Tropical cyclones are among the most dangerous natural hazards on earth. The rapidly rotating storms form over warm tropical waters, and can bring sustained wind speeds of more than 100 miles per hour. Communities near coasts are particularly vulnerable to the impacts of tropical cyclones, such as storm surges and intense rainfall that can lead to severe flooding. Since the 1970s, tropical cyclones have been detected using satellite data. However, before the widespread use of satellites, the observed record of tropical cyclones was patchy and the limited availability of high-quality observational data makes it tricky to find long-term trends in cyclone frequency.

As well as exploring global trends, the authors investigated changes in tropical cyclone frequency in different regions of the world. Of the seven regions studied, six showed a trend of decreasing tropical cyclone numbers from 1900 to 2012.  The figure below shows annual tropical cyclone frequency over 1850-2012 for seven different regions of the world. Blue lines show the annual tropical cyclone frequency, red lines show the five-year running average and black lines show the 1850-1900 and 1900-2012 trends. The bottom-right figure shows the seven regions.

As the climate warms, the conditions governing cyclone formation are changing. For example, increasing sea surface temperatures are giving tropical cyclones more energy, increasing their intensity and making them more destructive.

The figure below shows annual tropical cyclone frequency over 1850-2012, globally (top left), in the southern hemisphere (top right) and in the northern hemisphere (bottom left). Blue lines show the annual tropical cyclone frequency, red lines show the five-year running average and black lines show the 1850-1900 and 1900-2012 trends. The bottom-right figure shows tropical sea surface temperature rise, compared to the pre-industrial baseline.

The graphic on the left shows the physical changes in the Hadley and Walker cells, caused by variations in sea surface temperature changes (bottom) and sea level pressure (middle). The plots on the top right show changes in the index for different regions.

Annual tropical cyclone frequency over 1850-2012, for the globe (top left), the southern hemisphere (top right) and the northern hemisphere (bottom left). Tropical sea surface temperature rise anomaly, compared to a pre-industrial baseline, is shown in the bottom right. Source: Chand et al (2022).

the Walker and Hadley circulations – to weaken, with knock-on effects that create “more hostile conditions” for cyclones to form, the authors suggest.

The paper, published in Nature Climate Change, aims to fill this gap using “reanalysis” data that combines observations and model simulations. The findings show a 13% decrease in tropical cyclones around the world between 1850-1900 and 1900-2000. More specifically, they find a drop from more than 100 tropical cyclones a year in pre-industrial times to around 80 in 2012. The study does not look at changes in the intensity – or damage – of cyclones over this period.

Tropical cyclones are complex phenomena, which only form under specific atmospheric and oceanic conditions. Research suggests that, as the climate warms, changing conditions are making tropical cyclones less frequent. However, a lack of long-term cyclone data makes this trend difficult to quantify.

First, from the historical record was separated the frequency of TC activity by each TC intensity (from tropical depression to category 5 hurricane). To estimate where TCs were frequently generated, where intensity changes occur including hurricane intensity changes according to the Saffir-Simpson scale (Saffir,1973; Simpson, 1974), where maximum intensity was reached by each TC, and their track paths, the nonparametric kernel density estimation (KDE) was applied (Loader, 1999). An important limitation of the KDE application is that it allows the genesis or TCs intensification over land. In this article, for the statistical analyses, the points over land were eliminated according to the grid point of SST. Additionally, the statistical t-test was applied to determine the significance of the Pearson correlations at the 95% significance level.

Moreover, it was found a slight poleward migration of the mean latitude where TCs reached maximum intensity, but not statistically significant. Furthermore, the eastern regions of the NATL basin exhibit an increase in storm track density, which explains the decrease in track density near the Lesser Antilles Arc. This behaviour can be attributed to the variability of the NASH, but further studies are required to improve the understanding about the influence of NASH on the spatial distribution of TC trajectories.

