Literature Review On The Natural Variability Of Tropical Cyclones

Transcription

1 Literature Review On The Natural Variability Of Tropical Cyclones Ray Bell Apr Introduction Tropical Cyclone (TC) activity shows a range of variability on many different temporal and spatial scales shown in observations. Including variations of frequency (different TC intensities), location of genesis and lysis, landfalling, initial disturbances (African Easterly Waves, MJO, Monsoon depressions), associated rain rates, associated storm surge, maximum wind speed and structure. TC activity varies from one year to the next due to the internal variability of the large scale forcing of the climate system. Separating this variability from external forcing (CO2 inc.) is an essential aspect in my PhD, it is therefore important I get a good understanding of how TCs have varied in the past to help put my climate change results into perspective. More often than not there will be no TCs present around the world. However, there have been cases where 3/4 TCs are spinning within one basin. This short term modulation has been related to weather phenomenom such as the Madden-Julian Oscillation (MJO) (Madden and Julian, 1971). On a seasonal time scale the Atlantic basin, for example, has had as few as 4 TCs (e.g. 1983) compared to as most as 28 in These were related to the phase of the El Niño in the tropical Pacific, the local Sea Surface Temperatures (SST) in the tropical Atlantic and the phase of the Atlantic Meridional Mode (AMM) (see below) (Vimont and Kossin, 2007). These atmospheric and oceanic modes of variability are also related to other TC activity such as intensity. The variability of TCs on time scales from decades to centuries can be related to modes of variability on this time scale such as the Atlantic Multidecadal Oscillation (AMO) (Goldenberg et al., 2001) and the Pacific Decadal/Multidecadal Oscillation (PDO) (Mantua et al., 1997). It is still not clear if longer time scale modes of variability have an affect on TC activity as it is not possible to deduce it from the shortness of the observational record. Emanuel (1986) and Emanuel (1988) created Potential Intensity Theory (PI) to state the minimum mslp a hurricane can reach. However, PI is seldom used in statistical studies of TC variability and has also been challenged recently (Smith et al., 2008). For numbers of TCs there is no given probability of genesis given some large scale conditions, however, observations and statistical models show how TC activity generally corresponds to the large scale forcing. 1.1 Observations Our knowledge of the natural variability of TCs comes from years of different observational systems. There have been large changes in the operation systems present at the time to measure TCs since the early 1900s (Landsea et al., 1999) which have complicated trends in TC activity. Our best observations span back to the early 1970s with the introduction of remote sensing (Chan and 1

2 1 INTRODUCTION 2 Holland, 1989), however, before this time it has been shown many short lived storms especially away from the coast were missed. Another factor of confusion comes from changing practices of different centres defining TCs in different ways, especially between basins and over time (Landsea et al., 2010). The diagram below shows the types of observations available at the time of measurements (figure 1). Figure 1: Change in TC observations, data processing and communications since 1850 (Mcadie et al., 2009). Ship logs were used prior to the 1960s, but storms were likely to have been missed and there are large biases globally as the Atlantic was the most sailed basin. Aircraft reconnaissance since the 1950s was fairly limited to certain basins. The missions used dropsondes and related maximum wind speeds to the minimum surface pressure values with some empirical relationship which has evolved over time and there are still questions regarding the accuracy of the data prior to 1973 (start of the satellite data) (Landsea, 2000) and (Langford and Emanuel, 1993). There has been a great deal of research to use any existing data to extend the TC record back in time as far as possible. Mcadie et al. (2009) have used ship logs, newspaper articles and damage reports to create a climatology of TCs back to 1851 (any data before this period is believed to have serious limitations). Figure 2 shows TC frequency in the North Atlantic since However, accurate measurements of TC intensity came much later. 1.2 TC activity measurements ACE Accumulated Cyclone Energy (ACE) index can be defined as the energy used by a TC over its lifetime and is calculated every 6 hours. ACE = 10 4 Σv 2 max the units are 10 4 kt 2. This is usually summed up within basins for seasons. If a TC happens to cross years, its ACE counts for the previous year (Bell et al., 1999). The index is believed to not

3 1 INTRODUCTION 3 Figure 2: TC counts in the NAtl since 1851 (Mcadie et al., 2009). favour long weak storms compared to strong short lived storms. The global ACE index since the early 1970s is shown in the figure below and shows a current global inactivity (Maue, 2011). Figure 3: Global ACE index since 1971 (Maue, 2011). This gives additional information on top of the frequency which is often presented (figure 4). In many seasons, a small number of storms account for the majority of the season s ACE e.g. Atlantic 2010 season: Danielle, Earl, and Igor accounted for 90 of the 170 ACE units or 53

