Influence of Tropical Cyclones on the Estimation of Climate Variability in the Tropical
Western North Pacific
HUANG-HSIUNGHSU, CHING-HUIHUNG, AN-KAILO,AND CHUN-CHIEHWU
Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
CHIH-WENHUNG
Department of Atmospheric Sciences, Chinese Culture University, Taipei, Taiwan
(Manuscript received 10 January 2007, in final form 12 July 2007) ABSTRACT
By estimating the differences between the original and tropical cyclone (TC)-removed fields derived from the 40-yr (ECMWF) Re-Analysis (ERA-40) and NCEP–NCAR 40-Year Reanalysis, this study reveals that TCs contribute significantly (exceeding 50% in certain regions) to the seasonal mean and the intraseasonal and interannual variance of the 850-hPa vorticity along the TC tracks in the tropical western North Pacific. Similar effects on the precipitation are also seen, as presented by the examples located in Taiwan. While the low-frequency, large-scale circulation produces a clustering effect on TCs, the latter, which has a large positive vorticity and tends to occur in the positive vorticity background flow, significantly enhances the strength of the positive vorticity. The contribution from TCs, which is not offset by the synoptic systems with weak negative vorticity, can therefore leave marked footprints in the climate signal and variability. This effect is not removed by long-term averaging and low-pass filtering, which are often used to retrieve the climate perturbations. This study reveals that the climate variability, as it is defined, is not contributed to merely by the low-frequency large-scale fluctuations. Instead, the TC effect has to be taken into account to understand the climate variability in the tropical western North Pacific. Subsequently, the ensemble effect of TCs, at least in the statistical sense, has to be resolved in the climate model to obtain a better simulation of the climate variability in the TC-prone region, such as the tropical western North Pacific.
1. Introduction
A climate system is characterized by the interactions between multiscale fluctuations. Estimation of the cli-mate variability in different time scales is one of the cornerstones of climate research. Traditionally, time averaging and filtering are often used to numerically retrieve the low-frequency climate signals. The corre-sponding climate variability is estimated by calculating the variance. Such an approach is based on an implicit assumption that the time averaging and filtering pro-cesses remove higher-frequency signals. This assump-tion is normally not violated, as long as the amplitudes of the high-frequency fluctuations in the positive and negative phases are almost equal, and the averaging time is long enough to undergo several complete high-frequency fluctuation cycles.
On the other hand, the assumption may still be
vio-lated when the high-frequency fluctuations of signifi-cantly different amplitudes in the positive and negative phases are embedded in the time series, or when the step function–like fluctuations of large amplitude have the tendency to exist in one particular fluctuation phase. To demonstrate this effect on the time mean and variance, 150 time series of a combination of two sinu-soidal waves with random errors, that is, x ⫽ x0 ⫹
(a1cost⫹ b1sint)⫹ (a3cos3t⫹ b3sin3t) ⫹⑀, were
cre-ated to simulate 150 typhoon seasons. One to six step function–like perturbations with much larger ampli-tudes, representing the occasionally occurring extreme events, were randomly added to the 150 time series. Three time series are shown in Fig. 1 as examples. Without considering the extreme values, the ensemble mean and “inter–time series” (interannual) variance, which are defined as the variance of the 150 time series means, are 55.7 and 0, respectively. Zero variance is found, as the 150 time series are different only in the wake of random noises. Therefore, the corresponding means are essentially the same for all series. By adding these extreme values, the ensemble mean and
interan-Corresponding author address: Huang-Hsiung Hsu,
Depart-ment of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan.
E-mail: hsu@atmos1.as.ntu.edu.tw DOI: 10.1175/2007JCLI1847.1
© 2008 American Meteorological Society
nual variance increase significantly to 65.1 and 18.8, respectively. The presence of these extreme values ob-viously increases the mean of each time series to dif-ferent levels, depending on the amplitude and number of extreme events, which indirectly raises the variance between the means of the time series. This example reveals the possibility that the occurrence of a few ex-treme events may significantly enlarge the ensemble mean and variability.
Similar effects on the climate variability may occur in the areas where tropical cyclones (TC) are active, such as the tropical western North and South Pacific, the tropical North Atlantic, and the tropical Indian Ocean. This is because TCs are vortices of strong positive vor-ticity, which is not accompanied (or compensated) by vortices of negative vorticity with roughly equal strength. As demonstrated in Fig. 1, the climate vari-ability in the TC-prone areas may enlarge because of the occurrence of TCs. It has been known that TC ac-tivity is often modulated by low-frequency fluctuations such as intraseasonal oscillation (ISO; Gray 1978; Na-kazawa 1986; Heta 1990; Liebmann et al. 1994; Ma-loney and Dickinson 2003), El Niño–Southern Oscilla-tion (ENSO; Chan 1985, 2000; Chen et al. 1998, 2004; Landsea 2000; Chia and Ropelewski 2002; Wang and Chan 2002), and even decadal fluctuation (e.g., Ho et al. 2004). While TCs tend to appear in clusters, in light of the modulation of these low-frequency fluctuations, the mean vorticity in the TC-active periods is likely to be larger than its counterpart in the TC-inactive peri-ods. This asymmetric effect on the mean vorticity in the different phases of the low-frequency fluctuations may
therefore lead to an increase in the low-frequency vari-ance. The climate variability may therefore be larger under the existence of TCs.
