A Statistical Analysis of the Effects of Vertical Wind Shear on Tropical Cyclone Intensity Change over the Western North Pacific (original) (raw)
1. Introduction
Vertical wind shear (VWS) is one of the most important dynamical parameters of the large-scale environment related to tropical cyclone (TC) formation, as well as its structure and intensity changes [see Wang and Wu (2004) for a review]. It is generally believed that strong VWS is unfavorable for TC genesis and intensification (Gray 1968; McBride and Zehr 1981; DeMaria and Kaplan 1999; Zehr 2003; DeMaria et al. 2005; Paterson et al. 2005; Zeng et al. 2010; Riemer et al. 2010) and reduces TC maximum intensity (Zeng et al. 2007, 2008; Braun and Wu 2007; Tang and Emanuel 2010, 2012). VWS is also among the major factors that can lead to strong convective asymmetries in the core of a TC (Wang and Holland 1996; DeMaria 1996; Frank and Ritchie 1999, 2001; Corbosiero and Molinari 2003; Reasor et al. 2004; Wong and Chan 2004; Zhang and Kieu, 2005; Chen et al. 2006; Heymsfield et al. 2006; Xu and Wang 2013).
Although VWS influences TC structure and intensity changes and is considered as one of the major predictors in several statistical TC intensity prediction schemes (DeMaria and Kaplan 1999; DeMaria et al. 2005; Knaff et al. 2005), there has been no unique definition for VWS in the literature. In practice, VWS is commonly measured as the horizontal wind difference (in magnitude and direction) between 200 and 850 hPa averaged over an area within a given radius (e.g., within a radius of 500 km from the TC center) or in an annular region between two given radii (e.g., between 500 and 900 km from the TC center) (Emanuel 2000; Zehr 2003; Paterson et al. 2005; Heymsfield et al. 2006; Zeng et al. 2007, 2008). Although the difference in the mean horizontal winds between 200 and 850 hPa is a commonly used measure of tropospheric VWS, it has never been shown to best reflect the effects of environmental VWS on TC intensity change.
Recent theoretical studies based on both simplified dynamical models and high-resolution numerical experiments suggest three thermodynamic effects that are responsible for TC intensity change resulting from the effect of VWS [see a review by Wang (2012)]. The first effect is the ventilation of the warm core or convective eyewall heating in the upper troposphere (between 200 and 300 hPa). This upper-level ventilation weakens a TC either by the advection of the warm core as a whole (Gray 1968) or through the outward eddy heat flux by shear-induced asymmetric eddies (Frank and Ritchie 2001). Based on satellite observations, Knaff et al. (2004) showed that vertical wind shear acts to shrink the vertical extent of the axisymmetric vortex, lowering the warm core and leading to TC weakening.
The second effect is the so-called midlevel ventilation, which describes the inward flux of low-entropy air from the midtroposphere outside the eyewall by the shear-relative flow itself or shear-induced eddies, diluting the high-entropy air in the eyewall and thereby limiting TC intensity (Cram et al. 2007). Midlevel ventilation generally reduces the thermodynamic efficiency of the energy cycle of a TC Carnot engine (Tang and Emanuel 2010). It also promotes strong downdrafts very effectively by enhancing melting of ice species and evaporation of rain through midlevel dry air intrusion.
The downward flux of low-entropy air associated with shear-enhanced downdrafts into the inflow boundary layer can be easily transported inward into the eyewall and weaken the eyewall convection and thus the storm intensity (e.g., Riemer et al. 2010). This can be considered as low-level ventilation because the effect acts through the low-level inflow although the original low-entropy air comes from the midtroposphere. Recently, Gu et al. (2015) suggested a new path for the VWS effect on TC intensity, namely, upward flux of high-entropy air associated with shear-induced updrafts from the boundary layer outside the eyewall into the midlevel can weaken the radial gradient of moist entropy across the eyewall, and thus weaken the storm.
In addition to the thermodynamic effects, VWS also imposes dynamical effects on TC structure and intensity (e.g., DeMaria 1996; Frank and Ritchie 2001; Wong and Chan 2004; Wu and Braun 2004; Knaff et al. 2004). The main dynamical effect of VWS is to force a wavenumber-1 asymmetry in vertical motion and convection in the eyewall and also a quasi-steady downshear-left tilt of the vortex center (Wang and Holland 1996; Frank and Ritchie 2001; Reasor et al. 2004; Xu and Wang 2013). Both the tilting and the development of asymmetric structure may weaken the embedded TC by eddy-induced angular momentum mixing across the radius of maximum wind (Wu and Braun 2004; Xu and Wang 2013).
Both dynamical and thermodynamic effects of VWS can be expected to depend on the vertical distribution of the shear. The dependence of TC intensity change due to vertical shear on the depth over which the shear extends over the North Atlantic was statistically studied by Zeng et al. (2010). They showed that the strong, slow-moving, and low-latitude (south of 35°N) TCs are strongly affected by VWS over a deep layer, while the weak, fast-moving, and high-latitude TCs (north of 35°N) are strongly affected by VWS in the mid- to lower troposphere. They also showed that westerly VWS has a stronger weakening effect on TC intensity than easterly VWS. This is mainly due to a partial offset of the environmental VWS by the beta-induced northwesterly shear (Ritchie and Frank 2007). In this sense, the results of Zeng et al. (2010) suggest that there were no universal means by which VWS can be measured to best reflect the impact of VWS on TC intensity change.
