tropical cyclone genesis potential index over the western north pacific simulated by lasg/iap agcm

50 ACTA METEOROLOGICA SINICA VOL.27

Tropical Cyclone Genesis Potential Index over the Western NorthPacific Simulated by LASG/IAP AGCM

TIAN Fangxing1,2 (��), ZHOU Tianjun1∗ (��), and ZHANG Lixia1 (��)

1 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,

Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

2 Graduate University of the Chinese Academy of Sciences, Beijing 100049

(Received August 16, 2012)

ABSTRACT

Tropical cyclone genesis potential index (GPI) is a useful metric for gauging the performance of globalclimate models in the simulation of tropical cyclone (TC) genesis. The performance of LASG/IAP AGCMGAMIL2.0 in the simulation of GPI over the western North Pacific (WNP) is assessed in this paper. SinceGPI depends on large scale environmental factors including low-level vorticity at 850 hPa, relative humidityat 700 hPa, vertical wind shear between 850 and 200 hPa, maximum potential intensity (MPI), and verticalvelocity, the bias of GPI simulation is discussed from the perspective of thermal and dynamical factors. Theresults are compared with the ECMWF reanalysis data (ERA40). The analyses show that both the climato-logical spatial pattern and seasonal cycle of GPI over the WNP are reasonably simulated by GAMIL2.0, butdue to the overestimation of relative humidity, the simulated GPI extends to 170◦E, about 10◦ east to thatin the reanalysis data. It is demonstrated that the bias in the simulation of monsoon trough, which is about5◦ north to the reanalysis, leads to an overestimation of GPI during May–June and September–October, butan underestimation during July–August. Over the WNP, the response of GPI to ENSO is well captured byGAMIL2.0, including the eastward (westward) shift of TC genesis location during El Nino (La Nina) years.However, the anomalous convective center associated with El Nino shifts westward about 20◦ in comparisonto ERA40, which leads to the biases in both vertical velocity and relative humidity. These eventually resultin the westward deflection of the boundary between the positive and negative GPI centers along 20◦–30◦N.The results from this study provide useful clues for the future improvement of GAMIL2.0.

Key words: tropical cyclone genesis potential index (GPI), western North Pacific (WNP), model evalu-ation, GAMIL2.0

Citation: Tian Fangxing, Zhou Tianjun, and Zhang Lixia, 2013: Tropical cyclone genesis potential indexover the western North Pacific simulated by LASG/IAP AGCM. Acta Meteor. Sinica, 27(1),50–62, doi: 10.1007/s13351-013-0106-y.

1. Introduction

Tropical cyclones (TCs) always lead to extremeprecipitation and strong wind, which exert great im-pact on human life and social economy. Calculationsbased on the TC best track data indicate that thereare 680 TCs over the western North Pacific (WNP)during 1979–2000, accounting for 34% of the globaltotal. Located to the western coast of WNP, China isseriously influenced by TCs. Therefore, studies aboutTC genesis and forecast are of great importance.

In order to describe TCs, many kinds of in-dices have been defined. Maximum Potential Inten-sity (MPI) is used to estimate the possible maxi-mum velocity (Vmax) based on certain air-sea condi-tions (Emanuel, 1988). Accumulated Cyclone Energy(ACE) is used to evaluate the peak season TC activi-ty intensity (Bell et al., 2000). In order to describethe possibility of genesis of strong TCs or hurricanes,NCat45 (number of TCs with maximum intensity inCategory 4 or 5 on the Saffir-Simpson scale) is em-ployed (Saffir, 2003). The damage ability of certain

Supported by the National Basic Research and Development (973) Program of China (2010CB951904) and National NaturalScience Foundation of China (41125017).

∗Corresponding author: [email protected].

Chinese version to be published.

