Tropical cyclones and climate change

TL;DR: In this paper, the characteristics of tropical cyclones have changed or will change in a warming climate and if so, how, has been the subject of considerable investigation, often with conflicting results.

Abstract: Whether the characteristics of tropical cyclones have altered, or will alter, in a changing climate has been subject of considerable debate. An overview of recent research indicates that greenhouse warming will cause stronger storms, on average, but a decrease in the frequency of tropical cyclones. Whether the characteristics of tropical cyclones have changed or will change in a warming climate — and if so, how — has been the subject of considerable investigation, often with conflicting results. Large amplitude fluctuations in the frequency and intensity of tropical cyclones greatly complicate both the detection of long-term trends and their attribution to rising levels of atmospheric greenhouse gases. Trend detection is further impeded by substantial limitations in the availability and quality of global historical records of tropical cyclones. Therefore, it remains uncertain whether past changes in tropical cyclone activity have exceeded the variability expected from natural causes. However, future projections based on theory and high-resolution dynamical models consistently indicate that greenhouse warming will cause the globally averaged intensity of tropical cyclones to shift towards stronger storms, with intensity increases of 2–11% by 2100. Existing modelling studies also consistently project decreases in the globally averaged frequency of tropical cyclones, by 6–34%. Balanced against this, higher resolution modelling studies typically project substantial increases in the frequency of the most intense cyclones, and increases of the order of 20% in the precipitation rate within 100 km of the storm centre. For all cyclone parameters, projected changes for individual basins show large variations between different modelling studies.

Summary

1. Introduction:

• Much of their knowledge of gastropod innate immunity has come from investigations of snails that serve as intermediate hosts for the human blood flukes of the genus Schistosoma.
• Schistosomes infect over 200 million people worldwide causing both acute and chronic, debilitating diseases [1, 2].
• There are no effective vaccines against schistosomes, and treatment still relies on a single drug, praziquantel [3].
• As praziquantel resistance can be easily selected in the laboratory [4] and some human populations subjected to mass treatment now show evidence of reduced drug susceptibility [5], alternate control strategies are necessary, including strategies for blocking transmission via the snail intermediate host.
• Understanding the molecular mechanisms of the snail’s internal defense system, especially those mediating resistance to schistosomes (and other helminths), could give valuable clues for developing new strategies to disrupt disease transmission.

2. Insights from non-targeted transcriptomic studies

• Only a limited number have used non-targeted immune gene discovery methods to provide a survey of potentially immune-related genes.
• In addition to usual anticipated candidates such as transcripts coding for enzymes involved in oxidative response or cell adhesion proteins, several unexpected transcripts were identified.
• B. glabrata snails were exposed to various infectious agents such as trematodes [12-14], bacteria [12, 15, 16], or fungi [15], followed by cDNAs analyses using various techniques involving ORESTES or EST sequencing [14], microarrays [12, 13] and NGS technologies [15] in order to identify genes responding to these challenges.
• The potential existence and involvement of a MIF cytokine in B. glabrata immunity was investigated functionally by RNAi knockdown, in which snail MIF protein was shown to exhibit cellular immune activities similar to those of well known mammalian cytokines [17].
• Using a proteomic approach LBP/BPI was identified as a major protein of B. glabrata egg masses [20] and was shown to exhibit a protective its antimicrobial activity [21].

3. Insights from comparisons of susceptible and resistant snail strains

• The low prevalence of snails with patent schistosome infection usually observed in transmission foci [22, 23] raises the possibility that partial innate resistance could exist in natural snail populations.
• Interestingly, previous histological studies showed that hemocytic encapsulation reactions to E. caproni sporocysts occurred between 48 and 72 hours post exposure [28] in resistant snails, corresponding to the timing of hemocyte dermatopontin gene up-regulation.
• One of the more promising immune relevant candidates was a gene cluster displaying similarities to the defense factor Aplysianin A, originally purified from the albumen gland of Aplysia kurodai [33].
• A reverse genetic approach using linkage analysis of polymorphic expressed sequence tags (ESTs) - expressed simple sequence repeats - and previously identified bi-allelic microsatellite markers, genomic (g)SSRs were used, resulting in the identification of putative genomic locations for resistance gene loci [44].
• TEP1 is crucial for phagocytosis of bacteria and killing of Plasmodium parasites in the mosquito Anopheles gambiae.

