3798
MONTHLY WEATHER REVIEW
VOLUME 139
Sea Surface Temperature Response to Tropical Cyclones RICHARD A. DARE AND JOHN L. MCBRIDE Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, Victoria, Australia (Manuscript received 27 October 2010, in final form 9 May 2011) ABSTRACT The response of sea surface temperature (SST) to tropical cyclones is studied using gridded SST data and global cyclone tracks from the period 1981–2008. A compositing approach is used whereby temperature time series before and after cyclone occurrence at individual cyclone track positions are averaged together. Results reveal a variability of several days in the time of maximum cooling with respect to cyclone passage, with the most common occurrence 1 day after cyclone passage. When compositing is carried out relative to the day of maximum cooling, the global average response to cyclone passage is a local minimum SST anomaly of 20.98C. The recovery of the ocean to cyclone passage is generally quite rapid with 44% of the data points recovering to climatological SST within 5 days, and 88% of the data points recovering within 30 days. Although differences exist between the mean results from the separate tropical cyclone basins, they are in broad agreement with the global mean results. Storm intensity and translation speed affect both the size of the SST response and the recovery time. Cyclones occurring in the first half of the cyclone season disrupt the seasonal warming trend, which is not resumed until 20–30 days after cyclone passage. Conversely, cyclone occurrences in the later half of the season bring about a 0.58C temperature drop from which the ocean does not recover due to the seasonal cooling cycle.
1. Introduction It has been known for some decades that strong winds associated with tropical cyclones (TCs) induce reductions in the sea surface temperature (SST) beneath the storm (Fisher 1958; Leipper 1967; Brand 1971; Price 1981; Bender et al. 1993; Hart et al. 2007; Price et al. 2008; Jansen et al. 2010; Hart 2011). The cold oceanic surface ‘‘wake’’ left behind the TC may extend for hundreds of kilometers adjacent to the storm track (Nelson 1996; Emanuel 2001). An SST reduction that is initially small in spatial scale can also spread to larger scales over time (Sobel and Camargo 2005). Within the wake, SST reductions range from less than 18C (Cione et al. 2000), up to 38 (Shay et al. 1991), 48 (Price et al. 2008), 58 (Price 1981), 68 (Berg 2002), 78 (Walker et al. 2005), and 98C (Lin et al. 2003). The greatest reductions in SST are generally found to the right of the TC’s track in the Northern Hemisphere (Nelson 1996; Chen et al. 2007; Black and Dickey 2008; Davis et al. 2008), and to the left of the track in the
Corresponding author address: Dr. Richard A. Dare, CAWCR, Australian Bureau of Meteorology, 700 Collins St., Melbourne, 3001 VIC, Australia. E-mail:
[email protected] DOI: 10.1175/MWR-D-10-05019.1 Ó 2011 American Meteorological Society
Southern Hemisphere (Berg 2002). However, cooling maxima have also been observed to the left of the storm track in the Northern Hemisphere (Leipper 1967; Walker et al. 2005). Bender et al.(1993) also note that the ocean response exhibits large variations from one location to another. Many of the values of SST reduction reported in the literature refer to the maximum cooling observed in the wake to the rear of the TC and adjacent to its track. Cooling of the sea surface also occurs in the vicinity of the eyewall, although SST reductions are generally smaller than those in the adjacent wake (Schade and Emanuel 1999; Cione and Uhlhorn 2003; D’Asaro et al. 2007). Consideration of the TC-induced change in SST near the eyewall is important because reduced SSTs mean reduced fluxes of heat from the ocean to the storm. The reduced surface fluxes beneath the eyewall, rather than a change in surface flux at some distance from the TC’s center, have the potential to impact the storm’s intensity (Cione and Uhlhorn 2003; D’Asaro et al. 2007). A primary aim of the current work is to investigate TC-induced changes in SST beneath this important central region of the storm. Following the reduction in SST due to the TC, subsequent atmospheric and oceanic processes tend to return
DECEMBER 2011
DARE AND MCBRIDE
the SST to the climatological value. The number of days required for the SST to recover varies widely between different TCs, with time periods ranging from days to weeks. Hazelworth (1968) found a range from 1 to 36 days, with a mean period of approximately 20 days in the Gulf of Mexico, while along the East Coast of the United States the mean was approximately 10 days. Nelson (1996) found that cooling of the sea surface was still evident nearly one month after the storm’s passage, and Emanuel (2001) noted a restoration over a period of weeks. Using a large database of storm tracks and SSTs, Hart et al. (2007) found that SST returned to climatology approximately 35–40 days after the TC’s departure. While these sources discuss the overall recovery period, Leipper (1967) found that, following the initial rapid drop, the SST made a partial recovery toward pre-TC values over just two days, followed by a near-steady value for several more days. While much of the literature measures the time for the SST to return to the preTC or climatological value, it can also be convenient to express the recovery in terms of an e-folding period. Price et al. (2008) found that the low SST anomaly would disappear over an e-folding period of 5–20 days, depending on the thickness of the upper-ocean layer and on the post-TC