Editing IPCC:AR6/WGI/Chapter-11 (section)

=== 11.7.1 Tropical Cyclones ===

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==== 11.7.1.1 Mechanisms and Drivers ====

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The genesis, development, and tracks of TCs depend on conditions of the larger-scale circulations of the atmosphere and ocean ( [[#Christensen--2013|Christensen et al., 2013]] ). Large-scale atmospheric circulations, such as the Hadley and Walker circulations and the monsoon circulations can significantly affect TCs, as can internal variability acting on various time scales (Annex IV), from intra-seasonal (e.g., the Madden–Julian and Boreal Summer Intraseasonal oscillations and equatorial waves) and interannual (e.g., the El Niño–Southern Oscillation and Pacific and Atlantic Meridional Modes), to inter-decadal (e.g., Atlantic Multidecadal Variability and Pacific Decadal Variability). This broad range of natural variability makes detection of anthropogenic effects difficult, and uncertainties in the projected changes of these modes of variability increase uncertainty in the projected changes in TC activity. Aerosol forcing also affects sea surface temperature (SST) patterns and cloud microphysics, and it is ''likely'' that observed changes in TC activity are partly caused by changes in aerosol forcing ( [[#Evan--2011|Evan et al., 2011]] ; [[#Ting--2015|Ting et al., 2015]] ; [[#Sobel--2016|Sobel et al., 2016]] , 2019; [[#Takahashi--2017|Takahashi et al., 2017]] ; [[#Zhao--2018|Zhao et al., 2018]] ; [[#Reed--2019|Reed et al., 2019]] ). Among possible changes from these drivers, there is ''medium confidence'' that the Hadley cell has widened and will continue to widen in the future (Sections 2.3, 3.3 and 4.5). This ''likely'' causes latitudinal shifts of TC tracks ( [[#Sharmila--2018|Sharmila and Walsh, 2018]] ). Regional TC activity changes are also strongly affected by projected changes in SST warming patterns ( [[#Yoshida--2017|Yoshida et al., 2017]] ), which are highly uncertain (Chapters 4 and 9).

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==== 11.7.1.2 Observed Trends ====

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Identifying past trends in TC metrics remains a challenge due to the heterogeneous character of the historical instrumental data, which are known as ‘best-track’ data ( [[#Schreck--2014|Schreck et al., 2014]] ). There is ''low confidence'' in most reported long-term (multi-decadal to centennial) trends in TC frequency- or intensity-based metrics due to changes in the technology used to collect the best-track data. This should not be interpreted as implying that no physical (real) trends exist, but rather as indicating that either the quality or the temporal length of the data is not adequate to provide robust trend detection statements, particularly in the presence of multi-decadal variability.

There are previous and ongoing efforts to homogenize the best-track data ( [[#Elsner--2008|Elsner et al., 2008]] ; [[#Kossin--2013|Kossin et al., 2013]] , 2020; [[#Choy--2015|Choy et al., 2015]] ; [[#Landsea--2015|Landsea, 2015]] ; [[#Emanuel--2018|Emanuel et al., 2018]] ) and there is substantial literature that finds positive trends in intensity-related metrics in the best-track during the ‘satellite period’, which is generally limited to around the past 40 years ( [[#Kang--2012|Kang and Elsner, 2012]] ; [[#Kishtawal--2012|Kishtawal et al., 2012]] ; [[#Kossin--2013|Kossin et al., 2013]] , 2020; [[#Mei--2016|Mei and Xie, 2016]] ; [[#Zhao--2018|Zhao et al., 2018]] ; [[#Tauvale--2019|Tauvale and Tsuboki, 2019]] ). When best-track trends are tested using homogenized data, the intensity trends generally remain positive, but are smaller in amplitude ( [[#Kossin--2013|Kossin et al., 2013]] ; [[#Holland--2014|Holland and Bruyère, 2014]] ). [[#Kossin--2020|Kossin et al. (2020)]] extended the homogenized TC intensity record to the period 1979–2017 and identified significant global increases in major TC exceedance probability of about 6% per decade. In addition to trends in TC intensity, there is evidence that TC intensification rates and the frequency of rapid intensification events have increased within the satellite era ( [[#Kishtawal--2012|Kishtawal et al., 2012]] ; [[#Balaguru--2018|Balaguru et al., 2018]] ; [[#Bhatia--2018|Bhatia et al., 2018]] ). The increase in intensification rates is found in the best-track and the homogenized intensity data.

A subset of the best-track data corresponding to hurricanes that have directly impacted the USA since 1900 is considered to be reliable, and shows no trend in the frequency of USA landfall events ( [[#Knutson--2019|Knutson et al., 2019]] ). However, an increasing trend in normalized USA hurricane damage, which accounts for temporal changes in exposed wealth ( [[#Grinsted--2019|Grinsted et al., 2019]] ), and a decreasing trend in TC translation speed over the USA (Kossin, 2019) have also been identified in this period. A similarly reliable subset of the data representing TC landfall frequency over Australia shows a decreasing trend in Eastern Australia since the 1800s ( [[#Callaghan--2011|Callaghan and Power, 2011]] ), as well as in other parts of Australia since 1982 ( [[#Chand--2019|Chand et al., 2019]] ; [[#Knutson--2019|Knutson et al., 2019]] ). A paleoclimate proxy reconstruction shows that recent levels of TC interactions along parts of the Australian coastline are the lowest in the past 550–1500 years ( [[#Haig--2014|Haig et al., 2014]] ). Existing TC datasets show substantial inter-decadal variations in basin-wide TC frequency and intensity in the western North Pacific, but a statistically significant north-westward shift in the western North Pacific TC tracks since the 1980s ( [[#Lee--2020|]] [[#Lee--2020|T.-C. Lee et al., 2020]] ). Inthe case of the North Indian Ocean, analyses of trends are highly dependent on the details of each analysis (e.g., pre- and/or post-monsoon season period, or Bay of Bengal and/or Arabian Sea region). The most consistent trends are an increase in the occurrence of the most intense TCs, and a decrease in the overall TC frequency, in particular in the Bay of Bengal ( [[#Sahoo--2016|Sahoo and Bhaskaran, 2016]] ; [[#Balaji--2018|Balaji et al., 2018]] ; [[#Singh--2019|Singh et al., 2019]] ; [[#Baburaj--2020|Baburaj et al., 2020]] ). In the South Indian Ocean (SIO), an increase in the occurrence of the most intense TCs has been noted; however, there are well-known data quality issues there ( [[#Kuleshov--2010|Kuleshov et al., 2010]] ; [[#Fitchett--2018|Fitchett, 2018]] ). When the SIO data are homogenized, a significant increase is found in the fractional proportion of global Category 3–5 TC instances (6-hourly intensity estimates during the lifetime of each TC) to all Category 1–5 instances ( [[#Kossin--2020|Kossin et al., 2020]] ).