6. Conclusion:

• Research over the past decade has provided major advances in the field of molluscan immunity, particularly concerning the snail B. glabrata.
• A diversity of experimental systems comprising different strains of B. glabrata and trematode parasites that display various levels of compatibility has clearly shown that the mechanisms underlying the success or failure of parasite development are multigenic and variable among and within populations.
• To date, the contribution of environmental factors as determinant of compatibility between snails and their parasites has probably been underestimated.
• The assembly and annotation of the B. glabrata genome will facilitate continued development of molecular level studies that are likely to provide novel insights important for addressing this important question.
• Therefore, the resolution of a specific interaction between an individual B. glabrata and a particular digenean parasite depends on the integration of numerous individual parameters.

Figures

FIGURE 5 | Fractional future changes (%) in TC genesis number for each basin for a group of ensemble experiments, for three different convection schemes and four different future SST patterns. The error bars indicate 90% confidence intervals. Blue bars indicate that projected future changes that are statistically significant at 90% level according to the two-sided Student’s t-test. The number in the top left corner of each panel shows the standard deviation of twelve ensemble experiments (YG experiment is not included). (Reprinted with permission from Ref. 19 Copyright 2012 Springer)

FIGURE 2 | Climatology of a revised genesis index per year for (a) HiRAM climatology, (b) NCEP reanalysis, and (c) ERA-40. (Reprinted with permission from Ref. 139 Copyright 2014 American Meteorological Society)

FIGURE 4 | Global distribution of TC tracks during all seasons from 1979 to 2003 for (a) observations, (b) and (c), two current climate TC simulations, and (d) the global warming projection using the same GCM as for (c). The numbers for each basin show the annual mean number of TCs. TC tracks are colored according to the intensities of the TCs as categorized by the Saffir–Simpson hurricane wind scale (e.g., tropical depression [TD], tropical storms [TSs], and TC category C1–C5). (Reprinted with permission from Ref. 19 Copyright 2012 American Meteorological Society)

FIGURE 1 | Plots of quantiles (mean to 0.9) of the lifetime maximum intensity (LMI) of storms in the various tropical cyclone formation basins, from a homogenized satellite-based analysis of tropical cyclone intensity (1982–2009). (Reprinted with permission from Ref. 3 Copyright 2013 American Meteorological Society)

Full text

Advanced Review
Tropical cyclones and
climate change
Kevin J.E. Walsh,
1
John L. McBride,
2
Philip J. Klotzbach,
3
Sethurathinam Balachandran,
4
Suzana J. Camargo,
5
Greg Holland,
6
Thomas R. Knutson,
7
James P. Kossin,
8
Tsz-cheung Lee,
9
Adam Sobel
10
and Masato Sugi
11
Edited by Matilde Rusticucci, Domain Editor, and Mike Hulme, Editor-in-Chief
Recent research has strengthened the understanding of the links between cli-
mate and tropical cyclones (TCs) on various timescales. Geological records of
past climates have shown century-long variations in TC numbers. While no sig-
nificant trends have been identified in the Atlantic since the late 19th century,
significant observed trends in TC numbers and intensities have occurred in this
basin over the past few decades, and trends in other basins are increasingly
being identified. However, understanding of the causes of these trends is incom-
plete, and confidence in these trends continues to be hampered by a lack of con-
sistent observations in some basins. A theoretical basis for maximum TC
intensity appears now to be well established, but a climate theory of TC forma-
tion remain s elusive. Climate models mostly continue to predict future decreases
in global TC numbers, projected increases in the intensities of the strongest
storms and increased rainfall rates. Sea level rise will likely contribute toward
increased storm surge risk. Against the background of global climate change
and sea level rise, it is important to carry out quantitative assessments on the
potential risk of TC-induced storm surge and flooding to densely populated
cities and river deltas. Several climate models are now able to generate a good
distribution of both TC numbers and intensities in the current climate. Inconsist-
ent TC projection results emerge from modeling studies due to different down-
scaling methodologies and warming scenarios, inconsistencies in projected
changes of large-scale conditions, and differences in model physics and tracking
algorithms.
© 2015 Wiley Periodicals, Inc.
How to cite this article:
WIREs Clim Change 2016, 7:65– 89. doi: 10.1002/wcc.371
*Correspondence to: kevin.walsh@unimelb.edu.au
1
School of Earth Sciences, University of Melbourne, Parkville,
Australia
2
Centre for Climate Research Singapore (CCRS), Meteorological
Service Singapore, Singapore, Singapore
3
Department of Atmospheric Science, Colorado State University,
Fort Collins, CO, USA
4
Cyclone Warning Research Centre, Regional Meteorological Cen-
tre, Chennai, India
5
Lamont-Doherty Earth Observatory, Columbia University, Pali-
sades, NY, USA
6
National Center for Atmospheric Research, Boulder, CO, USA
7
National Oceanic and Atmospheric Administration (NOAA),
Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
8
National Oceanic and Atmospheric Administration (NOAA),
National Centers for Environmental Information, Asheville,
NC, USA
9
Hong Kong Observatory, Hong Kong, China
10
Columbia University, Palisades, NY, USA
11
Meteorological Research Institute, Japan Meteorological Agency,
Ibaraki, Japan
Conflict of interest: The authors have declared no conflicts of inter-
est for this article.
Volume 7, January/February 2016 © 2015 Wiley Periodicals, Inc. 65

