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The DOI for this manuscript is doi: 10.1175/JCLI-D-11-00719.1
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Weinkle, J., R. Maue, and R. Pielke, Jr., 2012: Historical global tropical cyclone
landfalls. J. Climate. doi:10.1175/JCLI-D-11-00719.1, in press.
© 2012 American Meteorological SocietyHistorical global tropical cyclone landfalls
Jessica Weinkle,1 * Ryan Maue2 and Roger Pielke, Jr.1 and1Center for Science and Technology Policy Research, University of Colorado, 1333 Grandview

Ave, Campus Box 488, Boulder, Colorado 80309
2

National Research Council*Corresponding author
Email: jessica.weinkle@colorado.edu
Office: 303-735-0451
Fax: 303-735-1576

1

Abstract2

In recent decades, economic damage from tropical cyclones (TCs) around the world has

3increased dramatically. Scientific literature published to date finds that the increase in losses can4

be explained entirely by societal changes (such as increasing wealth, structures, population, etc)

5in locations prone to tropical cyclone landfalls, rather than by changes in annual storm frequency

6or intensity. However, no homogenized dataset of global tropical cyclone landfalls has been

7

created that might serve as a consistency check for such economic normalization studies. Using

8

currently available historical TC best-track records, we have constructed a global database9

focused on hurricane-force strength landfalls. Our analysis does not indicate significant long-

10

period global or individual basin trends in the frequency or intensity of landfalling TCs of minor

11

or major hurricane strength. This evidence provides strong support for the conclusion that12

increasing damage around the world during the past several decades can be explained entirely by

13

increasing wealth in locations prone to TC landfalls, which adds confidence to the fidelity of

14

economic normalization analyses.

15

1. Introduction

16

The active North Atlantic tropical cyclone (TC) seasons of 2004 and 2005 coupled with17

their considerable social and economic impacts of several major hurricane landfalls precipitated18

a spirited scientific debate on the implications of changing climate conditions on TC behavior

19(Emanuel 2005a,b; Pielke Jr. 2005; Trenberth 2005; Webster et al. 2005). The current consensus

20is that an anthropogenic signal in historical TC activity metrics cannot be conclusively identified

21

independent of historically documented variability (Knutson et al. 2010). However, some in the22

public, media, and the insurance industry continue to point to human-caused climate change as a23

factor responsible for at least part of the observed increase in TC-related economic losses in

24recent decades (Gillis 2010; Mills 2005; Munich Re 2010).

25This paper specifically focuses on a subset of the historical record of TCs with the most26

direct relevance to understanding economic losses: landfalling TCs of at least hurricane force. To

27date, the scientific literature contains no global homogenized dataset of TC landfalls assembled

28

using a consistent methodology. This landfall dataset1 is important for understanding trends in29

TC-related economic losses and can aid in the quantitative determination of the relative30

contribution to losses by societal and climatic factors. Logically, with a trend in annual

31frequency of landfalls and/or intensity at landfall, one would expect to see a trend in economic

32

losses after normalizing for societal change. On the other hand, absent a trend in landfall

33

characteristics, there would be no reason to expect a residual climate related trend in losses.

1 The landfall dataset is printed in the Supplementary Online Material and is permanently

available at http://rogerpielkejr.blogspot.com
3

34
35

2. DataA main obstacle in constructing a homogeneous global TC landfall dataset concerns the36varying quality of the TC best-track historical records. Indeed, uncertainty in TC location and37

intensity data is a function of the evolving observation network throughout the past century

38

ranging from ship traffic, aerial reconnaissance, to satellite remote sensing. For instance, recent

39

research has attempted to quantify potential missing North Atlantic tropical storms in the late40

19th and early 20th centuries (Landsea et al. 2010; Vecchi and Knutson 2011) related to the41

ongoing Atlantic Hurricane Database Re-analysis Project (HURDAT; Landsea et al. 2003).

42

Also, issues related to the viewing angle of eye temperatures among different satellite platforms

43

have spurred research into reevaluating TC intensity during the past several decades in the

44

Northern Indian Ocean basin (Hoarau et al. 2011). While objective satellite methodologies have45

been applied to global TC satellite data (Kossin et al. 2007), a meticulous human-based46

reanalysis of all global TCs during the last several decades remains an unrealized endeavor.

47

Thus, it is important to acknowledge possible bias or errors in TC intensity and track information48

for each independent ocean basin prior to conducting long-period historical research.

