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Climate Dynamics

, Volume 52, Issue 12, pp 7225–7234 | Cite as

Role of ocean heat content in boosting post-monsoon tropical storms over Bay of Bengal during La-Niña events

  • Suchandra Aich BhowmickEmail author
  • N. Agarwal
  • M. M. Ali
  • C. M. Kishtawal
  • Rashmi Sharma
Article

Abstract

This study aims to analyze the role of ocean heat content in boosting the post-monsoon cyclonic activities over Bay of Bengal during La-Niña events. In strong La-Niña years, accumulated cyclone energy in Bay of Bengal is much more as compared to any other year. It is observed that during late June to October of moderate to strong La-Nina years, western Pacific is warmer. Sea surface temperature anomaly of western Pacific Ocean clearly indicates the presence of relatively warmer water mass in the channel connecting the Indian Ocean and Pacific Ocean, situated above Australia. Ocean currents transport the heat zonally from Pacific to South eastern Indian Ocean. Excess heat of the southern Indian Ocean is eventually transported to eastern equatorial Indian Ocean through strong geostrophic component of ocean current. By September the northward transport of this excess heat from eastern equatorial Indian Ocean to Bay of Bengal takes place during La-Nina years boosting the cyclonic activities thereafter.

Keywords

La-Niña Bay of Bengal Tropical cyclones Ocean heat content 

1 Introduction

Since India has an extensive coastline, a significant portion of its sustainable development depends on the ocean conditions. The subcontinent is surrounded by Indian Ocean and therefore vulnerable to devastations from tropical storms and cyclones. The ruinous impact of tropical cyclones (TCs) is much more pronounced in the east coast of India, adhering the Bay of Bengal (BoB) as compared to the west coast facing the Arabian Sea. The BoB is an enclosed ocean basin, and is a part of north Indian Ocean. The distribution of tropical storms/cyclones in BoB is bimodal (Li et al. 2013), the peak cyclonic activity is noticed during the post monsoon months of September to December and the secondary peak is during pre-monsoon months from April to June. For BoB, seasonal and intra-seasonal variability in both ocean and atmosphere plays key role in genesis, intensification, and movement of tropical cyclones (Goswami et al. 2003; Kikuchi and Wang 2010; Yanase et al. 2012). A strong temperature stratification is created in the BoB due to its high fresh water content and reduced surface salinity, making it perennially warmer and perfect hotspot for cyclogenesis. Apart from its own unique features conducive towards formation of TCs, BoB is also known for being remotely influenced by the dynamics of equatorial Indian Ocean and ENSO events (Felton et al. 2013; Girishkumar et al. 2015).

Recent study of Girishkumar et al. (2015) has established the remote influence of ENSO events on BoB cyclones. Study of Felton et al. (2013) has shown that La-Niña has a positive impact on the TCs of BoB while El-Niño suppresses it significantly. Unlike Felton et al. (2013), this study aims to analyze the role of oceanic heat content (OHC) during La-Niña years that play crucial role in modulating the BoB tropical storms during the post-monsoon season (September–December). Major inferences drawn from Felton et al. (2013) are during La-Niña phase, (1) zonal winds has greater variance favoring the development of low-level cyclonic vorticity, (2) low vertical wind shear over the central and northern BoB helps in development of tropical cyclones, (3) increased relative humidity due to enhanced moisture transport and higher precipitable water favors the cyclonic development.

In this study it is observed that there is large variability in the total accumulated cyclone energy (ACE) for various years. The years with large ACE eventually, coincides with the years of moderate to strong La-Niña events in Pacific Ocean whereas that with reduced ACE coincides with El-Niño events. The paper analyzes the role of ocean heat responsible for such modulation and establishes the remote influence of the event on post-monsoon BOB cyclones.

2 Data and methods

The data used in this study are (1) Cyclone records from U.S. Navy’s Joint Typhoon Warning Centre (JTWC) during 1982–2012, (2) gridded field of temperature and salinity from Argo available from Coriolis Oceanographic Re-Analysis (CoRA) climatological data records during 2005–2012, (3) Reynolds monthly sea surface temperature from NOAA during 1982–2012, and (4) absolute dynamic topography (ADT) and geostrophic current from altimeters along with (5) monthly temperature profiles and currents fields from NOAA Oceanic Re-Analysis (ORAS4).