©The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2013

NO.1 TIAN Fangxing, ZHOU Tianjun and ZHANG Lixia 51

TCs is evaluated by using the power dissipation index(PDI) (Emanuel, 2005). Among these indices, the gen-esis potential index (GPI) (Emanuel and Nolan, 2004)inherited from the Gray Index (Gray, 1968) is able toquantify the impact of large scale environmental fac-tors on tropical cyclone genesis. These factors includeboth dynamic factors such as vertical wind shear, ab-solutely vorticity, and vertical velocity; and thermo-dynamic factors such as relative humidity and Vmax.

Previous studies on GPI variations on multi-ple timescales reveal that TC formation is influencedstrongly by Madden–Julian Oscillation (MJO) andENSO (Camargo et al., 2007a, 2009). Some studiesalso indicate that global warming has potential impacton GPI (Zhang et al., 2009; Murakami et al., 2011). Inapplication, GPI is widely used in model evaluation.The climatology of GPI and its seasonal cycles aregenerally overestimated in most AGCMs (Camargo etal., 2007b). In most CGCMs, the climatology and sea-sonal cycle of GPI could also be reproduced. Duringthe peak season of TC genesis, GPI is underestimated,but overestimated before and after the peak season(Yokoi et al., 2009).

The atmospheric general circulation model(AGCM) named GAMIL (Grid-point atmosphericmodel of LASG/IAP) is widely used in studies of cli-mate change (Li et al., 2007), cloud radiative forcing(Guo et al., 2011), Asian-Australian monsoon (Zhouet al., 2009b, c), decadal climate change over East-Asia (Zhou et al., 2009a), and so on. However, theperformance of the model in simulating GPI is un-known. This study aims to assess the performanceof the GAMIL model in simulating GPI climatology,seasonal cycle, and interannual variability. The re-sults are useful for the development of high resolutionGAMIL, which targets to explicitly represent tropicalcyclones.

This paper is organized as follows. Section 2presents the model, data, and methods employed inthis study. Results of our exploration in GPI clima-tology, seasonal cycle, and interannual variability arepresented in Section 3. Conclusions and discussion aregiven in Section 4.

2. Model, data, and methods

2.1 Model and data

The model employed in this study is GAMIL2.0(Li et al., 2010), which is a grid-point atmosphericgeneral circulation model (AGCM) developed by theState Key Laboratory for Numerical Modeling for At-mospheric Sciences and Geophysical Fluid Dynamics(LASG), Institute of Atmospheric Physics (IAP), Chi-nese Academy of Sciences (CAS). The physical pack-age of GAMIL2.0 inherits from the US NCAR Com-munity Atmospheric Model. Its convection scheme isreplaced with a modified version of the Tiedtke scheme(Nordeng, 1994). The model is run at a horizontalresolution of 2.8◦×2.8◦, with 26 vertical levels in apressure coordinate system extending from surface to2.194 hPa. The model is driven by historical monthlysea surface temperature for 1975–2005. The outputfrom 1979 to 2000 is used in this work.

The following observational datasets are used inthis study: (1) Hadley Center Global Sea Ice andSea Surface Temperature (HadISST) data (Rayneret al., 2003); (2) European Center for Medium-Range Weather Forecast (ECMWF) 40-yr reanalysis(ERA40) data (Uppala et al., 2005); (3) Unisys Cor-poration website TC data (Unisys, 2011). The selectedperiod of all of the above datasets is 1979–2000.

2.2 Methods

2.2.1 Tropical cyclone genesis potential index (GPI)Following Emanuel and Nolan (2004), GPI is de-

fined as:

GPI =∣∣105η

∣∣32(RH

50

)3(Vmax

70

)3

(1 + 0.1Vs)−2,

where η is absolute vorticity at 850 hPa, RH is relativehumidity at 700 hPa, Vmax represents the maximumpotential intensity (MPI) (Emanuel, 1995), and Vs isthe magnitude of vertical wind shear between 850 and200 hPa.