4 2 PALEOTEMPESTOLOGY 4 Figure 4: Global TC count since 1971 (Maue, 2011) PDI Another measurement for TC activity is the Power Dissipation Index (PDI) (Emanuel, 2005). This was created from the parameter Power Dissipation which is of direct interest from the point of view of tropical cyclone contributions to upper ocean mixing and the thermohaline circulation. The PDI uses frequency, duration and intensity as a measure of the storms energy. This differs from ACE as Emanuel (2005) states the ACE index does not focus on landfalling??? PDI is defined as PDI = τ 0 V 3 max Where Vmax is the maximum wind speed at any time in the storm (10m height) and tau the lifetime of the storm. The units are m 3 /s 2. The environmental factors which influence PDI can be found in (Emanuel, 2007) and its relation to ocean heat transport in (Emanuel, 2001). The variations in ACE and PDI show large variability between each other from year to year (Bell et al., 1999). It is argued the ACE index corresponds well to the large scale external forcing factors such as ENSO on TCs (Camargo and Sobel, 2005) whereas, the PDI is more related to the local SSTs. The PDI is also useful for landfalling information as monetary loss from wind storms rises proportionally to the cube of the wind speed (Southern, 1979) as does the total power dissipation (Emanuel, 1998). 2 Paleotempestology Paleotempestology is the study of past TC activity on a paleo time scale. Most paleo studies require a proxy to diagnose the TC activity present at the time and its relation to TC activity. There is also an increasing number of studies simulating past climates and their TCs from our best estimates of the large scale forcing factors present at the time. Geological/Biological proxies for TC activity include (Taken from Landsea (2000):

5 3 CENTINNEAL VARIABILITY 5 Shallow coastal lake bed cores with relation to storm surge sand layers (Liu and Fearn, 1993). Cyclone-produced sediment deposits in shallow offshore waters (Keen and Slingerland, 1993). Pollen changes recorded in coastal forest floors due to canopy blow down (Bravo et al., 1997). Oxygen isotope variations found in coastal cavern stalactites (Malmquist, 1997) - due to the characteristic of primarily the most intense tropical cyclone rainfall having quite low oxygen-18 isotope concentrations compared with other types of local rainfall (Lawrence and Gedzelman, 1996). A more extensive list of Paleotempestology studies can be found at ( Liu and Fearn (1993) suggest one particular coastal location in Alabama had strikes of Cat 4 - Cat 5 hurricanes around the centuries 1400bc, 800bc, 200bc, 700 and 1300 giving a period of years for major hurricane strikes. Before 3400 years ago hurricanes cannot be diagnosed in the sediment record in this study as either the climate did not allow for strong hurricanes to be formed or the tracks did not pass over this location, although the geomorphology may have changed and limits this study. A limitation of paleo studies is that they need to be calibrated with TC activity in recent sediments, however, they still give us a good glimpse into the past TC activity. 2.1 Simulated Paleo Activity Fedorov et al. (2010) simulated TC activity in the early Pliocene Epoch (5 million years before present). A period with much warmer temperatures than today and similar CO2 concentrations - our best analogue to future greenhouse conditions. Figure 5 shows hows tracks differ from the early Pliocene to modern day. They found TCs in the Central Pacific (CPac) maintained the permanent El Niño. I assume in glacial periods the equator-pole temperature difference would have been large, therefore have less TCs present to help transfer heat to the poles. I can only guess an increase in TC activity would have had a helping hand in the transitional phase from glacial to interglacial periods. 3 Centinneal Variability 3.1 Observations There has been little success extending the TC dataset beyond 1851, simply due to the lack of information. There have been records for landfallings of hurricanes back to the 16th Century along a few US coastal locations by Ludlum (1989) using newspaper articles and ship logs as well as landfallings in South China from historical records (Liu et al., 2001). Other studies have also logged individual TCs pre-1900 in other basins (Evans, 1848) (CPCH, 2010). A time series of the Atlantic Multidecadal Oscillation has been extended back to 1550 using tree ring data (figure 6) (Gray et al., 2004) which has a strong influence on TCs in the Atl. Early records and theories were produced by some prominent TC researchers (Redfield, 1831) (Reid, 1838). However, any quantitative observations from this period can hardly be compared to todays unprecedented measurements of TC activity.

6 3 CENTINNEAL VARIABILITY 6 Figure 5: TC tracks simulated by the statistical downscalling model. The tracks shown in each panel are a 2 year subsample of 10,000 simulated TCs (Fedorov et al., 2010). Figure 6: Index of AMO constructed from tree ring data (Gray et al., 2004). 3.2 Simulations HiGEM offers 150 years at present day conditions and provides the ability to start assessing centinneal variability of TCs (Shaffrey et al., 2008). However, due to resolution constraints no category 4 or category 5 s are produced in HiGEM???. Delworth and Mann (2000) simulate multi-century integrations of the GFDL model (Geophysical Fluid Dynamics Laboratory) with a coarse resolution of atm. 7.5 o long and 4.5 o lat and the ocean component. 3.7 o long x 4.5 o lat. A Fourier transform on the time series shows there is a spectral peak in energy from the Atl SST and SLP fields around 70 years. Unfortunately the observed record does not go back this far and this mode of variability may not be an a strong influence on TCs. Long integrations of high resolution models are needed with some knowledge of past observations.