Generally, high SST values favour evaporation over the sea surface, which leads to an intense upward moisture transport due to persistent convection, phase changes, and the consequent release of latent heat, all of which are necessary for the genesis and intensification of TCs. Positive and negative SST anomalies are observed after and before 2000, respectively, for all months of the TC season. This supports the findings of Dare and McBride (2011) who pointed out that SST during TC genesis were warmer after 1995.

Pearson’s correlations between the different TC activity measures and the NAO were not significant (Table I). The strongest positive correlation coefficients were obtained for TC genesis and the AMO, AMM, TNA, and a negative correlation was obtained for the ENSO (r = 0.19); the negative phases of the QBO, NAO, and TSA are weakly correlated with TC genesis (r ranging from 0.13 to 0.3; Table I); and the ENSO is also inversely correlated with category 5 (r = -0.11) and category 1 (r = -0.23) hurricanes.

The trajectory density (Figure 4b) shows the highest values in the Gulf of Mexico and northeast of the Florida Peninsula, which are associated with the tracks of the systems formed within the region. In comparison, systems formed near the West African coast show more dispersed trajectories. In this area, TCs that move towards the Caribbean Sea are as frequent as those that move directly towards the central NATL. Similar to the behaviour observed for the genesis density, similar areas of the tropical North Atlantic and the Gulf of Mexico show the highest density values where TCs reach tropical storm status (Figure 4c). Furthermore, the regions where TCs reach hurricane status have the highest density values in the Caribbean Sea, the Gulf of Mexico, the tropical NATL region at the east of the Lesser Antilles Arc, and the northeast of the Bahamas archipelago (Figures 4 d-f). The tropical NATL (below 20° latitude) and the Gulf of Mexico show the highest density values, where TCs achieve maximum intensity (Figure 4g).

From the Centennial Time Scale (COBE SST2) dataset (Hirahara et al., 2014) of the National Oceanic and Atmospheric Administration (NOAA), freely available at https://psl.noaa.gov/data/gridded/data.cobe2.html, was obtained the monthly mean SST data. The COBE SST is a monthly mean of global fields created during June, 2011 at NOAA’s Physical Sciences Division (PSD) of the Earth System Research Laboratory (ESRL) using the gridded data from the Japanese Reanalysis Project (JRA). Moreover, all the climate variability modes used here were extracted from the NOAA Physical Sciences Laboratory at https://psl.noaa.gov/data/climateindices/list/. The NAO consists of a north-south dipole of anomalies, with a centre located over Greenland and the other centre of opposite sign that covers the central latitudes of the NATL between 35°N and 40°N. The NAO index is defined as the normalized pressure difference between the Azores and Iceland (Hurrell, 1995; Jones et al., 1997).

ea surface temperature (SST)

The tropical cyclone records were extracted from the International Best Track Archive for Climate Stewardship (IBTrACS; Knapp et al., 2010), which is freely available at https://www.ncdc.noaa.gov/ibtracs/index.php?name=climatology. The IBTrACS is officially recognized by the WMO Tropical Cyclone Programme as an official TC data resource and is under the auspices of the World Data Center for Meteorology located at NOAA’s National Centers for Environmental Information. It widely discuss by several authors (e.g. Bathia et al., 2019; Kossin et al., 2013; Vecchi and Knutson, 2008; Chang and Guo, 2007) that before the era of air reconnaissance flights and meteorological satellites, the detection of TCs depended on fortuitous encounters with ships or their impact on populated areas.

some climatic studies (e.g. Liu et al., 2019; Jiang et al., 2008) have found a significant increase in TCs rainfall rates that exceed the increase in environmental water vapour estimated with the Clausius-Clapeyron relationship. Furthermore, intense TCs tend to produce more rainfall than weak TCs. Thus, the increase in the intensity of the TC can explain the increase in TCs rainfall rates in a warmer climate, in agreement with Liu et al. (2019). In addition, the results of Bhatia et al. (2019) suggest an increase in the intensification rates of TCs in the NATL basin, with positive anthropogenic forcing.