weather, particularly wind speed. Jansen et al. (2010) noted an average e-folding period of about seven days. Two characteristics of TCs that affect the SST reduction are storm intensity (Brand 1971; Price 1981; Cione and Uhlhorn 2003; Hart et al. 2007; Sandery et al. 2010; Lin et al. 2008) and translation speed (Brand 1971; Price 1981; Bender et al. 1993; Cione and Uhlhorn 2003; Sandery et al. 2010; Lin et al. 2009). Larger reductions in SST are associated with more intense TCs and with storms that are translating slowly. Oceanic characteristics and dynamics are not discussed in detail here as this study is focused upon documenting the SST response to TCs rather than investigating physical mechanisms within the ocean. Briefly, mechanisms contributing to the cooling of the ocean surface are winddriven oceanic turbulence that causes vertical mixing and entrainment of cooler water from the thermocline into the overlying mixed layer, transient upwelling (Price 1981; Emanuel 2001), enhanced surface sensible and latent heat fluxes from the ocean to the atmosphere driven primarily by the high wind speeds near the radius of maximum winds (Price 1981; Emanuel 2001; Trenberth and Fasullo 2007), horizontal transports of warm water away from the storm center (Leipper 1967), rain falling onto the ocean surface, and radiative losses (Brand 1971). Despite this extensive list of processes, the SST reduction is generally dominated by mixing rather than surface heat fluxes. To the rear of the TC, inertial oscillations induce mixing
3799
that contributes to the large SST reductions observed in the wake. Consideration of the TC-induced sea surface cooling is important for a number of reasons. First, when the SST is reduced, the surface fluxes of moisture and heat to the TC are reduced, limiting the intensification of the storm if the eyewall region of the TC lingers over the cooled surface (Emanuel et al. 2004), or over the wake left by an earlier TC (Fisher 1958; Cione and Uhlhorn 2003; Hart et al. 2007; Sandery et al. 2010). Second, an area of reduced SST may also affect the path taken by a TC (Chang and Madala 1980; Bender et al. 1993). Third, Shay et al. (1987) mentioned that the asymmetric SST distributions induced by TCs may contribute to the asymmetric structure of the storm. Fourth, Wendland (1977) found that changes in SST by a few tenths of a degree celsius affect the number of monthly storms and the length of the storm season. Fifth, the persistence of oceanic anomalies for 1–2 months after the TC’s passage could potentially impact upon larger-scale atmospheric circulations (Hart et al. 2007). Sixth, Emanuel (2001), Sriver and Huber (2007), and Jansen et al. (2010) have argued that the turbulent mixing of the upper ocean associated with TCs plays a role in driving the thermohaline circulation of the global oceans. The first three points listed here affect the important task of forecasting TC intensity and track, while the fourth and fifth concern the larger-scale atmosphere, and the sixth involves the impacts on the global oceans and the climate system. Clearly, TC-induced SST cooling affects systems over a range of scales. A large amount of literature on this subject has focused on individual cases. In the current work, the aim is to carry out an exploratory analysis of TC-driven changes in SST over the entire globe, to document the magnitude of the SST reduction and the recovery time to climatological conditions. Similar to Hart et al. (2007), a compositing approach is used whereby temperature time series before and after cyclone occurrence at individual cyclone track positions are averaged together. Two basic parameters can be measured from these averaged time series: the sea surface temperature reduction in degrees celsius and the sea surface temperature recovery time in days. These two parameters are illustrated schematically in Fig. 1, with consideration of both the full recovery time and the e-folding period. Of course the temperature reduction and recovery time are not independent; in a mean sense, a larger reduction implies a longer recovery. This simple relationship is shown in Fig. 2, which is compiled from the entire global dataset, as will be described in the following sections. Means of both the full recovery time (Fig. 2a) and the e-folding period (Fig. 2b) have been computed. The error bars in Fig. 2 are the standard deviations for each of the bins of SST reduction
3800
MONTHLY WEATHER REVIEW
VOLUME 139
FIG. 1. Schematic of SSTA, full recovery time, and e-folding period in response to TC passage.
used to produce the figure. As can be seen, the overlap of the error bars from each SST-reduction bin to the accompanying bins is quite large. This is consistent with the findings of Hazelworth (1968), who noted previously that there is an insignificant relationship between the change in SST and the number of days to recover to normal. The purpose of the current paper is thus to document the range of magnitudes of these two primary variables and how they vary for different global cyclone basins and as a function of the intensity and translation speed of the tropical cyclone, with a specific focus on the innercore eyewall region of the storm. Following a discussion of our data and methods in section 2, mean quantities are presented in section 3. The influences of TC intensity and translation speed are discussed in section 4. Differences in the evolution of SST due to the temporal location of the storm within the season of TC activity are investigated in section 5. Results are summarized in section 6.