7 4 NORTH ATLANTIC BASIN Mechanisms There are likely to be centennial variability in some ocean dynamics such as the AMOC, subtropical gyres and planetary waves due to their slow moving nature and long residence time (Liu and Alexander, 2007) (figure 7). There are long time scale variability in atmospheric dynamics such as the location of the ITCZ and strength of the trade winds (Black et al., 1999). However, these may be related to centinneal variability of SST which will directly affect TC activity (Delworth and Zeng, 2008). However, it is still unknown if TC activity is forced by these modes of variability. Figure 7: Schematic for the major branches of climatic teleconnections in the atmosphere and ocean (Liu and Alexander, 2007). 4 NORTH ATLANTIC BASIN 4.1 North Atlantic Basin Statistics The table below shows the average Atlantic basin hurricane statistics for (Landsea, 2000). (figure ). For a more recent base period see figure 9. Figure 8: Average TC statistics in each basin and global, (Landsea, 2000).

8 4 NORTH ATLANTIC BASIN 8 Figure 9: Average TC statistics in each basin and globally in brackets, with a recent base period of (Masters, 2011). See figure 2 for information on frequency of TCs since 1851 and the related ACE index is shown in the figure 10. Figure 10: ACE index of TC activity in the NAtl since 1851 (Landsea, 2010). Maue (2011) provides the most up to date statistics of the Atlantic basin and updates his website regularly. A shift towards proportionally deep tropical systems began in the early to mid-1980s more than 10 year prior to the 1995 era of heighten activity (Kossin et al. 2010). Kossin et al.(2010) also looks at changing activities of different types of TCs. Largest increase in strong TCs which cause greatest damage. The increase in these storms is due to increase SSTs in the genesis location South and East. Large part in global ACE activity from (Maue et al. 2011)

9 4 NORTH ATLANTIC BASIN 9 Figure 11: TC count in the NAtl since 1945 (Maue, 2011). Figure 12: ACE index in the NAtl since 1950 (Maue, 2011). 4.2 Multi-Decadal Variability - AMO The North Atlantic has a mode of multi-decadal variability known as the Atlantic Multidecadal Oscillation (AMO). This is shown by decadal variability of SST in the North Atlantic between the Equator and Greenland (Schlesinger and Ramankutty, 1994) Klotzbach and Gray (2008) with cool and warm phases that may last for years at a time and cause a difference of about 0.5 o C between extremes. The AMO is related to variability in the Atlantic Meridional Overturning Circulation (AMOC). When the AMOC is stronger the warm SSTs in the equatorial North Atlantic are transported further Northward and vice versa with a weaker AMOC. What drives the variability of AMOC is still a subject of debate.

10 4 NORTH ATLANTIC BASIN 10 The AMO can be calculated from the first EOF of Natl SST (figure 13) (Goldenberg et al., 2001). Figure 13: First EOF of SST in the NAtl,(Goldenberg et al., 2001). The current warm phase is expected to last until and Enfield and Cid-serrano (2009) believe it will persist until Affect On Hurricanes The number of TSs that can mature into severe hurricanes is much greater during the warm phase than the cool phase, at least twice as many and the frequency is also increased. Klotzbach and Gray (2010) show this change in TC activity between (AMO +), (AMO -) and (AMO +). TC tracks in the Caribbean Sea are also more common (Goldenberg et al., 2001). It is believed hurricane damage is increased up to 5 times during AMO warm phases (Landsea et al., 1999). 4.3 Multi-decadal Variability - Sahel Rainfall Long term variations in the Saharan Air Layer (SAL) and vertical wind shear in the MDR are strongly correlated with Sahel rainfall. The drying of the Sahel corresponds to a equatorial shift in the African Easterly Jet (AEJ) and African Easterly Wave (AEW) activity which introduces the dry SAL to lower latitudes and increases the MDR wind shear (Wu and Tao, 2010). There is believed to be a reduction in wind shear of up to 7m/s when the Sahel rainfall is high (Goldenberg et al., 2001). The Indian Ocean is related to Sahel drying and can influence TC activity in the NAtl by increasing upper atmospheric temperatures anomalies, from the SSTs, showing the influence of remote SST changes on local TC activity (Vecchi and Soden, 2007). Smith et al. (2010) successfully replicated the multi-decadal variability of TCs in the Atl as well as Sahel rainfall using decadal integrations of the HadCM3 model hindcast. Zhang and Delworth (2006) also simulated the Sahel rainfall pattern which gave them good TC numbers in the Atlantic (Figure 14)

11 4 NORTH ATLANTIC BASIN 11 Figure 14: The colour shading is low-pass filtered and the green line unfiltered. (Zhang and Delworth, 2006). 4.4 Interannual Variability - ENSO El Niño activity in the tropical Pacific is known to be a strong modulator of hurricanes in the Atlantic. The El Niño or La Niña event changes the location of the Walker cell across the Equator, which mainly influences hurricanes by causing enhanced or reduced wind shear in the MDR and the Caribbean Sea (Madl, 2000) (Goldenberg and Shapiro, 1996) (Knaff, 1997) (figure 15). Along with the AMM strongly modulates the deep tropical systems (largest influence of PDI) (Kossin et al. 2010) Figure 15: Location of the Walker circulation in La Niña conditions, (Madl, 2000). La Niña years produce more hurricanes that El Niño years and similarly hurricanes are more intense in La Niña years and prolonged La Niña events (Lander and Gaurd, 1998). Knaff (1997) argues that ENSO has thermodynamics affects in the Atlantic such as increased mid level dryness during El Niño events (result of tropical upper tropospheric trough cause lower pressure in Atl, dec. subsidence, more mid level moisture and weaker trade wind inversion which leads to suppressed convection, less mid level IR, influence p again via. TUTT) cooling). Tang and Neelin (2004) state ENSO increases the static stability in the Atlantic as the upper tropospheric temperature are increased during El Niño events. However, some studies have shown it is more of the dynamics forcing rather than the thermodynamics caused by ENSO (Camargo et al., 2007b).