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[[File:0adffd6e83a0f7f63ea9c6dad9ad8006 IPCC_AR6_WGI_Figure_11_20.png]]

'''Figure 11.20 |''' '''Summary schematic of past and projected changes in tropical cyclone (TC), extratropical cyclone (ETC), atmospheric river (AR), and severe convective storm (SCS) behaviour.''' Global changes (blue shading) from top to bottom: ''(i)'' Increased mean and maximum rain rates in TCs, ETCs, and ARs [past ( ''low confidence'' due to lack of reliable data) and projected ( ''high confidence'' )]; ''(ii)'' Increased proportion of stronger TCs [past ( ''medium confidence'' ) and projected ( ''high confidence'' )]; ''(iii)'' Decrease or no change in global frequency of TC genesis [past ( ''low confidence'' due to lack of reliable data) and projected ( ''medium confidence'' )]; and (iv) Increased and decreased ETC wind speed, depending on the region, as storm tracks change [past ( ''low confidence'' due to lack of reliable data) and projected ( ''medium confidence'' )]. Regional changes, from left to right: ''(i)'' Poleward TC migration in the western North Pacific and subsequent changes in TC exposure [past ( ''medium confidence'' ) and projected ( ''medium'' ''confidence'' )]; ''(ii)'' Slowdown of TC forward translation speed over the contiguous USA and subsequent increase in TC rainfall [past ( ''medium confidence'' ) and projected ( ''low'' ''confidence'' due to lack of directed studies)]; and ''(iii)'' Increase in mean and maximum SCS rain rate and increase in spring SCS frequency and season length over the contiguous USA [past ( ''low confidence'' due to lack of reliable data) and projected ( ''medium confidence'' )].

As with all confined regional analyses of TC frequency, it is generally unclear whether any identified changes are due to a basin-wide change in TC frequency, or to systematic track shifts (or both). From an impacts perspective, however, these changes over land are highly relevant and emphasize that large-scale modifications in TC behaviour can have a broad spectrum of impacts on a regional scale.

Subsequent to AR5, two metrics have been analysed that are argued to be comparatively less sensitive to data issues than frequency- and intensity-based metrics. Trends in these metrics have been identified over the past 70 years or more ( [[#Knutson--2019|Knutson et al., 2019]] ). The first metric – the mean latitude where TCs reach their peak intensity – exhibits a global and regional poleward migration during the satellite period ( [[#Kossin--2014|Kossin et al., 2014]] ). The poleward migration can influence TC hazard exposure and risk ( [[#Kossin--2016a|Kossin et al., 2016a]] ) and is consistent with the independently observed expansion of the tropics ( [[#Lucas--2014|Lucas et al., 2014]] ). The migration has been linked to changes in the Hadley circulation ( [[#Altman--2018|Altman et al., 2018]] ; [[#Sharmila--2018|Sharmila and Walsh, 2018]] ; [[#Studholme--2018|Studholme and Gulev, 2018]] ). The migration is also apparent in the mean locations where TCs exhibit eyes ( [[#Knapp--2018|Knapp et al., 2018]] ), which is when TCs are most intense. Part of the Northern Hemisphere poleward migration is due to basin-wide changes in TC frequency ( [[#Kossin--2014|Kossin et al., 2014]] , 2016b; [[#Moon--2015|Moon et al., 2015]] , 2016) and the trends, as expected, can be sensitive to the time period chosen ( [[#Tennille--2017|Tennille and Ellis, 2017]] ; [[#Kossin--2018|Kossin, 2018]] ; [[#Song--2018|Song and Klotzbach, 2018]] ) and to subsetting of the data by intensity ( [[#Zhan--2017|Zhan and Wang, 2017]] ). The poleward migration is particularly pronounced and well-documented in the western North Pacific basin ( [[#Kossin--2016a|Kossin et al., 2016a]] ; [[#Oey--2016|Oey and Chou, 2016]] ; [[#Liang--2017|Liang et al., 2017]] ; [[#Nakamura--2017|Nakamura et al., 2017]] ; [[#Altman--2018|Altman et al., 2018]] ; [[#Daloz--2018|Daloz and Camargo, 2018]] ; J. [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|]] [[#Sun--2019|Sun et al., 2019]] ; [[#Lee--2020|]] [[#Lee--2020|T.-C. Lee et al., 2020]] ; [[#Yamaguchi--2020a|Yamaguchi and Maeda, 2020a]] ; [[#Kubota--2021|Kubota et al., 2021]] ).

A second metric that is argued to be comparatively less sensitive to data issues than frequency- and intensity-based metrics is TC translation speed ( [[#Kossin--2018|Kossin, 2018]] ), which exhibits a global slowdown in the best-track data over the period 1949–2016. TC translation speed is a measure of the speed at which TCs move across the Earth’s surface, and is very closely related to local rainfall amounts (i.e., a slower translation speed causes greater local rainfall). TC translation speed also affects structural wind damage and coastal storm surge by changing the hazard event duration. The slowdown is observed in the best-track data from all basins except the Northern Indian Ocean, and is also found in a number of regions where TCs interact directly with land. The slowing trends identified in the best-track data by [[#Kossin--2018|Kossin (2018)]] have been argued to be largely due to data heterogeneity. [[#Moon--2019|Moon et al. (2019)]] and [[#Lanzante--2019|Lanzante (2019)]] provide evidence that meridional TC track shifts project onto the slowing trends, and argue that these shifts are due to the introduction of satellite data. Kossin (2019) provides evidence that the slowing trend is real by focusing on Atlantic TC track data over the contiguous USA in the 118-year period 1900–2017, which are generally considered reliable. In this period, mean TC translation speed has decreased by 17%. The slowing TC translation speed is expected to increase local rainfall amounts, which would increase coastal and inland flooding. In combination with slowing translation speed, abrupt TC track direction changes – that can be associated with track ‘meanders’ or ‘stalls’ – have become increasingly common along the North American coast since the mid-20th century, leading to more rainfall in the region ( [[#Hall--2019|Hall and Kossin, 2019]] ).