2. Data and methods The International Best Track Archive for Climate Stewardship (IBTrACS; Knapp et al. 2009) was used to provide 6-hourly latitudes and longitudes of all TCs observed over the globe during the period of interest (1 September 1981–31 December 2008). All six TC basins (southern Indian Ocean, South Pacific Ocean, northern Indian Ocean, west North Pacific, east North Pacific, and North Atlantic) are included in this study. The IBTrACS project collects cyclone best-track data from all global forecast offices and warning centers. For most cyclones there are alternative versions of the track due to the cyclone having been tracked and forecast by more than
FIG. 2. (a) Mean and standard deviation of full recovery time vs 0.58C bins of local minimum SSTA, and (b) mean and standard deviation of e-folding period vs 0.58C bins of local minimum SSTA.
one warning center. In such cases the track was chosen from the primary forecast center with World Meteorological Organization (WMO) responsibility for that cyclone basin. Various warning centers designate the best track with different metrics for the cyclone intensity, including central pressure, 1-min-average sustained winds, and 10-min-average sustained wind. The analyses in the current study were restricted to cyclones including maximum sustained wind as the intensity measure, with the 1-min winds being converted to 10-min winds using the conversion factor 0.871 (Knapp and Kruk 2010). Although the TC dataset extended back well beyond 1981, this range of dates was used because the SST dataset covered only from September 1981 onward. This SST dataset provided by the National Oceanic and Atmospheric Administration/National Climatic Data Center (NOAA/ NCDC) has a high spatial and temporal resolution, providing daily observations over the ocean every 0.258 of latitude and longitude (Reynolds et al. 2007). The quality of the SST data is documented by Dash et al. (2010; information also online at www.star.nesdis.noaa.gov/sod/ sst/squam). Although there are uncertainties in the accuracy of the data, the current study involves computing mean values from a large dataset, and as such the bulk of the uncertainties cancel. An additional concern is the cloud cover associated with TCs, a factor that may affect the reliability of the observations. However, these data
DECEMBER 2011
DARE AND MCBRIDE
3801
FIG. 3. Percentage occurrence of day of local minimum SSTA, compiled from over 75 000 individual time series.
are used in this study because their use by Hart et al. (2007) for a similar purpose was well accepted, and it is desirable to document the mean state of the TC-induced SST reaction as many case studies have been presented in the past. For each 6-hourly position along a TC’s track, the daily SST grid corresponding to this point in time was found, and the SST at the exact location of the TC’s center was computed by linear interpolation from the 0.258 3 0.258 SST grid. The location was then held constant while linear interpolations were repeated for that location for a series of days before and after the day on which the TC was present. This method produced over 75 000 time series of SST values for each point along the TC tracks. Parallel time series for the same days and locations were computed using a climatological SST dataset that was constructed by averaged values at each point from the 1981–2008 daily SST Reynolds dataset. These climatological values were subtracted from the individual time series to produce a set of time series of SST anomalies (SSTAs).
3. Global mean quantities a. Global mean SSTA time series The day on which the local minimum occurs relative to the passage of each TC at each point is compiled for a range of days (27 to 17) (Fig. 3). For time series containing multiple local minima, the minimum, occurring on or after day 21 and that with the lowest SSTA, is considered. There is a notable signal as early as day 21 because a TC is a storm covering a wide area, and as such it can affect the SST at a point before the center of the
FIG. 4. (a) Percentage occurrence of day of local minimum SSTA, and (b) corresponding mean local minimum SSTA for each day.
storm arrives at that point. For the remainder of this study, only those time series with local minima on days 21 to 17 are considered (Fig. 4a), which includes approximately 96% of the total dataset. When the mean SSTA is computed corresponding to each day (Fig. 4b), values range from approximately 20.758 to 21.08C, with an overall mean of 20.918C. However, when the mean time series is computed (Fig. 5a), it reveals a mean local minimum of just 20.498C, which is of a lesser magnitude than every single mean in Fig. 4b. As there is a range of days on which the local minima occur, computing a mean (Fig. 5a) from all the time series without accounting for this range of days will effectively smooth the data and underestimate the mean minimum. As such, the mean time series in Fig. 5a must be rejected, as it is not representative of any of the mean local minima shown in Fig. 4b. To overcome this problem, individual time series are shifted in time so that their local minima are located on a common day. Day 1 is chosen for this purpose, as local minima occur most frequently on this day, as shown by Fig. 4a. The new mean time series (Fig. 5b) contains a local minimum with a value of 20.918C, consistent with the overall mean in Fig. 4b. A feature of this mean time series is the rapid partial recovery immediately following the local minimum. This feature is in agreement with the observations of Leipper (1967), who showed that the SST can make a rapid partial recovery over 2 days. Price et al. (2008) also noted rapid warming of the wake. This information
3802
MONTHLY WEATHER REVIEW
VOLUME 139
FIG. 6. Percentage occurrence of local minimum SSTA in 0.58C bins.