JTWC best-track data include information on the track of the cyclone, date and time of genesis and maximum sustained winds. These data are processed and only BoB TC and storms are retained. Thus, out of 167 cyclones only 113 (33 in pre-monsoon and 80 in post-monsoon seasons) are retained. In a year there could be several cyclones/storms with large variability in terms of the duration, intensity and occurrence. So when a year has to be labeled in terms of total cyclonic energy one has to carefully consider the strength of each cyclone, total number of cyclones, and duration of each cyclone. A single parameter representing of all these variables is the ACE, mathematically represented as
$${\text{ACE = 10}}^{ - 4} \sum {{\text{V}}_{\hbox{max} }^{2} }$$
(1)
where Vmax is the sustained maximum wind speed in knots from the 6-h JTWC observations. The values are summed over the life of each tropical system. For each year the ACE from all tropical storms/cyclones are summed up to calculate total yearly ACE. The years are also labeled as El-Niño or La-Niña year based on the Oceanic Niño Index (ONI) defined by NOAA. The ONI is a standard NOAA uses for identifying El Niño (warm) and La Niña (cool) events in the tropical Pacific. It is the running 3-month mean SST anomaly for the Niño 3.4 region (i.e., 5°N–5°S, 120°–170°W). Events are defined as 5 consecutive overlapping 3-month periods at or above the +0.5° anomaly for warm (El Niño) events and at or below the −0.5° anomaly for cold (La Niña) events. The threshold is further broken down into Weak (with a 0.5–0.9 SST anomaly), Moderate (1.0–1.4) and Strong (≥1.5) events (http://ggweather.com/enso/oni.htm). For this work years are categorized as weak, moderate or strong based on the ONI information and is shown in Table 1.
Table 1

ONI based categorization of different analysis years

El- Niño

La-Niña

Weak

Mod

Strong

Weak

Mod

Strong

2004–2005

1986–1987

1982–1983

1983–1984

1998–1999

1988–1989

2006–2007

1991–1992

1987–1988

1984–1985

2007–2008

1999–2000

 

1994–1995

1997–1998

1995–1996

 

2010–2011

 

2002–2003

 

2000–2001

  
 

2009–2010

 

2005–2006

  
   

2008–2009

  
   

2011–2012

  

In order to understand the role of ocean in boosting the TC and storms over BoB, SST has been analyzed in this study from 1982 to 2012. A monthly global SST from the National Climatic Data Center (NCDC) is used for this purpose. The actual coverage of this data is roughly between 60°S and 60°N globally. These data were based on ship and buoy SST data supplemented with satellite SST retrievals. The product is available on a monthly basis from NOAA and is referred as Reynolds SST after Richard Reynolds who developed this product. In order to investigate the role of SST, its monthly anomaly is obtained by taking the difference of the monthly SST of any particular year with respect to the monthly climatology constructed from Reynolds SST data from 1982 to 2012.

Albeit SST is a representative of the ocean surface heat and its value exceeding 26° C is one of the primary and essential conditions for genesis, development and sustenance of TC, one should not rule out the role of heat content in deeper layers of ocean that could play a key role in such activities (Ali et al. 2007; Shay et al. 2000; Goni and Trinanes 2003; Ali et al. 2013). Hence, to look into the contribution of the heat at deeper layers of ocean during the El-Niño and La-Niña years, the monthly temperature and salinity profile from ARGO have been used from CORA—Coriolis Oceanographic Re-Analysis (CORA3.4, Cabanes et al. 2013). The temperature and salinity fields are available at a spatial resolution of 0.5 degrees and have 152 vertical levels up to 2000 m. The heat content is calculated using this data by integrating up to required water depth D. The heat content is mathematically defined as
$${\text{Heat content = }}\int_{0}^{\text{D}} {\uprho{\text{CpTdz}}}$$
(2)
where, p is the density of the water. Density here is dependent on the salinity, and this dependence is important for the North Indian Ocean particularly in head Bay of Bengal region. However for southern Indian Ocean and western Pacific, which happens to be the region of interest, this dependence is much less. Thus, for the sake of simplicity in calculation it is considered as constant. Cp represents the specific heat and T is the temperature of dz infinitesimal depth of water. Two small regions are chosen off Indian Ocean coast of Australia (115E−120E) and off pacific coast of Australia (155E−160E) where the average temperature profile and heat content are analyzed. The heat content off Pacific coast of Australia is found to have a very high correlation with ACE of BoB. Thus, to find out any possible mechanism of this remote influence, the temperature and current fields of ocean available from ECMWF Ocean Re-Analysis System (ORAS4) have been analyzed. ORAS4 is a valuable data resource for climate variability studies and is based on Nucleus for European Modelling of the Ocean (NEMO) in which along track altimeter data is assimilated at every 10 days along with other conventional observations using 3D-Variational data assimilation technique (Balmaseda et al. 2013). The zonal and meridional heat transport has been computed using this data set and is mathematically defined as
$${\text{Zonal heat transport}} = \rho Cp\int {\int_{0}^{700} {uTdydz} }$$
(3)
$${\text{Meridional transport }} = \rho Cp\int {\int_{0}^{700} {vTdxdz} }$$
(4)
where dx and dy represents the small interval of longitude and latitude respectively. The upper limit of integration for heat content or transport ideally should be 0–2000 m. However heat content up to 2000 m shows similar trend as that up to 700 m except for higher amplitudes (Häkkinen et al. 2016). Thus, here the integration is performed till depth of 700 m from surface as upper-ocean would be most crucial for any air-sea interaction studies.