12 4 NORTH ATLANTIC BASIN 12 The correlations of the number of intense hurricanes with ENSO indices are higher than for weaker storms (Gray et al., 1993) (Landsea et al., 1999). The table below shows there is generally a decrease in all hurricanes of varying intensity in El Niñoyears (figure 16) (DAS, 2010). Figure 16: How ENSO affects TCs of different strength in the NAtl basin (time period of data not found???), (DAS, 2010) The year after an El Niño year usually has a large number of TCs such as the 2005 season with 28 TSs (Gray, 1984). Mehta (1998) uses a Fourier transform on Atlantic net tropical cyclone index (definition?) and shows there is strong peak at approx 3 years, as well as, a peak in the spectrum at 8 years (see figure. 17) showing the longer time scale variability ENSO induces on Atlantic hurricanes. Bell and Chelliah (2006) relate interannual variability and multidecadal variability of TC activity to ENSO and two Tropical Multidecadal Modes (TMM). Figure 17: Time series (left) and Fourier spectrum (right) of net tropical cyclone index, (Mehta, 1998) Strong TCs going w ward links to La Niña and + AMM less mature La Niña. Cluster 4 more N ward strong TCs are linked to cool SSTs in the EPac (upwelling equatorial ocean Rossby waves forced by eastern boundary reflections of equatorial Kelvin waves (Kossin et al. 2010). More mature La Niña Type of ENSO canonical El Niño = cold tounge, warming in EPac (Rasmusson and Carpenter, 1982) (classic) and central Pacific warming with major SSTa shifted w ward neat the warm pool (Yeh et al, 2009). During past 2 decades the occurence of CPac warm (El Niño Modoki) have increased beginning with the prolonged El Niño event with cold tounges have become less frequent (Lee and McPhaden, 2010). Ren and Jin (2011) created an index to seperate these. Kim et al (2009) looked at Modoki on Natl TCs.

13 4 NORTH ATLANTIC BASIN Landfalling There is an increased probability of hurricane landfalling in the US in La Niña years (O Brien et al. (1996) Bove et al. (1998) Pielke and Landsea (1999) Larson et al. (2005)). The probability of 2 or more hurricane landfalls during a La Niña event is 66 percent compared to 28 percent in an El Niño event. On a spatial scale it is believed landfalling in the East Coast (Georgia to Maine) varies with ENSO more than in Florida and the Gulf Coast (Smith et al., 2007) and even between certain Southeast states (Xie et al., 2002a). 4.5 Interannual Variability - AMM The Atlantic Meridional Mode (AMM) is the leading mode of coupled ocean-atmosphere variability in the tropical Atlantic and is associated with meridional displacements of the ITCZ, SST and winds (Chiang and Vimont, 2004). The AMM is thought to be excited by the AMO and also the NAO (Vimont and Kossin, 2007) (figure 18). Also known as the Atlantic dipole mode. Max variability in spring but still variability during summer. Figure 18: Tropical cyclogenesis points for the five strongest and five weakest AMM years, superimposed on SST (shaded) and shear (contour) anomalies. Crosses show the genesis points for all storms that reached TS strength, those that reach MH strength also have a circle around their genesis point, (Vimont and Kossin, 2007) With the AMM in a + phase there is a shift in genesis to the east and equatorward, as well as, an increase in activity. Shift southwards as well into the deeper tropics. It also causes an increase in storm duration and intense hurricane frequency. This mode implicitly accounts for Sahel rainfall and the AMO and provides a physically based framework for interpreting a variety of influences on Atlantic hurricanes on a range of time scales (Camargo et al., 2010). Warm SSTa in MDR and -SSTa in South Atl - northward shift of Atl ITCZ Slow variation of the AMM reflects the decadal variability that is generally described by the AMO but also comprises of interannual variability that is well correlated with TC activity.