INTRODUCTION
E
stimates of the effect of anthropogenic climate
change on tropical cyclones (TCs) generally fall
into two main topics: whether changes in the climate
to date have already had a detectable effect on TCs
and what portion of this is due to anthropogenic or
natural causes, and how climate change might affect
TCs in the future. Assessments of observed trends in
TC characteristics are affected by the same data
homogeneity issues that affect all climate variables,
so that a starting point for such an analysis is the
construction of a dataset that is free from spurious
trends due to changes in observing and assessment
practices. These issues are important for TCs, as the
most comprehensi ve datasets of TC records, the best
track datasets,
1
were designed as best estimates of
TC data using the techniques available at the time of
compilation. Thus, they were not primarily intended
as datasets to be used for trend analysis. Neverthe-
less, recent advances in the production of more tem-
porally homogeneo us datasets have been made, and
analysis of these datasets is starting to reveal impor-
tant new results.
2,3
The increasing use of atmospheric
reanalysis datasets
4– 9
has facilitated analysis of the
impact of changes in climate variables on TCs,
although reanalyses have significant potential homo-
geneity issues relevant to TC activity.
10,11
Current
reanalysis products have substantial limitations in
their explicit representation of TCs,
12
and their per-
formance in representing various TC climatological
metrics varies among available reana lyses.
13
Efforts
have also been made using geological techniques to
determine TC incidence in the distant past, and these
studies have revealed surprisin gly large long-term
variations in past TC activity.
14– 16
For future projections of TCs, climate models
(general circulation models or GCMs) have improved
to the point where the best models can now give
reasonable simulations of numbers of the average
number of TCs in all TC basins, including their
year-to-year variability.
17,18
While there is still room
for improvement, global GCMs are also showing
an increasing ability to simulate the intensity distri-
bution of observed TCs in both SST-forced
19
and
coupled
20
configurations. This latter task in particu-
lar requires high model resolution, so various
dynamical,
21
statistical,
22,23
and hybrid
5
‘downscal-
ing’ techniques have been developed to bridge the gap
between the relatively low resolution of most climate
models (e.g., the CMIP5 models used for many cli-
mate change projections
24
) and the fine detail required
for reliable simulation of TC intensities, an important
capability for future climate projections of TCs.
Confidence in future projections has been
increased by theoretical ideas and techniques that
relate clim ate variables to TCs. These have enabled
both a better understanding of TC dynamics and
improved confidence in future projections. While a
generally accepted climate theory of TC formation is
still lacking, a climate theory of TC maximum inten-
sity (potential intensity, PI) is now well estab-
lished.
25,26
Analysis and model experiments are
beginning to determine the crucial climate variables
associated with changes in TC formation rate.
This review summarizes recent research in the
interdisciplinary field of TCs and climate change. It
does not attempt an updated assessment of the sci-
ence beyond that given in the IPCC 5th Assessment
Report
27
; i.e., this review does not make synthesis
statements regarding our overall level of confidence
in predictions of climate change science. Neverthe-
less, this review is motivated by recent developments
in this field since the previous review and assess-
ment.
28
The increasing compilation and analysis of
improved, more homogeneous TC datasets has
enabled more conclusive results to be produced
about TC trends. There is an increasing availability
of datasets from before the period of written records.
Additionally, the best-performing climate modeling
systems are now producing not only a good simula-
tion of numbers and geographical patterns of TC for-
mation but also a rapidly improving representation
of the distribution of TC intensity. These three devel-
opments motivate a new review of this topic.
OBSERVATIONS OF TCS
Paleotempestology
Paleotempestology is the study of storm occurrence
in the past before written records became available.
The purpose of paleotempestology is to establish
whether there have been variations in the number
and intensity of TCs over geologic time periods. Thi s
provides a way of establishing a longer climate base-
line than the relatively short observational record
and also for exploring the dependence of TC activity
on climate change. Many types of geological proxies
have been tested for reconstructing past hurricane
activity; the most common proxies are based on
hurricane-induced overwash deposits of sediments of
coastal lakes and marshes.
16,29,30
Other emerging
proxies are based on oxygen isotopic ratios of hurri-
cane precipitation in caves (stalagmites), tree rings,
and corals.
15,31,32
As these studies typically focus on
a specific geographic location, a caveat that should
be considered is that they cannot distinguish between
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66 © 2015 Wiley Periodicals, Inc. Volume 7, January/February 2016