The above index has been modified by subsequentworks through adding a vertical wind velocity termthat enables correct reproducibility of TC genesis


over regions with strong convection, such as the ITCZ(Murakami et al., 2011). This work uses the modifiedversion of GPI as follows:

GPI =∣∣105η

∣∣32(RH

50

)3(Vmax

70

)3

·(1 + 0.1Vs)−2(−ω + 0.1

0.1

)

, (1)

where ω is the vertical wind velocity at 500 hPa.The equation is abbreviated as follows:

GPI = AV × RH × MPI × SH × W, (2)

where AV is absolute vorticity, MPI is maximum po-tential intensity, SH is vertical wind shear, and W isvertical wind velocity.2.2.2 Decomposition of GPI biases

Since GPI is the product of five factors, it is nec-essary to find the relative contribution of each factorto the simulation biases. This is achieved by calculat-ing the relative error in log form (Yokoi et al., 2009).The relative error is defined as:

lg GPI−g − lg GPI−e

=5∑

i=1

[

lg var−g(i) − lg var−e(i)]

, (3)

where

lg GPI =5∑

i=1

lg var(i), (4)

where GPI−g and GPI−e stand for GPI calculated bysimulation and from ERA40, respectively.2.2.3 Contributions of individual variables to interan-

nual variability of GPIIn order to find the contribution of each factor

to GPI’s response to ENSO, this work employs themethod developed by Carmargo et al. (2007b). Thatis, GPI is calculated using the climate mean of fourof the variables while the fifth variable is varying overtime. The result indicates the contribution of the fifthvariable on GPI’s interannual variability. The sameprocedure is repeated for every factor.2.2.4 Selection of ENSO years

El Nino and La Nina years in this work are de-fined according to the Nino 3.4 index values (Barnston

et al., 1997) averaged over TC peak season (JASO)over the WNP. About 25% of the total years with thelargest (smallest) values of Nino 3.4 index are definedas El Nino (La Nina) years; the left is defined as neu-tral years. This definition is widely used to investi-gate the interannual variability of TC (Camargo andSobel, 2005; Goddard and Dilley, 2005; Camargo etal., 2007a), because it avoids using thresholds, whichare seasonally varying and asymmetrical between coldand warm events. According to these criteria, 1982,1986, 1987, 1991, 1994, and 1997 are selected as ElNino years, and 1988, 1995, 1998, and 1999 are LaNina years, and the others are neutral years.

3. Results

The performance of GAMIL2.0 in simulatingGPI over the WNP is evaluated by comparison withERA40. We firstly examine the ability of GAMIL2.0in simulating climatology and seasonal cycle of GPI.Then, we assess the model’s skill in simulating the in-terannual variability of GPI.

3.1 Climatology of GPI

The climatology of GPI during 1979–2000 is pre-sented in Fig. 1. The black dots indicate the TC gene-sis positions. In the ERA40 reanalysis (Fig. 1a), TCsare distributed mainly between 5◦ and 30◦N over theWNP. The spatial structure of GPI−e reasonably re-sembles the climatology of TC distribution. The mainfeature of GPI−e can be well simulated by GAMIL2.0.However, the simulated large GPI value extends east-ward to 170◦E while the GPI in the reanalysis reducesand gradually vanishes to the east of 160◦E. This over-estimation could be identified in the difference field(Fig. 1c) more obviously.

In order to reveal the causes of the simulationbias, Fig. 2 shows distributions of the five linearizedcomponent variables of GPI based on Eq. (4). Amongthese variables, RH shows the largest positive error.Specifically, in the area 10◦–30◦N and east of 160◦E,GPI−e is relatively low; however, the simulated GPI−gis overwhelmingly larger than GPI−e. Hence, RH isthe most important contributor to the simulated biasof GPI east of 160◦E.


Fig. 1. Climatological GPI (×10−2) for (a) ERA40, (b) GAMIL2.0, and (c) difference between (b) and (a). The black

dots in (a, b) denote individual genesis events from 1979 to 2000.