14 4 NORTH ATLANTIC BASIN Interannual Variability - QBO The Quasi-biennial oscillation (QBO) is a global-scale, zonally symmetric oscillation of the zonal winds in the equatorial stratosphere (Wallace, 1973) which owes its existence to wave-mean flow interaction (?). The QBO has a period of 26 months and its largest amplitude occurs near 30 hpa. The QBO had a robust relationship with intense hurricane days (Gray et al., 1992). The number of intense hurricane days which occurred in the westerly phase was double that than in the easterly phase. The relationship since has not been statistically significant (Carmago and Sobel, 2010). It is still not fully understood how the QBO influences hurricanes in the Atlantic. Gray et al. (1992) believe the QBO modulates wind shear, however, the wind shear is occurring very high up in the atmosphere and may show how sensitive TCs are to wind shear even at their very top. Shapiro (1989) postulate the difference in tropospheric and stratospheric winds influencing pre-cyclogenesis easterly waves and states the QBO westerly phase results in a warm lower stratosphere which would reduce PI. 4.7 Interannual-Intraseasonal Variability - NAO The North Atlantic Oscillation (NAO) relates to a distribution of atmospheric mass between the Icelandic low pressure centre and the Azores high pressure centre, throughout the year, which oscillates between two extreme phases given by the pressure gradient (Hurrell and Deser, 2009). This can influence TC activity by affecting the steering flows of the TCs especially around the Azores high. However, the NAO is much weaker in summer compared to winter. Storms tracking westward along the southern flank of the ridge tend to turn northward through the weaking high. When NAO is - the western portion of the ridge is eroded and tracks tend to move northward through the eroded region (Kossin et al. 2010). Modulates storms that form N and W of their Cape Verde counterparts and closer to the NAO centres of action. Cluster 1 storms increase as NAO goes - (Kossin et al. 2010). 4.8 Intraseasonal Variability - MJO The Madden-Julian oscillation (MJO) is the strongest intraseasonal modulation of TCs across the tropics with a period of days. It consists of large-scale coupled patterns of convection which moves eastwards and influences many atmospheric variables. The deep convection is mainly confined to the Indian Ocean and West Pacific and stronger in boreal winter. The MJO can influence genesis location when it is in its active phase over the EPac (w ly). 4 times more hurricanes form in the Gulf of Mexico and western Caribbean than in the suppressed phase (Maloney and Hartmann, 2000a). During the active phase cyclogenesis tends to be shifted northward to the Gulf of Mexico. Modulates Gulf of mexico storms. More storms when MJO active in phase 2+3 and 8+1 with below activity when in phase 6+7.

15 5 NORTHWEST PACIFIC Interannual-Intraseasonal Variability - Easterly Wave Variability African Easterly Waves (AEW) has considerable interannual variability, in terms of number, intensity and tracks. The low frequency variability (decadal) is also correlated with Atlantic TC activity. However, on a interannual time scale the correlation of the number of AEWs are not correlated with TCs (Hopsch et al., 2007). The AEWs that form N of the jet are less effective at initiating cyclogensis and must generally track farther westward through a favorable environment before developing a warm core circulation. Affected by ENSO and AMM through upper level wind shear modulation (Maue et al. 2011). 5 NORTHWEST PACIFIC 5.1 Northwest Pacific Basin Statistics A time series of TS numbers in the NWPac is shown below (figure 19) (Chan and Liu, 2004). Average statistics can be found in the tables at the top (figure 2 and 3). Figure 19: Time series of the number of Typhoons (Chan and Liu, 2004). Shift in Pacific climate of and 1989 (Yeh et al, 2011). 5.2 Multi-decadal Variability - PDO Any results on this time scale must be interpreted with great caution due to the shortness of the data record. Observations in the NWPac are unreliable before the 1950s and perhaps even before the 1970s (Wu et al., 2006). Current Cold phase and 2 strong La Niña since The Pacific Decadal or Multidecadal Oscillation (PDO) in the dominant modes of low frequency variability in the Pacific Ocean (figure 20). The variability has a period of approximately years and is associated with SSTa in the extra-tropics (north of 20N). Each phase has an influence on ENSO and affect SST and SSH across the basin (Mantua et al., 1997). It is believed the mechanism behind this long time scale variability is caused by the North Pacific Ocean gyre circulation changes and related to ENSO teleconnections through the Hadley cell.

16 5 NORTHWEST PACIFIC 16 Figure 20: SST, slp and wind stress anom during warm and cool phases of the PDO, (Mantua, 2000) Affect On Typhoons The frequency of intense typhoons is believed to have a strong multi-decadal variability of years as Chan (2008) showed using wavelet analysis. He explains the variability in moist static energy (what is this?), smaller wind shear and the steering flows are more favourable for keeping TCs in lower latitudes and causing them to become more intense, these relate to the + phase of the PDO and ENSO. Matsuura et al. (2003) using a hi-res GCM (T?) showed decadal variability of TCs which were explained by warmer SST, stronger rel. vor. and more divergent outflow and 200hPa (include figs from this paper). Liu and Chan (2008) found TC tracks differ with the PDO and Ho et al. (2004) attribute the decadal changes with the westward expansion of the subtropical North Pacific High since Ho et al. (2004) also found the regions with the greatest inter-decadal variability are the East China Sea and Philippine Sea. 5.3 Multi-decadal Variability - NPGO North Pacific Gyre Oscillation (NPGO) (yeh et al. 2011). Decadal variations in this have been enhanced since 1989 and linked to SSTa similar to Modoki (DiLorenzo et al. 2010) 5.4 Interannual Variability - ENSO El Niño years result in more TC activity in the WPac (figure 21), although there seems to be no strong linear relation (Wang and Chan, 2002). A reduction in the number of TCs occurring in the summer following an El Niño event has also been found (Chan, 2000). ENSO has an important and well-documented impact on the mean TC genesis location, with a displacement to the southeast (northwest) in El Niño (La Niña) years (Wang and Chan, 2002) (See figure in Camargo(2010)). The displacement to the southeast in El Niño years allows typhoons to last longer and become more intense (Chan, 2007). The shift is genesis location has been commonly attributed to the dynamics factors of; eastward extension of the monsoon trough and westerlies (assoc with inr. cyc. low level vor.) and the reduction of wind shear near the dateline, which enhances genesis to the east of the climatological point (Wang and Chan, 2002). Camargo et al. (2007a) also explain the shift due a decrease in mid-level RH near the Asian continent during El Niño years. ENSO can also influence the track lengths and shapes. In El Niño years TCs have a