Although the significant effects from TCs may change our interpretation of the climate variability in the TC-prone region, it is minimally understood, as little attention has been addressed to this important issue. In this study, an unconventional approach was employed in removing the TCs from the global analysis. In addition, the potential contribution of TCs was esti-mated, which is defined as the variance difference be-tween the original and TC-removed vorticity fields at 850 hPa. The approach adopted in this study was de-signed to shed light on the TC contribution to the cli-mate variability along the TC tracks. Our results indi-cate that the differences in the time mean and variance are large enough for concern in the intraseasonal and interannual time scales. The arrangement of this article is as follows. Methodology and data are described in section 2. Effects on the interannual and intraseasonal variability are presented in section 3. A comparison between different global analyses is shown in section 4, while the conclusion and discussion are given in section 5.
2. Data and methodology
In reality, because of the possible nonlinear TC– climate interaction, it is impossible to exactly quantify the TC contribution. This essentially holds true for all studies involving multiple temporal and spatial scales. Despite this concern, temporal and spatial filtering has often been used to decompose a total field into pertur-FIG. 1. Three samples of 150 synthetic time series (see text) with randomly added step
function–like perturbations are revealed in demonstrating the effect of extreme values on the estimation of the mean and variance. Extreme values of three time series are shown by the solid bar, gray bar, and hollow bar. Only one sinusoidal curve is shown here, as the three sinusoidal curves differ only by negligible random noises. The X and Y axes indicate the time and strength of waves and perturbations, respectively.
bation and mean flow, or even into several subfields of distinct temporal or spatial scales. This linear-thinking approach has proved useful in providing important in-sights into climate processes, for example, the wave– mean flow interaction. To evaluate the possible TC contribution to climate variability, a spatial-filtering ap-proach was taken to remove the TC vortices from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; 2.5° longi-tude⫻ 2.5° latitude; Uppala et al. 2005) at 850 hPa in the typhoon season (June–October) from 1980 to 2004. This filtering procedure is similar to the decomposition of the total field into perturbation and background flow, but is performed in a more sophisticated manner. In contrast to the pre-1980 period, the 1980–2004 pe-riod was chosen in consideration of the better satellite network observation in pinpointing the TC position more accurately. The same analysis procedure was also applied to the 1958–79 period for comparison. The po-tential contribution of TCs to the climate variability was estimated by calculating the difference between the variance of the original and TC-removed vorticity at 850 hPa.
Both interannual and intraseasonal variances are
ex-amined in this study. The interannual variance is de-fined as the variance of seasonally averaged 850-hPa vorticity in each typhoon season. A 20–80-day Butter-worth bandpass filter was used to extract the intrasea-sonal fluctuations from the daily mean vorticity. To re-duce the end effect of the Butterworth filter, only data from 16 June to 15 October were used to calculate the intraseasonal variance.
Removal of TCs from the analysis data has been a common practice in the typhoon and hurricane simula-tion and forecast. The procedure, which has been used in the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane prediction system (Kurihara et al. 1993, 1995) and in typhoon simulations (Wu et al. 2002) for enhancing the representation of the environmental field in the initial condition, has proved effective in improving the overall TC track forecast. Following the procedure proposed by Kurihara et al. (1993, 1995), the four-time daily 850-hPa winds associated with each TC, based on the Joint Typhoon Warning Center (JTWC) best track, were subtracted from the 850-hPa wind field during the typhoon season.
The basic procedure, demonstrated in Fig. 2, is briefly described as follows. Readers are referred to FIG. 2. Flowchart of the TC-removal procedure.
Kurihara et al. (1993, 1995) for details. The zonal and meridional winds were individually separated into basic and disturbance fields using a smoothing operator. The winds associated with a TC were isolated in the filter domain, which defines the extent of a TC in the global analysis, and subtracted from the disturbance field to create a non-TC component. In the procedure, 1200 km is specified as the filter domain. This does not mean that everything in the domain is identified as the TC component. It is simply the longest distance for the procedure to automatically search the effective radius of a TC in 24 directions (for every 15°) surrounding the TC center. When the radius of a TC, for example, 500 km, is identified in a particular direction, the searching stops, and only the TC winds within the radius are sub-tracted. The reason for choosing a larger domain is to avoid missing the TC circulation in a TC (or typhoon) that owns a large radius and therefore leading to the underestimation of the TC circulation. After removing the TC component from the disturbance field, the non-TC component was added to the basic field to form the environmental flow, which is the TC-removed wind field in this study. The environmental flow outside the filter domain is identical to the original global analysis. The original and TC-removed vorticity fields were cal-culated based on the corresponding wind fields.
