Tropical Cyclone Climatology in a Global-Warming Climate

Journal of the Meteorological Society of Japan, Vol. 84, No. 2, pp. 259--276, 2006 259

Tropical Cyclone Climatology in a Global-Warming Climate

as Simulated in a 20 km-Mesh Global Atmospheric Model:

Frequency and Wind Intensity Analyses

Kazuyoshi OOUCHI

Advanced Earth Science and Technology Organization, Earth Simulator Center, Yokohama, Japan

Jun YOSHIMURA, Hiromasa YOSHIMURA

Meteorological Research Institute, Tsukuba, Japan

Ryo MIZUTA

Advanced Earth Science and Technology Organization, Tsukuba, Japan

Shoji KUSUNOKI and Akira NODA

Meteorological Research Institute, Tsukuba, Japan

(Manuscript received 30 April 2005, in final form 15 November 2005)

Abstract

Possible changes in the tropical cyclones in a future, greenhouse-warmed climate are investigated us-ing a 20 km-mesh, high-resolution, global atmospheric model of MRI/JMA, with the analyses focused onthe evaluation of the frequency and wind intensity. Two types of 10-year climate experiments are con-ducted. One is a present-day climate experiment, and the other is a greenhouse-warmed climate experi-ment, with a forcing of higher sea surface temperature and increased greenhouse-gas concentration. Acomparison of the experiments suggests that the tropical cyclone frequency in the warm-climate experi-ment is globally reduced by about 30% (but increased in the North Atlantic) compared to the present-day-climate experiment. Furthermore, the number of intense tropical cyclones increases. The maximumsurface wind speed for the most intense tropical cyclone generally increases under the greenhouse-warmed condition (by 7.3 m s�1 in the Northern Hemisphere and by 3.3 m s�1 in the Southern Hemi-sphere). On average, these findings suggest the possibility of higher risks of more devastating tropicalcyclones across the globe in a future greenhouse-warmed climate.

Corresponding author: Kazuyoshi Oouchi, Ad-vanced Earth Science and Technology Organiza-tion (AESTO), MRI Group, 3rd Floor, c /o ResearchExchange Group, Earth Simulator Center, 3173-25Showa-machi, Kanazawa-ku, Yokohama 236-0001,Japan.E-mail: [email protected]( 2006, Meteorological Society of Japan

1. Introduction

To understand the possible changes in trop-ical cyclones in a greenhouse-warmed world re-mains a high priority issue from both scientificand socio-economic viewpoints. Studies on thisissue have been undertaken with global cli-mate models (e.g., Broccoli and Manabe 1990;Haarsma et al. 1993; Bengtsson et al. 1996;Krishnamurti et al. 1998; Sugi et al. 2002;Tsutsui 2002; Yoshimura et al. 2005) and re-gional nested models (e.g., Knutson et al. 1998;Knutson and Tuleya 1999, 2004; Walsh andRyan 2000). Among the problems frequentlydiscussed, the possible changes in the fre-quency and intensity of future tropical cyclonesare more relevant to the present study. With ahigh-resolution regional model (using a 1/6 de-gree mesh at the innermost area in a triplynested model), Knutson et al. (1998) studiedthe tropical cyclones in the northwest Pacificunder a CO2-induced warming condition andreported that the model simulated significantlystronger hurricane intensities in a high-CO2

environment (in which the surface wind in-creases by 3–7 m s�1 from the present-day cli-mate condition). The finding was consistentwith the ‘‘theoretical prediction’’ from a poten-tial intensity theory proposed by Emanuel(1987) and, alternatively, by Holland (1997).On the other hand, the prediction of thefrequency change of tropical cyclone in agreenhouse-warmed climate has yielded mixedresults even regarding the signs of change.

The results from most of these studies withglobal models, however, have left room for fur-ther investigation, mainly because a typicalresolution of 100 kilometers was too coarse torepresent the typical inner structures (e.g.,mesoscale convection) of a tropical cyclone(Krishnamurti et al. 1989). Although we caninfer that coarse-resolution climate models areadequate for introducing some dynamical large-scale constraints on the tropical cyclone clima-tology as needed in the real atmosphere, it isinadequate for elaborated and accurate forecastof tropical cyclone intensity. Such a model maycreate tropical disturbances that could satisfygiven identification criteria (Camargo and So-bel 2004). Even if enough fine resolution is em-ployed with a regional-nesting technique, thecharacteristics of the tropical cyclones on a

global, climate scale will usually not be investi-gated due to limited computational resources.

Now that the Earth Simulator (Sato 2004) isoperating, a high-resolution, global climatemodel can be used to clarify the problem. Theadvantage of high resolution is that the modelis capable of representing a tropical cycloneboth as an ensemble of mesoscale convectivesystems and as a synoptic scale disturbance,which is developed as a result of its sponta-neous (not artificially forced) interaction withthe environmental disturbances within a cli-mate system. Therefore, the use of the 20 km-mesh model will provide more reliable informa-tion concerning how the greenhouse-warmedclimate would affect a tropical cyclone thanthat from previous studies that relied on cli-mate models with a coarser resolution. This ar-ticle reports the main findings of the problem.

On the other hand, this study also revealsthat the downscaling of the grid size alonedoes not address all the issues relating to theprojection of future tropical cyclones. Obvi-ously, the physical parameterizations in themodel must be refined to increase the reliabil-ity of the projection. This study is expected toserve as a step toward that goal given thatsome uncertainty coming from the coarse-resolution of the traditional model is excludedto some extent by using a 20 km mesh. The is-sue is discussed in more detail in Section 4.

This paper is organized as follows. An over-view of the model and experimental setup isprovided in Section 2, which is followed by apresentation of the simulated results in Section3. The article is concluded with a discussionand interpretation of the results in Section 4,which contains comparisons of the obtainedresults with those obtained by past modelingstudies on tropical cyclone projections.

2. Methodology

2.1 Overview of the model and numericalexperiments

The tropical cyclone projection discussed inthis paper is based on the outputs from twosets of 20 km-mesh, high-resolution AGCM ex-periments carried out on the Earth Simulator.The details of the model and the experimentsare described in Kusunoki et al. (2005) andMizuta et al. (2006). Briefly, the 20 km-mesh

260 Journal of the Meteorological Society of Japan Vol. 84, No. 2

model is a Meteorological Research Institute(MRI)/Japan Meteorological Agency (JMA) uni-fied model originated from the operational nu-merical weather prediction model, with somemodifications for a climate research objective(Mizuta et al. 2006). The time integration wasaccelerated with the introduction of a verticallyconservative semi-Lagrangian scheme (Yoshi-mura and Matsumura 2003; Yoshimura andMatsumura 2005), which allows nearly 4 hoursof wall-clock time for a 1-month time integra-tion with a time step of 360 seconds by using30 nodes of the Earth Simulator. The modelis configured with the horizontal spectral trun-cation of TL959 (equivalent to about 20 kmmesh), and 60 vertical levels with the modeltop at 0.1 hPa. The cumulus parameterizationused is a prognostic Arakawa-Schubert scheme(Randall and Pan 1993; Arakawa and Schubert1974).