Furthermore, Zeng et al. (2010) found that the linear negative correlation coefficients between the lagged TC intensity change and various definitions of VWS are generally below 0.3. This statistical result indicates that the large variability in TC intensity change cannot be explained by VWS as defined by the traditional metric of the vector wind difference between two pressure levels. On the other hand, Shu et al. (2013) showed that the VWS in the lower troposphere between 850 hPa and 10-m height is more important to TC intensification over the western North Pacific (WNP) than the commonly used VWS between 200 and 850 hPa, pointing to a higher relevance of low-level ventilation.
Recently, Velden and Sears (2014) provided a historical background on the definition of VWS in the literature and indicated that the two-level approach is oversimplified with view to the effect of VWS on TC intensity change. Instead, they proposed to use two mass-weighted layer-mean winds (i.e., one upper-tropospheric and one lower-tropospheric) to calculate the deep-tropospheric VWS. Compared to the traditional two-level wind difference this new two-layer measure for VWS seems to be more physically based because herein the variation of the wind field with height is given much greater consideration in measuring the mass-weighted layer-mean wind. However, similar to the classic two-level shear, the new two-layer VWS does not account for the different effects of shear in different vertical layers either.
As depicted by Velden and Sears (2014) in their Fig. 6, the correlation coefficient between TC intensity change over a 12-/24-h period and the traditionally defined VWS is generally around −0.24/−0.22 and that with VWS defined as a two-layer mass-weighted mean shear becomes −0.24/−0.26 for North Atlantic TCs during 2008–10 with a sample size of 824 cases. Hence, compared to the classic two-level VWS, the absolute value of the correlation coefficient with the two-layer VWS is slightly higher and is shown to increase further with the increased lagged time, reaching −0.34 for 72-h lagged intensity change. However, this still only explains a small portion of the variance, and provides motivation to have a look at the effect of various shear metrics on TC intensity change, although there are several other factors that significantly affect TC intensity change, such as SST, storm translation, concentric eyewall cycle, dry air intrusion, and so on (Wang and Wu 2004).
The objectives of this study are 1) to provide an initial survey on whether there exists a VWS measure that is highly correlated with the TC intensity change over the WNP; 2) to examine whether such a relationship displays a seasonal variation; and 3) to see how far it may be applicable to other ocean basins, such as the North Atlantic. The rest of the paper is organized as follows. Data and methodology are described briefly in section 2. The results are analyzed in section 3. Section 4 provides an extended discussion of the results and their implications. The main findings are summarized in the last section.
2. Data and methodology
The JTWC best track data include the longitude and latitude of the storm center and the maximum 1-min mean sustained 10-m wind speed of each TC at 6-h intervals. They were used to calculate the TC intensity change and translational speed during the 33-yr period from 1981 to 2013 over the WNP. Only named TCs with maximum 1-min mean sustained 10-m horizontal wind speeds greater than 17.2 m s−1 were considered in this study. The region of interest in the current study is 0°–35°N, 123°E–180°. This region is chosen to avoid any significant effects from the landmass. Note that there are still some small islands in the analysis domain, but their effect on the overall results should be negligibly small. Therefore, we focused our analysis on TCs in the tropical and subtropical WNP (excluding the South China Sea) and excluded TCs involving direct interaction with midlatitude troughs and those making landfall (Fig. 1a). Note that since only those cases equal to or above TS intensity (i.e., _V_max > 17.2 m s−1) were included in the analysis and with view to the fact that the analyzed region is distant from most coastlines, the number of weakening cases due to land interactions should only introduce a minimal “contamination” to the entire analysis.
Fig. 1.
Frequency distributions of TC occurrence in each 2.5° longitude by 2.5° latitude box in (a) the western North Pacific and (b) the North Atlantic for all seasons; and for (c) the active typhoon seasons from June to October and (d) the inactive seasons in the remaining months of the year in the western North Pacific during 1981–2013.
Citation: Monthly Weather Review 143, 9; 10.1175/MWR-D-15-0049.1
For the sake of comparison, we also used the best track data of Atlantic TCs from the National Hurricane Center (NHC) [i.e., the revised Atlantic hurricane database (HURDAT2)] during the same period of 1981–2013. The HURDAT2 consists of 6-hourly TC location (latitude–longitude), maximum 1-min mean sustained 10-m wind speed (_V_max), and central sea level pressure (Landsea and Franklin 2013). All TCs in the region 0°–40°N, 20°–80°W were included in the analysis (see Fig. 1b). Consistent with the WNP, extratropical transition stages of TCs and records after landfall were excluded from the analysis. Note, for the sake of a simple comparison, in our analysis of the North Atlantic we did not include the Caribbean Sea and the Gulf of Mexico, although considerable TCs occurred in those regions. Analogously, TCs in the South China Sea were not included either in our analysis of the WNP to avoid the effect of complex coastlines. Since our sample sizes for both basins are pretty large, we expect the results to be representative.
The 6-hourly wind fields used in the analysis are obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim), which has a horizontal resolution of 0.75° longitude × 0.75° latitude at 37 pressure levels. The ERA-Interim has been considered since it has substantial improvement over the earlier reanalysis of ERA-40 due to the use of more satellite data in the data assimilation system (Dee et al. 2011). In our analysis, two methods are used to calculate the horizontal mean winds on each pressure level. One is to calculate the horizontal mean winds directly using the original reanalysis, namely the total field of the reanalysis. The other one is to calculate the horizontal mean wind using the filtered basic wind field based on the algorithm described in Kurihara et al. (1993).