49

We examine landfalls in five global TC-active development regions including the North

50

Atlantic (NATL), North Eastern Pacific (EPAC), Western North Pacific (WPAC), Northern

51

Indian Ocean (NIO), and the Southern Hemisphere (SH) using the most recent version of the

52

International Best Track Archive for Climate Stewardship (IBTrACS v03r03; Knapp et al. 2010).

53This impressive resource compiles TC intensity and location data. It is important to note that54

this dataset is not a reanalysis and considerable uncertainties likely remain unresolved in the55

respective estimates of TC location and intensity.

56

We utilize the United States Department of Defense Joint Typhoon Warning Center

57(JTWC; Chu et al. 2002) best-tracks gleaned from the IBTrACS for TC lifecycle location and4

58

intensity estimates for the WPAC (1950-2010), NIO (1970-2010), and SH (1970-2010) for the

59time periods chosen in parentheses. While the WPAC basin was observed through aircraft

60

reconnaissance until 1987, routine satellite monitoring (Dvorak 1984) was also critical for

61

intensity estimates especially for the NIO and SH, and the time periods chosen roughly62

correspond to the beginning of the satellite era. As the JTWC data is not complete and less63

reliable prior to the mid-1980s in the SH and NIO, additional lifecycle points are filled in from64

the Neumann (Neumann 1999) and NCAR ds824.1 (Neumann et al. 1993) portions of the65

IBTrACS full dataset. Especially in the NIO prior to 1980, some TCs are simply categorized as66

a tropical storm or hurricane with maximum sustained winds listed generically at 35 or 65 knots

67

and are therefore likely biased low (NCAR ds824.1 Notes on Tropical Cyclone Data available at68

http://dss.ucar.edu/datasets/ds824.1/docs/format_ascii.html; Hoarau et al. 2011).

69

The United States National Hurricane Center (NHC) best-track dataset (Jarvinen et al.

701984) is used for the NATL (1944-2010) and EPAC (1970-2010) basins. While considerable

71

reliable data is available in the NATL back to at least 1900 (Neumann et al. 1993), as our focus

72

is on assembling a homogeneous global dataset, we begin with 1944 coinciding with the start of73

routine aircraft reconnaissance and a focal point of the Atlantic Hurricane Reanalysis Project74(Hagen et al. 2011). Northeast Pacific ocean TC data is reliable since about the mid-1960s

75

mainly due to routine satellite monitoring (NCAR ds824.1).

76
77
78

3. Methods
Each individual TC lifecycle in the best-tracks is individually examined through79

complimentary computer automated and manual detection techniques in order to compile a

80

global homogeneous landfall dataset. We adopt the current NHC (NHC 2010) online glossary5

81

definition of a TC landfall as the intersection of the surface center with a coastline. In our final

82

analysis, we do not include a relatively small number of TCs that have grazed coastal land yet

83

still caused hurricane force winds over land. These near-miss TC landfalls are responsible for84

only a small fraction of normalized economic losses and do not affect our overall conclusions.

85

To automate landfall detection, a straightforward binary decision process between land

86

and sea requires a very-high resolution geographical resource. Here we utilize an operational87

sea-surface temperature product (GHRSST OSTIA; Stark et al. 2007) as a land-mask with 1/20th88

degree global grid spacing (Supplementary Online Material Figure 1a & b). Coastlines and89

islands are very clearly demarcated at this spatial resolution. Since the IBTrACS best-track90

location points (latitude and longitude) are reported in increments of one-tenth degree, a ¼

91

degree square buffer is applied to allow for the expected uncertainty in reported TC locations at92

the 6-hourly intervals. We do exclude some small islands or chains of islands from our analysis.

93

Land included in the study is found in Table 1.

94

With each IBTrACS serial number from the software identified landfall candidates,

95

visual verification of landfall location and intensity is performed with an associated online TC

96graphics repository (available at http://storm5.atms.unca.edu/browse-ibtracs/browseIbtracs.php;

97

for details on the visual verification and descriptive imagery see the Supplemental Online

98

Material). As storms approach land, they tend to entrain dry air and their outer circulations may

99

interact with mountainous terrain. To account for the effects of land-based weakening in

100

categorizing TC landfall intensity, we also retrieve the 6-hourly observation time step101

immediately prior to the first on-land observation and use the highest value. If a TC makes

102

multiple landfalls, then it is only counted once and categorized at the highest determined landfall

103

intensity.