17 5 NORTHWEST PACIFIC 17 Figure 21: TC tracks in El Niño years and La Niña years tendency to form in the NE and reach more n ward latitudes as El Niño influences the steering flow via deepening of the east Asian trough (what is this?) in the midtrop (Wu and Wang, 2004). Zuki and Lupo (2008) found TCs affect the southern South China Seas more during La Niña years. Whilst TCs affect the CPac more in El Niño years which reach the NWPac (Clark and Chu, 2002). Pacific ACE (56% of global average) is well corellated with MEI. However, during the 1980s Pacific ACE has lass variation than PDO or MEI (Maue et al. 2011). Yeh et al (2011) noted NWPac TC freq and tropical Pacific SST indices varied greatly between and due to evolving Nino3 and Nino4 regions Landfalling ENSOs influence on TC tracks in the NWPac is also manifested in landfall rates throughout the region (Saunders et al., 2000) (Elsner and Liu, 2003). Wu et al. (2004) found a significant relationship with late season landfalls over China and ENSO (less landfalls in El Niñoyears except Japan the Korean peninsula). Fudeyasu et al. (2006a) founds an increase in landfalls in the Korean Peninsula and Japan during the early monsoon and in the Indochinese peninsula during the peak monsoons months in El Niñoyears. The SLP difference between Mongolia and west China, as well as SST over the NWPac explain the landfalls in North (south) China during La Niña(El Niño)??????? (Fogarty et al., 2006). 5.5 Interannual Variability - QBO Chan (1995) found during the w ly phase there are more Typhoons in the NWPac, attributed to a decrease in the upper-trop wind shear (Chan, 2005).

18 6 NORTH EAST PACIFIC Intraseasonal Variability - MJO The MJO modulate TCs in the NWPac and the genesis is favoured during the active phase of deep convection and changes the distribution of the tracks (Camargo et al., 2007a). The active phase enhances mid level humidity, low-level vor. and seeds more initial disturbances. The modulation of wave accumulation is also believed to be important (Sobel and Maloney, 2000). Idealized numerical calculations show how through dry dynamics the MJO-related circulation anomalies can amplify mixed Rossby-gravity waves and change their structure to be tropical depression types (Aiyyer and Molinari, 2003) which affects genesis. 5.7 Other Factors Of Variability Other factors thought to influence TC variability include: Tibetan plateau snow cover (Xie, L. Yan, 2007). NPac sea-ice cover (Fan, 2007). large-scale circulation in the extra-trop SH (Wang and Fan, 2007). Asian-Pacific Oscillation (APO) (Zhou et al., 2008) and North Pacific Oscillation (NPO) (Wang et al., 2007). 6 NORTH EAST PACIFIC The NEPac is the most active region of tropical cyclogenesis region per unit area (Molinari et al., 2000). Tropical cyclogenesis in the NEPac is influenced by wind surges (what are these?), AEWs, topographic effects, ITCZ breakdown, upper level PV and the confluence between monsoon westerlies and trade easterlies (see (Camargo et al., 2010) for references). 6.1 Northeast Pacific Basin Statistics See tables at top for average statistics. tables of TC activity for the NEPac can be see below and a time series of ACE index. 6.2 Multi-decadal Variability The hurricane activity in the NEPac had 2 inactive eras ( , ) and an active era ( ) shown by a Bayesian multiple charge-point analysis (Zhau and Chu, 2006). 6.3 Interannual Variability - ENSO There have been many studies investigating the influence of ENSO on TCs in the NEPac. Many of these studies find a robust relation between ENSO and various TC measurements. Frank and

19 6 NORTH EAST PACIFIC 19 Figure 22: EPac ACE index Figure 23: Average TC activity dependant on the type of season Figure 24: Average TC activity dependant on ENSO Young (2007) found a strong relation between storm counts and ENSO, Gray and Sheaffer (1991) showed there is an increase in intense hurricanes during El Niño and Camargo et al. (2008) found a westward shift in genesis locations which results in more TCs propagating into the central North Pacific region (Chu, 2004) (figure 25). Camargo et al. (2007b) used composites of a genesis potential index against the ENSO state (read this) and found wind shear is the main contributor as well as potential intensity (understand this). On a more regional basis it was shown east of 116W the conditions are nearly always favourable for TC formation, whereas to the west the conditions vary from year to year.