The procedure described above was able to correctly separate the TC and environmental components. One example is shown in Fig. 3a when three typhoons ap-peared simultaneously in the western North Pacific at 0600 UTC 28 August 2001. The three typhoons, lining up from the west to east near 20°N, were embedded in a zonally elongated belt of positive vorticity, which re-flects a well-established monsoon trough and a favor-able condition for the tropical cyclogenesis and devel-opment (Elsberry 2004). They appeared as three iso-lated vortices in Fig. 3b, after being separated from the background flow. The environmental flow, shown in Fig. 3c, was clearly characterized by a zonally elongated (but weaker) monsoon trough across the South China Sea and the Philippine Sea. This result, as with many other cases, indicates that the TC removal procedure removes mainly the TC vortex, and accurately retains the large-scale circulation, along with the climate vari-ability contributed by the large-scale fluctuations.
One potential problem in removing TCs from the global analysis, such as the ERA-40, is the possible mis-match between the JTWC TC track, which defines the center of a TC, and the position of the TC-correspond-ing vortex in the ERA-40. This is partially attributed to the coarse resolution of the datasets used in this study. The JTWC best track and the ERA-40 were not thor-oughly cross-checked, as the main goal of this study is
FIG. 3. The 850-hPa vorticity fields on 0600 UTC 28 Aug 2001
when three typhoons existed simultaneously in the tropical west-ern North Pacific: (a) total, (b) TC, and (c) TC removed. Contour interval is 10⫺5s⫺1. Solid and dashed lines denote positive and negative vorticity, respectively.
to statistically assess the gross contribution of TCs to the climate variability in a large domain covering the entire tropical western North Pacific. A mismatch of a few degrees in latitude and longitude would not seri-ously affect the overall results. As it will be shown later, the removed vorticity is collocated nicely with the TC tracks. The possible mismatch does not seem to cause problems in this study. TCs may be presented differ-ently in different global analysis or in different resolu-tion. Comparisons between the ERA-40 and National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) 40-Year Reanalysis and between different spatial resolutions were carried out. The results shown in section 4 indicate the consistency between the analyses.
Another concern is the underrepresentation of the wind speed and vorticity of TC in the ERA-40. This is an existing problem, which cannot be solved in this study. This study’s goal is to demonstrate how TCs con-tribute significantly to climate variance, based on pres-ently available global analysis. The results should be viewed as the TC effect represented in currently avail-able global analysis, which has been widely used to es-timate the climate variability. If the exact location and strength of TCs are represented in the global analysis, the actual contribution of TCs will likely be larger than what was found in this study.
3. Interannual variability
It is implicitly assumed that long-term averaging would eliminate (or minimize the contribution of) the transient perturbations, such as TCs, which fluctuate in a period much shorter than the averaging period. If this were true, the seasonal means from June to October for both the original and TC-removed fields would be al-most the same. Figure 4 presents the seasonally aver-aged 850-hPa vorticity and TC tracks for the two con-trasting typhoon seasons 1994 and 1998. Based on the statistics available from the Regional Specialized Me-teorological Center (RSMC) Tokyo-Typhoon Center http://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/RSMC_HP.htm), the total amount of named TCs in June–October (JJASO) 1994 and 1998 were 32 and 11, respectively, compared to the climatological-mean number of 20. The former (latter) is the year with the largest (least) number of named TCs during the typhoon seasons from 1951 to 2005.
During the 1994 typhoon season, the TC tracks can be roughly divided into three types (Fig. 4a). The first type follows a west-northwestward direction, crossing straightly from the Philippine Sea to the South China Sea. The second type moves northwestward in the
Phil-ippine Sea and recurves north-northwestward when ap-proaching Taiwan. The third type moves northward, covering a wide range of longitudes across the subtropi-cal western North Pacific. The seasonal-mean 850-hPa vorticity (Fig. 4b) exhibits maximum positive values (i.e., cyclonic) near these TC tracks, where the largest amplitude is found in the Philippine Sea and South China Sea. Along the TC tracks, there is always a local maximum of mean vorticity in both typhoon seasons. During the 1998 typhoon season, the TCs moved in many different directions in a less-organized manner (Fig. 4d). The genesis locations of TCs were located to the northwest of the climatological-mean position. The seasonal-mean 850-hPa vorticity was negative in the Philippine Sea and the subtropical western North Pa-cific, while the positive vorticity appeared only in the western South China Sea (Fig. 4e). These features re-flect the characteristics that are often observed in the La Niña summer: fewer TCs, northwestward shift of TC genesis region, and weaker monsoon trough (Chia and Ropelewski 2002; Wang and Chan 2002).
