 km (2Ro; see Methods) of this nearest point, we fit a straight line (solid blue line). The angle between the normal to this line (dashed blue line) and the incident direction (solid red line) is θ. f, Histogram of cosθ for the 71 events (Number) in our study. For most events, cosθ ≥ 0.9.

Extended Data Fig. 2 Analysis of North Atlantic landfalling hurricanes.

a, τ versus year for the 71 landfall events (marked in blue) of our study and the three events (marked in red) that are outliers. For the outlier events, the values of τ exceed the threshold (dotted line) of two standard deviations (2σ) above the mean value of τ from the 74 events. b, Effect of outlier landfall events (panel a) on the τ time series (grey line). The top panel shows τ versus year. We note that including the outliers increases the error bars (compare with Fig. 1c). The bottom panel shows τ versus year (grey line) and SST versus year (blue line). Including the outliers does not qualitatively affect the results. Quantitatively, the increase in τ over the past half century is more pronounced (compare with Fig. 1c). Also, while the τ time series echoes the SST time series, the correlation magnitude is lessened (compare with Fig. 1e). c, Landfall intensity, V(0), versus year (grey line) for all events (56 in total) from 1983–2018. (In Atlantic HURDAT2, the values of V(0) for the events before 1983 are not systematically recorded.) We note that there is no significant variation (at 95% CI) in V(0), consistent with previous studies, such as ref. 38. d, Effect of extratropical transitions on the τ time series. We compute the τ time series for 66 events (we exclude five events that underwent an extratropical transition within the first day past landfall). The top panel shows τ versus year (grey line). The bottom panel shows τ versus year (grey line) and SST versus year (blue line). Compared with the τ time series analysed in the main text (which includes these extratropical transitions), the results are similar, though the increase in τ is more pronounced. In panels b, c and d, we also show error bars (which correspond to ±1 s.e.m.), the linear regression line (solid black line), and the 95% confidence band about the regression line (dotted black lines). The relationship between τ and SST (panels b, d and e) is significant (at 95% CI); Extended Data Table 2. e, SST time series with SST computed using the months of landfall (as opposed to the whole hurricane season). We plot τ versus year (grey line) and SST versus year (blue line). The top panel shows, as in the main text, that the τ time series echoes the SST time series, but the correlation magnitude is lessened (see Fig. 1e). As we show next, this is largely due to two events. The middle panel is the same as the top panel but with two events from the month of June removed from both the τ time series and the SST time series. We note that the correlation magnitude is enhanced. The bottom panel is the same as Fig. 1e but, like the middle panel, with the June landfall events removed. The correlation magnitude is similar to Fig. 1e, signifying that as compared with the SST computed over months of landfalls, the SST is computed over the hurricane season is less sensitive to individual events. All the time series (panels be) are computed using the same procedure as employed for the τ time series in the main text.

Extended Data Fig. 3 Effect of various factors on τ.

a, Latitude versus year; b, τ versus latitude; c, Longitude versus year; d, τ versus longitude; e, vt versus year; f, τ versus vt. In each panel, we also show error bars (which correspond to ±1 s.e.m.), the linear regression line (solid black line), and the 95% confidence band about the regression line (dotted black lines). We note that there is no significant change (at 95% CI) in the latitude or vt over the past half-century and that there is no significant relationship (at 95% CI) between τ and latitude and between τ and vt (Extended Data Table 2). On the other hand, there is a significant eastward shift (at 95% CI) in the longitude over the past half-century and the relationship between τ and longitude is statistically significant (at 95% CI); Extended Data Table 2. All the time series are computed using the same procedure as employed for the τ time series in the main text.

Extended Data Fig. 4 Computational simulations.