Pearson’s correlation coefficients between tropical cyclone activity parameters and climatic variability modes. GEN: TC genesis, TS: tropical storm, HN (N = 1, 2, 3, 4, and 5): hurricanes with different categories on the Saffir-Simpson scale, LMI: mean TC maximum intensity, and MLT: mean TC lifetime. For the genesis, tropical storm, and hurricane categories, correlation was established between the number of TCs and the climatic variability modes. Statistically significant correlations are shown in bold text (p < 0.05)

Monthly time-series of TC genesis anomalies and the NAO, ENSO, AMM, AMO, TNA, TSA, NTA, and CAR climatic indices from 1980 to 2019.

Monthly time-series of SST and TC count (genesis and TC intensity categories) anomalies from 1980 to 2019.

Spatial linear trend of SST (°C/decade) in the North Atlantic basin in (a) June, (b) July, (c) August, (d) September, (e) October, and (f) November from 1980 to 2019. Dots represent statistically significant trends (p < 0.05).

(a) Monthly mean SSTs from June to November (top to bottom) during the period 1980-2019 and SST anomalies for (b) 1980-1999 and (c) 2000-2019 periods relative to 1980-2019. Dots represent statistically significant anomalies (p < 0.05).

Mean SST time-series (solid red line) and linear statistically significant trend in annual mean SST (p < 0.05). In the linear equation, t represents the number of years since 1980 (top) and annually SST anomalies (bottom).

Kernel density estimation (KDE) for the trajectory during (a) 1980-1999 and (b) 2000-2019, and (c) differences in KDE for trajectories between 2000-2019 and 1980-1999.

Temporal evolution in the latitude of lifetime-maximum-intensity TCs from 1980 to 2019. The dashed red line shows the trend of the series, which is not statistically significant.

Kernel density estimation for TCs (a) genesis, (b) trajectory, (c) locations of tropical storm status, (d) category 1 hurricanes, (e) category 2 hurricanes, (f) major hurricanes (category 3+), and (g) TC maximum intensity.

Temporal evolution and linear trends (dashed lines) in the number of TCs genesis events, tropical storms (TS), and hurricanes (HN, N = 1, 2, 3, 4, 5) on the Saffir-Simpson wind scale from 1980 to 2019 in the NATL basin. Statistically significant trends (95% significance level) were observed for TS. In the linear equation, t represents the number of years since 1980.

Monthly variation in the number of tropical cyclones (TCs) formed in the NATL basin from 1980 to 2019. The shaded grey area represents the NATL TC season from June to November. Note that the TCs that reached the hurricane category are included in the TS counts, likewise, in each hurricane category counts are included TCs that reached the next intensity category.

Number of TCs formed in the NATL basin from 1851 to 2019 using the HURDAT2 database (solid blue line). The red vertical lines represent the change points detected in the series applying the PELT and BINSEG methods. The dashed green, orange, and black lines represent the linear trend for the subperiods of 1980-1999, 2000-2019, and 1980-2019, respectively. No statistically significant trends were observed.

In this study, a climatology analysis of the cyclonic activity in the North Atlantic (NATL) basin was performed to improve our understanding of how sea surface temperature (SST) and climate variability modes modulate tropical cyclones (TCs) activity. The information on the TCs was extracted from the International Best Track Archive for Climate Stewardship database, while the SST was obtained from the Centennial Time Scale dataset. The SST analysis reveals a warming trend of approximately 0.23 °C/decade for the NATL basin during the TC season between 1980 and 2019, while the TC activity shows an increase of ~1.4 TC/decade in the number of TCs that reach the tropical storm category. Nevertheless, the observed increase in the frequency of hurricanes is not significant. The increasing frequency of TCs after 2000 concerning the 1980-1999 period was probably caused by increasing favourable conditions for cyclonic development, such as positive SST anomalies. Moreover, the eastern regions of the NATL basin exhibit an increase in storm track density, which explains the observed decrease in track density near the Lesser Antilles Arc. In addition, the Atlantic meridional mode, the Atlantic multidecadal oscillation, and El Niño-Southern Oscillation have a significant influence on the TCs activity; however, they cannot fully explain the tendency to increase the TCs frequency in the last decades.