Although the overall vorticity pattern in 1994 looked similar after the TC removal, notable changes can be seen in the Philippine Sea and South China Sea where the TC tracks were observed (Fig. 4c). For example, the positive mean vorticity in the Philippine Sea weakens significantly from above 9⫻ 10⫺6s⫺1to less than 3⫻ 10⫺6s⫺1. The largest reduction observed in the center of the positive mean vorticity exceeds 6⫻ 10⫺6s⫺1(Fig. 4a), accounting for more than 60% of seasonal-mean values. Similar changes can also be seen north of 20°N where northward but less-organized tracks were found. After removing TCs, the negative vorticity exhibits larger amplitudes and a more coherent spatial struc-ture. The change indicates a stronger and more coher-ent subtropical anticyclone after the removal of TCs.
In contrast, the TCs in the 1998 typhoon season seem to have a much smaller effect on the seasonal-mean vorticity. Although the mean TC-removed vorticity along the TC tracks tend to show a higher negative value in the Philippine Sea and the subtropical western North Pacific (Figs. 4e,f), the changes are much smaller than in 1994 (Fig. 4d). The comparison between the 1994 and 1998 typhoon seasons indicates that the de-gree of the TC effect on the seasonal-mean vorticity likely depends on the TC activity.
The results shown above indicate that TCs, although having a lifetime much shorter than the time span of the typhoon season, leave nonnegligible footprints in the 5-month seasonal means. Two factors may contribute to this effect. First, a TC is the strongest synoptic dis-turbance in the tropical western North Pacific. There is
no other synoptic feature with a negative vorticity strong enough in canceling out the contribution of the cyclonic vorticity associated with TCs. Second, TCs tend to be clustered by the large-scale circulation and move more or less along similar tracks. Under these circumstances, the clustering of strong vortices like TCs is likely to enlarge the seasonal-mean vorticity along the clustered tracks. These results are contrary to the assumption stated in the beginning of this section.
As seen in the comparison between the 1994 and
1998 typhoon seasons, the TC number and degree of clustering in the tropical western North Pacific vary significantly from year to year, depending on the char-acteristics of the large-scale circulation such as the monsoon trough and the Pacific subtropical anticy-clone, which are in turn affected by other climate fac-tors, such as ENSO. It follows that the TC contribution to the seasonal mean is likely different from one year to another. In view of these marked differences for each year, the interannual variance, which is interpreted as FIG. 4. Seasonal means of 850-hPa vorticity in JJASO (a)–(c) 1994 and (d)–(f) 1998: (a), (d) TC component and
TC tracks; (b), (e) total; and (c), (f) TC removed. Contour interval is 0.3⫻ 10⫺5s⫺1. Solid and dashed lines denote positive and negative vorticity, respectively.
the variability of seasonal means, is likely affected by the existence of TCs.
To examine this issue, the interannual variances of 850-hPa vorticity, which is computed based on the sea-sonal mean of each year, for the original and TC-removed fields from 1980 to 2001 are shown in Fig. 5. In the original field, a large vorticity variance is observed in the Philippine Sea and the tropical western North Pacific, the region surrounding Taiwan, the East China Sea and the Pacific to the south of Japan, and the South China Sea (Fig. 5a). In the TC-removed variance shown in Fig. 5b, the variance reduces notably in the Philip-pine Sea, the tropical western North Pacific and the region surrounding Taiwan. The regions with major dif-ferences shown in Fig. 5c are clearly collocated with the TC occurrence distribution seen in Fig. 5d. The reduc-tion in variance, expressed in percentage, is shown in Fig. 6a. A reduction exceeding 50% is found along the TC tracks from the tropical western Pacific to Taiwan. Similar results were also obtained in the analysis dur-ing 1958–79 and the entire 1958–2001 period. The re-duction in percentage for both periods is shown in Figs. 6b,c. The spatial patterns in both time frames are simi-lar to the pattern in 1980–2001 (Fig. 6a). Among the three periods, the reduction is largest in 1980–2001 and
smallest in 1958–2001. The region of major reduction in 1958–79 is largely located to the northeast of its coun-terpart in 1980–2001. The reason for this difference is not clear. More satellite observations and surveillances in recent decades may yield a better representation of TC structures in the analysis, which may lead to larger reductions. Whether the region shift between two sub-periods is significant enough requires further investiga-tion. The TC occurrence distribution in 1958–2001 is also presented in Fig. 6d. The collocation of the major reduction and TC-active regions is clearly seen in a comparison between Figs. 6c and 6d. Similar outcomes were also obtained for the 1958–79 period (not shown). These results reveal that TCs contribute significantly to the interannual variability in the tropical western North Pacific and the South China Sea where TCs are active. The strong TC vortices apparently leave significant footprints in the seasonal means and indirectly affect the estimation of interannual variability, which is often treated as the estimation of the interannual fluctuation in the seasonal-mean fields.
