Tropical Cyclone Rapid Intensification: An Explanation for the Global Increase

doi:10.21203/rs.3.rs-1617224/v1

PDF

Article

Tropical Cyclone Rapid Intensification: An Explanation for the Global Increase

https://doi.org/10.21203/rs.3.rs-1617224/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted 25 May, 2022

You are reading this latest preprint version

Tropical cyclone rapid intensification events often cause destructive hurricane landfalls because they are associated with the strongest storms and the worst forecasts. Multi-decade observational datasets of tropical cyclone behavior have recently enabled documentation of upward trends in tropical cyclone rapid intensification in several basins. However, a robust anthropogenic signal in global intensification trends and the physical drivers of intensification trends have yet to be identified.

To address these knowledge gaps, we compare the observed trends in intensification and tropical cyclone environmental parameters to simulated natural variability in a high-resolution global climate model. In multiple basins and the global dataset, we detect a significant increase in intensification rates with a positive contribution from anthropogenic forcing. Furthermore, thermodynamic environments around tropical cyclones have become more favorable for intensification, and climate models show anthropogenic warming has significantly increased the probability of these changes.

TC Observations

We used the International Best Track Archive for Climate Stewardship (IBTrACS¹⁸), v04r00, and the Advance Dvorak Technique-Hurricane Satellite-B1 (ADT-HURSAT^17,19) for the period 1982–2017. The ADT-HURSAT data record was recently expanded by 8 years to span the 39-year period between 1979-2017. We omit the first three years of the record where limited geostationary data results in missing storms and focus on the 36-year period between 1982-2017. For our IBTrACS analysis, we only consider best-track data from the National Hurricane Center for the Atlantic and east Pacific and the Joint Typhoon Warning Center for the remainder of the globe¹⁸. One of the benefits of only using data from these U.S. agencies is they follow the same definition of maximum winds: the highest 1-min average at 10 m height over a smooth surface³⁷. Best-track data start as operational estimates of the intensity and track of a TC and are refined at the end of a TC season with a combination of in situ (e.g., dropsondes, scatterometers, buoys), radar, and satellite measurements. Best-track intensity and position estimates are available every six hours at the four synoptic times (0000, 0600, 1200, and 1800 UTC) and are recorded to the nearest 5 knots (1 kt = 0.5144 m s⁻¹) and 0.1° latitude/longitude³⁸.

The creation of ADT-HURSAT consists of three main steps. Geostationary satellite imagery is first analyzed from International Satellite Cloud Climatology Project (ISCCP)-B1 data^39-41.Then, the data is centered on IBTrACS TCs and subsampled to be both spatially and temporally homogeneous. Finally, a simplified version of the advanced Dvorak technique⁴² is used to evaluate the data and determine a maximum TC wind speed. ADT-HURSAT data are produced every three hours based on satellite data that has been uniformly subsampled to a horizontal resolution of 8 km, and wind speeds are recorded to the nearest tenth of a Dvorak “T-number” (depending on the current intensity, between 1-3 knots).

Defining Storm-Local and Tropical-Mean Environments

Data for the analysis of cyclone environments were taken from ERA5²⁷ and MERRA-2^28,43. Reanalyses combine a forecast model with continuous assimilation of observational data to construct a global representation of historical atmospheric variability. The native spectral resolution of ERA5 is T639 (nominally 31 km) and the native resolution of MERRA-2 is 0.5º ´ 0.625º (nominally 50 km). We focus on these reanalyses because they capture realistic moisture, temperature, and wind values in the lower atmosphere^44,45. For storm-local environment calculations, we identified and tracked tropical cyclones in ERA5 and MERRA-2 objectively using the Lagrangian feature-tracking algorithm—TRACK—of Hodges³¹. This methodology was documented in greater detail by Hodges et al.⁴⁶.