To obtain a reasonable performance of the20 km-mesh climate model, careful tuning ofthe physical processes was needed. Our strat-egy in the tuning was to keep, or improve thequality of the climatological representation inthe original coarser-mesh version, while mak-ing the representation of convection associatedwith a tropical cyclone more realistic (Mizutaet al. 2006). It turned out that the manner ofthe organization of convection into a tropicalcyclone critically depends upon the amount ofcumulus momentum transport in the momen-tum transport parameterization used. To makeit more realistic, the transport amount, whichis tunable by an adjustment parameter, was re-duced by about 40% compared to the originalcoarser-mesh version at T106 (equivalent toabout 120 km mesh). The details will be foundin Mizuta et al. (2006).

The experiments follow the so-called ‘‘time-slice’’ method (Bengtsson et al. 1996; IPCC2001). First, a 10-year present-day experimentwas conducted with the forcing of the observedclimatological sea surface temperature (SST)average from 1982 to 1993. Next, an SSTchange between the future (average over 2080–99) and the present (average over 1979–98) ex-periments was added onto the observed SST.The SST change was predicted by the MRI-CGCM2.3 (Yukimoto et al. 2006) on the basisof the IPCC A1B emission scenario (IPCC 2000)with the CO2 concentration nearly doubled

around the 2080–99 period, and the globalmean surface air temperature rising by 2.5 de-grees. Under the SST condition, the model wasintegrated over 10 years as a future global-warmed climate experiment. In the future ex-periment, the concentrations of greenhouse gasand the aerosols are taken from the values ofthe year 2090 in the A1B scenario.

2.2 Tropical cyclone identification methodThis study focuses on elucidating the differ-

ences of tropical cyclone climatology betweenthe present-day climate condition and a future,greenhouse-warmed climate condition derivedfrom the respective 10-year time integrationperiod. The differences in the tracks (geograph-ical distributions), maximum wind speed arethe targets of our comparison. The data size ofthe default, 20 km-mesh output including thebasic meteorological elements of the dynamicaland thermodynamical physical quantities,amounts to about 60 GB per month. Due to thedifficulties in handling such an enormous data-set in an encompassing manner, we were forcedto carefully select only relevant output parame-ters featuring the tropical cyclones. In addi-tion, considering also the versatile aims of thecurrent numerical experiments (Mizuta et al.2006; Kusunoki et al. 2005), only a limitednumber of output variables, but minimizing sci-entific compromises, were allowed in the tropi-cal cyclone analysis.

To identify tropical cyclones from the outputsof the present-day and future experiments, weapplied the following method and criteria: thetarget area was specified between 45 S and45 N latitudinal belts over the ocean. The ini-tial position of each tropical cyclone was limitedto the region between 30 S and 30 N. Overthe specified area, the tropical cyclones weresearched with the following 6 sets of criteria,which are basically the same as those used bySugi et al. (2002) and originally in line withthose of Bengtsson et al. (1996).

(1) Across the 45 S–45 N latitudinal belt, thegrid point corresponding to a TC-centercandidate was defined as the one where theminimum surface pressure is at least 2 hPalower than the mean surface pressure overthe surrounding 7 degree � 7 degree gridbox.

April 2006 K. OOUCHI et al. 261

(2) The magnitude of the maximum relativevorticity at 850 hPa exceeds 3:5 � 10�5 s�1.

(3) The maximum wind speed at 850 hPa islarger than 15 m s�1.

(4) The temperature structure aloft has amarked warm core such that the sum ofthe temperature deviations at 300, 500 and700 hPa exceeds 2 K.

(5) The maximum wind speed at 850 hPa islarger than that at 300 hPa.

(6) The duration is not shorter than 36 hours.

Criteria (2)–(6) were applied to the region nearthe tropical cyclone center candidate satisfyingcriterion (1). The relevant physical properties ofthe tropical cyclones satisfying all the criteriawere saved every 6 hours for the analyses pre-sented in this article. Concerning criterion (3),the use of surface wind with the thershold of17 m s�1 turned out to be too stringent for sim-ulating the observed annual frequency of trop-ical cyclones. The use of a 15 m s�1 thresholdin criterion (3) is simply intended to makethe model-produced tropical cyclone frequencycomparable to the observed annual frequency(about 80 TCs per year). In fact, about 62.7% ofthe tropical cyclones simulated in the present-day experiment have a maximum surface windspeed of more than 17 m s�1. This demon-strates a reasonable model performance forsimulating a tropical cyclone. In the analysespresented in this article, surface wind speeddefined at 10 m height is used unless other-wise stated. Concerning criterion (4), a possibleglobal-warming-driven change in the warm-core structure itself merits investigation, but isbeyond the scope of this article. Relevantly, oneof the coauthors happened to have an opportu-nity to check the influence of the warm-core cri-terion upon the tropical cyclone frequency pro-jected in a T106 version of a global model, byincreasing a threshold of the vertically cumula-tive temperature anomaly. The result showedthat the tropical cyclone frequency remainedalmost unchanged for the intense events (mini-mum surface pressure of, for example, less than1,000 hPa). Criterion (5) is intended to removeextratropical cyclones.

2.3 The observational dataset for the outputverification

To verify the simulated tropical cyclone cli-matology, a global tropical cyclone data set ob-

tained from the website of the Unisys Corpora-tion is used; the data set combines the ‘‘besttrack’’ tropical cyclone data sets from the Na-tional Hurricane Center (NHC) of the U.S. Na-tional Oceanic & Atmospheric Administration,and from the U.S. Joint Typhoon Warning Cen-ter (JTWC). The NHC data set contains tracksfor the North Atlantic and the eastern NorthPacific basins. The JTWC data set containstracks for the western North Pacific Ocean, theNorth Indian Ocean, and the Southern Hemi-sphere. Out of the dataset, we retrieved the da-taset of the 1979–1998 period, with the maxi-mum surface wind speed at 17.2 m s�1 (34 kt)or higher for our analyeses.

3. Results

3.1 An example of a tropical cyclone rainfallmorphology

Compared to previous climate models with acoarser mesh, an advantage of using the 20 km-mesh model is the improvement in the repre-sentation of the inner structures of a tropicalcyclone. To gain insight into some morpho-logical aspects of the improvement, a typicaltropical cyclone in the present-day experimentis shown in Fig. 1. The figure illustrates (a) the6-hour averaged rainfall amount over a west-ern North Pacific region near Japan, (b) the 6-hour averaged rainfall amount near a tropicalcyclone in the mature stage, (c) a temperatureanomaly at 925 hPa as a deviation from thedisplayed area mean, (d) the water vapor mix-ing ratio at 925 hPa, and (e) the velocity at925 hPa. The area displayed in panels (d) and(e) is an expanded view of the area surroundedby thick-dashed lines in panel (b). In panels (d)and (e), the distribution of the precipitation isalso plotted with white contours.