Further, altimeter derived absolute dynamic topography and geostrophic currents have been used to understand the mechanism of heat propagation from Pacific Ocean to BoB. The absolute dynamic topography (which is similar to sea level but with respect to geoid) is available from AVISO, France. It is available at 1 degree spatial resolution on a daily basis. The coverage is from 1992 to 2010. It is derived from merged sea surface height (SSH) measurements from various satellites including EnviSAT, TOPEX/Poseidon, Jason-1, Jason-2 and is referenced to geoid. It is available from  http://www.aviso.altimetry.fr/en/data/data-access/ftp.html. The geostrophic current used here is available from MOSDAC (Meteorology and Oceanography Satellite Data Archival Centre). In this product global ocean surface current is defined as the average current for the top 0–30 m and is derived from the synergistic use of three different satellite derived parameters viz. Merged ADT from altimeters, the gridded ocean surface vector winds derived from ASCAT and gridded SST data derived from AVHRR. It contains both geostrophic component from altimeter data and the ageostrophic component from scatterometer and radiometer data (Sikhakolli et al. 2013).

3 Results and discussions

The ONI based categorization as discussed in Sect. 2 is used to label the analysis years (1982–2012) as El-Niño/La-Niña/normal year (Table 1). Corresponding to these years ACE for all BoB cyclones/storms is also considered simultaneously. The total ACE is calculated for these years as a sum of each storm energy occurring in post-monsoon months between September and December for each year. The total ACE shoots up during every moderate to strong La-Niña year during post-monsoon season over BoB (Fig. 1). The red circles represent the El-Niño and the green boxes the La-Niña years. The color less boxes denote the normal years. From Fig. 1 significant peaking of post-monsoon ACE is noticed during each of the strong (1988, 1999 and 2010) and moderate La-Niña events (2007). During the El-Niño years (1982, 1987, 1997, 2002, 2004, 2006 and 2009), the post-monsoon cyclonic activities are significantly suppressed as compared to any La-Niña year. All the La Niña years have more ACE compared to the El Niño years, excepting in 1987 where the ACE is more than that in 1998 and 2005.
Fig. 1

The accumulated cyclone energy in post-monsoon months over Bay of Bengal during different years from 1982 to 2012. The green boxes represent the La-Niña events and red circles present the El-Niño years while white boxes are for the normal years. The size of green boxes and red circles indicate week, moderate and strong events as shown in Table 1