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Figure 2(a) Satellite IR imagery, (b) modelled (grid 2; 12 km resolution) OLR (W m−2), (c) radar precipitation (from BoM Cairns radar) and (d) modelled (grid 3; 4 km resolution) 30 min precipitation rate (mm h−1) shortly before TC Yasi made landfall.DownloadFigure 3(a) Maximum simulated storm surge over the CTRL simulation. Open circles indicate location of tide gauge observations and simulation output locations, respectively. (b) Simulated and observed storm surge levels at locations plotted in (a).DownloadFigure 4(a) Tracks of TC Yasi from all nine simulations and time series showing the difference (SST runs minus CTRL) in (c) minimum pressure (hPa) and (b) maximum wind speed (m s−1).

Figure 5(a) Percentage difference between radius of maximum winds in SST experiments and CTRL run (%). (b) Radius to gale-force winds (m) and (c) integrated kinetic energy (TJ) at wind speeds > 17.5 m s−1 for all model simulations.DownloadFigure 6(a) Maximum simulated storm surge (m) for all runs. (b) Difference in storm surge area (defined as area of water levels > 1 m in km2) for each SST simulation minus the CTRL.DownloadFigure 7(a) Difference in precipitation rate within 500 km of the storm centre for each SST simulation minus the CTRL. (b) Accumulated precipitation (mm h−1; total over all grid points) within different radii for each simulation at landfall.

Differences in intensity between the SST experiments and the CTRL run are shown in Fig. 4b and c. The experiment setup means all simulations are initialised with the same pressure and wind fields. After 24 h there are clear differences in the intensities, with larger differences occurring with larger temperature anomalies. The larger the positive anomaly the more intense is the storm with lower pressures and higher wind speeds. The larger the negative anomaly the opposite is true with lower intensities occurring. Increasing the SST has a larger influence on the minimum pressure than decreasing it with a maximum difference of −60 hPa occurring in SST + 4 and 45 hPa in SST − 4. The minimum pressure also occurs earlier in the run as the positive SST anomaly is increased. The earlier landfall is evident in the wind speed differences between the positive SST anomaly runs as the difference becomes negative when they make landfall prior to CTRL.

In order to analyse the precipitation distribution in more detail, the precipitation was accumulated over different radii (Fig. 7b; 0–100, 100–200, 200–300, 300–400 and 400–500 km). The largest rainfall amounts occur between 100 and 200 km from the centre of the storm in all simulations. This is contrary to the maximum within 100 km shown in observations (e.g. Lonfat et al., 2004) and can be accounted for by the larger size of the eye relative to observations. A decrease in the SST leads to a decrease in the accumulated rainfall in all radii. However, increasing the SST from the CTRL value makes little difference to the precipitation rate within 100 km of the storm centre (red line Fig. 7b). This is inconsistent with climate change studies which project an increase in the precipitation rate within 100 km (Knutson et al., 2010) but, as previously mentioned, is likely to be due to the larger vortex in the current study. The increase in SST results in a change in the distribution of rainfall with more falling in the 200–300 km band than within 100 km. It could be argued that Yasi was such an extraordinary TC in terms of size compared to other TCs impacting Queensland that it does not necessarily fit climate change studies.

storm surge (wind-setup and inverse barometer effect)

The Weather Research and Forecasting (WRF) model, version 3.4, is used with a vortex-following, two-way nesting configuration. There are three domains. The outer grid has a horizontal resolution of 36 km. Both inner grids, 2 and 3, are able to move and have a grid spacing of 12 and 4 km respectively. All grids have 36 vertical levels with a model top of 20 hPa. Only the outer grid is forced with the atmospheric and SST data. The following parameterisations were selected based on a number of tests and using previous analysis results: the Thompson et al. (2008) microphysics, the Rapid Radiative Transfer Model (RRTM) for longwave radiation (Mlawer et al., 1997), the Goddard shortwave scheme (Chou and Suarez, 1994) and the Mellor–Yamada–Nakanishi–Niino Level 2.5 TKE scheme for the planetary boundary layer (Nakanishi and Niino, 2006). The Kain–Fritsch (K–F; Kain, 2004) cumulus parameterisation scheme was used on all grids.