Vertically averaged relative humidity (at 850, 700 and 600 hPa), vertical wind shear (computed as the square-root of the sum of the squared differences in the zonal and meridional winds between 850 and 200 hPa), and sea-surface temperature were spectrally filtered to T11 resolution to remove cyclonic circulation features and retain only the large-scale, background environmental fields. Sensitivity analysis of ERA5, the highest-resolution reanalysis available, shows that 95 % of the T639-resolution cyclonic circulation is removed at a T11 truncation (Supplementary Fig. 3). Along-track sampling of mean values from the spectrally filtered fields within a 5º storm-centered radius (geodesic) was performed. To compute potential intensity along cyclone tracks, reanalysis data (sea-surface temperature, mean sea-level pressure, air temperature, and specific humidity) were regridded first to 1º resolution. Regridding from the native reanalysis resolutions to 1º has a negligible effect on potential intensity and no spectral filtering was performed. Potential intensity was computed by taking input fields from the grid cell nearest to the storm center at each timestep, following Bister and Emanuel⁴⁷ and using published code^48,49 (Gilford et al., 2017; 2019). Vertical soundings of temperature and specific humidity were constructed from reanalysis data on 14 isobaric levels (1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, and 50 hPa). By default, the code allows reversible ascent and dissipative heating; the ratio of the exchange coefficients of enthalpy and momentum flux is 0.9; and output velocity is scaled by 0.8 to reflect surface drag. Further discussion of these constants is given in Emanuel⁵⁰ and Gilford et al.^48,49 (2017; 2019).

The tropical-mean of these fields are also calculated but with no spectral filtering. Annual averages are comprised of peak-TC-season means in each hemisphere, August-October (ASO) in the Northern Hemisphere and February-April (FMA) in the Southern Hemisphere and then averaged between the two Hemispheres (area-weighted). June-October (JJASO) in the northern hemisphere and December-February (DJFMA) were also tested but yielded comparable results. In the northern hemisphere, the tropical-mean is an area-average of environmental values over the ocean between 10°-30°N and 40°E-20°W. In the southern hemisphere, the bounds for averaging are 10°-30°S and 30°E-150°W.

HiFLOR experiments

HiFLOR control simulations introduced in Murakami et al.⁵¹ and Bhatia et al.⁶ were used here to represent natural (unforced) climate variability and provide the framework for exploring anthropogenic effects on TC intensification. We focused on the control simulation that used anthropogenic forcing fixed at 1860 (1860CTL) levels because it has the longest simulation length: 1500 years. The first 50 years of the simulation were disregarded to mitigate effects of model drift. Additional details on data preparation and methodology for the HiFLOR simulation can be found in Bhatia et al.⁶. The approach developed by Harris et al.⁵² is used to track TCs in HiFLOR and is applied using the parameter values of Zhang et al.⁵³ and Murakami et al.¹⁵.

CMIP6 Experiments

We examine linear trends of the four tropical-mean environmental fields from all available CMIP6 simulations over the period 1982-2014. The fields are defined in an identical way to the observed fields in MERRA-2 and ERA5. Models and the number of ensemble members for each model that contain the relevant environmental variables are listed in Supplementary Table 3.

Storm Criteria

For consistency, intensity change values in HiFLOR, and the observational datasets are rounded to the nearest five knots. We only consider TCs that are active for at least 72 hours and exceed wind speeds of 34 knots for at least 36 hours. We restrict our analysis sample to only consider cases where the TC center is located over the ocean, the starting and ending TC position are below 40 degrees of latitude, and the TC intensity stays above 34 knots. The warm core criteria discussed in Murakami et al.¹⁵ are also applied to the HiFLOR data before analysis. For the analysis involving storm-local environments, all storm fixes within 0.5 degrees of land in any direction are removed to reduce the number of spurious PI readings.

Uncertainty Quantification

For Fig. 1, we use Monte Carlo techniques to create random noise before analyzing the discretized data. Random noise prevents multiple data points from having the same value and provides an estimate of the typical error associated with measuring TC intensity. 1000 subsamples were produced by adding random noise from a uniform distribution on the interval ± knots to each intensity change value. The magnitude of this random noise is derived by adding 5 knots of error in quadrature (propagation of errors stemming from the intensity change calculation), which is a conservative estimate for the typical error associated with each TC intensity observation. Fig. 1 involved the calculation of the 5^th and 95^th percentile in each of the 1000 subsamples for each year. 1000 slopes of each percentile were calculated, and the mean of the slopes was considered the best estimate of the 1982-2017 slope of the quantile. The 5^th and 95^th percentiles of the 1000 slopes were shaded as the uncertainty bounds.

Data availability

The source code of the HiFLOR model can be found at https://www.gfdl.noaa.gov/cm2-5-and-flor. All the CMIP6 model output is available at https://esgf-node.llnl.gov/projects/cmip6/. Any of the data that support the findings of this study are available from the corresponding author on request.

Code Availability

The code that supports the findings of this study is available from the corresponding author on request. The code used for tracking TCs in ERA5 and MERRA-2 are available here: https://gitlab.act.reading.ac.uk/track/track.

Acknowledgments

The authors thank Dr. Shuai Wang and Pier Luigi Vidale for their suggestions and comments during the internal review process. Kieran Bhatia was supported by the Carbon Mitigation Initiative at Princeton University BP International 02085(7).