FIG. 5. (a) Time series of global mean SST anomaly (difference from climatology) relative to the arrival of a TC on day 0, and (b) as in (a), but with all minima aligned on the same day (11).
was lost when the mean time series was computed without aligning the local minima on a common day. After about day 10, the time series shows a gradual, slower recovery than the initial rapid response. After approximately 30–40 days, the SST returns to its climatological value. The mean time series for the Northern Hemisphere presented by Hart et al. (2007) appear to have shapes similar to both Figs. 5a and 5b, but with magnitudes comparable with those in Fig. 5a (minima of approximately 20.38 to 20.58C within about 2 days after the arrival of a TC). They used an areal-average SST over a TC-centered 58 3 58 box, which contains the wake region in which greater cooling than that found at the center of the TC is generally observed. As such, one may expect Hart et al.’s SSTA values to be lower than those in Fig. 5b, but this is not the case. The inclusion by Hart et al. of SSTA values on both sides of the TC track and, we assume, their nonalignment of the day of local minima are two factors that at least partially explain the differences between our results and those of Hart et al. The use of the Northern Hemisphere basins by Hart et al. and our use of all six global TC basins is not a point that contributes significantly to the differences between the two sets of results.
The percentage occurrences of different magnitudes of local minimum SSTA are shown in Fig. 6. The bulk of the reductions range from 08 to 21.58C. These values, along with the two largest peaks (20.58 and 21.08C), suggest that the mean value would probably lie between 20.58 and 21.08C, in agreement with Figs. 4b and 5b. (These 0.58C bins of SST reduction are used to define the data points and error bars in Fig. 2.)
b. Recovery period Three classes of recovery period are defined (Table 1) to represent 1) the rapid recovery observed in Fig. 5b, 2) the intermediate 30–40-day period noted by Hart et al. (2007), and 3) any recoveries exceeding 30 days. The end of the full recovery period is defined as the day following the local minimum on which the SSTA $ 20.18C, which is effectively a return to the climatological value for that location. The e-folding period is also considered, using the same three classes of recovery period. Figure 7a shows mean SSTA time series for each recovery class. The percentages of observations corresponding to each class are shown in Table 1. Most of the TABLE 1. Class number, recovery time, percentage of observations corresponding to each class based on recovery from local minimum SSTA to climatological value, and percentage of observations corresponding to each class based on e-folding recovery period. Period of Observations (%) Observations (%) SST recovery recovery of full recovery of e-folding class No. SST (days) time period 1 2 3
1–5 6–30 .30
44 44 12
64 33 3
DECEMBER 2011
DARE AND MCBRIDE
3803
FIG. 8. Percentage occurrence of number of recovery days following local minimum SSTA for full recovery period (black) and efolding period (gray).
FIG. 7. (a) Global mean time series of SSTA for classes of recovery time defined in Table 1. Recovery classes 1 (1–5 days, thick solid line), 2 (6–30 days, dashed), and 3 (.30 days, thin solid line) are shown. (b) As in (a), but for e-folding period instead of full recovery time.
time series make a full recovery in 30 days or less (88%), with half of these recovering within 5 days. The time series corresponding to class 1 (1–5 days) contains a sharp fall and rapid rise, similar to the short-term feature seen in Fig. 5. The class 2 (6–30 days) time series has a lower SST minimum, and it occurs 1 day later. It also contains the feature of a partial recovery (days 2–4) before undergoing a more gradual and prolonged return toward climatology. The mean time series for the third class (.30 days) also contains a lower local minimum and a rapid partial recovery, but its subsequent recovery is relatively slow, and it accounts for just 12% of the observations. When the e-folding period is considered (Fig. 7b), the three classes differ in similar ways to those already described for the data in Fig. 7a, except that approximately two-thirds of the data are contained within the 1–5-day class. This is a natural consequence of the definition of the e-folding period (Fig. 1). A comparison between the percentage occurrences of the full-recovery time and e-folding period is given in Fig. 8; both methods of assessing the recovery period are valid, but use of the efolding period may be beneficial when the time for the full recovery is very long (greater than about 30 days).
While the global mean time series in Fig. 7 reveal some interesting features, it is important to also examine differences between the individual TC basins. The percentage occurrences of the three classes of recovery periods for each of the six basins are shown in Fig. 9. Overall, there are only small differences between the six basins. It is interesting that for all four basins in the Northern Hemisphere, and for both the full recovery time and the e-folding period (Figs. 9a and 9b, respectively), the percentage of observations in the 1–5-day class exceeds those in the two basins of the Southern Hemisphere. Berg (2002) has previously noted that there are differences in SST response between basins. This is supported by Sandery et al. (2010), who note that the characteristics of the oceans differ between basins. The slower recovery class 3 data show little difference between basins and hemispheres. In agreement with the global mean results in Table 1 and Fig. 7, the majority of the time series (approximately 88%) in the individual basins recover to their climatological values within 30 days (Fig. 9a). For the e-folding period (Fig. 9b), the percentages of observations in the 1–5-day class are again fairly consistent between basins, ranging from 55% to 70%. Even more consistent is the fact that at least 95% of the time series in all basins show an e-folding period of 30 days or less.
