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Ocean currents show global intensification of weak tropical cyclones

Abstract

Theory1 and numerical modelling2 suggest that tropical cyclones (TCs) will strengthen with rising ocean temperatures. Even though models have reached broad agreement on projected TC intensification3,4,5, observed trends in TC intensity remain inconclusive and under active debate6,7,8,9,10 in all ocean basins except the North Atlantic, where aircraft reconnaissance data greatly reduce uncertainties11. The conventional satellite-based estimates are not accurate enough to ascertain the trend in TC intensity6,11, suffering from contamination by heavy rain, clouds, breaking waves and spray12. Here we show that weak TCs (that is, tropical storms to category-1 TCs based on the Saffir–Simpson scale) have intensified in all ocean basins during the period 1991–2020, based on huge amounts of highly accurate ocean current data derived from surface drifters. These drifters have submerged ‘holy sock’ drogues at 15 m depth to reduce biases induced by processes at the air–sea interface and thereby accurately measure near-surface currents, even under the most destructive TCs. The ocean current speeds show a robust upward trend of ~4.0 cm s−1 per decade globally, corresponding to a positive trend of 1.8 m s−1 per decade in the TC intensity. Our analysis further indicates that globally TCs have strengthened across the entirety of the intensity distribution. These results serve as a historical baseline that is crucial for assessing model physics, simulations and projections given the failure of state-of-the-art climate models in fully replicating these trends13.

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Fig. 1: Data distribution.
Fig. 2: Evolution of the near-surface ocean current speeds under weak TCs.
Fig. 3: Mean near-surface ocean current fields under weak TCs.
Fig. 4: Sea surface cooling induced by weak TCs.

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Data availability

The 6-hourly positions and upper-ocean current velocities of drifters are obtained from https://www.aoml.noaa.gov/phod/gdp/interpolated/data/all.php. TC occurrence also with 6 h temporal resolution is acquired from the best track data from the Joint Typhoon Warning Center (https://www.metoc.navy.mil/jtwc/jtwc.html?best-tracks) for the Western Pacific Ocean, the Indian Ocean and the Southern Hemisphere, and the National Hurricane Center and Central Pacific Hurricane Center (https://www.nhc.noaa.gov/data/#hudat) for the Atlantic and Northeast and Central Pacific Oceans. Daily SST is from the NOAA 1/4° Optimum Interpolation Sea Surface Temperature (OISST), and it is downloaded from https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/). The hourly current and wind data from the TAO/TRITON, RAMA and PIRATA buoy arrays are downloaded from https://www.pmel.noaa.gov/tao/drupal/disdel/. The JRA-55 Reanalysis dataset is downloaded from https://jra.kishou.go.jp/JRA-55/index_en.html. The IAP monthly ocean temperature analysis data are downloaded from ftp://www.ocean.iap.ac.cn/cheng/CZ16_v3_IAP_Temperature _gridded_1month_netcdfSource data are provided with this paper.

Code availability

Analysis and figure generation were performed using MATLAB. The code and scripts of the two main methods and four figures in the paper are available from Zenodo: https://doi.org/10.5281/zenodo.7013352.

References

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

    Article  ADS  Google Scholar 

  2. Knutson, T. R. & Tuleya, R. E. Impact of CO2-induced warming on simulated hurricane intensity and precipitation: Sensitivity to the choice of climate model and convective parameterization. J. Clim. 17, 3477–3495 (2004).

    Article  ADS  Google Scholar 

  3. Sobel, A. et al. Human influence on tropical cyclone intensity. Science 353, 242–246 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  4. Emanuel, K. Response of global tropical cyclone activity to increasing CO2: results from downscaling CMIP6 models. J. Clim. 34, 57–70 (2021).

    Article  ADS  Google Scholar 

  5. Knutson, T. et al. Tropical cyclones and climate change assessment: Part II: projected response to anthropogenic warming. Bull. Amer. Meteor. Soc. 101, E303–E322 (2020).