Figure 1a demonstrates that the 20 km-meshmodel reproduces a variety of precipitation fea-tures, with wide-ranging scales and a hierar-chy, including a remnant of the precipitationfrom a frontal system stretching across the Pa-cific coastal region of Japan, a tropical cycloneon its way to Japan, and some organized cloudensembles located to the south of the tropicalcyclone. In the following, the typical hierarchyof the simulated precipitation features in thetropical cyclone is described. In Fig. 1b, it isevident that a comma-shaped stronger rainfallregion, labeled A, spreads immediately outside


of the lowest surface pressure region. Region Amay be likely to correspond to the eyewall ofthe tropical cyclone. In the northeastern toeastern quadrant of the lowest pressure center,spiral-form rainbands B and C are present. Al-though not shown, an examination of the timesequence of B and C reveals that the rainbands,as they develop, move inward so as to eventu-ally merge into the innermost eyewall area. Al-though the present 20 km-mesh global model

does not incorporate any sophisticated cloudmicro-physics to allow a full-scale comparisonof the simulated behaviors of the rainbandswith the observations, it can be inferred that,in the organization and maintenance of B andC, some meso-beta scale thermodynamical pro-cesses are at work, even if not as elaboratelyas in nonhydrostatic tropical cyclone models.In Fig. 1c, conspicuous negative temperatureanomalies coincide with the stronger rainfall

Fig. 1. Horizontal view of a tropical cyclone simulated in the present-day experiment (Aug 29, 7th

year of the time integration): horizontal distributions of (a) 6-hour averaged precipitation amountin mm hour�1 in the near-Japan, and northwest Pacific region. The rest of the panels focus on atropical cyclone, displaying (b) 6-hour averaged precipitation amount in mm hour�1 (shaded) andsurface pressure (contour), (c) temperature anomaly at 925 hPa in K, (d) water vapor mixing ratioat 925 hPa in kg kg�1 (shaded) and precipitation amount (white contour), and (e) horizontal veloc-ity at 925 hPa in m s�1 (shaded and arrows) and precipitation amount in mm hour�1 (white con-tour). The domain of panel (d) and (e) is an expanded view of the region surrounded by a dottedbox in panel (b).


regions corresponding to A, B, and C. Thesenegative anomalies may partly originate fromthe evaporation of the rainwater from A, B,and C. At the eastern edge of rainband C, theboundary layer is relatively moist (Fig. 1d),and the low-level flow is from the southeast(Fig. 1e). These conditions may be favorable forthe maintenance of rainband C. It is interestingto note that rainband C is represented not as ahomogeneous rainfall area, but as an ensembleof substructures with varying rainfall intensity.The substructures are indicative of cellular con-vective structures, which are embedded in ob-served tropical cyclone rainbands (e.g., Wil-loughby et al. 1984). Thus, the typical innerstructure of a tropical cyclone is captured morereasonably compared to the previous coarser-mesh climate models, in which the representa-tion of the inner structure, including the eye,has been somewhat elusive (Krishnamurtiet al. 1989). In order to achieve a realistic sim-ulation of the inner structures, the modelshould be improved. To further explore the re-liability of the ‘‘inner structure,’’ we need toclarify the validity of the cumulus parameter-ization used. Although the problem itself mer-its investigation, it is beyond the scope of thisarticle and will be examined in a future study.Within the present purpose of investigatingthe tropical cyclone climatology, the simulatedinner structure of a tropical cyclone providesan important improvement over those in pre-vious studies.

3.2 Tracks and geographical distributionFigure 2 shows the tropical cyclone tracks si-

mulated over the 10-year integration period ofpresent-day (middle) and future (low) experi-ments, together with the best tracks of the ob-servational global tropical cyclone dataset from1979 to 1998 (upper). The tracks for thepresent-day experiment, which are in generalagreement with the observation, reveal thatthe 20 km-mesh model succeeds in represent-ing the geographical distributions reasonablywell.

Nevertheless, there are some inconsistenciesbetween the results of the observation andthose of the model result. Most noticeably,there is a deficiency in the occurrence in andaround the model western Pacific region. Themodel tropical cyclones tend to be generated

more to the north of the observed locations.The deficiency may be partly relevant to aweaker bias of the precipitation amount in thisregion compared to the observations (Mizutaet al. 2006). The model’s inability to sufficientlyrepresent the horizontal precipitation distribu-tion in the subtropical western Pacific was com-mon to most of the climate models, as revealedfrom an AMIP type intercomparison study(Kang et al. 2002).

Another inconsistency is evident in the southIndian Ocean, where the observed tropical cy-clones generally occur outside the 0–10 S latitu-dinal belt; however, this is not the case in themodel results. Though there is a case in whicha tropical cyclone is observed near the equator(Chang et al. 2003), the current model obvi-ously overpredicts the generation of tropical cy-clones at very low latitudes. A similar ‘‘equator-ward bias’’ in the tropical cyclone formationwas also found in other global climate modelswith coarser horizontal resolution (e.g., [email protected] degree in Camargo et al. 2005). Ca-margo et al. (2005) inferred that the bias mayoriginate from insufficient resolution of themodel, which may dilute some dynamical pro-cesses that are otherwise to be shared amongfiner adjacent grid boxes. As our model has rel-atively higher resolution at 20 km, we mayneed to explore other possibilities. Our modelalso has a bias to predict many more tropicalcyclones around the dateline than in the obser-vations. Moreover, the tracks for the present-day experiment indicate a small but nonnegli-gible number of tropical cyclones off the coastof Brazil, which was recently pointed out froman observational study (Pezza and Simmonds2005). To explain the discrepancy between themodel results and observations, we would needto scrutinize additional cases, by extendingour analysis into datasets containing otheryears. These shortcomings need to be amelio-rated to achieve a more accurate prediction ofthe geographical distribution of tropical cyclo-nes. A general comparison between the pres-ent-day and future experiments indicates thatthe geographical distribution of tropical cyclo-nes would not be altered significantly underthe greenhouse-warmed condition, supportingwhat has been inferred and projected from pre-vious studies (Haarsma et al. 1993; Bengtsson2001).


3.3 FrequencyIn Fig. 2, it is evident that the frequency of

tropical cyclones is reduced in the future exper-iment compared to the present-day experiment,

as may be noted in the differences in the densityof the tracks. The tropical cyclone frequency in-vestigated region-wise is indicated in Table 1,where the annual-mean numbers of tropical cy-

Fig. 2. Tropical cyclone tracks of the observational data (top), the present-day (middle), and thefuture climate experiments (bottom). The initial positions of tropical cyclones are marked with‘‘plus’’ signs. The tracks detected at different seasons of each year is in different colors (blue forJanuary, February, and March; green for April, May, and June; red for July, August, and Septem-ber; orange for October, November, and December).


clones are shown for the globe, the Northernand Southern Hemispheres, and six ocean ba-sins. The ocean basins were defined based onthe latitudes and longitudes indicated in the ta-ble. In Table 1 (and Table 2 as explained later),a two-sided Student’s t-test was applied to thedifferences between the values from the presentand future experiments. In the test, storm re-sults were aggregated for each year over eachgiven region that constitute sample elementsof the test for present (10 years) and future(10 years) experiments ðn1 ¼ n2 ¼ 10Þ. In thepresent-day experiment, the annual meannumber (78.3) is almost comparable to the ob-servation (83.7). In the future experiment, theannual mean number is reduced to 54.8, whichis nearly a 30% reduction from the present-dayexperiment.