In summary, there is mounting evidence that a variety of TC characteristics have changed over various time periods. It is ''likely'' that the global proportion of Category 3–5 tropical cyclone instances and the frequency of rapid intensification events have increased globally over the past 40 years. It is ''very likely'' that the average location where TCs reach their peak wind intensity has migrated poleward in the western North Pacific Ocean since the 1940s. It is ''likely'' that TC translation speed has slowed over the USA since 1900.

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==== 11.7.1.3 Model Evaluation ====

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Accurate projections of future TC activity have two principal requirements: accurate representation of changes in the relevant environmental factors (e.g., SSTs) that can affect TC activity, and accurate representation of actual TC activity in given environmental conditions.In particular, models’ capacity to reproduce historical trends or interannual variabilities of TC activity is relevant to the confidence in future projections. One test of the models is to evaluate their ability to reproduce the dependency of the TC statistics in the different basins in the real world, in addition to their capability of reproducing atmospheric and ocean environmental conditions. For the evaluation of projections of TC-relevant environmental variables, AR5 confidence statements were based on global surface temperature and moisture, but not on the detailed regional structure of SST and atmospheric circulation changes such as steering flows and vertical shear, which affect characteristics of TCs (genesis, intensity, tracks, etc.). Various aspects of TC metrics are used to evaluate how capable models are of simulating present-day TC climatologies and variability (e.g., TC frequency, wind intensity, precipitation, size, tracks, and their seasonal and interannual changes) ( [[#Walsh--2015|Walsh et al., 2015]] ; [[#Camargo--2016|Camargo and Wing, 2016]] ; [[#Knutson--2019|Knutson et al., 2019]] , 2020). Other examples of TC climatology/variability metrics are spatial distributions of TC occurrence and genesis ( [[#Walsh--2015|Walsh et al., 2015]] ), seasonal cycles and interannual variability of basin-wide activity ( [[#Zhao--2009|Zhao et al., 2009]] ; [[#Shaevitz--2014|Shaevitz et al., 2014]] ; [[#Kodama--2015|Kodama et al., 2015]] ; [[#Murakami--2015|Murakami et al., 2015]] ; [[#Yamada--2017|Yamada et al., 2017]] ) or landfalling activity ( [[#Lok--2018|Lok and Chan, 2018]] ), as well as newly developed process-diagnostics designed specifically for TCs in climate models (D. [[#Kim--2018|]] [[#Kim--2018|]] [[#Kim--2018|Kim et al., 2018]] ; [[#Wing--2019|Wing et al., 2019]] ; [[#Moon--2020|Moon et al., 2020]] ).

Confidence in the projection of intense TCs, such as those of Category 4–5, generally becomes higher as the resolution of the models becomes higher. The Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5/6) class climate models (around 100–200 km grid spacing) cannot simulate TCs of Category 4–5 intensity. They do simulate storms of relatively high vorticity that are at best described as ‘TC-like’, but metrics such as storm counts are highly dependent on tracking algorithms ( [[#Camargo--2013|Camargo, 2013]] ; [[#Wehner--2015|Wehner et al., 2015]] ; [[#Zarzycki--2017|Zarzycki and Ullrich, 2017]] ; [[#Roberts--2020a|Roberts et al., 2020a]] ). High-resolution GCMs (around 10–60 km grid spacing), as used in HighResMIP ( [[#Haarsma--2016|Haarsma et al., 2016]] ; [[#Roberts--2020a|Roberts et al., 2020a]] ), begin to capture some structures of TCs more realistically, as well as produce intense TCs of Category 4–5 despite the effects of parametrized deep cumulus convection processes ( [[#Murakami--2015|Murakami et al., 2015]] ; [[#Wehner--2015|Wehner et al., 2015]] ; [[#Yamada--2017|Yamada et al., 2017]] ; [[#Roberts--2018|Roberts et al., 2018]] ; [[#Moon--2020|Moon et al., 2020]] ). Convection-permitting models (around 1–10 km grid-spacing), such as used in some dynamical downscaling studies, provide further realism with capturing TC eye-wall structures ( [[#Tsuboki--2015|Tsuboki et al., 2015]] ). Model characteristics besides resolution, especially details of convective parametrization, can influence a model’s ability to simulate intense TCs ( [[#Reed--2011|Reed and Jablonowski, 2011]] ; [[#Zhao--2012|Zhao et al., 2012]] ; [[#He--2015|He and Posselt, 2015]] ; D. [[#Kim--2018|]] [[#Kim--2018|]] [[#Kim--2018|Kim et al., 2018]] ; [[#Zhang--2018|Zhang and Wang, 2018]] ; [[#Camargo--2020|Camargo et al., 2020]] ). However, models’ dynamical cores and other physics also affect simulated TC properties ( [[#Reed--2015|Reed et al., 2015]] ; [[#Vidale--2021|Vidale et al., 2021]] ). Both wide-area regional and global convection-permitting models without the need for parameterized convection are becoming more useful for TC regional model projection studies ( [[#Tsuboki--2015|Tsuboki et al., 2015]] ; [[#Kanada--2017a|Kanada et al., 2017a]] ; [[#Gutmann--2018|Gutmann et al., 2018]] ) and global model projection studies ( [[#Satoh--2015|Satoh et al., 2015]] , 2017; [[#Yamada--2017|Yamada et al., 2017]] ), as they capture more realistic TC eye wall structures ( [[#Kinter%20III--2013|Kinter III et al., 2013]] ) and are becoming more useful for investigating changes in TC structures ( [[#Kanada--2013|Kanada et al., 2013]] ; [[#Yamada--2017|Yamada et al., 2017]] ). Large ensemble simulations of GCMs with 60 km grid spacing provide TC statistics that allow more reliable detection of changes in the projections, which are not well captured in any single experiment ( [[#Yoshida--2017|Yoshida et al., 2017]] ; [[#Yamaguchi--2020|Yamaguchi et al., 2020]] ). Variable resolution global models offer an alternative to regional models for individual TC or basin-wide simulations ( [[#Yanase--2012|Yanase et al., 2012]] ; [[#Zarzycki--2014|Zarzycki et al., 2014]] ; [[#Harris--2016|Harris et al., 2016]] ; [[#Reed--2020|Reed et al., 2020]] ; [[#Stansfield--2020|Stansfield et al., 2020]] ). Computationally less intense than equivalent uniform resolution global models, they also do not require lateral boundary conditions, thus reducing this source of error ( [[#Hashimoto--2016|Hashimoto et al., 2016]] ). Confidence in the projection of TC statistics and properties is increased by the use of higher-resolution models with more realistic simulations.