    Article  Google Scholar 

  6. Landsea, C. W. Hurricanes and global warming. Nature 438, E11–E13 (2005).

    Article  ADS  CAS  PubMed  Google Scholar 

  7. Pielke, R. A. Are there trends in hurricane destruction? Nature 438, E11 (2005).

    Article  ADS  CAS  PubMed  Google Scholar 

  8. Knutson, T. et al. Tropical cyclones and climate change assessment: Part I: detection and attribution. Bull. Amer. Meteor. Soc. 100, 1987–2007 (2019).

    Article  ADS  Google Scholar 

  9. Klotzbach, P. et al. Trends in global tropical cyclone activity: 1990–2021. Geophys. Res. Lett. 49, e2021GL095774 (2022).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  11. Hennon, C. et al. Cyclone center: can citizen scientists improve tropical cyclone intensity records? Bull. Amer. Meteor. Soc. 96, 591–607 (2015).

    Article  ADS  Google Scholar 

  12. Weissman, D. E., Bourassa, M. A. & Tongue, J. Effects of rain rate and wind magnitude on sea winds scatterometer wind speed errors. J. Atmos. Oceanic Technol. 19, 738–746 (2002).

    Article  ADS  Google Scholar 

  13. Jing, R., Lin, N., Emanuel, K., Vecchi, G. & Knutson, T. R. A comparison of tropical cyclone projections in a high-resolution global climate model and from downscaling by statistical and statistical-deterministic methods. J. Clim. 34, 9349–9364 (2021).

    ADS  Google Scholar 

  14. Emanuel, K. Contribution of tropical cyclone to meridional heat transport by the oceans. J. Geophys. Res. 106, 14771–14781 (2001).

    Article  ADS  Google Scholar 

  15. Lin, I. et al. The interaction of Supertyphoon Maemi (2003) with a warm ocean eddy. Mon. Weather Rev. 133, 2635–2649 (2005).

    Article  ADS  Google Scholar 

  16. Sriver, R. & Huber, M. Observational evidence for an ocean heat pump induced by tropical cyclones. Nature 447, 577–580 (2007).

    Article  ADS  CAS  PubMed  Google Scholar 

  17. Korty, R. L., Emanuel, K. A. & Scott, J. R. Tropical cyclone-induced upper-ocean mixing and climate: application to equable climates. J. Clim. 21, 638–654 (2008).

    Article  ADS  Google Scholar 

  18. Zhang, Y., Zhang, Z., Chen, D., Qiu, B. & Wang, W. Strengthening of the Kuroshio current by intensifying tropical cyclones. Science 368, 988–993 (2020).

    Article  CAS  PubMed  Google Scholar 

  19. DeMaria, M., Sampson, C. R., Knaff, J. A. & Musgrave, K. D. Is tropical cyclone intensity guidance improving? Bull. Amer. Meteor. Soc. 95, 387–398 (2014).

    Article  ADS  Google Scholar 

  20. Dvorak, V. F. Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Weather Rev. 103, 420 (1975).

    Article  ADS  Google Scholar 

  21. Dvorak, V. F. Tropical cyclone intensity analysis using satellite data. NOAA Tech. Rep. NESDIS 11, 47 (1984).

    Google Scholar 

  22. Landsea, C. W., Harper, B. A., Hoarau, K. & Knaff, J. A. Can we detect trends in extreme tropical cyclones? Science 313, 452–454 (2006).

    Article  CAS  PubMed  Google Scholar 

  23. Knaff, J. A., Brown, D. P., Courtney, J., Gallina, G. M. & Beven, J. L. An evaluation of Dvorak technique–based tropical cyclone intensity estimates. Weather Forecast. 25, 1362–1379 (2010).

    Article  ADS  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

  26. Webster, P. J., Holland, G. J., Curry, J. A. & Chang, H.-R. Changes in tropical cyclone number, duration, and intensity in a warming environment. Science 309, 1844–1846 (2005).

    Article  ADS  CAS  PubMed  Google Scholar 

  27. Holland, G. & Bruyère, C. L. Recent intense hurricane response to global climate change. Clim. Dyn. 42, 617–627 (2014).