Author Contributions

Kieran Bhatia designed the study, completed a majority of the analysis, and wrote the entirety of the manuscript. Alexander Baker created Fig. 5 and Supplementary Fig. 3. Baker performed tracking of ERA5, produced the storm-local and tropical-mean reanalysis environments for trend analysis. Wenchang Yang created Fig. 6 and conducted the analysis of the CMIP6 tropical-mean environmental trends. Kevin Hodges developed the tracking algorithm to pair the environmental data to TC fixes and performed tracking of MERRA-2. Gabriel Vecchi helped develop the HiFLOR model and carry out the experiments. Vecchi also provided key suggestions on research topics to explore. Thomas Knutson provided guidance on the analysis. Hiroyuki Murakami provided key suggestions on the analysis and helped carry out the experiments. James Kossin developed ADT-HURSAT data and provided it for analysis. Keith Dixon helped design and write a description of the bias corrections for HiFLOR. Carolyn Whitlock was the primary developer of the bias corrections for HiFLOR. Ben Bronselaer conducted early analysis of the CMIP6 tropical-mean environmental trends and supported the development of comparison metrics.

Competing Interests

The authors declare no competing interests.

Kaplan, J. & DeMaria, M. Large-Scale Characteristics of Rapidly Intensifying Tropical Cyclones in the North Atlantic Basin. Weather and Forecasting 18, 1093–1108, doi:10.1175/1520-0434(2003)018<1093:lcorit>2.0.co;2 (2003).
DeMaria, M., Franklin, J. L., Onderlinde, M. J. & Kaplan, J. Operational Forecasting of Tropical Cyclone Rapid Intensification at the National Hurricane Center. Atmosphere 12, 683 (2021).
Trabing, B. C. & Bell, M. M. Understanding Error Distributions of Hurricane Intensity Forecasts during Rapid Intensity Changes. Weather and Forecasting 35, 2219–2234, doi:10.1175/waf-d-19-0253.1 (2020).
Kishtawal, C. M., Jaiswal, N., Singh, R. & Niyogi, D. Tropical cyclone intensification trends during satellite era (1986–2010). Geophysical Research Letters 39, doi:10.1029/2012GL051700 (2012).
Balaguru, K., Foltz, G. R. & Leung, L. R. Increasing Magnitude of Hurricane Rapid Intensification in the Central and Eastern Tropical Atlantic. Geophysical Research Letters 45, 4238–4247, doi:https://doi.org/10.1029/2018GL077597 (2018).
Bhatia, K. T. et al. Recent increases in tropical cyclone intensification rates. Nature Communications 10, 635, doi:10.1038/s41467-019-08471-z (2019).
Song, J., Duan, Y. & Klotzbach, P. J. Increasing trend in rapid intensification magnitude of tropical cyclones over the western North Pacific. Environmental Research Letters 15, 084043, doi:10.1088/1748-9326/ab9140 (2020).
Shimada, U., Yamaguchi, M. & Nishimura, S. Is the Number of Tropical Cyclone Rapid Intensification Events in the Western North Pacific Increasing? SOLA 16, 1–5, doi:10.2151/sola.2020-001 (2020).
Wang, S., Rashid, T., Throp, H. & Toumi, R. A Shortening of the Life Cycle of Major Tropical Cyclones. Geophysical Research Letters 47, e2020GL088589, doi:https://doi.org/10.1029/2020GL088589 (2020).
Klotzbach, P. J. et al. Trends in Global Tropical Cyclone Activity: 1990–2021. Geophysical Research Letters 49, e
2021GL095774
, doi:https://doi.org/10.1029/2021GL095774 (2022).
Bhatia, K., Vecchi, G., Murakami, H., Underwood, S. & Kossin, J. Projected Response of Tropical Cyclone Intensity and Intensification in a Global Climate Model. Journal of Climate 31, 8281–8303, doi:10.1175/jcli-d-17-0898.1 (2018).
Emanuel, K. Will Global Warming Make Hurricane Forecasting More Difficult? Bulletin of the American Meteorological Society 98, 495–501, doi:10.1175/bams-d-16-0134.1 (2017).
Frajka-Williams, E., Beaulieu, C. & Duchez, A. Emerging negative Atlantic Multidecadal Oscillation index in spite of warm subtropics. Scientific Reports 7, 11224, doi:10.1038/s41598-017-11046-x (2017).