The filtering algorithm is briefly summarized here. An original scalar field
(horizontal wind components u or υ) is split into the basic (environmental) field
and the disturbance field
, namely,
The basic field is obtained by iteratively applying a filtering operator in the zonal and meridional directions, respectively. The filtering operator is a local three-point smoothing operator, first being conducted in the zonal direction as follows:
where
is the variable being smoothed,
is the zonally smoothed value, and the subscripts
and
are the longitude and latitude, respectively, in degrees;
and
are the neighboring longitudes of
. The coefficient
is the filtering parameter defined as
where
sequentially varies as 2, 3, 4, 2, 5, 6, 7, 2, 8, 9, and 2. After completing the smoothing in the zonal direction,
is obtained by a similar filtering in the meridional direction. The filtering operator can be expressed as
where
and
are the neighboring latitudes of
. The application of the above filtering completely removes disturbances with wavelengths less than about 1000 km. The disturbance field then is obtained as the difference between the total and the basic field. In the following, the filtered basic wind field is referred to as the environmental wind field.
Figure 2 shows two examples of the original total wind fields at 850 hPa and the corresponding environmental and disturbance wind fields. The environmental wind field well represents the large-scale environmental flow. This also makes the calculated mean environmental flow less data resolution dependent because the TC center in the reanalysis field might be deviated from the actual TC center. Such a deviation can often lead to large errors in the area-averaged environmental wind field if the area average is taken as a circular area around the observed TC center. Our results show that in general the statistical relationship between TC intensity change and various definitions of environmental VWS is more robust if the filtered wind field is used to calculate the environmental VWS.
Fig. 2.
Two examples of the original total wind (m s−1) fields at 850 hPa and the corresponding filtered environmental and perturbation wind fields for (a)–(c) 0600 UTC 17 Aug and (d)–(f) 0600 UTC 22 Aug 1981. Note that “the active typhoon season” includes June–October of the year and the remaining months of the year are referred to as “the inactive season,” see section 3 for more details.
Citation: Monthly Weather Review 143, 9; 10.1175/MWR-D-15-0049.1
Although Velden and Sears (2014) suggest that the deep-tropospheric VWS defined as the difference in two mass-weighted layer-mean winds is superior to that simply defined as the difference in two-level mean winds, we will use the simpler two-level definition for VWS. We think that this is sufficient since we want to focus on the different effects of shear between various layers, such as lower, upper, and deep troposphere. In the radial direction, two areal averages were tested in our analysis: one is the average over a circular area within a given radius (circular mean) and the other one is the average over an annulus between two given radii (annular mean). We have tested five circular means (0°–2°, 0°–4°, 0°–6°, 0°–8°, and 0°–10° latitude) and five annular means (0°–2°, 2°–4°, 4°–6°, 6°–8°, and 8°–10° latitude). The VWS between two levels (lev1 and lev2) is defined as
where
is the area-averaged horizontal wind defined either within a given radius or over an annulus between two given radii as mentioned above. The subscripts “lev1” and “lev2” indicate two distinct pressure levels.
In (5), lev1 is varied from 1000 to 100 hPa. If lev2 is chosen to be located beneath lev1, lev2 represents the bottom level of shear, and (5) represents VWS in a layer just above lev2. Vice versa, if lev1 is located beneath lev2, (5) represents the shear in a layer just below lev2. As in Zeng et al. (2010), we have first systematically examined the sensitivity to the bottom pressure level by varying lev2 from 1000 to 500 hPa and found that the bottom pressure level at 1000 hPa exhibits the highest correlation between TC intensity change and VWS. In the following analysis, we will only present results for three bottom levels, namely, the commonly used 850 hPa, 600 hPa, and the most representative 1000 hPa, while varying the “top level” (i.e., lev1) from 1000 to 100 hPa as noted above.
The TC intensity change is estimated by the observed cumulative TC intensity change over given time lags (12- and 24-h lags were examined in this study) from the time when VWS is calculated based on the best track data. The Pearson correlation coefficient between the rate of TC intensity change and various definitions of VWS is calculated based on a linear fitting between TC intensity change over 12- and 24-h lags and VWS for all cases. There are in total 6-hourly samples of 10 443 cases, among which 7977 cases fall into the active typhoon season (June–October) and 2425 into other less active seasons (see Table 1). The significance of the correlation coefficient is examined using the Student’s t test with different assumptions on the degree of freedom (see Table 1). The results discussed below all passed the 99.95% confidence level. The TC translational speed is calculated using the centered time differencing based on the observed changes in longitude and latitude at 6-h intervals. The first and the last records for each individual TC are skipped in the analysis.
Table 1.
Lists of classification of different categories of intensity change and their respective cases during the whole period of years and during the active and inactive typhoon seasons over the western North Pacific (WNP) and during the whole period of years over the North Atlantic (NATL). Five categories of intensity change are rapid intensifying (RI: _V_max ≧ 15.42 m s−1 day−1), slow intensifying (SI: 2.57 < _V_max < 15.42 m s−1 day−1), neutral (N: −2.57 ≦ _V_max ≦ 2.57 m s−1 day−1), slow decaying (SD: −15.42 < _V_max < −2.57 m s−1 day−1), and rapid decaying (RD: _V_max ≦ −15.42 m s−1 day−1). The |r*| is the threshold value of correlation coefficient significant at the 99.95% confidence level for the corresponding sample size either of TC days or TC cases.
Since the results for the 12-h lagged intensity change are generally similar to those for the 24-h lagged intensity change, only the results for the latter will be discussed below. In addition, having compared the total and the filtered mean wind fields at a given pressure level, we found that the use of the filtered (environmental) wind field produces more robust results than the use of the total wind field (not shown). Therefore, all results discussed below are based on the use of the filtered wind field.