    Article  Google Scholar 

  28. Kossin, J. P., Olander, T. L. & Knapp, K. R. Trend analysis with a new global record of tropical cyclone intensity. J. Clim. 26, 9960–9976 (2013).

    Article  ADS  Google Scholar 

  29. Kossin, J. P., Knapp, K. R., Olander, T. L. & Velden, C. S. Global increase in major tropical cyclone exceedance probability over the past four decades. Proc. Natl Acad. Sci. 117, 11975–11980 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  30. Price, J. F., Sanford, T. B. & Forristall, G. Z. Forced stage response to a moving hurricane. J. Phys. Oceanogr. 24, 233–260 (1994).

    Article  ADS  Google Scholar 

  31. Pallàs-Sanz, E., Candela, J., Sheinbaum, J. & Ochoa, J. Mooring observations of the near-inertial wave wake of Hurricane Ida (2009). Dyn. Atmospheres Oceans 76, 325–344 (2016).

    Article  ADS  Google Scholar 

  32. Sanabia, E. R. & Jayne, S. R. Ocean observations under two major hurricanes: evolution of the response across the storm wakes. AGU Advances 1, e2019AV000161 (2020).

    Article  ADS  Google Scholar 

  33. Li, R. et al. Slope-intensified storm-induced near-inertial oscillations in the South China Sea. J. Geophys. Res. Oceans 126, e2020JC016713 (2021).

    Article  ADS  Google Scholar 

  34. Hsu, J. ‐Y., Lien, R. ‐C., D'Asaro, E. A. & Sanford, T. B. Scaling of drag coefficients under five tropical cyclones. Geophys. Res. Lett. 46, 3349–3358 (2019).

    Article  ADS  Google Scholar 

  35. Fan, S. et al. Observed ocean surface winds and mixed layer currents under tropical cyclones: Asymmetric characteristics. J. Geophys. Res. Oceans 127, e2021JC017991 (2022).

    Article  ADS  Google Scholar 

  36. Chang, Y. C., Chen, G. Y., Tseng, R. S., Centurioni, L. R. & Chu, P. C. Observed near-surface flows under all tropical cyclone intensity levels using drifters in the northwestern Pacific. J. Geophys. Res. 118, 2367–2377 (2013).

    Article  ADS  Google Scholar 

  37. Niiler, P. P., Sybrandy, A. S., Bi, K., Poulain, P. M. & Bitterman, D. Measurements of the water following capability of holey-sock and TRISTAR drifters. Deep-Sea Res. 42, 1951–1964 (1995).

    Article  Google Scholar 

  38. Batts, M. E., Russell, L. R. & Simiu, E. Hurricane wind speeds in the United States. J. Struct. Div. 106, 2001–2016 (1980).

    Article  Google Scholar 

  39. Peng, Q. et al. Surface warming-induced global acceleration of upper ocean currents. Sci. Adv. 8, eabj8394 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Huang, P., Lin, I. I., Chou, C. & Huang, R. H. Change in ocean subsurface environment to suppress tropical cyclone intensification under global warming. Nat. Commun. 6, 7188 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  41. Mei, W. & Pasquero, C. Spatial and temporal characterization of sea surface temperature response to tropical cyclones. J. Clim. 26, 3745–3765 (2013).

    Article  ADS  Google Scholar 

  42. Mei, W., Xie, S. P., Primeau, F., McWilliams, J. C. & Pasquero, C. Northwestern Pacific typhoon intensity controlled by changes in ocean temperatures. Sci. Adv. 1, e1500014 (2015).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  43. Stewart, R. H. Introduction to Physical Oceanography (OAKTrust, 2008); https://oaktrust.library.tamu.edu/handle/1969.1/160216.

  44. Hwang, P. A., Reul, N., Meissner, T. & Yueh, S. H. Whitecap and wind stress observations by microwave radiometers: global coverage and extreme conditions. J. Phys. Oceanogr. 49, 2291–2307 (2019).