20 6 NORTH EAST PACIFIC 20 Figure 25: TC tracks around an El Niño event (Gray, 1984). Several studies have found a see-saw of TC activity with the North Atlantic and NEPac and thought a stronger anti-correlation exists for stronger TCs (Frank and Young, 2007). Is this the result of ENSO on the two basins differently or that one basin influences the other??? 6.4 Intraseasonal Variability - MJO The MJO has a strong modulation on TCs in the NEPac (figure 26) (Camargo et al., 2008). Whilst in the convective phase cyclonic anomalies and anomalous westerlies occur over the eastern Pacific and Gulf of Mexico. There are 4 times more TCs in the active phase than in the suppressed phase (Maloney and Hartmann, 2000b). Aiyyer and Molinari (2008) use idealized numerical calculations and show that in the active phase, easterly waves tend to propagate into the EPac, while during the suppressed phase they are steered into the Gulf of Mexico. Figure 26: Number of TCs as a function of MJO phase, May-Nov, , error bars are 95 percent (Maloney and Hartmann, 2000b). The barotropic dynamics can explain how the MJO influences the TCs in the NEPac. Barotropic energy conversion from the mean state to the eddies occurs during the convection phase of the MJO (Aiyyer and Molinari, 2008). Aiyyer and Molinari (2008) noticed in the active phase of the MJO, the vertical shear is relatively weak and TCs tend to form mainly within the ITCZ. In contrast,

21 7 CENTRAL NORTH PACIFIC 21 during the suppressed phase, the vertical wind shear greater than 10m/s and development is shifted northward, nearer the Mexican coastline. Additionally, the MJO is thought in influence the thermodynamics as Camargo and Barnston (2009) show midlevel RH to be important. consistent with the EPIC (East Pacific Investigation of Climate) field experiment on the MJO s influence on generalized ITCZ convection (Raymond et al., 2003) Landfalling In the active phase TCs form closer to the Mexican coast, increasing the chance of landfall (Maloney and Hartmann, 2000a). 7 CENTRAL NORTH PACIFIC 7.1 Central North Pacific Statistics TC occurrences in the CPac are low, with an average 4-5 TCs per year. 7.2 Multi-decadal Variability - Chu and Zhao (2004) found inactive periods of and with an active period of In the more active epoch there were warmer SSTs, lower mslp, stronger low level anom cyc. vor., reduced wind shear and increased total precipitable water. The steering flows were slightly different in the warmer epoch as during October-November NEPac TCs had a higher changes of entering the CPac and likely to affect the Hawaiian Islands. 7.3 Interannual Variability - ENSO In El Niño years there are more TCs, unintuitively not caused by the warmer SSTs but by smaller wind shear and low-level vorticity ((Wu and Lau, 1992), (Chu, P.-S. Wang, 1997), (Clark and Chu, 2002), (Camargo et al., 2007a)). 8 NORTH INDIAN OCEAN In contrast to the other basins the TCs that form in the North Indian Ocean typically form in the monsoon seasons - spring and autumn - not in late summer. It is thought that the monsoon trough being located over the ocean favours the TCs (Singh et al., 2000), however wind shear with the peak of the monsoon is likely to play a role in suppressing activity. 8.1 North Indian Ocean Statistics See average statistics in the tables at the top (figure 2 and 3).

22 8 NORTH INDIAN OCEAN 22 Figure 27: Number of TCs in the NInd Ocean of varying strength, Interannual Variability - ENSO In the El Niño year 1997 there was a particularly close association between intense monsoon depressions and TCs in the NWPac (Fudeyasu et al., 2006b). In additions, during the months of July and August, there is usually a higher number of monsoon depressions (Singh et al., 2001). There is a strong association between the Pacific Ocean SST and the Indian Ocean SST (Pan, Y. H. Oort, 1983), with a lag of 3-6 months in the Indian Ocean after the peak SST in the central and eastern Pacific. The Indian Ocean generally has lower wind speeds as a result, though the tropospheric temperatures increase around the global tropics, leading to increased stability (Chiang and Sobel, 2002). Singh et al. (2000) found lower TCs during May and November in the Bay of Bengal during El Niño years. 8.3 Interannual Variability - NAO Frank and Young (2007) observed a reduced activity during the + phase of the NAO. 8.4 Intraseasonal Variability - Monsoon Depressions There is a link between monsoons depressions over the Indian Ocean and TCs (Chen and Weng, 1999). Many monsoon depressions can be traced back to NWPac TCs. They can re-intensify in the Bay of Bengal, where the warm SST and high moisture content is present in the monsoon flow. 8.5 Intraseasonal Variability - MJO More activity when MJO is in + phase over the Indian Ocean (Liebmann et al., 1995).