Operational forecasting models also reproduce TCs, and their use for climate projection studies shows promise. However, there is limited application for future projections as they are specifically developed for operational purposes, and TC climatology is not necessarily well evaluated. Intercomparison of operational models indicates that enhancement of horizontal resolution can provide more credible projections of TCs ( [[#Nakano--2017|Nakano et al., 2017]] ). Likewise, high-resolution climate models show promise as TC forecast tools ( [[#Zarzycki--2015|Zarzycki and Jablonowski, 2015]] ; [[#Reed--2020|Reed et al., 2020]] ), further narrowing the continuum of weather and climate models, and increasing confidence in projections of future TC behaviour. However, higher horizontal resolution does not necessarily lead to an improved TC climatology ( [[#Camargo--2020|Camargo et al., 2020]] ).

Atmosphere–ocean interaction is an important process in TC evolution. Atmosphere–ocean coupled models are generally better than atmosphere-only models at capturing realistic processes related to TCs ( [[#Murakami--2015|Murakami et al., 2015]] ; [[#Ogata--2015|Ogata et al., 2015]] , 2016; [[#Zarzycki--2016|Zarzycki, 2016]] ; [[#Kanada--2017b|Kanada et al., 2017b]] ; [[#Scoccimarro--2017|Scoccimarro et al., 2017]] ). However, the basin-scale SST biases commonly found in atmosphere–ocean models can introduce substantial errors in the simulated TC number ( [[#Hsu--2019|Hsu et al., 2019]] ). Higher-resolution ocean models improve the simulation of TCs by reducing the SST climatology bias ( [[#Li--2018|Li and Sriver, 2018]] ; [[#Roberts--2020a|Roberts et al., 2020a]] ). Coarse resolution atmospheric models may degrade coupled model performance as well. For example, in a case study of Hurricane Harvey, [[#Trenberth--2018|Trenberth et al. (2018)]] suggested that the lack of realistic hurricane frequency and intensity within coupled climate models hampers the models’ ability to simulate SST and ocean heat content and their changes.

Even with higher-resolution atmosphere–ocean coupled models, TC projection studies still rely on assumptions in experimental design that introduce uncertainties. Computational constraints often limit the number of simulations, resulting in relatively small ensemble sizes and incomplete analyses of possible future SST magnitude and pattern changes ( [[#Zhao--2011|Zhao and Held, 2011]] ; [[#Knutson--2013|Knutson et al., 2013]] ). Uncertainties in aerosol forcing also are reflected in TC projection uncertainty ( [[#Wang--2014|Wang et al., 2014]] ).

Regional climate models (RCM) with grid spacing around 15–50 km can be used to study the projection of TCs. RCMs are run with lateral and surface boundary conditions, which are specified by the atmospheric state and SSTs simulated by GCMs. Various combinations of the lateral and surface boundary conditions can be chosen for RCM studies, and uncertainties in the projection can be further examined in general. They are used for studying changes in TC characteristics in a specific area, such as Vietnam ( [[#Redmond--2015|Redmond et al., 2015]] ) and the Philippines ( [[#Gallo--2019|Gallo et al., 2019]] ).

Less computationally expensive downscaling approaches that allow larger ensembles and long-term studies are also used in the projection of TCs ( [[#Emanuel--2006|Emanuel et al., 2006]] ; C.Y. [[#Lee--2018|]] [[#Lee--2018|]] [[#Lee--2018|Lee et al., 2018]] ). A statistical–dynamical TC downscaling method requires assumptions of the rate of seeding of random initial disturbances, which are generally assumed to not change with climate change ( [[#Emanuel--2008|Emanuel et al., 2008]] ; [[#Emanuel--2013|Emanuel, 2013]] ). The results with the downscaling approach might depend on the assumptions, which are required for the simplification of the methods.

In summary, various types of models are useful to study how TCs change in response to climate changes, and there is no unique solution for choosing a model type. However, higher-resolution models generally capture TC properties more realistically ( ''high confidence'' ). In particular, models with horizontal resolutions of 10–60 km are capable of reproducing strong TCs with Category 4–5 and those of 1–10 km are capable of the eye wall structure of TCs. Uncertainties in TC simulations come from details of the model configuration of both dynamical and physical processes. Models with realistic atmosphere–ocean interactions are generally better than atmosphere-only models at reproducing realistic TC evolutions ( ''hi'' ''gh confidence'' ).

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==== 11.7.1.4 Detection and Attribution, Event Attribution ====

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There is general agreement in the literature that anthropogenic greenhouse gases and aerosols have measurably affected observed oceanic and atmospheric variability in TC-prone regions (see Chapter 3). This underpinned the SROCC assessment of ''medium confidence'' that humans have contributed to the observed increase in Atlantic hurricane activity since the 1970s (Chapter 5, [[#Bindoff--2013|Bindoff et al., 2013]] ). Literature subsequent to AR5 lends further support to this statement ( [[#Knutson--2019|Knutson et al., 2019]] ). However, there is still no consensus on the relative magnitude of human and natural influences on past changes in Atlantic hurricane activity, and particularly on which factor has dominated the observed increase ( [[#Ting--2015|Ting et al., 2015]] ) and it remains uncertain whether past changes in Atlantic TC activity are outside the range of natural variability. A recent result using high-resolution dynamical model experiments suggested that the observed spatial contrast in TC trends cannot be explained only by multi-decadal natural variability, and that external forcing plays an important role ( [[#Murakami--2020|Murakami et al., 2020]] ).Observational evidence for significant global increases in the proportion of major TC intensities ( [[#Kossin--2020|Kossin et al., 2020]] ) is consistent with both theory and numerical modelling simulations, which generally indicate an increase in mean TC peak intensity and the proportion of very intense TCs in a warming world ( [[#Knutson--2015|Knutson et al., 2015]] , 2020; [[#Walsh--2015|Walsh et al., 2015]] , 2016). In addition, high-resolution coupled model simulations provide support that natural variability alone is ''unlikely'' to explain the magnitude of the observed increase in TC intensification rates and upward TC intensity trend in the Atlantic basin since the early 1980s ( [[#Bhatia--2019|Bhatia et al., 2019]] ; [[#Murakami--2020|Murakami et al., 2020]] ).