The difference of linearized variables betweenGAMIL2.0 and ERA40 according to Eq. (3) is shownin Fig. 3. Over the region between 10◦ and 30◦N,the bias of RH is higher and located eastward. Onthe contrary, the biases of MPI, W , AV, and SH arerelatively small. This further confirms that the biasof GPI is mainly caused by the bias of RH. The sim-ulated biases of the five factors are also compared inTable 1. By comparing the pattern correlation coeffi-cient (PCC) and root mean square error (RMSE), itshows that AV and MPI can be successfully simulated,but the model skill is relatively low in simulating W

and RH; in particular, the bias of RH is the highest.In conclusion, it is the overestimation of simulated

relative humidity that leads to larger GPI extendingeastward to 170◦E in the GAMIL2.0. Therefore, themodel is unable to correctly capture the significant de-cline of TC genesis frequency to the east of 160◦E.

3.2 Seasonal cycles of GPI

The seasonal cycles of GPI are analyzed next.Figure 4 shows monthly variations of the zonal meanGPI between 120◦ and 150◦E. In ERA40 (Fig. 4a),GPI−e locates in south of 10◦N before April, ex-

tends northward and strengthens in May, eventuallyreaches 35◦N in August. From July to October, GPI−egets enhanced over 20◦N and reaches peak values inSeptember. From November, GPI−e retreats south-ward and is weakened. The seasonal cycles of GPI canbe generally reproduced by GAMIL2.0 (Fig. 4b), butGPI−g is intensifying and stretching in April, whichis one month earlier than GPI−e. The maximum GPIarea appears from June to October, but is betweenMay and November in GAMIL2.0. The peak value ofGPI−g appears in October, a month later than thatof GPI−e.

The simulation bias of GPI−g and its componentvariables (Fig. 5) are calculated based on Eq. (3) andshown in Fig. 5. From April to November, the dif-ference between GPI−g and GPI−e shows an overesti-mation of GPI−g over 15◦–25◦N (Fig. 5a). The max-imum bias appears during April–May and October–November. The underestimation of GPI−g is seen be-tween 5◦ and 15◦N from May to November. The rela-tive biases of the five component factors are presentedin Figs. 5b–5f. From May to November, the underesti-mations of AV and SH lie between 5◦ and 15◦N, whichis consistent with the bias of GPI−g. This indicates


Fig. 2. Distributions of (a, b) MPI, (c, d) W , (e, f) AV, (g, h) SH, and (i, j) RH. Left panels are for ERA40 and right

for GAMIL2.0. Solid and dashed lines indicate positive and negative values, respectively.


that from May to November, underestimations of ab-solute vorticity and vertical wind shear are responsible

for the bias of GPI−g between 5◦ and 15◦N. Mean-while, RH is overestimated from April to November

Fig. 3. Relative errors of (a) climatology GPI, (b) MPI, (c) W , (d) AV, (e) SH, and (f) RH.

Fig. 4. Time-latitude cross-sections of monthly mean GPI integrated within 120◦–150◦E for (a) GPI−e and (b) GPI−g.


Fig. 5. Seasonal variations of relative errors of (a) GPI, (b) MPI, (c) W , (d) AV, (e) SH, and (f) RH. Solid lines and

shadings indicate positive values and dashed lines indicate negative values.

Table 1. PCC and RMSE of the climatology pattern of GPI and its component variables between GAMIL2.0 and ERA40

Variable GPI MPI W AV SH RH

RMSE 0.38 0.83 1.01 0.93 0.81 1.13

PCC 0.86 0.92 0.78 0.99 0.89 0.84

within 15◦–25◦N, reaching the maximum bias dur-ing April–May and October–November. Therefore,the overestimation of RH leads to the earlier north-ward moving and the delayed southward retreatmentof GPI.