4. Influence of TC intensity and translation speed The intensity of the TC and its translation speed are often noted as two important factors affecting the magnitude of the SST response, as discussed in the introduction. To investigate the mean influence of these
3804
MONTHLY WEATHER REVIEW
VOLUME 139
TABLE 2. MSW speed (intensity) class (WSC) number, range of MSW, and the percentage of global observations corresponding to each class.
FIG. 9. Percentage occurrence of classes of recovery time (defined in Table 1) for each basin of TC activity: southern Indian Ocean (SI), South Pacific (SP), northern Indian Ocean (NI), west North Pacific (WNP), east North Pacific (ENP), and North Atlantic (NA). Recovery classes 1 (1–5 days, black), 2 (6–30 days, white), and 3 (.30 days, gray) for (a) full recovery time and (b) e-folding period.
two factors, data are stratified into four classes of intensity and four classes of translation speed. The four (maximum sustained) wind speed (intensity) classes (WSCs) are defined here based on the thresholds shown in Table 2. The mean local minimum SSTA was computed for each intensity class (Fig. 10). The global mean shows general agreement with the results from the individual basins, showing greater cooling response in SST as the intensity increases, with mean local minimum SSTAs ranging from approximately 20.78 to 21.48C. In addition to differences in SST minima between intensity classes, recovery times are also examined by finding the percentage of observations in each recovery class (Table 1, left and middle columns) for each intensity class (Table 2, left and middle columns). These results (Fig. 11) show that as the intensity increases, there is a large reduction in the number of observations with recovery periods of 1–5 days (down from 52% to 28%), which is largely balanced by the increase in recovery periods of 6–30 days (up from 38% to 57%). The total of these two classes (recovery times within 30 days) falls from 90% to 85% with increase in intensity, while there is an increase in the occurrence of the longer period recovery
WSC No.
Range of wind speeds (m s21)
Observations (%)
1 2 3 4
,17 17–32 33–43 $44
49 33 10 8
class 3 (up from 10% to 15%). These trends in the longest recovery times (class 3) are not obvious when the efolding period is considered (Fig. 11b) because 97% of the observations are contained within the 1–5- and 6– 30-day e-folding periods. In this case, it may be beneficial to use the full recovery time data rather than the e-folding period so that changes in the longest recovery periods (class 3) are revealed. The four translation speed classes (TSCs) are defined in Table 3. As the translation speed increases (Fig. 12), mean local minimum SSTAs are higher (impacted less). The corresponding relationships for the individual basins (dashed lines) agree well with the global mean. Comparing the mean local minimum SSTAs corresponding to the four TSCs with the impacts corresponding to the intensity classes (Fig. 10), there is less variability in mean local minimum SSTAs between the various TSCs (21.18 to 20.758C, a range of 0.358C) than there is between the WSCs (a range of 0.78C), although the particular definitions of the classes in Tables 2 and 3 may be partly responsible for these differences.
FIG. 10. Global mean local minimum SSTAs corresponding to the wind speed (intensity) classes (WSCs) defined in Table 2. For comparison, the corresponding relationships for the six individual basins are also plotted (dashed), showing general agreement with the global mean (thick black).
DECEMBER 2011
3805
DARE AND MCBRIDE
TABLE 3. TSC number, the corresponding range of translation speeds, and the percentage of global observations corresponding to each class.
FIG. 11. (a) Percentage occurrence of classes of recovery time (defined in Table 1) for each wind speed (intensity) class (WSC) defined in Table 2. Recovery classes 1 (1–5 days, thin solid line), 2 (6–30 days, thick solid line–dots), class 1 1 2 (1–30 days, dashed), and 3 (.30 days, dot–asterisk) are shown. (b) As in (a), but for e-folding period.
TSC No.
Range of translation speeds (km h21)
Observations (%)
1 2 3 4
,8 8–16 17–24 $25
18 38 27 17
evolving. In previous sections, the SSTAs were computed as the difference from climatological SSTs. In this section, an alternative SST anomaly is computed. A fixed value (the mean SST over 7 days prior to day 0) is subtracted from each daily SST value. This alternative type of anomaly provides a seasonal context for the TCinduced SST reduction. For simplicity, the active TC season in each basin is assumed to be of 4 months duration. For the two Southern Hemisphere basins, the first half of the season is defined as December and January, while for the Northern Hemisphere, July and August are used. The second half of the season is February and March for the south, and September and October for the north. Observations lying outside of these months are not considered in this analysis. The north Indian Ocean Basin is not included in this particular analysis because its active TC season does not contain an obvious single peak around which the season could be divided into halves.
The period of recovery of SST is less variable between TSCs (Fig. 13) than they were for WSCs (Fig. 11). As the translation speed increases, the percentage of time series with recovery periods of 1–5 days increases from 41% (TSC1) to 50% (TSC4). When the TC passes over the ocean surface rapidly, there is less impact and the SST is more able to return to its pre-TC state. The change to more frequent 1–5-day recovery periods with increasing translation speed is largely balanced by a reduction in the 6–30-day periods of recovery. Overall, there is only a very small change (89%–87%) in the 1–30-day recovery period. Although the magnitudes of the percentages are different for the e-folding data, the trends across TSC classes are of similar magnitude in both Figs. 13a and 13b.