4. Intraseasonal variability
As discussed in the introduction, TCs are often modulated by ISO, and appear in clusters in the cy-FIG. 5. Interannual variance of 850-hPa vorticity in 1980–2001: (a) total, (b) TC removed, (c) TC, and (d)
clonic phase of the ISO (e.g., Liebmann et al. 1994; Maloney and Dickinson 2003). This section explores whether this clustering effect significantly affects the intraseasonal variability (ISV) as in the interannual time scale.
The intraseasonal variance of the original and TC-removed 850-hPa vorticity fields during 1980–2001 are shown in Fig. 7. In the original field (Fig. 7a), a large variance is found in the oceanic region southwest of Taiwan and south of Japan, the South China Sea, and east of the Philippines. After removing TCs, excluding the area south of Japan, the variance in all the other regions reduces a great deal (Fig. 7b). The regions with the largest reduction, which is more than 50% in the core region, appear in the oceanic region to the east of Taiwan, the Philippines, and the South China Sea (Figs. 7c,d). These regions are also the major regions of clus-tered TC tracks (Fig. 5d). Similar results were obtained for 1958–79 and 1958–2001 (not shown).
To further delineate how the occurrence of TCs af-fect the ISV, the daily time series of area-mean vorticity in an ISO-active region (15°–30°N, 123°–138°E), where large ISV and TC tracks were located during the 1999 typhoon season, is shown by dotted curve in Fig. 8. There were two ISO events from June to October, with
peaks in late-July/early August and mid-October. These two time periods are also when the TC activities in the region are active, as indicated in the bottom of Fig. 8. This confirms the well-known fact that TCs tended to appear in the positive vorticity phase. These two ISO events can still be seen in the TC-removed time series (dashed curve in Fig. 8), but with amplitudes reduced substantially in the positive vorticity phases. The levels of reduction, shown by the light solid curve, are often larger than the TC-removed amplitudes dur-ing the peak phases, while the vorticity in the negative vorticity phases remains unchanged. The vorticity re-duction shown in Fig. 8 is larger than the original vor-ticity in a few occasions. This is because the positive vorticity contributed by the TC to the area-mean vor-ticity shown in Fig. 8 is partially cancelled by the nega-tive vorticity within the area. The original vorticity can be smaller than the vorticity reduction when the can-cellation is more significant in certain periods.
Results of the spectral analysis applied to the original and TC-removed time series in Fig. 8 are shown in Fig. 9. Before applying a spectral analysis, fluctuations with periods longer than 120 days were removed to empha-size the intraseasonal time scale. In the original time series, two spectral peaks (exceeding 95% confidence FIG. 6. Interannual variance contributed by TC in percentage: (a) 1980–2001, (b) 1958–79, (c) 1958–2001, and
level) are found near 50 days and 17 days. After the removal of TCs, the 50-day peak drops substantially by more than 60%. A huge drop is also observed at the 17-day peak. Both spectral and wavelet analysis were
applied to each respective year from 1979 and 2001. Similar results were obtained.
The significant variance reduction can be viewed as the TC contribution to the ISV. The clustered TCs with strong positive vorticity enlarged the amplitude of posi-tive vorticity in the cyclonic phase of ISO. On the
con-FIG. 8. Total (dotted curve), TC removed (dashed curve), and TC contribution (solid curve) time series of the 850-hPa vorticity (unit: 10⫺5 s⫺1) averaged over (15°–30°N, 123°–138°E) in JJASO 1999. Occurrence of TC is marked at the bottom of the figure.
FIG. 9. Spectra of the total (dark line) and TC-removed (gray line) time series (10⫺10s⫺2) shown in Fig. 8. The corresponding red noise spectra at 95% confidence level are shown in the smooth dark and gray curves.
FIG. 7. Intraseasonal (20–80 days) variance of 850-hPa vorticity in 1980–2001: (a) total, (b) TC removed, (c) TC, and (d) TC contribution in percentage. Contour interval is 0.05⫻ 10⫺12s⫺2in (a) and (d) and 0.03⫻ 10⫺12s⫺2 in (c). Contour interval is 10% in (d).
trary, the amplitude in the anticyclonic phase is virtu-ally unaffected because much fewer TCs appear in that phase. The enlargement of the positive vorticity appar-ently leads to the larger variance (and stronger variability) in the intraseasonal time scale. The ISV evidently be-comes larger through the clustering effect of ISO on TCs.
5. Comparison between different analyses
TCs may be represented differently in various analy-ses because of the different design of data assimilation systems and models. Influence of data resolution (e.g., difference between 2.5° and 1.125° resolution) on our results is another issue that requires attention. These two issues are investigated in this section by comparing the results based on the ECMWF and NCEP–NCAR reanalyses, and also the analysis of different spatial resolution. Comparisons were done for the case studies, seasonal mean, and intraseasonal variance.