In the WNP, monsoon trough is the hotbed of TCgenesis, where significantly higher relative humidity isobserved. The wind field from GAMIL2.0 is analyzedin Fig. 6. We find that the bias of simulated GPI’sseasonal cycle is mainly caused by the simulated ad-vance and retreat of monsoon trough. In ERA40 (Figs.6a, 6c, and 6e), the monsoon trough locates at 15◦N

in June, arrives at 20◦N in July, and retreats south-ward to 15◦N in August. Both the TC genesis fre-quency and GPI−e possess very high values aroundthe monsoon trough. In GAMIL2.0 (Figs. 6b, 6d,and 6f), from June to August, the monsoon troughis about 5◦ northward compared with ERA40. Thus,larger GPI−g shows up in the north of observed TChigh-frequency genesis region. The simulated mon-soon trough passes 20◦N twice a year, hence the largevalue area of GPI−g maintains over 20◦N longer thanERA40, and GPI−g moves northward earlier and re-treats southward later compared to ERA40.


Fig. 6. Horizontal wind fields (vector; m s−1) at 850 hPa and GPI (shaded; ×10−3). (a, b) June, (c, d) July, and (e,

f) August. Left panels are for ERA40 and right for GAMIL2.0. The thick blue line indicates the monsoon trough.

Fig. 7. Interannual variations of TC genesis frequency

and GPI over the WNP. Solid line is observed TC genesis

frequency, dashed line denotes GPI of ERA40, and dot line

represents GPI of GAMIL2.0.

3.3 Modulations of ENSO on GPI

Previous research shows that, in addition to theclimatology of TC genesis frequency, the interan-nual variability of TC can also be represented by GPI(Camargo et al., 2007a). Figure 7 shows the time se-ries of TC genesis frequency and GPI averaged overthe WNP. The change of GPI derived from ERA40 isconsistent with the observed TC. However, the corre-lation coefficient between GAMIL2.0 and TC is only0.16, indicating that the ability of the model in re-producing the interannual variation of GPI is quitelimited. In the following analysis, we will try to iden-tify the reasons for this model limitation.

The composite GPI during June–October is


shown in Fig. 8. This composite is derived fromthe difference between El Nino and La Nina years. InERA40 (Fig. 8a), GPI−e is intensified (weakened) tothe east of 150◦E, and weakened (intensified) tothe west of 150◦E in the warm (cold) phase of ENSO.Previous works documented that over the WNP, theactive TC genesis region moves southeastward in ElNino years and northwestward in La Nina years (Chan,1985; Chu, 2004), which is consistent with the per-formance of GPI. This zonal dipole pattern over theWNP is well simulated by GAMIL2.0 (Fig. 8b), butthe boundary between positive and negative areas lo-cates to the east of the observations. The relativebias, shown in Fig. 8c, further indicates that the GPIanomaly over 25◦–30◦N, 140◦–160◦E is overestimatedby GAMIL2.0.

The response of each component factor of GPI toENSO is evaluated individually based on Eq. (3). Itis seen that the responses of W and RH are the sameas GPI while the response of AV is opposite to thatof GPI. The interannual variations of SH and MPI areunrelated to that of GPI. In order to identify the mainfactors responsible for the GPI bias, the bias of eachvariable during June–October is shown in Fig. 9. The

simulated W and RH are higher over 15◦–30◦N, 140◦–160◦E, where the GPI is overestimated. The bias ofMPI is the smallest, while the biases of AV and SHterms are not evident either. Therefore, it is the over-estimation of W and RH over 15◦–30◦N, 140◦–160◦Ethat leads to the westward shift of the boundary be-tween the positive and negative values of GPI.

Since both W and RH are influenced by convec-tions, the wind field at 850 hPa and the correspond-ing velocity potential field at 200 hPa are analyzedfurther. There is westerly anomaly over 0◦–10◦N inERA40 (Fig. 10a), and strong cyclonic anomaly domi-nates the WNP in GAMIL2.0 (Fig. 10c). This formsthe cyclonic anomaly around 20◦N (Fig. 10e), whichis located in the same area of the overestimated GPI.