5. Intraseasonal variations In this section we compare TC-induced changes in SST at different times within the active TC season relative to the seasonal background SST signal that is itself continually
FIG. 12. Global mean local minimum SSTAs corresponding to the TSCs defined in Table 3. For comparison, the corresponding relationships for the six individual basins are also plotted (dashed), showing general agreement with the global mean (thick black).
3806
MONTHLY WEATHER REVIEW
FIG. 13. (a) Percentage occurrence of classes of recovery time (defined in Table 1) for each TSC defined in Table 3. Recovery classes 1 (1–5 days, thin solid line), 2 (6–30 days, thick solid line– dots), 1 1 2 (1–30 days, dashed), and 3 (.30 days, dot–asterisk) are shown. (b) As in (a), but for e-folding period.
The mean time series of the SST anomaly from 60 days before to 60 days after the arrival of the TC, representing the first and second halves of the season, are shown by Figs. 14a and 14b, respectively. There is a partial and relatively rapid recovery in SST after the initial reduction, and a more gradual recovery over a period of about 30 days toward the climatological values. When compared with the range of SSTs present in the seasonal cycle patterns from 260 to 160 days (covering a period of approximately 4 months), the mean reduction in SST of approximately 218C near day 0 is obviously not a minor change. Further, these are mean patterns, and some individual time series exhibit much larger variations in SST over the same period. During the first half of the TC season (Fig. 14a), the SST is still climbing toward its seasonal peak from days 260 to 210. Following the TC-induced drop in SST, it recovers over about 20–30 days, and then the SST continues to climb, exceeding the prestorm value. More than 1 month after the TC’s arrival, the SST begins to decline gradually. For the second half of the season (Fig. 14b), the SST increase from day 260 to 210 is very gradual compared
VOLUME 139
FIG. 14. Mean time series of SST anomaly (computed relative to SST 1 week before the TC’s arrival rather than using the difference from climatology) for (a) the first 2-month half of the active TC season and (b) the second 2-month half of the TC season.
with the first-half season case. By this time of the season, the background SST has already reached close to its mean seasonal maximum. After the SST reduction around day 0, it takes 5–10 days to make a partial recovery, but it does not reach or exceed prestorm values. At this late stage of the season, the SST is heading toward, or undergoing, seasonal cooling, and therefore any recovery in SST is not supported by processes driving the large-scale seasonal cycle. After about 20–30 days, this SST time series appears to blend into the inevitable seasonal drop in SST. If some minimum value of SST, such as 268C, is required to support the genesis and intensification of TCs, then the time series for the second half of the season implies that TCs themselves may shorten the season of TC activity. In contrast, the SST did recover during the first half of the season. This effect will vary depending on the magnitude of the SST reduction relative to the pre-TC SST, and on the area of ocean affected. If there is not an opportunity for processes associated with the seasonal cycle to recharge the TC-induced reduction in SST, then the reduced SST will not fully recover but will contribute to an early decline in the SST at the locations affected by the TC. Although these results are specifically relevant to the SST reduction associated with the inner core of the TC, it is expected that wider and cooler wakes located
DECEMBER 2011
DARE AND MCBRIDE
adjacent to the TC track would vary from the seasonal SST cycle in a similar manner.
6. Concluding remarks We have documented the response of the upper oceans to tropical cyclones through compositing on a 0.258 3 0.258 latitude by longitude grid the climatological anomalies of sea surface temperature before and after tropical cyclone passage. Two primary variables have been defined as measures of the sea surface temperature response: sea surface temperature reduction and sea surface temperature recovery time (Figs. 1 and 2). The main findings of this research are as follow: d
d
d
d
d
d
d
There is a variability in the time of maximum cooling with respect to cyclone passage, with maximum cooling observed between day 21 to day 17 relative to cyclone passage. The most common occurrence of the maximum sea surface temperature reduction is 1 day after cyclone passage (Figs. 3 and 4a). When compositing is carried out relative to the day of maximum cooling, the global average response to cyclone passage is an SST reduction of 0.98C (Figs. 4b and 5b). The recovery of the ocean to cyclone passage is generally quite rapid, with 44% of the data points recovering to the precyclone sea surface temperature within 5 days and 88% of the data points recovering within 30 days or less (Fig. 7 and Table 1). Both the full recovery time and e-folding period have been used to assess the recovery time of SST following passage of the cyclone. The two methods are generally useful and have provided consistent results in this study. The global average local minimum SSTA for weak storms is approximately 20.78C, while that for intense systems with sustained winds exceeding 44 m s21 is 21.48C (Table 2 and Fig. 10). Stratifying by cyclone translation speed, it is found that the slowly moving systems (less than 8 km h21) have an average minimum SSTA of 21.18C compared with 20.758C for rapidly moving systems (Table 3 and Fig. 12). Moderate rates of recovery occur frequently with over 80% of time series recovering within 30 days for all basins (Fig. 9), all intensity classes (Fig. 11), and all translation speed classes (Fig. 13). For the most intense systems, approximately 27% have rapid recovery rates (1–5 days), while 52% of the weakest systems recovery rapidly (Fig. 11). Approximately 40% of slow-moving systems recover in 5 days or less, while 50% of fast-moving systems recovery rapidly (Fig. 13).