In the North Indian Ocean, Eastern NorthPacific, North Atlantic, and Southern Pacific,the model has reasonably simulated the ob-served frequency and distribution of tropical cy-clones. On the basin-wise scale, the model is,despite its high resolution, incapable of captur-ing the observed frequency in some basins: inthe western North Pacific, the model under-predicts the number of the observed frequencyfor the tropical cyclone of any intensity class,

as also inferred from the frequency deficiencyin that region reported in 3.2; in the SouthIndian Ocean, on the other hand, the relativenumbers are practically reversed, and themodel overpredicts the number of observedfrequency. In general, the model tends tooverestimate/underestimate the occurrencenumber in the Southern/Northern Hemisphere.

A comparison between the future andpresent-day experiments reveals that the oc-currence number in the future experiment isgenerally reduced, showing approximately 30%total global reduction, as stated before. Thereduction trend remains evident across theNorthern Hemisphere, Southern Hemisphere,North Indian Ocean, western North Pacific,South Indian Ocean, and South Pacific, at the99% level of statistical significance (based onthe two-sided Student’s t-test). On the otherhand, a tendency of increase in the future ex-periment is found in the North Atlantic Ocean,where the occurrence number increases byabout 30%. Though the reason for this contrastin the tendency is not presently clear, a cluemay be obtained by investigating the change inthe sea surface temperature (SST) between thetwo experiments. The upper panel of Fig. 3shows the difference of the SST between the

Table 1 Frequency of tropical cyclone occurrence in the observational dataset and in thepresent-day and the future experiments. The annual-mean numbers are shown for the globe,the Northern and Southern Hemispheres, and six ocean basins (conveniently defined basedon the latitudes and longitudes indicated in the table). Standard deviations are shown in pa-rentheses. The two-sided Student’s t-test was applied to the differences between the valuesfrom the present-day experiment and those from the future experiment.

Number of TCs Latitudes LongitudesObservation

20 yearsPresent10 years

Future10 years

Global 45 S–45 N ALL 83.7 (10.0) 78.3 (8.4) 54.8 (8.4)**Northern Hemisphere 0–45 N ALL 58.0 (7.1) 42.9 (7.0) 30.8 (5.8)**Southern Hemisphere 0–45 S ALL 25.7 (5.6) 35.4 (3.8) 24.0 (6.1)**

North Indian Ocean 0–45 N 30 E–100 E 4.6 (2.4) 4.4 (2.5) 2.1 (1.7)**Western North Pacific Ocean 0–45 N 100 E–180 26.7 (4.2) 12.4 (3.7) 7.7 (2.6)**Eastern North Pacific Ocean 0–45 N 180–90 W 18.1 (4.8) 20.5 (3.4) 13.5 (5.7)*North Atlantic Ocean 0–45 N 90 W–0 8.6 (3.6) 5.6 (2.7) 7.5 (1.8)#

South Indian Ocean 0–45 S 20 E–135 E 15.4 (3.8) 25.8 (3.0) 18.6 (5.1)**South Pacific Ocean 0–45 S 135 E–90 W 10.4 (4.0) 9.4 (3.3) 5.4 (1.9)**

**Statistically significant decrease at 99% confidence level.* Statistically significant decrease at 95% confidence level.# Statistically significant increase at 95% confidence level.


experiments (‘‘future minus present-day’’) ascalculated from the average over the borealsummer months of the tropical cyclone season(July, August, September, October), while thelower panel displays the mean precipitation dif-ference between the experiments. We can seethat a positive region of SST spreads over thesouth-eastern part of the North Atlantic Ocean.The region approximately corresponds to thearea of generation for most of the hurricanesthat approach and threaten the eastern coastof the United States. Of the several signaturesof such warm tongues over the globe, the oneover the North Atlantic is conspicuous in size,and, more importantly, coincides with the trop-ical cyclone source. In addition, the accompa-nied difference in the rainfall amount, which is

shown in the lower panel of Fig. 3, exhibits atrend of increase, indicative of activated con-vection. This should help ‘‘seed’’ the tropicalcyclone generations. These features can be re-sponsible for the increased frequency of tropicalcyclones over the North Atlantic Ocean.

3.4 IntensityThe simulated reduction in the number of fu-

ture tropical cyclones may be a positive signfrom a socio-economic viewpoint; however, aconcern is the increased devastation of tropicalcyclones in a greenhouse-warmed world. A clueto answer the problem may be obtained byexamining the simulated maximum surfacewind speeds of the tropical cyclones, which areshown in Fig. 4. The annual mean numbers

Fig. 3. Difference between the future and the present-day experiments (future minus present-day)in the sea surface temperature (upper) and precipitation amount (lower) derived from the averagein the boreal tropical-cyclone season (July, August, September, and October) for the entire integra-tion years.


of the simulated tropical cyclones for the‘‘present-day (thick solid lines)’’ and ‘‘future(thin solid lines)’’ experiments are plottedagainst the observation (dotted, black). In Fig.4, a two-sided Student’s t-test was applied tothe difference between the ‘‘present’’ and ‘‘fu-ture’’ curves, to check the statistical signifi-cance. In deriving the significance level, stormresults aggregated for the entire storm seasoneach year were used as the sample elements ofthe t-test (n1 ¼ n2 ¼ 10, for each 10 years timeintegration). The segment of the graphs satisfy-ing 95% significance level are marked with openand closed circles. The circles on thin/thicklines denote that the trend of frequency in-crease/decrease in the future experiment arestatistically significant (95% level). Focusingon the global frequency, it is noteworthy thatthe maximum surface wind speeds, in the pres-ent simulation, are generally lower than those

that are observed. The model is likely to under-estimate the surface wind speeds and, there-fore, the intensity of the tropical cyclones. Thediscrepancy may originate from some insuffi-cient performance of the physical schemes inthe 20 km-mesh model, including the cumulusparameterization. Originally formulated andincorporated in a coarser-mesh (e.g., T42,which is equivalent to horizontal mesh at a few100 s of kilometers) version climate model, thephysical schemes may fail to capture sufficientphysical processes necessary for the tropicalcyclone development. A considerable influenceof the change in the physical processes upontropical cyclone intensity, has also beensuggested by other studies (e.g., Knutson andTuleya 2004). To clarify the critical problem,detailed analyses on the interactions amongthe grid-scale physical processes, and subgrid-scale cumulus-scale processes, are necessary.

Fig. 4. (a, b, c) Annual mean occurrence frequency, (d, e, f ) Occurrence rate (%) normalized by thetotal number of simulated tropical cyclones counted in the region specified as functions of the trop-ical cyclone intensities (abscissa) for (a, d) global, (b, e) Northern Hemisphere, and (c, f )Southern Hemisphere. The abscissa is the largest maximum surface wind speed, and the maxi-mum wind speed attained in the lifetime of a tropical cyclone is plotted. Dotted lines indicate theobservation; results from the present-day experiment are shown by thick solid lines, and thosefrom the future experiment, by thin solid lines. For the difference between the ‘‘present’’ and‘‘future’’ results, the plot at the 95% statistical significance level is marked with an open or closedcircle (according to a two-sided Student’s t-test). Note that plots from the observation include notropical cyclone with the maximum surface wind speed at less than 17.2 m s�1.