3. Results
As explained in section 1, it is not clear whether there exists any optimal definition of VWS that can give the best indication of the VWS effect on TC intensity change. In this section, we thus first examine this possibility by calculating the linear correlations between the lagged TC intensity change and variously defined VWS at different vertical levels. In the first step, based on the results from the correlation analysis, we can identify layers over which VWS shows potentially high impacts on TC intensity change (section 3a). We then discuss the probability distributions of the lagged TC intensity change as a function of the preselected VWSs to further refine the selection. Finally we examine the sensitivity of the selected VWS–TC intensity change relationships to SST and storm translational speed (section 3b).
a. Correlation analysis
Figure 3 shows the correlation coefficients between the 24-h lagged TC intensity change and VWS with respect to 850-, 1000-, and 600-hPa mean winds, respectively. The correlation coefficients are calculated over a circle (top panels) and an annular area (bottom panels) using the environmental wind field for all TC cases over the WNP during 1981–2013. We can see that the correlation coefficients between the 24-h lagged TC intensity change and the VWS with respect to 850-hPa mean winds are all negative, indicating the overall weakening effect of VWS on TC intensity. Interestingly, the maximum absolute value is not obtained for the commonly used VWS between 200 and 850 hPa; instead, it occurs for VWS between 300 and 850 hPa regardless of the extent of the areal average (Figs. 3a and 3d).
Fig. 3.
Correlation coefficients between the 24-h lagged tropical cyclone intensity change and the vertical wind shear between a given pressure level (vertical axis) and (a),(d) 850; (b),(e) 1000; and (c),(f) 600 hPa (top) as a function of the circular mean within a given radius (horizontal axis) and (bottom) as a function of the annular mean within 2° latitudes centered at a given radius (horizontal axis) for all seasons during 1981–2013 over the western North Pacific.
Citation: Monthly Weather Review 143, 9; 10.1175/MWR-D-15-0049.1
Surprisingly, the shear between the mean wind at any level from 200 to 850 hPa and the mean wind at 1000 hPa is most negatively correlated with the 24-h lagged TC intensity change (Figs. 3b and 3e). The maximum absolute correlation coefficient occurs for low-level VWS between 850/700 and 1000 hPa within a radius of about 5° latitudes. A second maximum appears for the deep layer shear between 300 and 1000 hPa, consistent with the VWS between 300/400 and 850 hPa shown in Figs. 3a and 3d, but with higher absolute correlation coefficients when the bottom level is at 1000 hPa. Consistent with the results above, the VWS with respect to 600 hPa (Figs. 3c and 3f) shows the highest negative correlation at lev1 = l000 hPa; while the upper-tropospheric shear (above 600 hPa) has a much lower negative correlation with the 24-h lagged intensity change. These results indicate that the bottom level of 850 hPa commonly used in VWS calculation is not the best choice for predicting TC intensity change in response to VWS in the WNP. Instead the shear with respect to 1000-hPa mean winds appears to be more suitable for this purpose. Moreover, the maximum negative correlations with the low-level shear imply that the low-level shear has a stronger detrimental effect on TC intensification than tropospheric deep-layer shear over the WNP.
The results above seem to be different from those of Zeng et al. (2010) for the North Atlantic, where the effect of low-level shear on TC intensity change often is not as strong as that of shear within the mid- to upper troposphere. This difference might reflect the sensitivity of the VWS effect on TC intensity change to the large-scale dynamical and thermodynamic environmental conditions. Although the mechanism is not well understood at present, the basin-dependent results motivated us to investigate whether the correlations we gained for the WNP vary seasonally. The region is largely controlled by the WNP monsoon during the active typhoon season, while frequently affected by midlatitude systems in winter and spring. The summer monsoon season over the WNP extends from June to October (JJASO) and is often referred to as the active typhoon season for this basin (Zhan et al. 2011; Wu et al. 2013). Hereafter, the rest of the year is referred to as the inactive season. The sample cases for the active season and inactive season are listed in Table 1.
Figure 4 shows the correlation coefficients between the 24-h lagged TC intensity change and VWS with respect to 850-, 1000-, and 600-hPa mean winds, respectively, for the TC cases in the active typhoon season (top panels) and for those in the inactive season (bottom panels) over the WNP during 1981–2013. Note we only display results with the mean winds calculated over an annular area, since they are quite similar to those calculated over a circular area. Comparing Fig. 4 with Fig. 3, we can see that the maximum negative correlation at the low levels is largely dominated by the results from the active typhoon season, while the second maximum in the deep layer shear between 300 and 850/1000 hPa is mainly contributed by the results from the inactive season.
Fig. 4.
As in Fig. 3 (bottom), but (a)–(c) for TC cases in the active typhoon season (June–October) and (d)–(f) for those in the inactive season.
Citation: Monthly Weather Review 143, 9; 10.1175/MWR-D-15-0049.1
We can also see from Fig. 4 that in the active typhoon season the vertical shear between 300 and 850 hPa shows slightly higher correlations with the TC intensity change than the commonly used shear between 200 and 850 hPa (Fig. 4d). In the inactive season, however, the commonly used shear between 200 and 850 hPa shows slightly higher negative correlations with the TC intensity change than the shear between 300 and 850 hPa, in particular for mean winds averaged between 4° and 8° latitude (Fig. 4d). In the active typhoon season, shear in the mid- to lower troposphere between 700/850 and 1000 hPa dominates the effect on TC intensity change (Fig. 4b), while the effect from shear in the mid- to upper troposphere is rather small (Fig. 4c). In contrast, in the inactive season, the absolute values of correlation coefficients are higher than those in the active typhoon season for shear between any two levels, with the shear between 200/300 and 1000 hPa being more influential (Figs. 4d and 4e). This strongly suggests that TCs in the inactive season are more vulnerable to the effect of environmental shear, in particular the deep-layer shear. This might be partially due to the overall weaker TCs and lower SSTs, or different shear profiles in the inactive season (see the discussion in section 4).