basin-wide trends or variability and systematic
changes in TC tracks, both of which may be modu-
lated by climate.
33
One method to evaluate the signif-
icance of the obtained patterns of overwash
reconstructions is to employ a large set of synthetic
hurricane tracks for a specific site.
34
A number of significant recent advances have
been made in this work. In the Australian region,
oxygen isotopic analysis of stalagmite records has
been used
35
to infer that the TC incidence in north-
east Queensland is less at present than at any time in
the past 1500 years. In the Atlantic basin, e.g., Bran-
don et al.
30
use overwash deposits to show that a
period of increased intense hurrica ne frequency is
observed between ~1700 and ~600 years before the
present (B.P.) and decreased intense storm frequency
is observed from ~2500 to ~1700 and ~600 years
B.P. to the present. Thus, there have been large,
unexplained variations in TC climatology in the rela-
tively recent geologic past.
16,29,36
These sedimentary records only go back a few
millennia, however. Therefore, dynamical,
37
statistical,
38
and statistical– dynamical
39
modeling
studies of more distant past climates have become
more common. For both the Holocene (6 ka before
the present) and the Last Glacial Maximum (LGM;
21 ka), Korty et al.
40,41
show that even though condi-
tions during the LGM were cooler than today, there
was not a systematic decrease in either PI or other
environmental variables associated with TC fre-
quency. Sugi et al.
42
found that their model-
simulated TC frequency significantly increases in a
4 K cooler climate GCM experiment, indicating that
there may be more TCs in a glacial period, consistent
with the consensus finding that overall TC frequency
decreases with warming
43
and showing that a climate
with generally cooler SSTs does not imply fewer
TCs.
44
They also pointed out that TC formation
under these conditions can occur at sea-surface tem-
peratures (SSTs) well below 26

C, a value conven-
tionally used to indicate the minimum threshold for
TC formation in our present-day climate.
45
Merlis
46
used the GFDL HiRAM model to show that TC fre-
quency was simulated to increase during the LGM.
Historical and Satellite Era
Observed TC data during what is called the ‘histori-
cal’ era (in this case typically from the 19th century
onward, the period when ship observations became
more routine) are compiled in what is known as ‘best
track’ datasets. These compilations are the best esti-
mates of TC characteristics such as position and
intensity, but are subject to ongoing review.
47
The recent introduction of the IBTrACS compi-
lation of best track datasets
1
has considerably facili-
tated analysis of global climate trends. Creating an
homogenous climate record from best track data con-
tinues to be a challenge, however, especially for
cyclone intensity data,
48
as the quality of measuring
techniques has generally improved over time. The
parallel creation of more homogeneous satellite-
derived datasets
3
increasingly enables analysis of cli-
mate variability for the recent decades.
In addition to cyclone intensity, both size and
translation speed have been shown to be substantial
contributors to the impacts resulting from hurricane
passage.
49– 52
Emphasis on increased attention to
these parameters in the development of future data-
sets is therefore desirable, along with creative meth-
ods for developing new approaches to their
determination from past climate.
53
It has been shown
that for observed Atlantic TC intensities and sea sur-
face cooling due to the passage of a TC, storm trans-
lation speed affects the amount of local cooling, but
the cooling does not increase monotonically for all
intensities.
54
This indicates a role for ocean subsur-
face thermal structure in modulating the ability of
storms to intensify and thus alter the degree of cool-
ing under intense TCs.
55
Global Observations
A previous review of this topic
28
concluded that ‘it
remains uncertain whether past changes in TC activ-
ity have exceeded the variability expected from natu-
ral causes. ’ While there remain concerns about the
temporal homogeneity of the best track record,
56,57
recent studies using satellite-based temporally homo-
genized datasets are beginning to suggest climate
trends of various kinds in the TC record over the
past few decades. For example, a summary of trends
in the lifetime maximum intensity of TCTCs in vari-
ous ocean basins
3
is shown in Figure 1, for the
period 1989– 2009. Globally, there are modest signif-
icant trends in this quantity (at the 90% level), but
individual basins have greater significance. In the
North Atlantic and Western North Pacific, trends are
highly significant at the 95% level, with increases in
the North Atlantic and decreases in the Western
North Pacific. More modest trends significant at the
90% level are seen in the South Pacific and the South
Indian basins (both upwards). Trends have not been
significant in the Eastern North Pacific and the North
Indian basin has insufficient data. More recently,
Kossin et al.
58
find significant poleward movement in
the latitude of the maximum intensity of TCs over
the period 1982– 2012. This result is potentially
important as an indicator of the poleward expansion
WIREs Climate Change Tropical cyclones and climate change
Volume 7, January/February 2016 © 2015 Wiley Periodicals, Inc. 67