23 9 SOUTH PACIFIC AND AUSTRALIA REGION 23 9 SOUTH PACIFIC AND AUSTRALIA REGION 9.1 South Pacific and Australian Region Statistics Average stats at top (Figure 2 and 3). Figure 28: Number of TCs around the Australian region of varying strength, Interannual Variability - ENSO Neville Nicholls was the first to investigate the forcing factors on TC activity in the Australian especially with relation to ENSO (Nicholls, 1979), (Nicholls, 1992). Generally, years with below normal SST in the eq. Pac, low Darwin pressure and high North Australia SST enhance the number of TCs. In contrast, years with relatively few TCs are preceded by El Niño events (see (Camargo et al., 2010)). The clearest relationship is for storms of moderate intensity, with a weak relationship with intense or weak storms (Nicholls et al., 1988). The total number of TCs in the southwest Pacific tends to increase with El Niño (Basher, R. Zheng, 1995). The region of mean TC genesis shifts with ENSO in the southwest Pacific (similar to the NWPac) with an eastward shift in the warm ENSO phase (Kuleshov et al., 2008), so TCs are likely to form away from the coast in El Niño years. In some El Niño events TCs occur in French Polynesia, a location not usually affected by TCs. During La Niña TCs form more westward and are more likely to make landfall (Evans and Allan, 1992). The relationship in the Western region is much weaker than the East. The genesis location also shifts northward during El Niño and southward during La Niña (Camargo et al., 2007a), therefore TC landfalls in Indonesia are more likely in El Niño years, even though precipitation is suppressed there during El Niño events (Camargo et al., 2010), but the landfalls are still rare.

24 10 SOUTH INDIAN OCEAN 24 Work has been done on how the ENSO signal influences specific environmental factors on TC stats. Ramsay et al. (2008) found a stronger relationship with ENSO indices than local SST in the region of most frequent cyclogenesis (similar to NWPac (Chan, 2008)). Relations with low-level rel. vor. and vertical shear are also significant. 9.3 Intraseasonal Variability - MJO More TCs in MJO active, with the modulation more pronounced to the northwest of Australia and is strengthened during El Niño events (Hall et al., 2001). Low levels rel. vor. anom. related to the MJO have been associated with changes in large-scale cyclogenesis patterns. 10 SOUTH INDIAN OCEAN 10.1 South Indian Ocean Statistics Average stats at top (tables). Figure 29: Number of TCs in the South Indian Ocean of varying strength, Interannual Variability - ENSO Jury (1993) did not find a relationship to ENSO and explained it due to opposing influences of increased upper-level w ly wind and enhanced convection during El Niño years. Whereas, Xie et al. (2002b) found an association between local SST influenced by ENSO and TC activity in the SW Ind. Oc. Kuleshov (2003) found an increase in TC numbers east of 70E during La Niña years. Maximum TC frequency in the SH occurs at the end of January during La Niña years and at the end of February in early March during El Niño years. The TC season usually starts one month earlier in the S. Ind. Oc. in La Niña years.

25 11 GLOBAL AND HEMISPHERIC ACTIVITY 25 TC tracks are more zonal in La Niña years and tend to be more frequent when local SSTs are warmer. This happened in 2000 which led to a exceptional number of TCs making landfall in Mozambique, which could be reproduced using a high-resolution model (Vitart et al., 2003). Ho et al. (2006) found that TC genesis is shifted westward during El Niño events, enhancing cyclogenesis west of 75E and reducing it east of that longitude. They explain this shift due to changes in the Walker circulation which leads to anom. anticyc. low-level circ. in the eastern part of the South Indian Ocean during El Niño years. There is a change in track with a decrease in activity SE of Madagascar and a moderate increase in activity in the central subtropical SInd Oc during El Niño events (Kuleshov et al., 2008) Interannual Variability - QBO Jury (1993) found more TCs occur in the e ly phase than in the w ly phase, which is opposite to the relation of QBO in the NWPac and NAtl??? 10.4 Intraseasonal Variability - MJO Bessafi and Wheeler (2006) attributed the MJO modulation to perturbations in vor. and shear. Ho et al. (2006) noticed changes in TC tracks depending on the MJO phase (what were they?) 11 GLOBAL AND HEMISPHERIC ACTIVITY There have been many figures which sum up activity over the different basins in each hemisphere and globally but it is almost impossible to relate it to forcing factors. Global parameter values usually mask out regional changes and globally average temperature most certainly does not relate to TC activity. It can be explained by looking at each individual basin separately, however for example NAtl and NEPac are usually in opposite phase. Cooling of trop Pacific explains the recent decrease (Maue et al. 2011). 12 Conclusions I now feel I have a good handle on how the large scale forcing factors drive TC activity in each basin and the influence that the large scale forcing factors have on local and remote parameters. It will be interesting to see if any future change in TC activity arises due a change in the modes of atmospheric/oceanic variability e.g. ENSO on NAtl. I have built up an extensive reference list and if I/we decide to look at certain factors I can easily have a full read of the papers mentioned in this lit. rev. I have only listed how each mode of variability influences TCs activity, however, there is an increase amount of literature on how TCs can influence these large scale forcing factors e.g. initiating El Niño events and influencing ocean heat storage. This is something I am interested in and look forward to studying e.g. the role of ocean forcing on TCs.

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