Based on the above results, we select three optimal shear metrics, which display the overall highest negative correlations with TC intensity change: shears between 300 and 850 hPa (VWS300–850), 300 and 1000 hPa (VWS300–1000), and 850 and 1000 hPa (VWS850–1000). For comparison, we also consider the results for the commonly used shear between 200 and 850 hPa (VWS200–850). Note that all four vertical shear metrics are calculated using the environmental wind fields averaged over a circular area within a radius of 5° latitude. The scatter diagrams in Fig. 5 show the relationships of the 24-h intensity change with each of the four shears in both active and inactive seasons. The corresponding linear correlation coefficient for each shear and the 95th, 75th, and 50th percentiles are also displayed (yellow curves). Among the four shears, VWS300–1000 and VWS850–1000 exhibit the highest correlations with the 24-h TC intensity change (with correlation coefficients of −0.41 and −0.42, respectively). VWS300–850 is also highly correlated with the TC intensity change but with a lower correlation coefficient (−0.38) than VWS300–1000 and VWS850–1000. The commonly used deep-layer shear VWS200–850 has the lowest absolute correlation coefficient of −0.36 among all four shear metrics. That is to say, VWS300–1000 and VWS850–1000 can explain the variance of TC intensity change by 16.81% and 17.64%, respectively, while the commonly used deep-layer shear VWS200–850 can explain only 12.96% (i.e., the former is about 36% higher). This suggests that shear with respect to the 1000-hPa mean winds (VWS300–1000 or VWS850–1000) better represents the effect of VWS shear on TC intensity change than the commonly used shear with respect to 850 hPa. Thus, using both VWS300–1000 and VWS850–1000 may help to improve the prediction skill of TC intensity change.
Fig. 5.
Scatter diagram of the 24-h lagged tropical cyclone intensity change (m s−1 day−1, horizontal axis) vs various environmental vertical wind shears (as labeled on top of each panel) averaged over a circular area within a radius of 5° latitudes, optimized based on the correlation coefficient phase diagram shown in Fig. 3, for all seasons over the western North Pacific. The red vertical lines in each panel show the boundaries of (right) rapid intensification (RI) and (left) rapid weakening (RD) and the yellow curves show 95th, 75th, and 50th percentiles in each given intensity change bin.
Citation: Monthly Weather Review 143, 9; 10.1175/MWR-D-15-0049.1
As shown in Fig. 4, the correlation exhibits a seasonal dependence. Therefore, we also plot the scatter diagrams of the 24-h TC intensity change against three shears (VWS200–850, VWS300–1000, and VWS850–1000) for the active typhoon and inactive seasons, respectively (Fig. 6). Note that for the inactive season, shear between 200 and 1000 hPa calculated using the mean wind fields averaged over an annular area outside the inner core between radii of 4° and 8° latitude would give slightly higher correlations (Figs. 4d and 4e). However, for consistency (i.e., in analogy to Fig. 5), we display the average over a circular area within a radius of 5° latitude for both the active typhoon and inactive season in Fig. 6. Again, in the active typhoon season the negative correlation between TC intensity change and low-level shear VWS850–1000 (Fig. 6e) is higher than that with deep-layer shear VWS200–850 or VWS300–1000 (Figs. 6a and 6c). In the inactive season, the negative correlation is higher for deep-layer shear (Figs. 6b and 6d) than that for low-level shear (Fig. 6f). Note that the maximum absolute value of the correlation coefficients in the inactive season is about 29% higher than that in the active typhoon season (−0.53 vs −0.41). Another feature, which is not apparent from the representation in Figs. 3 and 4, is the overall stronger magnitude of the deep-layer shear in the inactive season than in the active typhoon season as inferred from the three percentiles of shears as a function of intensity change shown in Fig. 6. This feature indicates that the effect of deep-layer shear on TC intensity change is dominated by rather strong VWS in the inactive season.
Fig. 6.
Scatter diagram of the 24-h lagged tropical cyclone intensity change (m s−1 day−1, horizontal axis) vs various environmental vertical wind shears (as labeled on top of each panel) averaged over a circular area within a radius of 5° latitudes, optimized based on the correlation coefficient phase diagram shown in Fig. 4, for the (a),(c),(e) active typhoon season and the (b),(d),(f) inactive season over the western North Pacific. The red vertical lines in each panel show the boundaries of (right) rapid intensification (RI) and (left) rapid weakening (RD) and the yellow curves show 95th, 75th, and 50th percentiles in each given intensity change bin.
Citation: Monthly Weather Review 143, 9; 10.1175/MWR-D-15-0049.1
b. Probability distribution and dependence on SST and TC translational speed
Based on the results from the analysis in section 3a, VWS300–1000 and VWS850–1000 are selected as the optimal representatives of the deep-layer shear and the low-level shear, respectively. To perform a probability distribution analysis, both shears are stratified into bins according to their magnitude. The bins for VWS300–1000 are 0–2, 2–4, …, 16–18 m s−1; and those for VWS850–1000 are 0–1, 1–2, …, 6–7 m s−1. Each observed VWS is assigned to the nearest midpoint of the corresponding VWS bin in the analysis (e.g., VWS300–1000 between 0 and 2 m s−1 is assigned to 1 m s−1). The 24-h TC intensity changes of all cases over the WNP during 1981–2013 are grouped into five categories, namely RI, SI, N, SD, and RD episodes (see Table 1 for definitions and Fig. 7 for their frequency distribution; note, for the sake of comparison, we also display the respective number of cases for the North Atlantic). Since the frequency distributions for TCs in the active typhoon season and in the inactive season are roughly similar (Fig. 7b), cases occurring during the active typhoon season and those during the inactive season will not be considered separately in the following discussion.