The cause of the observed slowdown in TC translation speed is not yet clear. [[#Yamaguchi--2020|Yamaguchi et al. (2020)]] used large ensemble simulations to argue that part of the slowdown is due to actual latitudinal shifts of TC tracks, rather than data artefacts, in addition to atmospheric circulation changes. G. [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|Zhang et al. (2020)]] used large ensemble simulations to show that anthropogenic forcing can lead to a robust slowdown, particularly outside of the tropics at higher latitudes. [[#Yamaguchi--2020b|Yamaguchi and Maeda (2020b)]] found a significant slowdown in the western North Pacific over the past 40 years and attributed the slowdown to a combination of natural variability and global warming. The slowing trend since 1900 over the USA is robust and significant after removing multi-decadal variability from the time series (Kossin, 2019). Among the hypotheses discussed is the physical linkage between warming and slowing circulation ( [[#Held--2006|Held and Soden, 2006]] ; see also [[IPCC:Wg1:Chapter:Chapter-8#8.2.2.2|Section 8.2.2.2]] ), with expectations of Arctic amplification and weakening circulation patterns through weakening meridional temperature gradients ( [[#Coumou--2018|Coumou et al., 2018]] ; see also Cross-Chapter Box 10.1), or through changes in planetary wave dynamics ( [[#Mann--2017|Mann et al., 2017]] ). The tropics expansion and the poleward shift of the mid-latitude westerlies associated with warming is also suggested as the reason of the slowdown (G. [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|Zhang et al., 2020]] ). However, the connection of these mechanisms to the slowdown has not been robustly shown. Furthermore, slowing trends have not been unambiguously observed in circulation patterns that steer TCs, such as the Walker and Hadley circulations ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4|Section 2.3.1.4]] ), although these circulations generally slow down in numerical simulations under global warming (Sections 4.5.1.6 and 8.4.2.2).

The observed poleward trend in western North Pacific TCs remains significant after accounting for the known modes of dominant interannual to decadal variability in the region ( [[#Kossin--2016a|Kossin et al., 2016a]] ), and is also found in CMIP5 model-simulated TCs (in the recent historical period 1980–2005), although it is weaker than observed and is not statistically significant ( [[#Kossin--2016a|Kossin et al., 2016a]] ). However, the trend is significant in 21st-century CMIP5 projections under the RCP8.5 scenario, with a similar spatial pattern and magnitude to the past observed changes in that basin over the period 1945–2016, supporting a possible anthropogenic greenhouse gas contribution to the observed trends ( [[#Kossin--2016a|Kossin et al., 2016a]] ; [[#Knutson--2019|Knutson et al., 2019]] ).

The recent active TC seasons in some basins have been studied to determine whether there is anthropogenic influence. For 2015, [[#Murakami--2017b|Murakami et al. (2017b)]] explored the unusually high TC frequency near Hawaii and in the eastern Pacific basin. W. [[#Zhang--2016b|Zhang et al. (2016b)]] considered unusually high Accumulated Cyclone Energy (ACE) in the western North Pacific; and S.-H. [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|Yang et al. (2018)]] and [[#Yamada--2019|Yamada et al. (2019)]] looked at TC intensification in the western North Pacific. These studies suggest that the anomalous TC activity in 2015 was not solely explained by the effect of an extreme El Niño (see Box 11.4) and that there was also an anthropogenic contribution, mainly through the effects of SSTs in subtropical regions. In the post-monsoon seasons of 2014 and 2015, tropical storms with lifetime maximum winds greater than 46 m s <sup>−1</sup> were first observed over the Arabian Sea, and [[#Murakami--2017a|Murakami et al. (2017a)]] showed that the probability of late-season severe tropical storms is increased by anthropogenic forcing compared to the preindustrial era. [[#Murakami--2018|Murakami et al. (2018)]] concluded that the active 2017 Atlantic hurricane season was mainly caused by pronounced SSTs in the tropical North Atlantic and that these types of seasonal events will intensify with projected anthropogenic forcing. The trans-basin SST change, which might be driven by anthropogenic aerosol forcing, also affects TC activity. [[#Takahashi--2017|Takahashi et al. (2017)]] suggested that a decrease in sulphate aerosol emissions caused about half of the observed decreasing trends in TC genesis frequency in the south-eastern region of the western North Pacific during 1992–2011.

Event attribution is used in TC case studies to test whether the severities of recent intense TCs are explained without anthropogenic effects. In a case study of Hurricane Sandy (2012), [[#Lackmann--2015|Lackmann (2015)]] found no statistically significant impact of anthropogenic climate change on storm intensity, while projections in a warmer world showed significant strengthening. However, [[#Magnusson--2014|Magnusson et al. (2014)]] found that, in European Centre for Medium-Range Weather Forecast (ECMWF) simulations, the simulated cyclone depth and intensity, as well as precipitation, were larger when the model was driven by the warmer actual SSTs than the climatological average SSTs. In Super Typhoon Haiyan, which struck the Philippines on 8 November 2013, [[#Takayabu--2015|Takayabu et al. (2015)]] took an event attribution approach with cloud system-resolving (around 1 km) downscaling ensemble experiments to evaluate the anthropogenic effect on typhoons, and showed that the intensity of the simulated worst-case storm in the actual conditions was stronger than that in a hypothetical condition without historical anthropogenic forcing in the model. However, in a similar approach with two coarser parametrized convection models, Wehner et al. (2019) found conflicting human influences on Haiyan’s intensity. [[#Patricola--2018|Patricola and Wehner (2018)]] found little evidence of an attributable change in intensity of hurricanes Katrina (2005), Irma (2017), and Maria (2017) using a regional climate model configured between 3 km and 4.5 km resolution. They did, however, find attributable increases in heavy precipitation totals. These results imply that higher resolution, such as in a convective permitting 5 km or less mesh model, is required to obtain a robust anthropogenic intensification of a strong TC by simulating realistic rapid intensification ( [[#Kanada--2016|Kanada and Wada, 2016]] ; [[#Kanada--2017a|Kanada et al., 2017a]] ), and that whether the TC intensification can be attributed to the recent warming depends on the case.