The findings will be discussed in a futurearticle.

Here, the focus is on the comparison of thewind speeds between the present and future ex-periments. It is evident in Fig. 4 that the simu-lated change in the number of tropical cycloneoccurrences in the globe is different in tropicalcyclones of intense (>43 m s�1), and weak-to-moderate (<43 m s�1) classes. In the future ex-periment, tropical cyclones of the intense classincrease in number, while those of weak-to-moderate class decline. In particular, the sta-tistically significant (at 95% confidence level)increase is found for the class of 45–60 m s�1.A similar contrasting trend of frequency changein the ‘‘weak-to-moderate’’ and ‘‘intense’’ classis equal across most of the ocean basins (notshown), though detailed estimates of its statis-tical significance may require a larger samplingnumber; the current result is highly dependenton the variance in the number sampled amongthe basins (Fig. 2, Table 1). The result is consis-tent with the finding from a lower-resolution(T106) GCM (Yoshimura et al. 2006) for the fre-quency change, and from the regional model(Knutson et al. 1998), in that the more intensetropical cyclones are likely to increase underthe greenhouse-warmed climate.

To understand the quantitative difference inthe maximum surface wind speeds of intensetropical cyclones, between the present-day andfuture experiments, we have monitored thelargest maximum surface wind speed attainedthroughout the lifetime of all the tropical cy-clones in the respective regions in respectiveyears, and then calculated those statistics for10 years. Table 2a contains the results from acomparison of the largest maximum surfacewind speed in the present-day experiment withthat from the future experiment. Aggregatedover the globe, and over each hemisphere, themost intense tropical cyclones in the future ex-periment tend to become much stronger thanthose in the present-day experiment. In partic-ular, at the statistical significance level of morethan 95%, the increase is 6.7 m s�1 over theglobe, 7.3 m s�1 in the Northern Hemisphere,and 8.7 m s�1 in the North Atlantic Ocean.The tendency is almost the same in the South-ern Hemisphere. On a regional basis, this in-crease only occurs in the North Atlantic (95%significance) and South Indian (90% signifi-

cance) basins. A significant (95% level) decreaseoccurs in the South Pacific, and the remainingbasins have decreases which are not statisti-cally significant. The tendency, including asign of change, is basically true for the annualmean maximum wind speed averaged over allthe simulated tropical cyclones in each region,except for the western North Pacific and theeastern North Pacific.

The discussion regarding the tropical cycloneintensity up to here has been devoted to themaximum wind speed. One might argue thatthe model may have trouble in simulating verystrong wind speeds, but nonetheless can simu-late fairly low surface pressure of tropical cy-clone, and vice versa. To look briefly into this,we examine the relationship between the mini-mum surface pressure and the maximum sur-face wind of the simulated tropical cyclones inour model. The results are shown in Fig. 5, inwhich scattered diagrams of both physical vari-ables are shown for (left) present-day experi-ment and (right) warmed climate experiment.Superimposed on both diagrams is a nonlinearregression line that specifies a relationship be-tween minimum sea level pressure and maxi-mum sustained wind speed derived from theobservation of tropical cyclones in the westernNorth Pacific (Atkinson and Holliday 1977). Itseems that the range of scattered plots of thesimulated maximum wind speeds and surfacepressure in both experiments fit reasonablywell into the regression line. In addition, wecan see clear expansion of the surface pressuresto lower values in the future experiment. Thismay demonstrate a reasonable performance ofthe present model in simulating tropical cy-clones. On the other hand, it is noted thatthe model does not simulate central pressuresnearly as low as observed in nature (down to870 hPa). It appears that the lowest pressureis about 932 hPa in the present experiment,and about 912 hPa in the future experiment.We should note, however, that the observatio-nally derived regression line probably containsuncertainty in the measurement, and carefulinterpretation may be needed for full explora-tion of the problem.

3.5 Seasonal variation of intensityIn this subsection, the target of the intensity

comparison of the tropical cyclone between the


present-day and future experiments is shiftedfrom the annually averaged trend to its sea-sonal (monthly) change. We investigate how theoccurrence rate of the maximum wind speed ofa tropical cyclone varies at different times ofthe year under a greenhouse-warmed conditionwhen compared with the present-day condition.Figure 6 shows the occurrence rate of the maxi-mum surface wind speeds from the (a) observa-tion, (b) present-day experiment, and (c) futureexperiment, for all the simulated tropicalcyclones for each experiment over the globe

(GLOBAL) and in the Northern Hemisphere(NH), Southern Hemisphere (SH), North In-dian Ocean (NI), and western North Pacific(WNP). The observational dataset used is thesame as that described in Section 3.2. The oc-currence rate is plotted in time-intensity coor-dinates, where the area integration spanningthe panel equals 100%. Generally, the simu-lated seasonal change of the tropical cycloneoccurrence rate in the present-day experimentagrees with our expectation, indicating a higherrate from July to September in the Northern

Table 2 Maximum surface wind speed (m s�1) of TCs in the present-day and the future experi-ments. The two-sided Student’s t-test was applied to the difference between the values of thetwo experiments. Standard deviations and percent changes are shown in parentheses.(a) For each year of the integrations, the maximum surface wind speed attained in the life-time of all the TCs was selected, and the values for 10 years were then averaged. (b) Themaximum surface wind speed attained in the lifetime of each TC was selected, and the val-ues of all the TCs were averaged for each year of the integrations. The values for 10 yearswere then averaged.

(a) Annual maximumsPresent 10

years Ave. (SD)Future 10

years Ave. (SD)F minus P(% change)

Global 49.3 (2.2) 56.0 (5.3) 6.7 (13.7) ###

Northern Hemisphere 47.0 (5.4) 54.2 (6.4) 7.3 (15.5) ##

Southern Hemisphere 47.6 (2.3) 50.8 (4.5) 3.3 (6.9) #

North Indian Ocean 30.2 (9.7) 25.2 (14.6) �5.0 (�16.7)Western North Pacific Ocean 45.8 (5.0) 44.8 (12.0) �0.9 (�2.0)Eastern North Pacific Ocean 39.9 (4.6) 37.9 (7.8) �2.0 (�5.0)North Atlantic Ocean 43.4 (7.1) 52.2 (4.9) 8.7 (20.1) ##

South Indian Ocean 46.5 (2.7) 50.3 (5.0) 3.8 (8.2) #

South Pacific Ocean 44.0 (3.7) 34.1 (9.9) �9.9 (�22.5)**

(b) Annual averagesPresent 10

years Ave. (SD)Future 10

years Ave. (SD)F minus P(% change)

Global 24.0 (1.0) 26.5 (1.8) 2.6 (10.7) ###

Northern Hemisphere 23.1 (1.4) 25.1 (2.2) 2.0 (8.5) #

Southern Hemisphere 25.0 (1.6) 28.5 (2.9) 3.5 (14.1) ###

North Indian Ocean 21.8 (7.2) 19.0 (5.3) �2.8 (�12.8)Western North Pacific Ocean 25.3 (2.9) 26.4 (5.2) 1.1 (4.2)Eastern North Pacific Ocean 21.6 (2.0) 21.7 (3.3) 0.1 (0.6)North Atlantic Ocean 28.1 (7.0) 31.2 (5.8) 3.1 (11.2)

South Indian Ocean 25.6 (1.6) 30.1 (3.5) 4.4 (17.3) ###

South Pacific Ocean 23.7 (2.9) 23.2 (4.3) �0.5 (�2.0)

### Statistically significant increase at 99% confidence level.## Statistically significant increase at 95% confidence level.# Statistically significant increase at 90% confidence level.** Statistically significant decrease at 95% confidence level.* Statistically significant decrease at 90% confidence level.