The problem of spatial resolution is examined by a comparison between ERA-40 and the NCEP–NCAR reanalyses (2.5° resolution for both), and between ERA-40 and the ECMWF analysis in 1.125° resolution during the 1996 typhoon season. The removing proce-dure was found to be effective in removing the TC from all three analyses, although the representation of TCs may vary respectively. One example is shown in Fig. 10, which presents the total 850-hPa vorticity field and the TC vortices on 0600 UTC 26 July 1996 in all three analyses. Two typhoons lining up zonally were ob-served simultaneously in the western North Pacific, and were clearly identified in each analysis (Figs. 10a–c). The vortices identified in the NCEP–NCAR reanalysis were the weakest ones, while those in the ECMWF analysis of 1.125° resolution exhibited the largest am-plitude and sharpest structure because of the higher resolution (Figs. 10d–f). Although differences between the three analyses were notable, the identification of the TC vortices from the total field was done success-fully, and the major characteristics of the TCs were all revealed. A comparison was done for every 6-hourly analysis from 1 June to 30 October 1996. Results (not shown) indicate a consistency between each analysis during the entire period.
Seasonal means estimated from the ERA-40 and the NCEP–NCAR reanalysis in the 2.5° resolution, and the ECMWF analysis in the 1.125° resolution in JJASO 1996 are presented in Figs. 11a–c. The ECMWF analy-sis in both resolutions (Figs. 11a–c) reveal more details in the spatial distribution, and generally exhibits larger amplitudes than the NCEP–NCAR reanalysis (Fig. 11b). The ECMWF analysis in the 1.125° resolution shows more localized features with larger amplitudes
owing to the higher resolution. Despite these discrep-ancies, all three analyses exhibited a similar spatial pat-tern, which is characterized by the positive vorticity in western China, the South China Sea, and the Philippine Sea, reflecting the extension of the monsoon trough from China to the Philippine Sea. To the north of this positive vorticity region is a zonal belt of negative vorticity, rep-resenting the subtropical anticyclone. The positive vortic-ity region and the TC track are collocated as in other years (not shown). After removing the TCs, the major differences in the seasonal-mean fields shown in Figs. 11d–f are 1) virtual disappearance of the positive vorticity in the Philippine Sea, 2) weakened positive vorticity in the South China Sea, and 3) enhanced zonal belt of nega-tive vorticity. These features reflect the weakened mon-soon trough and strengthened anticyclone. The major differences are the results of reduced vorticity along the TC tracks. The TC effect on the seasonal means of the three analyses reveals similar characteristics.
Because of its unique characteristics, the 2004 ty-phoon season is chosen for a comparison of the in-traseasonal variance. This particular season was char-acterized by the strong in-phase relationship between intraseasonal signal and TCs (Nakazawa 2006). The record-breaking number (10) of typhoon making land-fall in Japan was another interesting feature observed in that season. Our calculation (not shown) indicates a predominant 32–76-day fluctuation. Comparison of the 32–76-day intraseasonal variance of the 850-hPa vortic-ity between each analysis is shown in Fig. 12. The origi-nal ISV in both the NCEP–NCAR reaorigi-nalysis and the ERA-40 (both in the 2.5° resolution; Figs. 12a,c) dem-onstrate similar spatial distribution, despite quantita-tive differences being observed. Large variances are evident in the Philippine Sea and in the oceanic region south of Japan and east of Taiwan. The ECMWF analy-sis again exhibits a larger variance and spatial distribu-tion in greater detail. Figures 12b and 12d present the variance difference between the original and TC-removed 850-hPa vorticity fields, that is, the TC contri-bution, in the NCEP–NCAR and ECMWF reanalyses, respectively. Again, both figures are characterized by similar spatial distributions, which reveal the major dif-ferences in the Philippine Sea and the oceanic region south of Japan and east of Taiwan along the TC tracks. The TC contribution can be larger than 50% along the clustered TC tracks in both analyses. This result indi-cates the consistency between the ECMWF and NCEP–NCAR reanalyses.
6. Rainfall
As shown in the previous section, TCs contribute significantly to the climate variability of the 850-hPa
vorticity along the TC tracks. Rainfall is another im-portant climate variable that is likely to be affected. Being a positive-definite variable, rainfall has no cor-responding negative value to compensate. The heavy rainfall brought by TCs often occurs like a large-amplitude step function, and is likely to have a signifi-cant impact on the estimation of seasonal mean and
interannual variability. In this section, the rainfall in Taiwan, which is on average affected by 3–4 landfalling typhoons (i.e., named TCs) annually during the ty-phoon season, is analyzed as an example to demon-strate the TC contribution on the interannual variabil-ity of rainfall. Taiwan was chosen partly because of the easier access to station data for the authors. In addition, FIG. 10. (a)–(c), Total and (d)–(f) TC component of 850-hPa vorticity at 0600 UTC 26 Jul 1996: (a), (d) ECMWF
analysis in 2.5° resolution; (b), (e) NCEP–NCAR reanalysis in 2.5° resolution; and (c), (f) ECMWF analysis in 1.125° resolution. Contour interval is 3⫻ 10⫺5s⫺1. Solid and dashed lines denote positive and negative vorticity, respectively.