The dominant factor in the extreme rainfall amounts during Hurricane Harvey’s passage onto the USA in 2017 was its slow translation speed. But studies published after the event have argued that anthropogenic climate change contributed to an increase in rain rate, which compounded the extreme local rainfall caused by the slow translation. [[#Emanuel--2017|Emanuel (2017)]] used a large set of synthetically-generated storms and concluded that the occurrence of extreme rainfall as observed in Harvey was substantially enhanced by anthropogenic changes to the larger-scale ocean and atmosphere characteristics; [[#Trenberth--2018|Trenberth et al. (2018)]] linked Harvey’s rainfall totals to the anomalously large ocean heat content from the Gulf of Mexico; and [[#van%20Oldenborgh--2017|van Oldenborgh et al. (2017)]] and [[#Risser--2017|Risser and Wehner (2017)]] applied extreme value analysis to extreme rainfall records in the Houston, Texas region, both attributing large increases to climate change. Large precipitation increases during Harvey due to global warming were also found using climate models ( [[#van%20Oldenborgh--2017|van Oldenborgh et al., 2017]] ; S.-Y.S. [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ). Harvey precipitation totals were estimated in these papers to be three to 10 times more probable due to climate change. A best estimate from a regional climate and flood model is that urbanization increased the risk of the Harvey flooding by a factor of 21 (W. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ), using a regional climate and flood model, found that surface roughness from urbanization increased the risk of the Harvey flooding by a factor of 21. Anthropogenic effects on precipitation increases were also predicted in advance from a forecast model for Hurricane Florence in 2018 ( [[#Reed--2020|Reed et al., 2020]] ).

In summary, it is ''very likely'' that the recent active TC seasons in the North Atlantic, the North Pacific, and Arabian basins cannot be explained without an anthropogenic influence. The anthropogenic influence on these changes is principally associated to aerosol forcing, with stronger contributions to the response in the North Atlantic. It is ''more likely than not'' that the slowdown of TC translation speed over the USA has contributions from anthropogenic forcing. It is ''likely'' that the poleward migration of TCs in the western North Pacific and the global increase in TC intensity rates cannot be explained entirely by natural variability. Event attribution studies of specific strong TCs provide ''limited evidence'' for anthropogenic effects on TC intensifications so far, but ''high confidence'' for increases in TC heavy precipitation. There is ''high confidence'' that anthropogenic climate change contributed to extreme rainfall amounts during Hurricane Harvey (2017) and other intense TCs.

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==== 11.7.1.5 Projections ====

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A summary of studies on TC projections for the late 21st century, particularly studies since AR5, is given by [[#Knutson--2020|Knutson et al. (2020)]] , which is an assessment report mandated by the World Meteorological Organization (WMO). Studies subsequent to [[#Knutson--2020|Knutson et al. (2020)]] are generally consistent, and the confidence assessments here closely follow theirs ( [[#Cha--2020|Cha et al., 2020]] ), although there are some differences due to the varying confidence calibrations between the IPCC and WMO reports.

There is not an established theory for the drivers of future changes in the frequency of TCs. Most, but not all, high-resolution global simulations project significant reductions in the total number of TCs, with the bulk of the reduction at the weaker end of the intensity spectrum as the climate warms ( [[#Knutson--2020|Knutson et al., 2020]] ). Recent exceptions based on high-resolution coupled model results arenoted in [[#Bhatia--2018|Bhatia et al. (2018)]] and [[#Vecchi--2019|Vecchi et al. (2019)]] . [[#Vecchi--2019|Vecchi et al. (2019)]] showed that the representation of synoptic-scale seeds for TC genesis in their high-resolution model causes different projections of global TC frequency, and there is evidence for a decrease in cyclone seeds in some projected TCsimulations ( [[#Sugi--2020|Sugi et al., 2020]] ; Yamada et al., 2011). However, other research indicates that TC seeds are not an independent control on climatological TC frequency, rather the seeds covary with the large-scale controls on TCs ( [[#Patricola--2018|Patricola et al., 2018]] ). While empirical genesis indices derived from observations and reanalysis describe well the observed subseasonal and interannual variability of current TC frequency ( [[#Camargo--2007|Camargo et al., 2007]] , 2009; [[#Tippett--2011|Tippett et al., 2011]] ; [[#Menkes--2012|Menkes et al., 2012]] ), they fail to predict the decreased TC frequency found in most high-resolution model simulations ( [[#Zhang--2010|Zhang et al., 2010]] ; [[#Camargo--2013|Camargo, 2013]] ; [[#Wehner--2015|Wehner et al., 2015]] ), as they generally project an increase as the climate warms. This suggests a limitation of the use of the empirical genesis indices for projections of TC genesis, in particular due to their sensitivity to the humidity variable considered in the genesis index for these projections ( [[#Camargo--2014|Camargo et al., 2014]] ). In a different approach, a statistical–dynamical downscaling framework assuming a constant seeding rate with warming ( [[#Emanuel--2013|Emanuel, 2013]] , 2021) exhibits increases in TC frequency consistent with genesis indices-based projections, while downscaling with a different model leads to two different scenarios depending on the humidity variable considered (C.-Y. [[#Lee--2020|]] [[#Lee--2020|Lee et al., 2020]] ). This disparity in the sign of the projected change in global TC frequency, and the difficulty in explaining the mechanisms behind the different signed responses, further emphasize the lack of process understanding of future changes in tropical cyclogenesis ( [[#Walsh--2015|Walsh et al., 2015]] ; [[#Hoogewind--2020|Hoogewind et al., 2020]] ). Even within a single model, uncertainty in the pattern of future SST changes leads to large uncertainties (including the sign) in the projected change in TC frequency in individual ocean basins, although global TCs would appear to be less sensitive ( [[#Yoshida--2017|Yoshida et al., 2017]] ; [[#Bacmeister--2018|Bacmeister et al., 2018]] ).

Changes in SST and atmospheric temperature and moisture play a role in tropical cyclogenesis ( [[#Walsh--2015|Walsh et al., 2015]] ). Reductions in vertical convective mass flux due to increased tropical stability have been associated with a reduction in cyclogenesis ( [[#Held--2011|Held and Zhao, 2011]] ; [[#Sugi--2012|Sugi et al., 2012]] ). [[#Satoh--2015|Satoh et al. (2015)]] further posit that the robust simulated increase in the number of intense TCs, and hence increased vertical mass flux associated with intense TCs, must lead to a decrease in overall TC frequency because of this association. The Genesis Potential Index can be modified to mimic the TC frequency decreases of a model by altering the treatment of humidity ( [[#Camargo--2014|Camargo et al., 2014]] ). This supports the idea that increased mid-tropospheric saturation deficit ( [[#Emanuel--2008|Emanuel et al., 2008]] ) controls TC frequency, but the approach remains empirical. Other possible controlling factors, such as a decline in the number of seeds (held constant in Emanuel’s downscaling approach, or dependent on the genesis index formulation in the approach proposed by C.-Y. [[#Lee--2020|]] [[#Lee--2020|Lee et al., 2020]] ) caused by increased atmospheric stability have been proposed, but questioned as an important factor ( [[#Patricola--2018|Patricola et al., 2018]] ). The resolution of atmospheric models affects the number of seeds, hence TC genesis frequency ( [[#Vecchi--2019|Vecchi et al., 2019]] ; [[#Sugi--2020|Sugi et al., 2020]] ; [[#Yamada--2021|Yamada et al., 2021]] ). The diverse and sometimes inconsistent projected changes in global TC frequency by high-resolution models indicate that better process understanding and improvement of the models are needed to raise confidence in these changes.