of the tropics, an outcome of anthropogenic climate
change that is predicted by both theory
59
and model
projections.
60
An expansion of the tropics implies a
potential for TC effects to be felt further poleward in
a warmer world. The causes of the observed trends
have not been fully established, however. Holland
and Bruyère
61
considered trends in numbers of
intense TCs using an early version of the homoge-
nized satellite-based dataset of Kossin et al.
3
and in
the IBTrACS data.
1
They find substantial relation-
ships between an index of global warming and the
observed proportion of very intense TCs (Saffir-
Simpson categories 4 and 5) in the IBTrACS data
from 1975 to 2010, and similar although smaller
trends in the Kossin dataset. No change in global
cyclone frequency or average intensity was found,
but they concluded there has been a substantial
increase in the proportion of intense hurricanes, both
globally and individ ually in all basins except for the
eastern North Pacific. This is consistent with
trends in intense cyclones found by previous studies
for individual basins.
62– 68
Recently, Klotzbach and
Landsea
69
added an additional 10 years of data
to the analysis of Webster et al.,
62
finding a flattening
out of the upward trend in categories 4 and
5 storm numbers. They also find a continuation in
the increasing trend in the proportion of these
storms, but that this trend is not statistically signifi-
cant during the more recent period 1990– 2014.
Since the early 1990s, global accumulated cyclone
energy (ACE) values have undergone a fairly
sizeable decrease, with 24-month running mean
values in mid-2015 the lowest that they have been
since the mid-1970s.
70
Western North Pacific
Several recent reviews have been conducted on trends
in the western North Pacific (WNP) region.
71– 73
There are several different best-track datasets that
have been compiled for this region and they have dif-
ferences in their representation of TC intensities.
74
Existing datasets show pronounced inter-decadal var-
iations in a number of TC metrics in the western
North Pacific basin, such as storm frequency, inten-
sity, and power dissipation index (PDI; a measure
that combines TC intensity and frequency with life-
time). Multidecadal trends in WNP basin-wide TC
frequency are highly dependent on which best track
dataset is used. Among the four best track datasets
assessed, the China Meteorological Administration
(CMA, 1949– 2010) and Hong Kong Observatory
(HKO, 1961– 2010) datasets show significant
FIGURE 1
|
Plots of quantiles (mean to 0.9) of the lifetime maximum intensity (LMI) of storms in the various tropical cyclone formation
basins, from a homogenized satellite-based analysis of tropical cyclone intensity (1982– 2009). (Reprinted with permission from Ref. 3 Copyright
2013 American Meteorological Society)
Advanced Review wires.wiley.com/climatechange
68 © 2015 Wiley Periodicals, Inc. Volume 7, January/February 2016