    Article  ADS  Google Scholar 

  45. Zijlema, M., van Vledder, G. P. & Holthuijsen, L. H. Bottom friction and wind drag for wave models. Coast. Eng. 65, 19–26 (2012).

    Article  Google Scholar 

  46. Peduzzi, P., Concato, J., Kemper, E., Holford, T. R. & Feinstein, A. R. A simulation study of the number of events per variable in logistic regression analysis. J. Clin. Epidemiol. 49, 1373–1379 (1996).

    Article  CAS  PubMed  Google Scholar 

  47. Kobayashi, S. et al. The JRA-55 Reanalysis: general specifications and basic characteristics. J. Meteor. Soc. Japan. 93, 5–48 (2015).

    Article  ADS  Google Scholar 

  48. Emanuel, K. A. The maximum intensity of hurricanes. J. Atmos. Sci. 45, 1143–1155 (1988).

    Article  ADS  Google Scholar 

  49. Holland, G. J. The maximum potential intensity of tropical cyclones. J. Atmos. Sci. 54, 2519–2541 (1997).

    Article  ADS  Google Scholar 

  50. Tang, B. & Emanuel, K. A ventilation index for tropical cyclones. Bull. Amer. Meteor. Soc. 93, 1901–1912 (2012).

    Article  ADS  Google Scholar 

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Acknowledgements

G.W. and L.W. were supported by the National Key R&D Program of China (grant no. 2019YFC1510100) and the National Natural Science Foundation of China (grant no. 41976003).

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G.W. initiated the idea, designed the study and interpreted the results. L.W. processed the data and performed the analyses. All the authors developed the idea and wrote the paper. W.M. and S.-P.X. discussed the results and commented on the manuscript.

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Correspondence to Guihua Wang.

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Extended data figures and tables

Extended Data Fig. 1 Buoy current and wind observations under TC conditions.

a, Relationship of observed wind speed and ageostrophic current speed considering latitudes under TC conditions (blue dots). Linear regression (red line) is also plotted between the binned averages of the observed wind speeds and current speeds (red dots), with \({V}_{0}\) in the regression equation being coupling between the ageostrophic current speed \(V\) and latitude \(\phi \) as \({V}_{0}=V\times \sqrt{{\rm{\sin }}\left|\phi \right|}\), and the slope of the fitted line (along with the 95% margin of error and the p-value of the t-test) is reported in the bottom right corner. The binned averages of the current speeds are calculated from the ageostrophic current speeds in every 0.1 m s−1 bin, and the corresponding observed wind speeds are taken to calculate the binned averages of the observed wind speeds. b, Comparison of the binned averages of the theoretical wind speeds estimated from the ageostrophic currents and observed wind speeds (red dots). The binned averages of the observed wind speeds are calculated from the observed wind speeds in every 1 m s−1 bin, and the corresponding theoretical wind speeds are taken to calculate the binned averages of the theoretical wind speeds. c, Distribution of buoys with current and wind observations in the TC-coordinate system (dots), with the purple dots indicating that the maximum wind speeds of the corresponding TCs are larger than 35 kt. The results are based on observations from the TAO/TRITON, RAMA, and PIRATA buoy arrays.

Extended Data Fig. 2 Winds of weak TCs derived from drifter current measurements based on typhoon wind field model of Batts.

a, The difference of spatial averages between the theoretical wind fields and composite wind fields for each five-consecutive-year period from 1991 to 2020. The length of the error bar for each period is twice the standard deviation divided by the square root of the effective number of observations during that period (i.e., twice the standard error of the mean). The effective number of observations is approximated as the number of observations that are separated by at least 500 km in distance or at least 10 days in time. b, The theoretical and composite wind fields for each five-consecutive-year period during 1991–2020, and differences between the two winds (theoretical winds minus composite winds). c, The composite wind fields for the periods of 1991–2005 and 2006–2020, and change of the wind fields between the two periods (2006–2020 minus 1991–2005).