Most TC-permitting model simulations (10–60 km or finer grid spacing) are consistent in their projection of increases in the proportion of intense TCs (Category 4–5), as well as an increase in the intensity of the strongest TCs defined by maximum wind speed or central pressure fall ( [[#Murakami--2012|Murakami et al., 2012]] ; [[#Tsuboki--2015|Tsuboki et al., 2015]] ; [[#Wehner--2018a|Wehner et al., 2018a]] ; [[#Knutson--2020|Knutson et al., 2020]] ). The general reduction in the total number of TCs, which is concentrated in storms weaker than or equal to Category 1, contributes to this increase. The models are somewhat less consistent in projecting an increase in the frequency of Category 4–5TCs (Wehner et al., 2018a; [[#Knutson--2020|Knutson et al., 2020]] ). The projected increase in the intensity of the strongest TCs is consistent with theoretical understanding (e.g., [[#Emanuel--1987|Emanuel, 1987]] ) and observations (e.g., [[#Kossin--2020|Kossin et al., 2020]] ). For a 2°C global warming, the median proportion of Category 4–5 TCs increases by 13%, while the median global TC frequency decreases by 14%, which implies that the median of the global Category 4–5 TC frequency is slightly reduced by 1% or almost unchanged ( [[#Knutson--2020|Knutson et al., 2020]] ). [[#Murakami--2020|Murakami et al. (2020)]] projected a decrease in TC frequency over the coming century in the North Atlantic due to greenhouse warming, as consistent with [[#Dunstone--2013|Dunstone et al. (2013)]] , and a reduction in TC frequency almost everywhere in the tropics in response to +1% CO <sub>2</sub> forcing. Exceptions include the central North Pacific (Hawaii region), east of the Philippines in the North Pacific, and two relatively small regions in the northern Arabian Sea and Bay of Bengal. These projections can vary substantially between ocean basins, possibly due to differences in regional SST warming and warming patterns ( [[#Sugi--2017|Sugi et al., 2017]] ; [[#Yoshida--2017|Yoshida et al., 2017]] ; [[#Bacmeister--2018|Bacmeister et al., 2018]] ). A summary of projections of TC characteristics is schematically shown by Figure 11.20.

The increase in global TC maximum surface wind speeds is about 5% for a 2°C global warming across a number of high-resolution multi-decadal studies ( [[#Knutson--2020|Knutson et al., 2020]] ). This indicates the deepening in global TC minimum surface pressure under the global warming conditions. A regional cloud-permitting model study shows that the strongest TC in the western North Pacific can be as strong as 857 hPa in minimum surface pressure with a wind speed of 88 m s <sup>–1</sup> under warming conditions in 2074–2087 ( [[#Tsuboki--2015|Tsuboki et al., 2015]] ). TCs are also measured by quantities such as ACE and the power dissipation index (PDI), which conflate TC intensity, frequency, and duration ( [[#Murakami--2014|Murakami et al., 2014]] ). Several TC modelling studies ( [[#Yamada--2010|Yamada et al., 2010]] ; H.S. [[#Kim--2014|]] [[#Kim--2014|Kim et al., 2014]] ; [[#Knutson--2015|Knutson et al., 2015]] ) project little change or decreases in the globally accumulated value of PDI or ACE, which is due to the decrease in the total number of TCs.

A projected increase in global average TC rain rates of about 12% for a 2°C global warming is consistent with the Clausius–Clapeyron scaling of saturation-specific humidity ( [[#Knutson--2020|Knutson et al., 2020]] ). Increases substantially greater than Clausius–Clapeyron scaling are projected in some regions, which is caused by increased low-level moisture convergence due to projected TC intensity increases in those regions ( [[#Knutson--2015|Knutson et al., 2015]] ; [[#Phibbs--2016|Phibbs and Toumi, 2016]] ; [[#Patricola--2018|Patricola and Wehner, 2018]] ; M. [[#Liu--2019|Liu et al., 2019]] a). Projections of TC precipitation using large-ensemble experiments ( [[#Kitoh--2019|Kitoh and Endo, 2019]] ) show that the annual maximum one-day precipitation total is projected to increase, except for the western North Pacific where only a small change (or even a reduction) is projected, mainly due to a projected decrease of TC frequency. They also show that the 10-year return value of extreme Rx1day associated with TCs will greatly increase in a region extending from Hawaii to the south of Japan. TC tracks and the location of topography relative to TCs significantly affect precipitation, thus, in general, areas on the eastern and southern faces of mountains have more impacts of TC precipitation changes ( [[#Hatsuzuka--2020|Hatsuzuka et al., 2020]] ). Projection studies using variable-resolution models in the North Atlantic ( [[#Stansfield--2020|Stansfield et al., 2020]] ) indicate that TC-related precipitation rates within North Atlantic TCs and the amount of hourly precipitation due to TC are projected to increase by the end of the century compared to a historical simulation. However, the annual average TC-related Rx5day over the eastern USA is projected to decrease because of a reduction in landfalling TCs. RCM studies with around 25–50 km grid spacing are used to study projected changes in TCs. The projected changes of TCs in South East Asia simulated by RCMs are consistent with those of most GCMs, showing a decrease in TC frequency and an increase in the amount of TC-associated precipitation or an increase in the frequency of intense TCs ( [[#Redmond--2015|Redmond et al., 2015]] ; [[#Gallo--2019|Gallo et al., 2019]] ).