Hemisphere and from January to March in theSouthern Hemisphere. The pattern of the veloc-ity range and the seasonal advance in the fu-ture experiment is remarkably similar to thatin the present-day experiment; the tropical cy-clones, with a higher rate of occurrence, stilllie within the velocity range similar to that ofthe present-day experiment. A notable changeis that the occurrence rate of the tropical cy-clones with the maximum wind speed exceed-ing 50 m s�1 increases somewhat in the futureexperiment.

On hemispheric and basin scales, the simu-lated seasonality of the tropical cyclone activityvaries from region to region. Broadly speaking,however, the comparison between the present-day experiment and observation reveals thatthe seasonal change of the simulated wind pat-terns is in agreement with the observationin the Northern Hemisphere, Southern Hemi-sphere, North Indian Ocean, and westernNorth Pacific. We confirm that tropical cyclonesare absent from June to September in theNorth Indian Ocean. The period is character-ized by enhanced vertical shear around theregion, due to the summer monsoon season,which tends to suppress the formation anddevelopment of tropical cyclones. Our model,

thus, simulates the relevant basic climate ofthis region reasonably well (Kusunoki et al.2005), demonstrating an improved representa-tion of the seasonal change of the basic fields.On the other hand, in terms of the quantitativecomparison, the model has a bias to simulateweaker wind speed compared to observation,which will be discussed later.

A comparison between the present-day andfuture experiments reveals no significant differ-ences in the seasonal change of the occurrencerate aggregated over the globe (GLOBAL).However, one may notice that the monthlychange in the pattern varies among the re-gions. Considering typhoons, for instance,which are a kind of tropical cyclone residing inthe western North Pacific, the season usuallyends in October in the present-day climate;however, in the future climate, the season usu-ally continues until November. There is also anindication that intense typhoons (with a veloc-ity exceeding 50 m s�1), have a higher rate ofoccurrence from September to October. Thesechanges should have something to do with thechanges in the environmental conditions oftropical cyclones, a topic which will require fur-ther investigation.

Fig. 5. Scattered diagram of minimum surface pressure versus maximum surface wind speed for allthe simulated tropical cyclones in the (left) present-day experiment and (right) warmed climate ex-periment. Blue line is the nonlinear regression line of ‘‘surface wind (Vsfc in knot) and surfacepressure (mb)’’ relationship from Atkinson and Holliday (1977).


4. Discussion and remarks

The previous studies using a global modelwith relatively high-resolution at that time(e.g., 100 km) suggested a general trend thatthe tropical cyclone frequency would be signifi-cantly reduced under the greenhouse-warmedenvironments (Bengtsson et al. 1996; Sugi et al.2002). Yet, the resolution of the model was stillinsufficient to represent the typical inner struc-tures of the observed tropical cyclone, and,partly for that reason, no general consensus onthe suggested trend was achieved (IPCC 2001).It has been suggested from a theoretical line ofthought, that an accurate representation of the

intensity of a tropical cyclone requires a hori-zontal resolution that is fine enough to resolvethe pressure gradients existing near the radiusof the maximum winds (Persing and Montgom-ery 2005). In addition, a numerical simulationof a super typhoon, using a global model, indi-cated that high resolution is critical for repre-senting the intense warm core in the innerrain area of a tropical cyclone (Krishunamurtiand Oosterhof 1989). Thus, there has been agrowing demand for a high-resolution climatemodel for studying the climatological aspects offuture tropical cyclones. In the present study,the use of the 20 km-mesh climate model(AGCM), one of the highest-resolution models

Fig. 6. Occurrence rate of the maximum surface wind speed of the tropical cyclones versus themonth for (a) the observation, (b) the present-day experiment, and (c) the future experiment. Thefractional rates (%) with respect to all the total tropical cyclones detected in the global (GLOBAL),Northern Hemisphere (NH), Southern Hemisphere (SH), North Indian Ocean (NI), and westernNorth Pacific (WNP) regions are displayed.


of its kind, allows a reasonable representationof the inner structures of a tropical cyclone(Fig. 1), and may provide a more reliable pro-jection of future tropical cyclones.

The results suggested that the frequency re-duction of tropical cyclones is probable in thefuture, which supports findings from the previ-ous studies. Though the mechanisms responsi-ble for the frequency reduction under the futuregreenhouse-warmed condition are not fully un-derstood, we infer that the stabilization of theatmosphere, caused by the greenhouse warm-ing, could be a reason, as suggested by Knut-son and Manabe (1995), Sugi et al. (2002),and Yoshimura et al. (2006). The inference isbroadly supported by a comparison of the drystatic stability between the future and present-day experiments; the future experiment showsan increase of approximately 10% more thanthat in the present-day experiment. The drystatic stability here is defined as a difference inpotential temperature between 250 hPa andthe surface.

In terms of the intensity change, it appearsthat more intense tropical cyclones will in-crease in the future greenhouse-warmed cli-mate. The finding is in agreement with pre-vious studies using regional models (Knutsonet al. 1998; Walsh and Ryan 2000; Knutsonand Tuleya 2004) as well as a theoretical esti-mation using the thermodynamical factors rele-vant to tropical cyclone (Emanuel 1987; Hol-land 1997; Henderson-Sellers et al. 1998). Thepresent study supports the finding in a morecomprehensive manner, because of the long-term climate integration with the 20 km-meshmodels. Another advantage of our model in cop-ing with this problem would be that the dynam-ical effects of the environmental flow of atropical cyclone, including the effect of verticalshear, might be represented consistentlythroughout the integration period. This featureshould be checked in a future study. The impor-tance of the effects in assessing the influence ofglobal-warming upon the intensity of tropicalcyclones was suggested by Walsh and Ryan(2000), but these effects have not been incorpo-rated in most of the past modeling studies.Knutson and Tuleya (1999) suggested that thetropical cyclone, in the global warming environ-ment, will be more intense in all the ocean ba-sins, a suggestion which is at odds with our

finding that the manner of change is basin-dependent. A reason for the discrepancy maybe relevant to the absence of environmental dy-namical effects on the tropical cyclones in theirmodel.