6

104

Even with the above caveats, we still rely on the reported best-track locations that105

represent a contemporary real-time and/or post-season assessment. Furthermore, we

106

discriminate between two groups of hurricane force TCs at landfall: Category 1 and 2 storms on107

the (NATL based) Saffir Simpson scale (one-minute maximum sustained winds of 64 to 95

108

knots) described as minor hurricanes, and Category 3-5 storms (wind exceeding 96 knots) often

109

referred to as major hurricanes. Of course, the exact intensity at the point of landfall is often

110

unknowable due an acknowledged under-sampling of the atmospheric environment, yet we have111

confidence in the discrimination between minor and major landfalls. The term hurricane is used112

generically across all global basins to denote a TC with one-minute maximum sustained winds

113

exceeding 64-knots.

114
115
116

4. Results
Overall, TC occurrence is a basin-dependent function of large-scale climate variability on

117interannual time scales (Gray 1984) as well as shorter-term fluctuations in atmospheric118

conditions favorable for the organization of convection (Emanuel 1989). While considerable

119

research has been conducted on TC climatology in each basin, the annual number of collective

120

global landfalls has not been previously quantified. From the homogeneous dataset, it is121

apparent that the frequency of global hurricane landfalls is dominated by the WPAC, which is

122

climatologically the most active basin (Maue 2011), followed by the NATL. The typical steering

123

flow in the EPAC does not favor tracks that would result in Mexico coastal landfalls. Australia

124

and Madagascar are the most commonly affected large landmasses in the SH. Conversely, the125

Bay of Bengal in the NIO experiences few landfalls but they tend to cause extremely large social

126

impacts (Figure 1).

7

127

The collective global frequency of all global hurricane landfalls and the minor and major

128

subsets shows considerable interannual variability but no significant linear trend (Figure 2).

129

Furthermore, when considering each basin individually during the entire time periods analyzed,

130

it is not possible to ascertain a positive or negative trend in minor, major, or overall hurricane

131

landfall frequency in all basins except the SH. In the SH a significant positive trend in major

132

hurricane landfalls was detected; yet the sample size is still small (Table 2). This result is not

133

unexpected considering the known multidecadal signals in TC activity, which cannot be

134

adequately resolved by our comparatively short historical record.

135

Thus, in the context of climate variability, it is important to recognize that certain shorter

136

time-periods during the past half-century may indeed show significant trends (upward and137

downward) in TC landfall activity on decadal time scales (e.g. Callaghan and Power 2011). The

138

NATL basin has been in an active period since about 1995 which some have attributed to the139

positive phase of the Atlantic Multidecadal Oscillation (Goldenberg et al. 2001). A linear trend

140

analysis shows a significant upward trend in NATL activity (R2=0.13, p=0.011) during the past141

several decades (1970-2010), consideration of the longer period of 1944-2010 exhibits no secular142

trend in hurricane landfalls (and even longer periods show no increasing trend, see, e.g., Pielke

143

Jr. 2009). Intense typhoon frequency has also been shown in the WPAC to be modulated by

144

multidecadal variability (Chan 2008) on time scales of 16-32 years associated with the Pacific145

Decadal Oscillation (PDO) and variability of the El Niño Southern Oscillation (ENSO), and no146

significant trend is found in hurricane landfalls during the period examined (1950-2010).

147

The conclusion of the NATL 2011 hurricane season sets a new record of days (greater

148

than 2,321 days) between major US hurricane landfalls. The most recent major hurricane US

149

landfall was Hurricane Wilma in 2005. For calendar year 2011, according to available NHC and

8

150

JTWC best-track and preliminary information, a total of 10 hurricane force TCs made landfall

151

with three at major strength (> 96 knots) including Yasi (Australia), Nanmadol (Philippines,

152

Taiwan), and Nalgae (Philippines). Elsewhere of note, Irene in the NATL was a weak hurricane

153

when it struck North Carolina and Jova impacted southwest Mexico in the EPAC. Characterized154

as a La Niña year, 2011 saw considerably fewer TC landfalls than, for instance, 1971, also a155strong La Niña year with a record 32 global hurricane frequency landfalls. On a global scale,156

future research may shed light on the uneven distribution of TC existence and the proportion that157

make landfall.

158
159

5. Conclusions

160

From currently available historical TC records, we constructed a long-period global

161

hurricane landfall dataset using a consistent methodology. We have identified considerable

162

interannual variability in the frequency of global hurricane landfalls, but within the resolution of163

the available data, our evidence does not support the presence of significant long-period global

164

or individual basin linear trends for minor, major, or total hurricanes within the period(s) covered165

by the available quality data. Therefore, our long-period analysis does not support claims that166

increasing TC landfall frequency or landfall intensity has contributed to concomitantly

167

increasing economic losses. Due to documented multidecadal variations in TC frequency and168

intensity on global and basin scales, our findings strongly support the usage of long-period

169

historical landfall datasets for trend analysis (cf. Liebmann et al. 2010).