Projected changes in TC tracks or TC areas of occurrence in the late 21st century vary considerably among available studies, although there is better agreement in the western North Pacific. Several studies project either poleward or eastward expansion of TC occurrence over the western North Pacific region, and more TC occurrence in the central North Pacific ( [[#Yamada--2017|Yamada et al., 2017]] ; [[#Yoshida--2017|Yoshida et al., 2017]] ; [[#Wehner--2018a|Wehner et al., 2018a]] ; [[#Roberts--2020b|Roberts et al., 2020b]] ). The observed poleward expansion of the latitude of maximum TC intensity in the western North Pacific is consistently reproduced by the CMIP5 models and downscaled models, and these models show further poleward expansion in the future; the projected mean migration rate of the mean latitude where TCs reach their lifetime-maximum intensity is 0.2±0.1° from CMIP5 model results, while it is 0.13±0.04° from downscaled models in the western North Pacific ( [[#Kossin--2014|Kossin et al., 2014]] , 2016a). In the North Atlantic, while the location of TC maximum intensity does not show clear poleward migration observationally ( [[#Kossin--2014|Kossin et al., 2014]] ), it tends to migrate poleward in projections ( [[#Garner--2017|Garner et al., 2017]] ). The poleward migration is less robust among models and observations in the Indian Ocean, eastern North Pacific, and South Pacific (e.g., [[#Tauvale--2019|Tauvale and Tsuboki, 2019]] ; Ramsay et al. 2018; Cattiaux et al. 2020). There is presently no clear consensus in projected changes in TC translation speed ( [[#Knutson--2020|Knutson et al., 2020]] ), although recent studies suggest a slowdown outside of the tropics (Kossin, 2019; [[#Yamaguchi--2020|Yamaguchi et al., 2020]] ; G. [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|]] [[#Zhang--2020|Zhang et al., 2020]] ), but regionally there can even be an acceleration of the storms ( [[#Hassanzadeh--2020|Hassanzadeh et al., 2020]] ).

The spatial extent, or ‘size’, of the TC wind field is an important determinant of storm surge and damage. No detectable anthropogenic influences on TC size have been identified to date, because TCs in observations vary in size substantially ( [[#Chan--2015|Chan and Chan, 2015]] ) and there is no definite theory on what controls TC size, although this is an area of active research ( [[#Chavas--2014|Chavas and Emanuel, 2014]] ; [[#Chan--2018|Chan and Chan, 2018]] ). However, projections by high-resolution models indicate future broadening of TC wind fields when compared to TCs of the same categories ( [[#Yamada--2017|Yamada et al., 2017]] ), while [[#Knutson--2015|Knutson et al. (2015)]] simulate a reasonable interbasin distribution of TC size climatology, but project no statistically significant change in global average TC size. A plausible mechanism is that, as the tropopause height becomes higher with global warming, the eye wall areas become wider because the eye walls are inclined outward with height to the tropopause. This effect is only reproduced in high-resolution convection-permitting models capturing eye walls, and such modelling studies are not common. Moreover, the projected TC size changes are generally on the order of 10% or less, and these size changes are still highly variable between basins and studies. Thus, the projected change in both magnitude and sign of TC size is uncertain.

The coastal effects of TCs depend on TC intensity, size, track, and translation speed. Projected increases in sea level, average TC intensity, and TC rainfall rates each generally act to further elevate future storm surge and fresh-water flooding (see [[IPCC:Wg1:Chapter:Chapter-9#9.6.4.2|Section 9.6.4.2]] ). Changes in TC frequency could contribute toward increasing or decreasing future storm surge risk, depending on the net effects of changes in weaker vs stronger storms. Several studies ( [[#McInnes--2014|McInnes et al., 2014]] , 2016; [[#Little--2015|Little et al., 2015]] ; [[#Garner--2017|Garner et al., 2017]] ; [[#Timmermans--2017|Timmermans et al., 2017]] , 2018) have explored future projections of storm surge in the context of anthropogenic climate change with the influence of both sea level rise and future TC changes. [[#Garner--2017|Garner et al. (2017)]] investigated the near-future changes in the New York City coastal flood hazard, and suggested a small change in storm-surge height because effects of TC intensification are compensated by the offshore shifts in TC tracks, but concluded that the overall effect due to the rising sea levels would increase the flood hazard. Future projection studies of storm surge in East Asia, including China, Japan and Korea, also indicate that storm surges due to TCs become more severe ( [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|]] [[#Yang--2018|J.A. Yang et al., 2018]] ; [[#Mori--2019|Mori et al., 2019]] , 2021; J. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] b). For the Pacific Islands, [[#McInnes--2014|McInnes et al. (2014)]] found that the future projected increase in storm surge in Fiji is dominated by sea level rise, and projected TC changes make only a minor contribution. Among various storm surge factors, there is ''high confidence'' that sea level rise will lead to a higher possibility of extreme coastal water levels in most regions, with all other factors assumed equal.

In the North Atlantic, vertical wind shear, which inhibits TC genesis and intensification, varies in a quasi-dipole pattern, with one centre of action in the tropics and another along the south-east USA coast ( [[#Vimont--2007|Vimont and Kossin, 2007]] ). This pattern of variability creates a protective barrier of high shear along the USA coast during periods of heightened TC activity in the tropics ( [[#Kossin--2017|Kossin, 2017]] ), and appears to be a natural part of the Atlantic ocean–atmosphere climate system ( [[#Ting--2019|Ting et al., 2019]] ). Greenhouse gas forcing in CMIP5 and the Community Earth System Model Large Ensemble ( [[#Kay--2015|Kay et al., 2015]] ) simulations, however, erodes the pattern and degrades the natural shear barrier along the USA coast. Following the RCP8.5 emissions scenario, the magnitude of the erosion of the barrier equals the amplitude of past natural variability (time of emergence) by the mid-21st century ( [[#Ting--2019|Ting et al., 2019]] ). The projected reduction of shear along the USA East Coast with warming is consistent among studies (e.g., [[#Vecchi--2007|Vecchi and Soden, 2007]] ).

In summary, average peak TC wind speeds and the proportion of Category 4–5 TCs will ''very likely'' increase globally with warming. It is ''likely'' that the frequency of Category 4–5 TCs will increase in limited regions over the western North Pacific. It is ''very likely'' that average TC rain rates will increase with warming, and ''likely'' that the peak rain rates will increase at rate greater than the Clausius–Clapeyron scaling rate of 7% per 1°C of warming in some regions due to increased low-level moisture convergence caused by regional increases in TC wind intensity. It is ''likely'' that the average location where TCs reach their peak wind intensity will migrate poleward in the western North Pacific Ocean as the tropics expand with warming, and that the global frequency of TCs over all categories will decrease or remain unchanged.

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