Fig. 7.
Frequency distributions of different categories in intensity change for TCs over the western North Pacific (dark blue) and over the North Atlantic (light blue) during 1981–2013 for (a) the whole period of years and (b) those over the western North Pacific in the active typhoon season (dark blue) and the inactive season (light blue).
Citation: Monthly Weather Review 143, 9; 10.1175/MWR-D-15-0049.1
Figures 8a and 8b show the probability distributions of RI, SI, N, SD, and RD episodes in each bin of VWS300–1000 and VWS850–1000, respectively. It is clear that for both deep-layer shear and low-level shear, the intensification cases (RI and SI) bias toward the low-magnitude shear side while the weakening cases (RD and SD) bias toward the high-magnitude shear side. In particular, RI mostly occurs during much weaker shear conditions compared to other episodes, while RD mainly occurs under stronger shear conditions. Note that the probabilities of intensifying and decaying TCs intersect at VWS300–1000 = 7–9 m s−1 and VWS850–1000 = 2.0–2.5 m s−1, implying that a TC has a better chance to develop than to decay under deep layer shear less than 7–9 m s−1 and low-level shear less than 2.0–2.5 m s−1. Nevertheless, the probability distribution of “neutral” (N) TCs is relatively flat across all VWS bins. Since our region of interest is located south of 35°N, the majority of TCs undergo either intensification or SD episodes. Note that the relatively high percentages of SD and RD on the high-magnitude shear side for both deep-layer shear and low-level shear are related to the fact that the total number of cases is small (Table 1), in particular for VWS300–1000 > 11 m s−1 (Fig. 8e) and for VWS850–1000 > 3.5 m s−1 (Fig. 8f).
Fig. 8.
(a),(b) Percentage distribution of different intensity change categories in each vertical wind shear bin for shears between 300–1000 and 850–1000 hPa. (c),(d) The probability distributions of each of the five intensity change categories as a function of the shears between 300–1000 and 850–1000 hPa. (e),(f) As in (c),(d), but for all TC episodes.
Citation: Monthly Weather Review 143, 9; 10.1175/MWR-D-15-0049.1
Figures 8c and 8d show the probability distributions of each of the five intensity change categories as a function of deep-layer shear and low-level shear, respectively. From this it becomes very clear that the peak in intensity change shifts to the high shear side from RI to RD. The majority of cases are SI, which shows a probability distribution similar to that of all cases together (i.e., RI, SI, N, SD, and RD) for each shear-magnitude (Figs. 8e and 8f). The probability of RD is quite small here, because our analysis is limited to regions where the RD cases are relatively rare as mentioned above. A high probability of RI occurs mainly when the deep-layer shear is below 7 m s−1 and the low-level shear below 2.5 m s−1.
To explore the sensitivity of the relationship between VWS and TC intensity change to SST, Fig. 9 breaks down the probability distributions shown in Figs. 8c and 8d into different SST categories. The number of cases for each SST category is listed in Table 2. When the SST is below 27°C, the likelihood of intensification is very low and TCs have a much higher probability to decay regardless of the definition and magnitude of shear. Nevertheless, cases with no significant intensity change (N) are still biased toward the weak shear side and those of RD are biased toward the strong shear side for both deep-layer shear and low-level shear (Figs. 9a and 9b). For SSTs between 27° and 28°C (Figs. 9c and 9d), the majority of cases either do not exhibit any significant intensity change or undergo SD independent of the shear conditions (i.e., either deep-layer or low-level shear). A considerable number of intensification cases start to appear under weak shear conditions but RI cases are still rare. A relatively small percentage of RD cases appear under relatively high-magnitude shear conditions, reaching a maximum for deep-layer shear larger than 7 m s−1 and low-level shear larger than 2.0 m s−1.
Fig. 9.
Percentage distribution of different tropical cyclone intensity change categories as a function of various vertical wind shears in different SST (°C) groups as given in each panel.
Citation: Monthly Weather Review 143, 9; 10.1175/MWR-D-15-0049.1
Table 2.
The number of cases in each SST category or each translational speed (_V_trans) group as used in the analysis in section 3b.
At SSTs between 28° and 29°C (Figs. 9e and 9f), TCs are mostly embedded in a weak shear environment and, as expected, they exhibit a higher probability to intensify than at lower SSTs. Moreover, a higher percentage of TCs experience RI under weak shear, whereas the probability of RD is relatively low. This suggests that for SSTs above 28°C, VWS may be less critical to TC intensification and other factors should be considered more carefully instead. For SSTs higher than 29°C (Figs. 9g and 9h), TCs are even more often embedded in a weak shear environment (VWS300–1000 < 9 m s−1 and VWS850–1000 < 2.5 m s−1), which is in itself less deleterious, and, hence, it is more likely that TCs undergo intensification or even RI. In sharp contrast, under such high SST conditions, the probability of decay is lowest with very few RD cases.
Figure 10 shows the probability distributions of the five intensity change categories as a function of storm translational speed grouped into four categories for deep-layer shear and low-level shear, respectively (Table 2). It can be seen that the probability distribution of TC intensity change in response to both deep-layer shear and low-level shear depends strongly on the translational speed of TCs. A particularly drastic change occurs for a translational speed at around 8 m s−1. For the slow-moving storms with a translational speed of less than 8 m s−1, intensification dominates with very low probabilities of RD (Figs. 10a–d). The RI is more likely to occur for translational speeds between 3 and 8 m s−1 under relatively wider VWS conditions than for translational speeds of less than 3 m s−1. This is often attributed to the stronger negative ocean feedback for very slow-moving storms, which acts to cool the SST underneath the TC (Zeng et al. 2007, 2008; Lin et al. 2009). Note that most of the slow-moving storms are embedded in an environment of relatively weak shear (viz., VWS300–1000 < 11 m s−1 and VWS850–1000 < 3.5 m s−1). Coherently, the occurrence of all other intensity change categories becomes more likely on the stronger shear side associated with relatively fast-moving storms (_V_tran > 8 m s−1, Figs. 10e and 10f). When the translational speed is larger than 12 m s−1, most TCs tend to decay, with no chance for RI at all (Figs. 10g and 10h). This is often explained as a result of the development of a strong asymmetric structure in fast-moving storms (e.g., Shapiro 1983; Chen et al. 2006; Zeng et al. 2007, 2008; Uhlhorn et al. 2014).