Extended Data Fig. 3 PDFs of the maximum sustained wind under global weak TCs.

for 1991–2005 (blue) and 2006–2020 (red) derived from (a) all drifter observations, and (b) the same as (a) but with the results for 2006–2020 obtained with a random sampling method. Specifically, the random sampling procedure was repeated 10,000 times. Each time, 25,538 observations (i.e., the number available for the period of 1991–2005) were drawn from the entire 59,643 observations during 2006–2020, and a PDF of TC intensities was computed. The red curve shows the average PDF using the obtained 10,000 individual PDFs, with the error bars indicating the standard deviations of the 10,000 realizations. Bin size is 3 m s−1.

Extended Data Fig. 4 Drifter data quantity statistics and mean near-surface ocean current fields under weak TCs.

(a1, b1, c1, d1) are numbers of drifter records within \({r < 7R}_{{\rm{\max }}}\) for the period of 1991–2005, and (a2, b2, c2, d2) are the same as (a1, b1, c1, d1) but for 2006–2020, respectively. Global mean current fields, (e1, f1, g1) are for 1991–2005 and (e2, f2, g2) are for 2006–2020, and (e3, f3, g3) are changes of the mean currents between the two periods (2006–2020 minus 1991–2005). a1, a2 are for weak TCs range from 17 to 42 m s−1. b1, b2, e1-e3 are for weak TCs range from 17 to 32 m s−1, c1, c2, f1-f3 are for weak TCs whose LMI is 17 to 32 m s−1, d1, d2, g1-g3 are for weak TCs whose LMI is 17 to 42 m s−1, respectively.

Extended Data Fig. 5 Evolution of the drifter measured ocean current speeds within r < 7Rmax under weak TCs.

from 1991 to 2020. The color shading and solid lines represent the current speeds at different distances from the corresponding TC centers along the cross-track direction, and the dashed lines are the associated current speed trends. The lower and upper limits of all the lines are 18 and 45 cm s−1, respectively.

Extended Data Fig. 6 Translation speed (m s−1) of weak TCs during the period of 1991–2020.

The red and blue lines are the average during the periods of 1991–2005 and 2006–2020, respectively.

Extended Data Fig. 7 Linear trends in the large-scale atmospheric environment and upper-ocean thermal condition during 1991–2020.

a, Potential intensity. b, Relative humidity at 600 hPa. c, Upper-ocean thermal stratification (represented by the difference of water temperature between ocean surface and 75-m depth, i.e., SST - T75m). d, Mixed layer depth (MLD, depth where the density change reaches a threshold value of 0.03 kg m−3 relative to the surface). The results in a and b are based on the JRA-55 reanalysis, and in c and d are calculated using the IAP ocean analysis data. Red dots denote regions where the linear trend is significant at the 0.05 level. Note this figure is produced by MATLAB.

Extended Data Fig. 8 Evolution of the near-surface ocean current speeds derived from drifters under TCs.

for individual basins and the globe. a is for weak TCs range from 17 to 32 m s−1, b is for weak TCs whose LMI is 17 to 32 m s−1, c is the same as b but LMI is 17 to 42 m s−1, and d is based on drifter data under strong TCs (Category-2–5) over NWP. The dashed lines indicate the fitted linear trends of the curves, and the estimated trends (along with the 95% margin of errors) and the p-values of the t-test for the trends are also reported. In each panel, the length of the error bar for each year is twice the standard deviation divided by the square root of the effective number of observations in that year. Discontinuity points along the curves in b and c represent missing values due to insufficient data.

Extended Data Table 1 TC Intensity estimates by JTWC (Joint Typhoon Warning Center), JMA (Japan Meteorological Agency) and CMA (China Meteorological Agency)
Extended Data Table 2 Trends in drifter-measured ocean current speeds within varying Rmax of weak TCs during the period of 1991–2020

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Wang, G., Wu, L., Mei, W. et al. Ocean currents show global intensification of weak tropical cyclones. Nature 611, 496–500 (2022). https://doi.org/10.1038/s41586-022-05326-4

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