d
3807
The tropical cyclones have a large impact on the seasonal cycle of ocean surface temperature. Cyclones occurring in the first half of the season disrupt the seasonal warming trend, which is not resumed until 30 days after cyclone passage. Conversely, cyclone occurrences in the latter half of the season bring about a mean temperature drop of approximately 0.58C, from which the ocean does not recover due to the seasonal cooling cycle (Fig. 14).
Overall, our results validate the suppositions of Emanuel (2001) and Hart et al. (2007) that tropical cyclones can have a major impact on the heat balance of the underlying oceans. From the case studies and modeling studies in the literature it is clear that the major physical cause of the cooling is subsurface mixing with water from below the thermocline, rather than enhancement of ocean to atmosphere enthalpy fluxes. The restoration to climatological values is believed to be through net surface flux. There are a number of further directions in which this study can go. It is likely that this approach has underestimated the impacts of tropical cyclones on sea surface temperature as it is has documented the sea surface temperature response immediately beneath and behind the cyclone. A number of Northern Hemisphere case studies have found the major response is to in the rear and to the right of the cyclone’s passage. Hence, further work would include a documentation of the response on a cyclonecentered grid oriented toward the direction of motion. Through choosing a global examination this study has been exploratory, aimed at determining the overall magnitude of the signal and its global character. It is planned in a future study to examine an individual basin in greater detail to obtain a better understanding of the precise large-scale conditions leading to slow temperature recovery rates. It would also be useful to carry out further case studies employing oceanographic observations as well as coupled tropical cyclone models. A striking result from the current study is that both rapid and slow recoveries can occur across the range of cyclone intensity classes and the range of translation speeds. Further observational and modeling research is recommended to determine the conditions governing slow versus fast recovery. Acknowledgments. We are grateful for the expert assistance provided by Dr. Helen Beggs, Ilia Bermous, Robin Bowen, Dr. Noel Davidson, Jin Lee, Tan Le, Les Logan, and Arnold Mavromatis. We thank Drs. Paul Sandery and Kevin Tory for their constructive comments on the manuscript. We also thank the three anonymous reviewers for their insightful comments. This work was supported by the Government of Western Australia’s Indian Ocean Climate Initiative.
3808
MONTHLY WEATHER REVIEW REFERENCES
Bender, M. A., I. Ginis, and Y. Kurihara, 1993: Numerical simulations of tropical cyclone–ocean interaction with a high-resolution coupled model. J. Geophys. Res., 98, 23 245–23 263. Berg, R., 2002: Tropical cyclone intensity in relation to SST and moisture variability: A global perspective. Preprints, 25th Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Amer. Meteor. Soc., 16C.3. [Available online at http:// ams.confex.com/ams/pdfpapers/37899.pdf.] Black, W. J., and T. D. Dickey, 2008: Observations and analyses of upper ocean responses to tropical storms and hurricanes in the vicinity of Bermuda. J. Geophys. Res., 113, C08009, doi:10.1029/ 2007JC004358. Brand, S., 1971: The effects on a tropical cyclone of cooler surface waters due to upwelling and mixing produced by a prior tropical cyclone. J. Appl. Meteor., 10, 865–874. Chang, S. W., and R. V. Madala, 1980: Numerical simulation of the influence of sea surface temperature on translating tropical cyclones. J. Atmos. Sci., 37, 2617–2630. Chen, S. S., J. F. Price, W. Zhao, M. A. Donelan, and E. J. Walsh, 2007: The CBLAST-Hurricane Program and the nextgeneration fully coupled atmosphere–wave–ocean models for hurricane research and prediction. Bull. Amer. Meteor. Soc., 88, 311–317. Cione, J. J., and E. W. Uhlhorn, 2003: Sea surface temperature variability in hurricanes: Implications with respect to intensity change. Mon. Wea. Rev., 131, 1783–1796. ——, P. Molina, J. Kaplan, and P. G. Black, 2000: SST time series directly under tropical cyclones: Observations and implications. Preprints, 24th Conf. on Hurricanes and Tropical Meteorology, Fort Lauderdale, FL, Amer. Meteor. Soc., 1A.1. D’Asaro, E. A., T. B. Sanford, P. P. Niiler, and E. J. Terrill, 2007: Cold wake of Hurricane Frances. Geophys. Res. Lett., 34, L15609, doi:10.1029/2007GL030160. Dash, P., A. Ignatov, Y. Kihai, and J. Sapper, 2010: The SST Quality Monitor (SQUAM). J. Atmos. Oceanic Technol., 27, 1899–1917. Davis, C., and Coauthors, 2008: Prediction of landfalling hurricanes with the Advanced Hurricane WRF model. Mon. Wea. Rev., 136, 1990–2005. Emanuel, K., 2001: Contribution of tropical cyclones to meridional heat transport by the oceans. J. Geophys. Res., 106, 14 771– 14 781. ——, C. DesAutels, C. Holloway, and R. Korty, 2004: Environmental control of tropical cyclone intensity. J. Atmos. Sci., 61, 843–858. Fisher, E. L., 1958: Hurricanes and the sea-surface temperature field. J. Meteor., 15, 328–333. Hart, R. E., 2011: An inverse relationship between aggregate Northern Hemisphere tropical cyclone activity and subsequent winter climate. Geophys. Res. Lett., 38, L01705, doi:10.1029/ 2010GL045612. ——, R. N. Maue, and M. C. Watson, 2007: Estimating local memory of tropical cyclones through MPI anomaly evolution. Mon. Wea. Rev., 135, 3990–4005. Hazelworth, J. B., 1968: Water temperature variations resulting from hurricanes. J. Geophys. Res., 73, 5105–5123. Jansen, M. F., R. Ferrari, and T. A. Mooring, 2010: Seasonal versus permanent thermocline warming by tropical cyclones. Geophys. Res. Lett., 37, L03602, doi:10.1029/2009GL041808.