Taiwan is located in one of the favorite regions of TC tracks and its weather and climate are significantly af-fected by typhoons.
Rainfall data from 18 weather stations in Taiwan dur-ing 1958–2001, provided by Taiwan’s Central Weather Bureau (CWB), were used in this study. The averages of these 18 weather stations, which cover the entire island quite evenly, were analyzed. Seasons in Taiwan
are traditionally classified according to the dominant weather and rainfall characteristics. The annual rainfall cycle in Taiwan can be separated into spring rain, mei-yu, typhoon, and winter dry seasons (Fig. 13a; Chen and Chen 2003). To depict the characteristics of the annual rainfall cycle in Taiwan, the time series of 31-day run-ning mean of climatological-mean rainfall averaged over the 18 CWB weather stations is shown in the thick FIG. 11. Seasonal means of (a)–(c) total and (d)–(f) TC-removed 850-hPa vorticity in JJASO 1996: (a), (d)
ECMWF analysis in 2.5° resolution; (b), (e) NCEP–NCAR reanalysis in 2.5° resolution; and (c), (f) ECMWF analysis in 1.125° resolution. Contour interval is 0.2⫻ 10⫺5s⫺1. Solid and dashed lines denote positive and negative vorticity, respectively.
black curve in Fig. 13a. The rainfall corresponding to the 25th, 50th, and 75th percentiles are also shown in dashed lines. Mei-yu (May and June) and typhoon [July to October (JASO)] seasons are the two major rainy seasons in Taiwan. The mean rainfall is between the 50th and 75th percentiles for both seasons, but is much closer to the 75th percentile in the typhoon season than in the mei-yu season. This implies that the JASO sea-son rainfall is contributed substantially by various ex-tremely heavy rainfall events, which are usually related to typhoons.
To estimate the potential contribution of typhoon-related rainfall on the total rainfall during the JASO season, a designated region (19°–28°N, 117°–125°E) surrounding Taiwan is chosen to separate the typhoon-related rainfall (hereafter, typhoon rainfall) and the nontyphoon-related rainfall (hereafter, non-typhoon rainfall). When the center of a typhoon enters this re-gion, the rainfall occurring in Taiwan is counted as “ty-phoon rain.” The remaining rainfall occurring during other periods is deemed as “non-typhoon rainfall.” As some typhoons decay into tropical cyclones after mak-ing landfall in Taiwan, all cases were taken into account
as long as the tropical cyclones were once classified as a typhoon during their lifetime. The region used to de-fine the typhoon rainfall was slightly changed, where it covered a larger or smaller domain to test the reliability of the results shown below. Consistency is found be-tween the different domain sizes. The definition of the typhoon and nontyphoon rainfall cannot separate the large-scale rainfall from the rainfall contributed by the mesoscale convective systems in a typhoon. Therefore, the typhoon rainfall is likely overestimated because of the inclusion of large-scale rainfall. On the other hand, the rainfall associated with mesoscale convective sys-tems is usually at least one order of magnitude larger than the rainfall when no typhoons exist in the vicinity. The overestimation is negligible, and will not affect the results presented here.
During JASO, the long-term average of the total rainfall is 1261.8 mm, in which typhoon and nonty-phoon rainfalls account for 43.2% (532.4 mm) and 56.8% (699.4 mm), respectively. During the 123-day JASO season, the 43.2% total rainfall contributed by typhoons occurs on average for only 14 days, while the remaining 56.8% occurs in the other 109 nontyphoon FIG. 12. (a), (c) Intraseasonal (32–76 days) variance and (b), (d) intraseasonal (32–76 days) variance contributed
by TCs in JJSAO 2004 in the (a), (b) NCEP–NCAR reanalysis and (c), (d) ECMWF analysis. Contour interval is 1.5⫻ 10⫺10s⫺2.
days. This implies that a handful of short-term extreme rainfall events related to typhoons can have a large impact on the monthly and seasonal rainfall in Taiwan. Seasonal means of typhoon and nontyphoon rainfall from 1950 to 2004 are shown in Fig. 13b. Both types of rainfall fluctuate significantly from year to year. The interannual standard deviations are 239.4 and 205.5 mm for the seasonal-mean typhoon and nontyphoon pre-cipitation, respectively. The largest contribution of ty-phoon rainfall accounting for 65.6% of total rainfall occurred in 1986, while the smallest contribution taking up only 7.7% occurred in 1964. The number of days with typhoon affecting Taiwan is also shown in Fig. 13b, which reveals a fluctuation between 2 and 30 days. The level of contribution from the typhoon is closely asso-ciated with the number of typhoon day. The correlation coefficient between the two time series is 0.78. This is expected because a typhoon usually brings torrential rainfall to Taiwan. Monthly and even seasonal rainfall in Taiwan can be changed from below normal to above normal simply by one single typhoon landfall event. Inclusion of the typhoon rainfall significantly increases the interannual standard deviation from 205.5 (of nontyphoon rainfall) to 247.7 mm (of total rainfall). This clearly reflects the significant contribution of ty-phoon rainfall to the interannual variability of total rainfall. The results presented above reveal a big chal-lenge for the seasonal rainfall forecast in the JASO
season, as almost half of the total rainfall, along with a significant portion of the interannual variance in the Taiwan’s typhoon season, is contributed by a handful of extreme rainfall events associated with typhoons.