a, b, Choosing the instance of landfall. As noted in the main text, we evolve each hurricane over a warm ocean until its intensity reaches a threshold at about 60 m s−1, after which we subject it to a complete landfall. In each simulation over the ocean, as V reaches close to the threshold, we output ‘restart’ files every 3 h. (A restart file contains the entire flow field at one instant of time and the file can be used to restart the simulation.) a, V versus time; SST = 301 K (same as Fig. 2a). The table shows the times of the restart files (the times are marked by black vertical lines in the panel) and the corresponding V. For the instance of landfall, we pick the flow field whose V is closest to the threshold (red box in the table). This intensity, 60.7 m s−1, becomes V(0) for the landfall. b, Effect of V(0) on the decay past landfall. From the restart files, we evolve two hurricanes past landfall. One, plotted in red, corresponds to V(0) = 60.7 m s−1; this plot of V versus t is also shown in Fig. 2a. The other, plotted in blue, corresponds to V(0) = 62.3 m s−1, the first instance of time (blue box in the table of panel a) when V crosses the threshold. Although their V(0) values are different, their decay past landfall is indistinguishable, showing that the results are robust to the choice of V(0); also see panels c and d. (We note that for t < 0, the V(t) are shifted, reflecting the differences in the times of landfall.) c, d, Sensitivity tests for the effect of the landfall intensity, V(0). For the simulations in the main text, V(0) ≈ 60 m s−1. Here we conduct simulations for V(0) ≈ 65 m s−1. c, V versus t for four SSTs (compare with Fig. 2a); b, τ versus SST for the simulations of panel c (compare with Fig. 2b). We note that the results for V(0) ≈ 65 m s−1 and for V(0) ≈ 60 m s−1 are comparable. e, f, Sensitivity tests for the effect of the surface drag past landfall. e, V versus t for different CD past landfall. (Prior to the landfall, the hurricane develops over an ocean of SST = 300 K, till its intensity reaches 60  m s−1.) f, τ versus CD for the simulations of panel e. (We do not plot the data for the default CD as the value of the default CD changes with V.) Note that the value of τ decreases with an increase in the value of CD.

Extended Data Fig. 5 Sensitivity tests with axisymmetric simulations.

The size of the physical domain is 1,500 km (radial) × 25 km (vertical); the spatial resolution is 192 (radial) × 59 (vertical); all other parameters are the same as in the three-dimensional simulations (Extended Data Table 1). The intensity at landfall, V(0) ≈ 60 m s−1. a, b, Sensitivity tests for the effect of SST. a, Representative plots of V versus t for different SSTs (compare with Fig. 2a); b, τ versus SST (compare with Fig. 2b). Note that for both three-dimensional and axisymmetric simulations, the value of τ increases with an increase in the value of SST. In some cases, V(t) past landfall re-intensifies for a brief period; see, for example, V(t) for SST = 302 K and 303 K in panel a. This behaviour, which we see in a few of the axisymmetric simulations (but not the three-dimensional simulations), appears to be caused by a contracting ring of convection around the eye of the hurricane. We also show dry simulations; their decay, similar to the case in three-dimensional simulations, is unaffected by the SST. c, d, Sensitivity tests for the effect of the surface drag past landfall. c, V versus t for different CD past landfall. (Prior to the landfall, the hurricane develops over an ocean of SST = 299.5 K, till its intensity reaches 60 m s−1.) d, τ versus CD for the simulations of panel c (compare with Extended Data Fig. 4c). (We do not plot the data for the default CD because the value of the default CD changes with V.) We note that for both three-dimensional and axisymmetric simulations the value of τ decreases with an increase in the value of CD; also see ref. 23.

Extended Data Fig. 6 Hurricane size and SST.

a, A typical example of contours of the surface wind. The data are from the wind field at the lowest elevation (z = 25 m) for a simulated hurricane at SST = 300 K. We identify the centre of the hurricane as the location of the minimum value of the surface wind. (Using the minimum of the pressure yields similar results.) In general, this location is close to, but not the same as, the geometric centre of the simulation. b, Surface wind velocity (normalized by its peak value) versus radius for different SST. (The profiles are azimuthally averaged; the hurricane centre is identified using the method described above.) The data correspond to the simulated hurricanes of Fig. 2a at the time of landfall. Inset, RMW versus SST. We compute the RMW using the radius corresponding to the peak of a profile. We note that RMW increases with SST.

Extended Data Fig. 7 A simple model of a landfalling hurricane.

a, Schematic of an axisymmetric hurricane of effective radius Ro moving from ocean to land with a constant translational speed vt and at an angle θ with respect to the normal to the coastline. b, Normalized enthalpy flux versus t. We have normalized the enthalpy flux using the enthalpy flux for a hurricane over the ocean with the same velocity profile as the landfalling hurricane at t = 0. The timescale for the enthalpy flux to drop to 10% of its value over the ocean is about 3.5 h, and for it to vanish is about 6 h.

Extended Data Table 1 Parameters for computational simulations
Extended Data Table 2 Statistical significance of the relationship between two variables
Extended Data Table 3 Smoothing and robustness of results

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Li, L., Chakraborty, P. Slower decay of landfalling hurricanes in a warming world. Nature 587, 230–234 (2020).

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