Fig. 10.
Percentage distribution of different tropical cyclone intensity change categories as a function of various vertical wind shears in different storm translation speed (m s−1) groups as indicated in each panel.
Citation: Monthly Weather Review 143, 9; 10.1175/MWR-D-15-0049.1
Overall it can be seen that the separation between decaying and intensifying cases is more distinct under low-level shear than under deep-layer shear for different categories in both SST and translational speed (Figs. 8–10). This suggests that the low-level shear provides a better representation of the VWS effect on TC intensity change than the deep-layer shear in the WNP. Consequently, the use of low-level shear could lead to more skillful TC intensity forecasts in this region, especially during the active typhoon season.
4. Discussion
The results presented in section 3 demonstrate that the effect of environmental VWS on TC intensity change over the WNP during the active typhoon season could be quite different from that in the inactive season. A high relevance of low-level shear with view to TC intensity change over the WNP was also noticed by Shu et al. (2013) based on reanalysis data during 2000–06. They found that VWS between 850 hPa and 10-m height has a stronger detrimental effect on TC intensification than the deep-layer shear between 200 and 850 hPa. However, in the study at hand, we showed that the effect of low-level shear on TC intensity change is essentially prevailing in the active typhoon season. In other seasons, we found that shear between 200/300 and 1000 hPa has a stronger effect on TC intensity change. Two questions arise as to 1) why the relevance of either low-level shear or deep-layer shear with regard to TC intensity change displays a seasonal dependence over the WNP, and 2) whether similar relationships apply to other ocean basins. In this section we discuss some initial thoughts on these questions based on physical reasoning. Detailed studies to address these issues will be the subject of our future work.
Shu et al. (2013) hypothesized that low-level shear can induce downward fluxes of low equivalent potential temperature (θ e) air into the boundary layer from above, significantly depressing θ e in the inflow boundary layer and thereby impeding TC intensification. However, they did not discuss how the low-level shear may either induce or enhance these downward fluxes. At this point, we think of two possible processes that may be responsible for the strong influence of low-level shear between 700/850 and 1000 hPa on TC intensity change. On one hand, the vertical θ e profile in the tropics has a minimum value near 700 hPa [see Fig. 11.1 in Holton and Hakim (2004)]. This implies that the low-level shear could lead to an enhanced efficiency of the midlevel ventilation as discussed by Tang and Emanuel (2010). On the other hand, the radially inward intrusion of low θ e air by the shear flow above the inflow boundary layer may promote evaporation of raindrops in rainbands, thus strengthening the downdrafts (with lowered θ e air). The enhanced downdrafts may flush low θ e air downward into the inflow boundary layer, where the low θ e air is transported into the eyewall, acting to reduce buoyancy, thereby weakening eyewall convection. This second process can be considered as an amplification of the low-level ventilation effect of VWS proposed by Riemer et al. (2010).
The difference in the relative importance of deep-layer shear and low-level shear between the active typhoon season and the inactive season could be ascribed to various reasons. First, we notice that the SST is higher in the active typhoon season than in the remaining seasons in the WNP. Since convection is generally stronger under higher SSTs (Evans and Waters 2012), TCs tend to intensify at a higher intensification rate and are generally stronger in the active typhoon season. On the other hand, the atmospheric environment over the WNP is predominantly controlled by the summer monsoon trough. In such a monsoon environment TCs tend to have a larger extent (Wang 2009; Xu and Wang 2010, Chavas and Emanuel 2010; Lee et al. 2010; Knaff et al. 2014). Stronger and larger TCs are generally more resilient to VWS (Reasor et al. 2004; Xu and Wang 2013). This suggests that the dynamical effect of VWS, which is often associated with deep-layer shear, might be less influential in the active typhoon season than in the inactive season. In contrast, the low-level shear effect is dominated by the thermodynamic effects discussed above. This gives way to the hypothesis that the thermodynamic effect of VWS on TC intensity change outweighs the dynamical effect in the active typhoon season, while the dynamical effect of VWS dominates the thermodynamic effect in the inactive season. In addition, our analysis revealed that the magnitude of low-level shear VWS850–1000 does not display any considerable seasonal dependence, while the deep-layer shear VWS300–1000 is stronger in the inactive season than in the active typhoon season. Presumably this also contributes to the higher negative correlation with TC intensity change during the inactive season. Other possible reasons for the observed seasonal dependence might be related to a different stratification of the shear flow. However, after some preliminary analysis, we have not found any robust conclusions.
To investigate whether the same statistical relationships between VWS and TC intensity change over the WNP apply to other ocean basins, we have done a similar statistical analysis for North Atlantic TCs during 1981–2013 in the region 0°–40°N, 20°–80°W. Again, all landfalling cases and close-to-coast episodes were excluded in the statistical analysis. The results for all TC cases are shown in Fig. 11. We can see that the distribution of correlation coefficients between VWS and TC intensity change are very different from those over the WNP shown in Fig. 3. Although the shear between 300 or 200 hPa and 1000 hPa is still rather highly negatively correlated with the 24-h lagged TC intensity change, the low-level shear turns out to be not highly influential in the North Atlantic.