It is noteworthy that the downscaling fromlow to high (fine) resolution AGCM alone doesnot resolve all the problems around accuratetropical cyclone projection. Even in the contextof climate statistics, the model still has someshortcomings in capturing the observed geo-graphical distribution, frequency, and intensityof a tropical cyclone. In particular, the defi-ciency of the intense (e.g., high wind speed ofmore than 60 m s�1) tropical cyclone in ourmodel was the most notable unanticipated re-sult; however, the reasons are complex and dif-ficult to understand. There are other issues tobe clarified, which will make tropical cycloneprojections more accurate and reliable. Onemay argue that the use of 20 km mesh is stillcoarse for representing the inner mesoscalestructures of tropical cyclones. From anotherpoint of view, one may question the validity ofthe cumulus parameterization used in such a‘‘high-resolution’’ hydrostatic model becausemoist process plays a key role in the develop-ment of tropical cyclones. It will be importantto clarify the extent to which the cumulus pa-rameterization, and other physical schemesare involved in the success of the currentmodel. It will help clarify the caveats identifiedin our model including its bias for underpre-dicting the intensities of tropical cyclones whencompared to observation. Since other physicalparameterization schemes, such as a boundarylayer treatment, should be of equal importancefor improving the prediction of tropical cy-clones, additional studies should be needed toclarify the intensity question.

This study does not treat some importantquestions concerning future tropical cycloneprojection, which need to be investigated as afuture work. First, this study does not fully in-vestigate possible changes in the maximumcentral pressure of the future tropical cyclones,which is an important research theme to deepenour understanding on the characteristics oftropical cyclones in the future greenhouse-warmed climate. Also there remains a questionof how the duration of tropical cyclone willchange under a greenhouse-warmed climate.


This is also an important research topic. Evi-dence is emerging that the tropical cycloneactivity in the past 30 years in the North Pa-cific and North Atlantic is increasing signifi-cantly, and that the storms are characterizedby greater duration and intensity, which are at-tributed, at least partially, to anthropogenic ac-tivities (Emanuel 2005).

Even with these unresolved issues, the trop-ical cyclone projections with the 20 km-meshclimate model represent an advancement, andthe general trend of tropical cyclones in thefuture greenhouse-warmed climate is repre-sented to a reasonable degree and justified asfollows. First, extreme events other than thetropical cyclone, including the Baiu front, havebeen reproduced reasonably well with the model(Kusunoki et al. 2005), along with the success-ful representation of long-term averaged clima-tology (Mizuta et al. 2006). Furthermore, theexperiments have been used to provide someboundary conditions for the nonhydrostatic re-gional modeling of an Asian monsoon at 5 kmmesh, and these conditions turned out to workwell in the nonhydrostatic model (Yoshizakiet al. 2005). Thus, the model suitably repro-duces the fundamental climate features and at-tendant larger-scale background disturbancesof tropical cyclones. Second, the spontaneousdevelopment of a tropical cyclone with a typicalinner structure (eye, eyewall-like, and spiral-bank-like structures) is represented to a morerealistic extent in a hydrostatic modelingframework. To understand a genesis process ofthe tropical cyclones, a challenging and appeal-ing issue, remains to be attempted in the pres-ent work, and will be investigated in a futurework. It is our opinion that the major advance-ment from the past AGCM studies is that thetropical cyclone, including its inner structures,is represented not as a result of some artificiallyforcing or seeding from initial disturbances but,rather, as a result of its spontaneous interac-tion with environmental disturbances through-out the 10-year integration period. From a16-day integration of a 10 km-mesh globalhydrostatic model on the Earth Simulator, Oh-fuchi et al. (2004) reported that the model is ca-pable of representing the typical mesoscale con-vective structure of a typhoon (tropical cycloneresiding in and around the North Pacific) rea-sonably well. From a similar line of reasoning,

but based on the time integration and analysisspanning a global and climate scale, our strat-egy of using a 20 km-mesh hydrostatic modelis appropriate for the representation of theconvective structure of a tropical cyclone thatdevelops and decays in a climate system. Theresults reported in this article are expected toprovide further insights into a potentially morereliable projection of future tropical cyclone cli-matology.

Acknowledgments

The calculations were performed on the EarthSimulator as a part of the project RR2002,‘‘Development of Super-High-Resolution Globaland Regional Climate Models,’’ funded by theMinistry of Education, Culture, Sports, Science,and Technology (MEXT), Japan. Relevant sub-project members, other than the authors ofthis article, include T. Aoki, M. Hosaka, H.Ishizaki, A. Kitoh, T. Matsuo, T. Shibata, T.Uchiyama, T. Yasuda, and S. Yukimoto (Meteo-rological Research Institute, MRI), M. Hirai, T.Hosomi, T. Kadowaki, K. Katayama, H. Kawai,M. Kazumori, H. Kitagawa, C. Kobayashi, M.Kyoda, S. Maeda, T. Matsumura, S. Murai, M.Nakagawa, A. Narui, T. Ose, R. Sakai, T. Saka-shita, Y. Takeuchi, K. Yamada, and M. Yama-guchi (Japan Meteorological Agency, JMA), andK. Fukuda, K. Horiuchi, K. Miyamoto, and H.Murakami (Advanced Earth Science & Technol-ogy Organization, AESTO), all in alphabeticalorder. The timely and thoughtful comments onthe research by Prof. Akio Arakawa and Drs.Tatsushi Tokioka and Kozo Ninomiya are ac-knowledged. Last but not the least, the authorswould like to thank two anonymous referees fortheir insightful and constructive comments onthe original manuscript.

References

Arakawa, A. and W.H. Schubert, 1974: Interaction ofcumulus cloud ensemble with the large-scaleenvironment. Part I. J. Atmos. Sci., 31, 674–701.

Atkinson, G.D. and C.R. Holliday, 1977: Tropical cy-clone minimum sea level pressure/maximumsustained wind relationship for the westernNorth Pacific. Mon. Wea. Rev., 105, 421–427.

Bengtsson, L., M. Botzet, and M. Esch, 1996: Willgreenhouse gas-induced warming over the next50 years lead to higher frequency and greaterintensity of hurricanes? Tellus, 48A, 57–73.


———, 2001: Huricane threats. Science, 293, 440–441.

Broccoli, A.K. and S. Manabe, 1990: Can existing cli-mate models be used to study anthropogenicchanges in tropical cyclone climate? Geophys.Res. Lett., 17, 1917–1920.

Camargo, S.J. and A.H. Sobel, 2004: Formation oftropical storms in an atmospheric general cir-culation model. Tellus, 56A, 56–67.

———, A.G. Barnston, and S.E. Zebiak, 2005: A sta-tistical assessment of tropical cyclone activityin atmospheric general circulation models.Tellus, 57A, 589–604.

Chang, C.-P., C.-H. Liu, and H.-C. Kuo, 2003: Ty-phoon Vamei: An equatorial tropical cycloneformation. Geophys. Res. Lett., 30, No. 3, 1150,doi:10.1029/2002GL016365.

Emanuel, K.A., 1987: The development of hurricaneintensity on climate. Nature, 326, 483–485.

———, 2005: Increasing destructiveness of tropicalcyclones over the past 30 years. Nature, 438,686–688.

Haarsma, R.J., J.F.B. Mitchell, and C.A. Senior,1993: Tropical disturbances in a GCM. Clim.Dyn., 8, 247–257.

Henderson-Sellers, A. and Coauthors, 1998: Tropicalcyclones and global climate change: A post-IPCC assessment. Bull. Amer. Meteor. Soc.,79, 19–38.

Holland, G.J., 1997: The maximum potential inten-sity of tropical cyclones. J. Atmos. Sci., 54,2519–2541.