decreasing trends in TC (tropical storm or above) fre-
quency, but for the Joint Typhoon Warning Center
(JTWC, 1945– 2010) and Regional Specialized Mete-
orological Center Tokyo (1977– 2010) datasets only
a nominal, statistically insignifi cant decreasing trend
was found. Hsu et al.
75
reported an abrupt decrease
in the late-season TC frequency that is consistent
among best track datasets. Causes for interdecadal
changes in typhoon genesis in the WNP have been
examined by Choi et al.
76
who conclude that the hor-
izontal distribution of SST, as opposed to its magni-
tude, can be a key factor.
For TC intensity, trends in intense categories
4– 5 typhoon frequencies in the WNP are particularly
divergent among datasets in recent decades, and so
remain uncertain. Increasing trends in intense
typhoon frequencies in the WNP have been found by
a number of studies.
61– 66
However, these trends are
not apparent in all the datasets available for the
basin,
74
and satellite-based intensity trends since
1981
3
show more modest trends.
Efforts have been made to reconcile the differ-
ences in intensities of WNP TCs from the various
agencies using a quantile method,
64
whereby more
agreement is shown between the various best track
datasets in this region when intensity ranks are used
rather than raw intensity estimates themselves. Kang
and Elsner
64
showed that the statistical analysis of
consensus trends between the JWTC and JMA data-
sets indicate fewer but stronger storms since 1984.
Nevertheless, the continued discrepancies between
the various datasets in the WNP region continue to
be a matter of concern.
In principle, a good numerical simulation of TC
trends in this basin cou ld help resolve some of these
issues. Wu and Zhao
66
used the Emanuel et al.
5
sta-
tistical downscaling approach to examine recent TC
trends for the WNP. They found an increasing inten-
sity trend that was similar to though less than that
in the JTWC data archive. This trend was not con-
sistent with the decreasing or zero trends in other
available datasets for the region. Reconciling the sub-
stantial differences in WNP TC archives would aid
future research in this area.
Despite the uncertainty in basin-wide changes
in TCs, observations indicate some regional shifts in
TC activity in the basin, such as a decreasing trend in
TC occurrence in part of the South China Sea and an
increasing trend along the east coast of China during
the past 40 years. This change is apparently related
to local circulation variations in eastern Asia and the
WNP.
71,72,77
Further observations and research will
still be required to understand the influence and con-
tribution of natural variability and anthropogenically
forced climate change on TC track changes in the
WNP. Kossin et al.
58
identified a poleward migration
over the past 30 years or so of around 0.5

latitude
per decade in the location where WNP TCs reach
their peak intensity. In this case, the migration was
consistently found in the data from each regional
data source. The migration appears to be related to
systematic decadal changes in regional PI and vertical
wind shear and is occurring at roughly the same rate
as the independently observed expansion of the tro-
pics.
78
Zhao and Held
79
simulated some poleward
migration of TC genesis frequency with climate
warming in the North Pacific and North Atlantic
Ocean basins.
North Atlantic
There are clear increasing trends in observed intensi-
ties of TCs in the Atlantic basin in the past few dec-
ades.
80
Kossin et al.
3
note very significant increases
since 1982. Holland and Bruyère
61
also find an
upward trend in the proportion of intense hurricanes
since 1975, significant at the 95% level when com-
pared to their gl obal warming index. Landse a et al.
81
emphasize the data homogeneity issues that exist in
the Atlantic best track data over the past 100 years,
but also note that observed increases have occurred
in TC activity in the Atlantic since the 1970s.
Murakami et al.
82
find that the recent increase in TC
genesis number in the Atlantic is the primary driver
of the increase in aggregate activity measures such as
the ACE or PDI. However, the Atlantic basin is
noted for having significant multidecadal variability
in TC activity levels. The basin was characterized by
a more active period from the mid-1870s to the late
1890s as well as the mid-1940s to the late 1960s.
These periods may have had levels of activity similar
to what has been observed since the mid-1990 s.
83
Crucial to this analysis has been the construction of
more homogeneous datasets over this longer period
of time.
81
Analysis of these suggests a general lack of
long-term trends in this basin for both total numbers
of TCs and for hurricanes only, as well as for land-
falling hurricanes since 1900.
2,84
Causes for these trends are summarized in the
latest Intergovernmental Panel on Climate Change
(IPCC) report (AR5), which provided a synthesis of
the relationship between climate and TCs,
85
with
particular emphasis on the North Atlantic basin.
Confidence has now increased to ‘medium’ that in
the Atlantic basin, external forcing factors such as
anthropogenic greenhouse gases and aerosols are
partly responsible for the increase in TC formation
since the comparatively quiescent 1970s– 1980s. In
particular, the influence of atmospheric aerosols
WIREs Climate Change Tropical cyclones and climate change
Volume 7, January/February 2016 © 2015 Wiley Periodicals, Inc. 69

Frequently asked questions

Q1. What are the contributions mentioned in the paper "Tropical cyclones and climate change" ?