The velocity potential and divergent wind at 200hPa are shown in Figs. 9b, 9d, and 9f. In ERA40, thedivergence center is located around 140◦W while inGAMIL2.0, it is located around 160◦W, with strongerintensity. The difference between reanalysis and sim-ulation indicates that divergence anomaly (cyclonicanomaly) dominates the area east of 150◦E at 200 hPa(850 hPa). This leads to the overestimation of W andRH, and thereby the bias of GPI.

Fig. 8. Composite difference of GPI during June–October between El Nino and La Nina years for (a) GPI from ERA40

and (b) GPI from GAMIL2.0, and (c) difference between (b) and (a). Solid and dashed lines indicate positive and

negative values, respectively.


Fig. 9. Simulated errors of GPI and its component variables during June–October between El Nino and La Nina years.

(a) GPI, (b) MPI, (c) W , (d) AV, (e) SH, and (f) RH. Solid and dashed lines indicate positive and negative values,

respectively.

4. Conclusions and discussion

This study aims to evaluate the performance ofthe AGCM developed by LASG/IAP in simulating thegenesis potential index of tropical cyclone. The cli-matology, seasonal cycle, and interannual variabilityof GPI simulated by GAMIL2.0 are examined in com-parison with those from ERA40 data. The main re-sults are summarized as follows.

(1) The GPI derived from ERA40 reasonably rep-resents the distribution of the maximum area (5◦–20◦N, 110◦–160◦E) of the observed tropical cyclonegenesis frequency. To the east of 160◦E, GPI of ERA40decreases rapidly, which is consistent with the declineof observed tropical cyclone genesis frequency. Thisclimatology of GPI distribution is well simulated by

GAMIL2.0 except that the simulated GPI is higherthan the reanalysis over 10◦–30◦N due to overestima-tion of relative humidity.

(2) The GPI of ERA40 is located to the southof 10◦N before May, then stretches northward andreaches 35◦N in August. The maximum GPI intensityappears in September. After October, GPI retreatssouthward and becomes weaker. The seasonal cycleof GPI is reasonably reproduced by GAMIL2.0. Butfrom April to July, the simulated relative humidity ishigher than the reanalysis over 5◦–25◦N. This leads toan earlier northward movement and later southwardretreat of GPI in GAMIL2.0, and the maximum valueof GPI appears one month later than ERA40. Thebias of relative humidity is ascribed to the bias in thesimulated location of monsoon trough, which affects


Fig. 10. (a, c, e) Difference of GPI (shaded; ×10−3) and 850-hPa horizontal wind (vector; m s−1) during June–October

between El Nino and La Nina years. (b, d, f) Difference of velocity potential (shaded; 106 m s−1) and divergent wind

(vector; m s−1) at 200 hPa during June–October between El Nino and La Nina years. (a, b) for ERA40, (c, d) for

GAMIL2.0, (e) for (c)–(a), and (f) for (d)–(b).

relative humidity distribution, and thereby leads tothe bias of GPI.

(3) The GPI of ERA40 is intensified to the eastof 150◦E and weakened to the west of 150◦E duringEl Nino years. By contrast, for La Nina years, GPIis weakened to the east of 150◦E and intensified to

the west of 150◦E. In GAMIL2.0, this zonal dipolarpattern is well simulated, but the boundary betweenthe positive and negative regions is located to the westof 150◦E. The anomalous upward motion in El Ninoyears is located to the west of that in ERA40, thus theboundary of upward movement and relative humidity


is located to the west of reanalysis and finally leads tothe bias of GPI.

Because of the relatively lower resolution,GAMIL2.0 is unable to explicitly describe tropicalcyclones. However, GPI is a useful measure of themodel’s potential ability in simulating the tropicalcyclone genesis. Our analysis on the performance ofGAMIL in reproducing the observed features of GPIindicates that, while the model is potentially able todepict the tropical cyclones in its high resolution ver-sion, improvements are needed in its accurate simu-lation of the monsoon trough and the response of trop-ical circulation to ENSO.

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tropical cyclone genesis potential index over the western north pacific simulated by lasg/iap agcm

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