IPCC (Intergovernmental Panel on Climate Change),Special Report on Emissions. A Special Reportof Working Group III of the IntergovernmentalPanel on Climate Change. Cambridge Univer-sity Press, Cambridge, UK (2000).

IPCC (Intergovernmental Panel on Climate Change),Climate Change 2001: The Scientific Basis.Contribution of Working Group I to the ThirdAssessment Report of the IntergovernmentalPanel on Climate Change. Cambridge Univer-sity Press, United Kingdom and New York,NY, USA, 881pp.

Kang, L.S., K. Jin, B. Wang, K.M. Lau, J. Shukla, V.Krishnamurthy, S.D. Schubert, D.E. Waliser,W.F. Stern, A. Kitoh, G.A. Meehl, M. Kana-mitsu, V.Y. Galin, V. Satyan, C.K. Park, andY. Liu, 2002: Intercomparison of the climato-logical variations of Asian summer monsoonprecipitation simulated by 10 GCMs. Clim.Dyn., 19, 383–395.

Knutson, T.R. and S. Manabe, 1995: Time-mean re-sponse over the tropical Pacific to increasedCO2 in a coupled ocean-atmosphere model. J.Climate, 8, 2181–2199.

———, R.E. Tuleya, and Y. Kurihara, 1998: Simu-

lated increase of hurricane intensities in aCO2-warmed climate. Science, 279, 1018–1020.

——— and R.E. Tuleya, 1999: Increased hurricaneintensities with CO2-induced warming as si-mulated using the GFDL hurricane predictionsystem. Clim. Dyn., 15, 503–519.

——— and ———, 2004: Impact of CO2-Inducedwarming on simulated hurricane intensity andprecipitation: Sensitivity to the choice of cli-mate model and convective parameterization.J. Climate, 17, 3477–3495.

Krishnamurti, T.N. and D.K. Oosterhof, 1989: Pre-diction of the life cycle of a supertyphoon witha high-resolution global model. Bull. Amer. Me-teor. Soc., 70, 1218–1230.

———, ———, and N. Dignon, 1989: Hurricane Pre-diction with a high resolution global model.Mon. Wea. Rev., 117, 631–669.

———, R. Correa-Torres, M. Latif, and G. Daughen-baugh, 1998: The impact of current and possi-bly future sea surface temperature anomalieson the frequency of Atlantic hurricanes. Tellus,50A, 186–210.

Kusunoki, S., J. Yoshimura, H. Yoshimura, A. Noda,K. Oouchi, and R. Mizuta, 2005: Change ofBaiu in global warming projection by an atmo-spheric general circulation model with 20-kmgrid size. J. Meteor. Soc. Japan. (Submitted.)

Mizuta, R., K. Oouchi, H. Yoshimura, A. Noda, K.Katayama, S. Yukimoto, M. Hosaka, S. Kusu-noki, H. Kawai, and M. Nakagawa, 2006:20 km-mesh global Climate simulations usingJMA-GSM model. J. Meteor. Soc. Japan, 84,165–185.

Ohfuchi, W., H. Nakamura, M.K. Yoshioka, T. Eno-moto, K. Takaya, X. Peng, S. Yamane, T. Nish-imura, Y. Kurihara, and K. Ninomiya, 2004:10-km mesh meso-scale resolving simula-tions of the global atmosphere on the EarthSimulator—preliminary outcomes of AFES(AGCM for the Earth Simulator)—. J. EarthSimulator, 1, 8–34.

Persing, J. and M.T. Montgomery, 2005: Is environ-mental CAPE important in the determinationof maximum possible hurricane intensity? J.Atmos. Sci., 62, 542–550.

Pezza, A.B. and I. Simmonds, 2005: The first SouthAtlantic hurricane: Unprecedented blocking,low shear and climate change. Geophys. Res.Lett., 32, L15712, doi:10.1029/2005GL023390.

Randall, D.A. and D.M. Pan, 1993: Implementationof the Arakawa-Schubert cumulus parameter-ization with a prognostic closure. The Repre-sentation of Cumulus Convection in NumericalModels, Meteor. Monogr., Amer. Meteor. Soc.,46, 137–144.


Sato, T., 2004: The current status of the Earth Simu-lator. J. Earth Simulator, 1, 6–7.

Sugi, M., A. Noda, and N. Sato, 2002: Influence of theglobal warming on tropical cyclone climatology:An experiment with the JMA global model. J.Meteor. Soc. Japan, 80, 249–272.

Tsutsui, J., 2002: Implications of anthropogenic cli-mate change for tropical cyclone activity: Acase study with the NCAR CCM2. J. Meteor.Soc. Japan, 80, 45–65.

Walsh, K.J.E. and B.F. Ryan, 2000: Tropical cycloneintensity increase near Australia as a result ofclimate change. J. Climate, 13, 3237–3254.

Webster, P.J., G.J. Holland, J.A. Curry, and H.-R.Change, 2005: Changes in tropical cyclonenumber, duration, and intensity in a warmingenvironment. Science, 309, 1844–1846.

Willoughby, H.E., F.D. Marks Jr., and R.J. Feinberg,1984: Stationary and moving convective bandsin hurricanes. J. Atmos. Sci., 41, 3189–3211.

Yoshimura, H. and T. Matsumura, 2003: A semi-Lagrangian scheme conservative in the verticaldirection. CAS/JSC WGNE Research Activitiesin Atmospheric and Ocean Modeling, 33, 3.19–3.20.

——— and ———, 2005: A two-time-level vertically-

conservative semi-Lagrangian semi implicitdouble Fourier series AGCM. CAS/JSC WGNEResearch Activities in Atmospheric and OceanModeling, 35, 3.27–28.

——— and ———, 2004: A vertically conservativetwo-time-level semi-Lagrangian semi-implicitscheme. The 2004 Workshop on the Solution ofPartial Differential Equations on the Sphere.Yokohama.

Yoshimura, J., M. Sugi, and A. Noda, 2006: Influenceof greenhouse warming on tropical cyclone fre-quency. J. Meteor. Soc. Japan, 84, 405–428.

Yoshizaki, M., C. Muroi, S. Kanada, Y. Wakazuki, K.Yasunaga, A. Hashimoto, T. Kato, K. Kurihara,A. Noda, and S. Kusunoki, 2005: Changes ofBaiu (Mei-yu) frontal activity in the globalwarming climate simulated by a non-hydrostatic regional model. SOLA, 1, 25–28.

Yukimoto, S., A. Noda, A. Kitoh, M. Hosaka, H.Yoshimura, T. Uchiyama, K. Shibata, O. Ara-kawa, and S. Kusunoki, 2006: Present-Day Cli-mate and Climate Sensitivity in the Meteoro-logical Research Institute Coupled GCM,Version 2.3 (MRI-CGCM2.3). J. Meteor. Soc.Japan, 84, 333–363.

Note: After submitting the final version of this ar-ticle, the authors noticed that Webster et al. (2005)provided a significant, new observational evidencefor emergence of global upward trend in tropicalcyclone intensity based on an observational data-set of 35-year period. The trend is consistent withour major findings.


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Tropical Cyclone Climatology in a Global-Warming Climate