In this paper, the authors focus on the potential risk of TC-induced storm surge and flooding to densely populated cities and river deltas. 

Q2. What are the future works mentioned in the paper "Tropical cyclones and climate change" ?

Additionally, the future changes in climate models such as ENSO and the resulting effects on TC projections are still unclear, given the lack of agreement and poor simulation by many climate models of those climate modes. Analysis of these experiments for changes in TCs has only started156,158,163 and more results will need to be produced before a consensus can emerge on the likely effect of the CMIP5 experimental results on TCs. 

Q3. Why is the time-slice method used for TC simulations?

Because of the very significant computational cost of running highresolution coupled ocean–atmosphere GCMs, most very high-resolution simulations of the effect of climate change on TCs have been performed using the time-slice method, whereby simulated SSTs from a coarser-resolution model are used as a boundary condition for the simulation of a finer-resolution atmosphere-only climate model. 

Q4. What are the reasons for inconsistent TC projection results?

Inconsistent TC projection results emerge from modeling studies due to different downscaling methodologies and warming scenarios, inconsistencies in projected changes of large-scale conditions, and differences in model physics and tracking algorithms. 

Q5. What is the effect of a longer return period on flood levels?

At longer return periods, changes in the frequencies and intensities of TCs play a larger role, and increase the uncertainty in the flood levels for those more extreme events. 

Q6. What is the confident prediction of the effects of climate change on TCs?

an increase in storm-relative rainfall rates from composite storms in a warmer climate is one of the more confident predictions of the effects of future climate on TCs. 

Q7. What are the main criteria used to determine if a disturbance is a TC?

153,159 Traditional direct detection methods use (1) structural criteria to ensure a disturbance resembles a TC and (2) a wind speed threshold to determine if the disturbance is sufficiently intense to be considered a TC. 

Q8. What is the main reason for the parallel creation of more homogeneous datasets?

The parallel creation of more homogeneous satellitederived datasets3 increasingly enables analysis of climate variability for the recent decades. 

Q9. What are the factors that are important for societal impacts of TCs?

Note that physical factors like storm size and intensity are difficult for global models to simulate directly unless very fine resolution is used, yet these are factors that are important for societal impacts of TCs, which are largely dominated by the rarer, high-intensity storms. 

Q10. What are the proxies of hurricane precipitation in caves?

Other emerging proxies are based on oxygen isotopic ratios of hurricane precipitation in caves (stalagmites), tree rings, and corals. 

Q11. How did they find the correlation between TC frequency and CO2?

Using idealized climate experiments, Held and Zhao43 and Sugi et al.129 showed that both the CO2 increase and SST increase contribute about equally to the reduction of future upward mass flux, which they suggested could lead to a reduction of TC frequency. 

Q12. What are the main sources of uncertainty in the TC frequency change projections?

Murakami et al.19,160 indicated that there were three major source of uncertainty in the TC frequency change projections: (1) resolution, (2) physics (convection scheme), and (3) SST pattern change. 

Q13. How did they estimate the changes in TC tracks in the North Atlantic?

Colbert et al.171 used a statisticaldynamical modeling approach to estimate future changes in TC tracks in the North Atlantic, finding a decrease in westward tracks and in increase in recurving tracks. 

Q14. What is the need for a reliable TC detection method for the analysis of climate models?

It is also noted that not all disturbances in the tropics with low-level wind speeds greater than 17ms−1 are TCs (e.g., monsoon gyres196), so there will continue to be a need for a reliable TC detection method for the analysis of climate model output. 

Q15. What is the reason that confidence in future regional projections of TCs remains limited?

One of the reasons that confidence in future regional projections of TCs remains limited is the variation between climate models of projections of meanpatterns of change of regional SSTs. 

Q16. What are the probable speeds of CDs over the Arabian Sea?

Geetha and Balachandran104 have studied the decadal variations in the translational speeds of cyclonic disturbances (CDs) over the North Indian Ocean over the period 1961–2010 and have noted that during the most recent decade, the most probable speeds of CDs have increased over the Bay of Bengal, but have decreased over the Arabian Sea.