VOLUME 139
Knapp, K. R., and M. C. Kruk, 2010: Quantifying interagency differences in tropical cyclone best-track wind speed estimates. Mon. Wea. Rev., 138, 1459–1473. ——, ——, D. H. Levinson, and E. J. Gibney, 2009: Archive compiles new resource for global tropical cyclone research. Eos, Trans. Amer. Geophys. Union, 90, 46, doi:10.1029/ 2009EO060002. Leipper, D. F., 1967: Observed ocean conditions and Hurricane Hilda, 1964. J. Atmos. Sci., 24, 182–196. Lin, I., and Coauthors, 2003: New evidence for enhanced primary production triggered by tropical cyclone. Geophys. Res. Lett., 30, 1718, doi:10.1029/2003GL017141. ——, C.-C. Wu, and I.-F. Pun, 2008: Upper-ocean thermal structure and the western North Pacific category 5 typhoons. Part I: Ocean features and the category 5 typhoons’ intensification. Mon. Wea. Rev., 136, 3288–3306. ——, I.-F. Pun, and C.-C. Wu, 2009: Upper-ocean thermal structure and the western North Pacific category 5 typhoons. Part II: Dependence on translation speed. Mon. Wea. Rev., 137, 3744– 3757. Nelson, N. B., 1996: The wake of Hurricane Felix. Int. J. Remote Sens., 17, 2893–2895. Price, J. F., 1981: Upper ocean response to a hurricane. J. Phys. Oceanogr., 11, 153–175. ——, J. Morzel, and P. P. Niiler, 2008: Warming of SST in the cool wake of a moving hurricane. J. Geophys. Res., 113, C07010, doi:10.1029/2007JC004393. Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution blended analyses for sea surface temperature. J. Climate, 20, 5473– 5496. Sandery, P. A., G. B. Brassington, A. Craig, and T. Pugh, 2010: Impacts of ocean–atmosphere coupling on tropical cyclone intensity change and ocean prediction in the Australian region. Mon. Wea. Rev., 138, 2074–2091. Schade, L. R., and K. A. Emanuel, 1999: The ocean’s effect on the intensity of tropical cyclones: Results from a simple coupled atmosphere–ocean model. J. Atmos. Sci., 56, 642–651. Shay, L. K., R. L. Elsberry, and P. G. Black, 1987: Mesoscale ocean temperature and current patterns induced by hurricanes. Preprints, 17th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 388–392. ——, P. G. Black, J. D. Hawkins, R. L. Elsberry, and A. J. Mariano, 1991: Sea surface temperature response to Hurricane Gilbert. Preprints, 19th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 574–578. Sobel, A. H., and S. J. Camargo, 2005: Influence of western North Pacific tropical cyclones on their large-scale environment. J. Atmos. Sci., 62, 3396–3407. Sriver, R. L., and M. Huber, 2007: Observational evidence for an ocean heat pump induced by tropical cyclones. Nature, 447, 577–580, doi:10.1038/nature05785. Trenberth, K. E., and J. Fasullo, 2007: Water and energy budgets of hurricanes and implications for climate change. J. Geophys. Res., 112, D23107, doi:10.1029/2006JD008304. Walker, N. D., R. R. Leben, and S. Balasubramanian, 2005: Hurricane-forced upwelling and chlorophyll a enhancement within cold-core cyclones in the Gulf of Mexico. Geophys. Res. Lett., 32, L18610, doi:10.1029/2005GL023716. Wendland, W. M., 1977: Tropical storm frequencies related to sea surface temperatures. J. Appl. Meteor., 16, 477–481.