Vecchi, G. A., Landsea, C., Zhang, W., Villarini, G. & Knutson, T. Changes in Atlantic major hurricane frequency since the late-19th century. Nature Communications 12, 4054, doi:10.1038/s41467-021-24268-5 (2021).
Murakami, H. et al. Simulation and Prediction of Category 4 and 5 Hurricanes in the High-Resolution GFDL HiFLOR Coupled Climate Model. Journal of Climate 28, 9058–9079, doi:10.1175/jcli-d-15-0216.1 (2015).
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958, doi:10.5194/gmd-9-1937-2016 (2016).
Kossin, J. P., Olander, T. L. & Knapp, K. R. Trend Analysis with a New Global Record of Tropical Cyclone Intensity. Journal of Climate 26, 9960–9976, doi:10.1175/jcli-d-13-00262.1 (2013).
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J. & Neumann, C. J. The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying Tropical Cyclone Data. Bulletin of the American Meteorological Society 91, 363–376, doi:10.1175/2009bams2755.1 (2010).
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. Proceedings of the National Academy of Sciences of the United States of America 117, 11975–11980, doi:10.1073/pnas.1920849117 (2020).
Cannon, A. J., Sobie, S. R. & Murdock, T. Q. Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? Journal of Climate 28, 6938–6959, doi:10.1175/jcli-d-14-00754.1 (2015).
Allen, M. a. P., D and Whitaker, K and Marshall, TR and van Langen, J and Kievit, RA. Raincloud plots: a multi-platform tool for robust data visualization [version 2; peer review: 2 approved]. Wellcome Open Research 4, doi:10.12688/wellcomeopenres.15191.2 (2021).
Davis, C. A. Resolving Tropical Cyclone Intensity in Models. Geophysical Research Letters 45, 2082–2087, doi:https://doi.org/10.1002/2017GL076966 (2018).
Emanuel, K. Evidence that hurricanes are getting stronger. Proceedings of the National Academy of Sciences 117, 13194–13195, doi:doi:10.1073/pnas.2007742117 (2020).
Hong, J. & Wu, Q. Modulation of global sea surface temperature on tropical cyclone rapid intensification frequency. Environmental Research Communications 3, 041001, doi:10.1088/2515-7620/abf39b (2021).
Kossin, J. P. Validating Atmospheric Reanalysis Data using Tropical Cyclones as Thermometers. Bulletin of the American Meteorological Society 96, 1089–1096, doi:10.1175/bams-d-14-00180.1 (2015).
Kossin, J. P. & Camargo, S. J. Hurricane track variability and secular potential intensity trends. Climatic Change 97, 329–337, doi:10.1007/s10584-009-9748-2 (2009).
Hersbach, H. et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 1999–2049, doi:https://doi.org/10.1002/qj.3803 (2020).
Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Journal of Climate 30, 5419–5454, doi:10.1175/jcli-d-16-0758.1 (2017).
Bister, M. & Emanuel, K. A. Dissipative heating and hurricane intensity. Meteorology and Atmospheric Physics 65, 233–240, doi:10.1007/BF01030791 (1998).
Lee, W., Kim, S.-H., Chu, P.-S., Moon, I.-J. & Soloviev, A. V. An Index to Better Estimate Tropical Cyclone Intensity Change in the Western North Pacific. Geophysical Research Letters 46, 8960–8968, doi:https://doi.org/10.1029/2019GL083273 (2019).
Hodges, K. I. Feature Tracking on the Unit Sphere. Monthly Weather Review 123, 3458–3465, doi:10.1175/1520-0493(1995)123<3458:ftotus>2.0.co;2 (1995).
Hodges, K. I. Adaptive Constraints for Feature Tracking. Monthly Weather Review 127, 1362–1373, doi:10.1175/1520-0493(1999)127<1362:acfft>2.0.co;2 (1999).
Forthofer, R. N., Lee, E. S. & Hernandez, M. in Biostatistics (Second Edition) (eds Ronald N. Forthofer, Eun Sul Lee, & Mike Hernandez) 387–419 (Academic Press, 2007).
Wilks, D. S. in Statistical Methods in the Atmospheric Sciences (Fourth Edition) (ed Daniel S. Wilks) 143–207 (Elsevier, 2019).
Andrade, J. M. & Estévez-Pérez, M. G. Statistical comparison of the slopes of two regression lines: A tutorial. Analytica Chimica Acta 838, 1–12, doi:https://doi.org/10.1016/j.aca.2014.04.057 (2014).