7. Conclusions and discussion
By estimating the differences between the original and TC-removed fields, this study reveals that TCs con-tribute a great deal to the seasonal mean and the in-traseasonal and interannual variability of the 850-hPa vorticity along the TC tracks.
This large reduction in both seasonal mean and vari-ance can be explained as follows. While the low-frequency large-scale circulation has a clustering effect on TCs, the latter tends to occur with large amplitude in the positive vorticity background flow, and significantly enhances the total strength of the positive vorticity. Conversely, when no TC exists or when only the syn-optic anticyclone is found, the contribution to the total vorticity field from the synoptic- and mesoscale distur-bances is relatively small. This is because the negative vorticity associated with these disturbances is much weaker than the positive vorticity of TCs. The contri-bution of TCs is therefore not offset by the disturbances of the negative vorticity. This contrast leads to the en-largement of the mean and variance, where the effects, which account for more than 50% of variance along TC FIG. 13. (a) The 31-day running means of rainfall seasonal cycle in Taiwan (thick dark line)
and the corresponding rainfall at the 25, 50, and 75 percentiles. (b) Typhoon rainfall (thick solid line; mm; left axis), nontyphoon rainfall (dashed line; mm; left axis), and number of typhoon days (bar; days; right axis) in Taiwan during 1950–2004.
tracks, are found in both intraseasonal and interannual variability. Similar situations can also occur in the rain-fall, which is positive-definite and cannot be balanced by negative values. Strong impacts from the typhoon rainfall on the seasonal mean and interannual variance of the rainfall in Taiwan is found.
In reality, the relationship between tropical cyclones and the large-scale circulation is nonlinear. Bearing this in mind, our study took an unconventional approach to remove TC vortices from the circulation to demon-strate that even a simple arithmetic approach can greatly affect the computed mean and variance in both intraseasonal and interannual time scales. Since the in-traseasonal and interannual variability is usually inter-preted based on the low-pass filtered data or seasonal means, the contribution of the TC activity, which is imprinted in these means, can possibly be viewed as climate variability. Similar effects are likely to occur in other TC-prone regions. Further studies are needed to evaluate this possible impact.
Our finding raises a question about the definition of climate variability in the TC-prone regions. Tradition-ally, climate variability is often interpreted as the vari-ability of low-frequency climatic perturbations. Our re-sults, however, point out that these climatic perturba-tions may contain the contribution from TCs, which fluctuate in much shorter periods and smaller spatial scale. In the region such as the tropical western North Pacific, where the meso–synoptic-scale and large-scale systems are closely intertwined, and the multiscale in-teraction is likely one of the key processes affecting the climate variability, the contribution from the severe weather systems like TCs has to be taken into account to understand the mechanisms leading to the climate variability.
In the tropical western North Pacific, the tropical cyclone often has a lifetime longer than 10 days. As the moving tropical cyclones own a strong and long-lasting energy source, large-scale circulations are likely in-duced, followed by energy emanation to remote re-gions. This feedback may affect the environmental flow, such as the subtropical anticyclone and monsoon trough, and leave notable footprints in the climate vari-ability in the regions away from TC tracks. This poten-tial effect cannot be estimated by the methodology adopted in the present study and will be explored in future works. Another problem is the coarse resolution of the datasets used in this study. Although the TCs are reasonably represented, the strength of the TCs is un-derestimated and the spatial structure is not as sharp as in the real world. As a result, the actual contribution of TCs to the climate variability cannot be accurately es-timated. Moreover, the contribution is likely
underes-timated. However, despite not being able to exactly quantify the effect, this research explains in a qualita-tive sense why the contribution from TCs cannot be overlooked.
Most of the general circulation models used to simu-late past climate suffers from the poor simulation of the climate variability in the tropical western North Pacific during the boreal summer (Wang et al. 2004). The re-sults reported here imply that the inability to resolve and simulate TCs may be one of the key weaknesses of the GCM leading to the poor simulation. Using coarse-resolution GCM may lead to an inaccurate climate simulation and prediction in the intraseasonal, interan-nual, and, perhaps, even climate change time scales. High-resolution models that are able to reasonably simulate the ensemble effect of TCs, at least in the statistical sense, seem needed to resolve the multiscale interaction and to produce better simulations of the climate variability in TC-prone regions, such as the tropical western North Pacific.
Acknowledgments. The authors thank anonymous
re-viewers for their valuable comments. This study is sup-ported by the National Science Council in Taiwan un-der Grant NSC-95-2111-M-002-010-MY3.
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