170

While there is continued uncertainty surrounding future changes in climate (Knutson et171

al. 2010), current projections of TC frequency or intensity change may not yield an172anthropogenic signal in economic loss data for many decades or even centuries (Crompton et al.

9

173

2011). Thus, our quantitative analysis of global hurricane landfalls is consistent with previous

174

research focused on normalized losses associated with hurricanes that have found no trends once

175

data is properly adjusted for societal factors (e.g. Pielke Jr. et al. 2008; Crompton and McAneney

176

2008; Neumayer and Barthel 2011; Barthel, Fabian and Neumayer 2012; Bouwer 2011;177

Raghavan and Rajesh 2003).

10

Acknowledgements
The authors would like to thank Chris Landsea, Ryan Crompton, Buck Sampson, and Edward
Fukada. We thank Ami Nacu-Schmidt and Evan Pugh for helping to prepare the paper for
publication. This work was supported by the U.S. National Science Foundation DMUU Grant
SES 0345604. Dr. Maue acknowledges National Research Council Research Associateshipwhile at Naval Research Laboratory in Monterey, CA.
Figure Caption Text
Figure 1: Hurricane tracks and landfall location points for storms that make landfall at hurricane
intensity (maximum one-minute sustained > 64 knots) for the (a) North Atlantic and Eastern
North Pacific, (b) Western Pacific, (c) North Indian, and (d and e) Southern Hemisphere. EachTC track line connects the 6-hourly best-track positions with red squares indicating a hurricane
force landfall location point and blue circles indicating over land observations of tropical storm
strength (wind speed between 34-63 knots). For reference, non-tropical overland or extratropical
positions are indicated with a black cross where such information exists in the best-track
database.

Figure 2: Global and basin hurricane landfall annual frequencies of storms of major (red) andboth major and minor (blue) hurricane intensity at landfall.

Table 1: Land areas considered for study

Table 2: Global hurricane landfall trend significance partitioned according to basin andminor/major hurricane intensity. Total hurricanes observed include all tropical cyclones

observed with at least maximum lifecycle wind speed of 64-knots (Saffir- Simpson Category 1
and above).
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Figure 1: Hurricane tracks and landfall location points for storms that make landfall at hurricane
intensity (maximum one-minute sustained > 64 knots) for the (a) North Atlantic and Eastern
North Pacific, (b) Western Pacific, (c) North Indian, and (d and e) Southern Hemisphere. EachTC track line connects the 6-hourly best-track positions with red squares indicating a hurricane
force landfall location point and blue circles indicating over land observations of tropical storm
strength (wind speed between 34-63 knots). For reference, non-tropical overland or extratropical
positions are indicated with a black cross where such information exists in the best-track
database.

16

Figure 2: Global and basin hurricane landfall annual frequencies of storms of major (red) andboth major and minor (blue) hurricane intensity at landfall.

17

Table 1: Land areas considered for study
Land Area
Remarks
Coastline of continental Africa
Southern coastline of continental Asia from Including Sri Lanka and China’s Hainan Island
Yemen to Russia
Mainland Australia
BahamasNew Providence Island only
Mainland Cuba
Mainland Hispaniola (Dominican Republic andHaiti)
Mainland JamaicaJapan
Excluding islands south and east of the mainisland of Kyushu
Mainland Madagascar
Coastline of continental North, Central, and Including MI/LA delta region, FL Keys, HI,
South America
US barrier islands, Puerto Rico, Nova Scotia
and Newfoundland
Philippines
TaiwanTable 2: Global hurricane landfall trend significance partitioned according to basin andminor/major hurricane intensity. Total hurricanes observed include all tropical cyclones
observed with at least maximum lifecycle wind speed of 64-knots (Saffir- Simpson Category 1
and above).
Total
Period of
Minor
Minor R2
Major R2
Total R2
Basin
Landfalling
Analysis(Major)(p value)(p value)(p value)
Hurricanes
111
0.00270.00130.0003
NATL 1944-2010
180(69)(0.68)(0.77)(0.89)
38
0.00340.00380.0063EPAC 1970- 201047
(9)(0.72)(0.70)(0.62)
345
0.03780.03970.0016
WPAC 1950- 2010
494
(149)
(0.13)(0.12)(0.76)
34
0.06270.04840.0086
NIO
1970- 201048
(14)
(0.11)(0.17)(0.56)
57
0.07250.12670.0087
SH
1970- 2010
105(48)
(0.08)(0.02)(0.56)
442
3e-06
0.08890.0268Global 1970- 2010
637
(195)
(0.99)(0.06)(0.31)

18