Keil, P., Schmidt, H., Stevens, B. & Bao, J. Variations of Tropical Lapse Rates in Climate Models and their Implications for Upper Tropospheric Warming. Journal of Climate, 1–50, doi:10.1175/jcli-d-21-0196.1 (2021).
Harper, B. A., Kepert, J. D. & Ginger, J. D.
Landsea, C. W. & Franklin, J. L. Atlantic Hurricane Database Uncertainty and Presentation of a New Database Format. Monthly Weather Review 141, 3576–3592, doi:10.1175/mwr-d-12-00254.1 (2013).
Kenneth, R. K. & James, P. K. New global tropical cyclone data set from ISCCP B1 geostationary satellite observations. Journal of Applied Remote Sensing 1, 1–6, doi:10.1117/1.2712816 (2007).
Kenneth, R. K. Scientific data stewardship of international satellite cloud climatology project B1 global geostationary observations. Journal of Applied Remote Sensing 2, 1–21, doi:10.1117/1.3043461 (2008).
Knapp, K. R. Calibration Assessment of ISCCP Geostationary Infrared Observations Using HIRS. Journal of Atmospheric and Oceanic Technology 25, 183–195, doi:10.1175/2007jtecha910.1 (2008).
Olander, T. L. & Velden, C. S. The Advanced Dvorak Technique: Continued Development of an Objective Scheme to Estimate Tropical Cyclone Intensity Using Geostationary Infrared Satellite Imagery. Weather and Forecasting 22, 287–298, doi:10.1175/waf975.1 (2007).
Molod, A., Takacs, L., Suarez, M. & Bacmeister, J. Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2. Geosci. Model Dev. 8, 1339–1356, doi:10.5194/gmd-8-1339-2015 (2015).
Taszarek, M., Allen, J. T., Marchio, M. & Brooks, H. E. Global climatology and trends in convective environments from ERA5 and rawinsonde data. npj Climate and Atmospheric Science 4, 35, doi:10.1038/s41612-021-00190-x (2021).
Luo, B. & Minnett, P. J. Evaluation of the ERA5 Sea Surface Skin Temperature with Remotely-Sensed Shipborne Marine-Atmospheric Emitted Radiance Interferometer Data. Remote Sensing 12, doi:10.3390/rs12111873 (2020).
Hodges, K. I., Cobb, A. & Vidale, P. L. How Well Are Tropical Cyclones Represented in Reanalysis Datasets? Journal of Climate 30, 5243–5264, doi:10.1175/jcli-d-16-0557.1 (2017).
Bister, M. & Emanuel, K. A. Low frequency variability of tropical cyclone potential intensity 1. Interannual to interdecadal variability. Journal of Geophysical Research: Atmospheres 107, ACL 26-21-ACL 26 – 15, doi:https://doi.org/10.1029/2001JD000776 (2002).
Gilford, D. M., Solomon, S. & Emanuel, K. A. On the Seasonal Cycles of Tropical Cyclone Potential Intensity. Journal of Climate 30, 6085–6096, doi:10.1175/jcli-d-16-0827.1 (2017).
Gilford, D. M., Solomon, S. & Emanuel, K. A. Seasonal Cycles of Along-Track Tropical Cyclone Maximum Intensity. Monthly Weather Review 147, 2417–2432, doi:10.1175/mwr-d-19-0021.1 (2019).
Emanuel, K. A. The Maximum Intensity of Hurricanes. Journal of Atmospheric Sciences 45, 1143–1155, doi:10.1175/1520-0469(1988)045<1143:tmioh>2.0.co;2 (1988).
Murakami, H., Vecchi, G. A. & Underwood, S. Increasing frequency of extremely severe cyclonic storms over the Arabian Sea. Nature Climate Change 7, 885–889, doi:10.1038/s41558-017-0008-6 (2017).
Harris, L. M., Lin, S.-J. & Tu, C. High-Resolution Climate Simulations Using GFDL HiRAM with a Stretched Global Grid. Journal of Climate 29, 4293–4314, doi:10.1175/jcli-d-15-0389.1 (2016).
Zhang, W. et al. Improved Simulation of Tropical Cyclone Responses to ENSO in the Western North Pacific in the High-Resolution GFDL HiFLOR Coupled Climate Model. Journal of Climate 29, 1391–1415, doi:10.1175/jcli-d-15-0475.1 (2016).

There is NO Competing Interest.

supplementaryfiguresMAY2.docx
Supplementary Figures and Tables

PDF

Version 1

posted 25 May, 2022

You are reading this latest preprint version

Tropical Cyclone Rapid Intensification: An Explanation for the Global Increase

Status:

Version 1

Abstract

Figures

Full Text

Methods

Declarations

References

Competing Interests

Supplementary Files

Status:

Version 1