This may also provide insights into how we might expect the potential destructiveness of TCs (track, intensity, size, precipitation and storm surge) to change in a warmer climate.

Simulations of TC Yasi have been performed previously by Parker et al. (2017), who examined the influence of atmospheric and SST initialisation data as well as the choice of parameterisation schemes on the track, intensity, landfall location and translational speed of Yasi. They found the choice of cumulus parameterisation made the biggest difference with a trade-off needed between accurate trajectory and more realistic intensities.

Tropical cyclones (TCs) result in widespread damage associated with strong winds, heavy rainfall and storm surge.

The importance of warm SSTs for the development and intensification of TCs has been long known: surface fluxes of latent and sensible heat from the oceans provide the potential energy to TCs (Ooyama, 1969; Emanuel, 1986). Palmén (1948) was the first to document that TCs only occur over oceans warmer than a critical temperature of 26–27 ∘C and subsequently the values of 26 and 26.5 ∘C have been widely used throughout TC research (e.g. Gray, 1968; Holland, 1997) as a threshold SST for the formation of TCs. This threshold temperature was recently revisited by Dare and McBride (2011) using observations from 1981 to 2008 with results consistent with these earlier studies. They found that the majority (93 %) of TCs occur at SSTs greater than 26.5 ∘C and over 98 % at SSTs greater than 25.5 ∘C. The positive trend in SSTs over their study period has not led to a shift in this threshold temperature.

An increase in SST results in an increase in intensity, precipitation and integrated kinetic energy of the storm; however, there is little influence on track prior to landfall. In addition to an increase in precipitation, there is a change in the spatial distribution of precipitation as the SST increases.

The resulting surface winds and pressure are used to force a barotropic storm surge model to examine the influence of SST on the associated storm surge of TC Yasi.

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Because it is the interaction of warm air and warm seawater that spawns these storms, they form over tropical oceans between about 5 and 20 degrees of latitude.

speeds of 74 miles per hour or more.

a weather disturbance, such as a thunderstorm, that pulls in warm surface air from all directions

The same type of disturbance in the Northwest Pacific is called a “typhoon” and “cyclones” occur in the South Pacific and Indian Ocean.

The same type of disturbance in the Northwest Pacific is called a “typhoon” and “cyclones” occur in the South Pacific and Indian Ocean.

Recent studies have shown a link between ocean surface temperatures and tropical storm intensity – warmer waters fuel more energetic storms.

Eventually, hurricanes turn away from the tropics and into mid-latitudes. Once they move over cold water or over land and lose touch with the hot water that powers them, these storms weaken and break apart.

As long as the base of this weather system remains over warm water and its top is not sheared apart by high-altitude winds, it will strengthen and grow

At higher altitudes, water vapor starts to condense into clouds and rain, releasing heat that warms the surrounding air, causing it to rise as well.

Hurricanes – Multimedia Discovery Missions Hurricanes – NOAA National Hurricane Center

Hurricanes start simply with the evaporation of warm seawater, which pumps water into the lower atmosphere. This humid air is then dragged aloft when converging winds collide and turn upwards. At higher altitudes, water vapor starts to condense into clouds and rain, releasing heat that warms the surrounding air, causing it to rise as well. As the air far above the sea rushes upward, even more warm moist air spirals in from along the surface to replace it.

they form over tropical oceans between about 5 and 20 degrees of latitude

water at the ocean’s surface that is at least 80° Fahrenheit (27° Celsius)