Fig. 11.
As in Fig. 3, but for Atlantic TCs during 1981–2013 in the region 0°–40°N, 20°–100°W based on the HURDAT2 best track data and ERA-Interim. Landfalling cases and close-to-coast episodes were excluded in the analysis.
Citation: Monthly Weather Review 143, 9; 10.1175/MWR-D-15-0049.1
Moreover, we can see from Fig. 12 that the overall VWS is much weaker over the North Atlantic than over the WNP. This suggests that the VWS effect on TC intensity change over the North Atlantic is in general less important than over the WNP. A similar contrast between the deep-layer shear and low-level shear effects between the active hurricane season and other seasons is not found in our analysis either. This further supports the hypothesis that the stronger and larger TCs associated with the monsoon environment in the active typhoon season in the WNP are more resilient to the dynamical effects of the deep-layer shear; and, therefore, the thermodynamic effects of the mid–lower level ventilation may play a more important role in TC intensity change in that region.
In addition, it is found that the deep-layer shear with respect to the 300-hPa level has a slightly stronger negative correlation with TC intensity change than that with respect to 200 hPa over the WNP (Fig. 5). We note that the outflow layer in a strong TC is generally located between 150 and 300 hPa and often centered at 200 hPa. As a result, the environmental flow could be blocked by the strong outflow and is unable to reach the inner core region of a strong TC. Since the upper-level outflow is generally stronger in larger and stronger storms, such as those over the WNP, the shear between 200 and 300 hPa might not be very important to TC intensity change over the WNP. Note that correlations of TC intensity change with the shear between 200 and 850 hPa and that between 300 and 850 hPa are comparable over the North Atlantic (Fig. 11). Nevertheless, although not being significantly superior, overall the use of 300 hPa as the top level for deep-layer shear should be less basin dependent in representing the deep-layer shear effect on TC intensity change.
5. Conclusions
Based on the JTWC best track data of TCs in the WNP and the ERA-Interim data during 1981–2013 the effect of VWS between different pressure levels on the lagged intensity change of TCs over the subsequent 24 h were statistically analyzed. Overall, the results show that TC intensity change and VWS are negatively correlated, giving evidence for the detrimental effect of VWS on TC intensity, in agreement with previous studies. It is also found that the commonly used deep-layer shear between 200 and 850 hPa does not provide the optimal measure for the detrimental effect of VWS on TC intensity change as the deep-layer shear between 300 and 1000 hPa shows a higher negative correlation with TC intensity change.
Moreover, our analysis revealed that the negative correlation of the low-level shear between 700 (or 850) and 1000 hPa averaged over an area within a radius of 5° latitude and the 24-h lagged TC intensity change is stronger than that with the deep-layer shear between 300 and 1000 hPa over the WNP. By distinguishing between the active and inactive typhoon seasons, we discovered a seasonal dependence of this correlation. In the active (inactive) season, the low-level (deep layer) shear is more negatively correlated with the TC intensity change than the deep-layer (low level) shear. This suggests that the use of the low-level shear in statistical intensity prediction schemes may improve the intensity forecast skill for TCs in the active season (June–October) over the WNP.
The analysis also shows that for SSTs above 28°C, TCs tend to be embedded in an environment of relatively weak shear (i.e., VWS300–1000 < 11 m s−1 and VWS850–1000 < 3.5 m s−1). Consequently they are much more likely to intensify than to decay. On the other hand, TCs are not likely to intensify at all when the SST is below 27°C while still have a low probability to intensify rapidly when the SST is below 28°C. The highest probability for TCs to undergo RI and little chance to weaken rapidly is found when the SST is above 29°C. Furthermore, the VWS effect on TC intensity change depends also on the translational speed of the storm. The TCs moving at a translational speed of less than 8 m s−1 are more likely to intensify than to decay and have a very low probability to decay rapidly. However, TCs are found to intensify both slowly and rapidly under relatively weaker shear conditions for translational speeds less than 3 m s−1 than for translational speeds between 3 and 8 m s−1. When the translational speed is larger than 12 m s−1, most TCs tend to decay regardless of the shear condition, with little chance to intensify and no chance of RI at all.
To address how the relationships identified for the WNP as summarized above may apply to other ocean basins, we also analyzed the response of TC intensity change to different VWS metrics in the North Atlantic in the same period from 1981 to 2013. Different from that over the WNP, our analysis showed that deep-layer VWS between 300 (or 200) and 1000 hPa has a higher negative correlation with the 24-h lagged TC intensity change than the low-level shear over the North Atlantic. Furthermore, the overall effect of VWS (both deep-layer shear and low-level shear) on TC intensity change over the North Atlantic is less pronounced than over the WNP. This seems to suggest that the relative importance of the VWS effect is basin dependent and may be subject to other environmental conditions, such as the dominant control of the WNP by the summer monsoon trough in the active season, a topic that requires further investigation.
Acknowledgments
The authors are grateful to Dr. John Knaff and another anonymous reviewer for their constructive comments and stimulating suggestions, which aided to improve our manuscript. We also highly appreciate the reviewers’ encouragement for a continuation of our work on this subject. This study has been supported in part by NSF Grant AGS-1326524 and USGS/PICSC G13AC00363 and in part by the National Natural Science Foundation of China under Grant 41130964. Additional support has been provided by the JAMSTEC through its sponsorship of the International Pacific Research Center (IPRC) in the School of Ocean and Earth Science and Technology (SOEST) at the University of Hawai‘i at Mānoa.
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