To investigate the possible reason behind this boosting impact of La-Niña on BoB storms as observed in the 31-year record of cyclones the following analysis has been carried out. Firstly, Reynold’s monthly SST anomalies have been analyzed over the study period by taking the SST difference with respect to the monthly climatology. Figure 2 represents the SST anomaly of September during the major La-Niña and El-Niño events. The figure indicates a warmer (cooler) western Pacific during La-Niña (El-Niño) years. A well organized positive SST anomaly (> 0.5–1 °C) is observed during each of the La-Niña years between the 10°S–20°S (black boxes). This belt happens to be a major ocean mixing belt (between Indian Ocean and Pacific Ocean) in absence of any land boundary near the northern part of Australia. The north -west Pacific is also warmer in La-Nina phase. Thus, Indian Ocean potentially receives the warmer water from western Pacific from both eastern part of Australia and by Indonesian through flow (ITF). This warming is evident from late June and continues till December of the La-Niña years. However during El-Niño years west Pacific has negative SST anomaly that ranges from 0.5 to 1.5 °C (Fig. 2). To examine whether warming of water mass off Pacific coast of north-east of Australia during La-Niña is a mere surface phenomenon, we analysed the temperature profiles of this region. Figure 3 shows the temperature profiles of the water mass during October of various El-Niño and La-Niña years. Clearly, during La-Niña (2007 and 2010) warming of this water mass is evident up to deeper layers as compared to El-Niño years of 2006 and 2009. The temperature difference between the Indian Ocean and Pacific Ocean water masses is also more during the La-Niña years of 2007, 2008, 2010 and 2011 as compared to El Niño years, 2006 and 2009, particularly, for the surface layer up to 30 m (Fig. 4). Most interesting feature is the upper ocean heat content (up to 50 m) of this area (which is shown in left panel of Fig. 5a) that has a high correlation during September –November with the ACE of BoB tropical storms/cyclones. SST of this area however lacks any such strong correlation. This has been shown in right panel of Fig. 5a. Apart from western Pacific, the ITF region also shows similar trends in 50 m heat content pattern (left panel of Fig. 5b) except with a lower variability. The correlation of 50 m heat content at ITF region shows significant positive trend from May to August with maximum correlation in July (0.68), implying importance of entire West Pacific and ITF region in injecting heat into south eastern Indian Ocean. Figure 5c shows the inter-annual variability of the OHC up to 50 m, integrated over the longitudinal belt of 155°–160° E along the 10°S. Clearly La-Niña years are marked with remarkable heat buildup in this region. For most of the moderate to strong El-Niño/La-Niña cases the total OHC (July–October) of this belt is having correlation of more than 0.7, (significant at 92%) to the ACE of BoB. Arguably, however, a higher correlation alone does not establish any remote impact of ocean heat of this area on TCs over BoB during La-Niña as the distance between the two is huge. Hence, to investigate possible tele-connection of this area with BoB, the ORA-S4 temperature profiles and current fields have been analyzed. The heat transport from this area in western Pacific near Australia to southern part of Indian Ocean has been computed and is shown in Fig. 6 for various El-Niño and La-Niña years. The red bars represents the El-Niño years while the green bars are the La-Niña years. Very clearly there is significant enhancement of the heat transport from western Pacific to southern Indian Ocean during the La-Niña phase. This excess heat flow in the southern Indian Ocean is also visible in the altimeter ADT data of La-Niña years. Figure 7 shows the ADT of 1999 (La-Niña) and 1997 (El-Niño) overlaid with the geostrophic current available over the study area. The black boxes show the high/low ADT during La-Niña/El-Niño in the southern Indian Ocean during September. This heat eventually is likely to get transported via strong geostrophic component of ocean current to eastern and central part of the equatorial Indian Ocean (5S-5N/80E−100E), thereby increasing the heat content of this area. However, modeling efforts are required to confirm this, which is beyond the scope of the present study. Figure 8 shows the heat content of the south-eastern part of equatorial Indian Ocean computed from the ORAS4 temperature profiles. Clearly the heat content of equatorial Indian Ocean is also more during the La-Niña phase. Thus, it is justified to mention that by September equatorial Indian Ocean gets an excess heat during La-Niña years as compared to El-Niño years. The way this excess heat enters BoB (Northern Indian Ocean) can be explained by the strong eastward equatorial ocean current also known as Wyrtki Jet which hits the Sumatra coast during fall and initiates the northward propagation of the ocean heat into BoB. Wyrtki Jet has been well established by several studies (Wyrtki 1973; Yuan and Han 2006; Nagura and McPhaden 2008). Thus, during the La-Niña events strong meridional heat transport is evident from equatorial Indian Ocean to BoB as seen in Fig. 9. Very clearly from mid of September onward the stronger northward transport of heat is evident during the 1999 La-Niña as compared to 1997 El-Niño.
Fig. 2

El-Niño (in left panel) and La-Niña (in right panel) years mean SST anomaly (°C) pattern in Pacific and Indian Ocean regions during September computed from Reynold’s SST. Black boxes in right panel indicate warmer water mass in Western Pacific that enters Indian Ocean through the channel near Australia during La-Niña. Here, 1982 (strong), 1997(strong) and 2006(moderate) are El-Niño years and 1999 (strong), 2010 (strong) and 2007(moderate) are La-Niña years. a El-Niño b La-Niña periods

Fig. 3

The average temperature profile between 155 and 160°E at off Pacific Coast of Australia up to 100 m

Fig. 4

The temperature difference profile between Pacific Ocean (155–160°E) and Indian Ocean (115- to 120°E) (on east and west coast of Australia) at 10°S up to 100 m depth

Fig. 5

aLeft panel Variability in ocean heat content (50 m) among La-Niña years (2010, 2007), and El-Niño years (2006) in Western Pacific. Right panel The correlation coefficient (significance up to 92%) of total ACE (Sept–Dec) over BoB with monthly average heat content (up to 50 m depth)/SST anomaly at Pacific Ocean (155–160°E) along 10°S b Same as in a but for ITF region c The inter-annual variability of integrated ocean heat content (integration between 155 and 160ºE) in this region along 10°S. Green circles are La-Niña years, Red circles are El-Niño years and White circles are normal years

Fig. 6

Average zonal transport of ocean heat (integrated from surface up to 700 m) from Pacific Ocean to Indian Ocean during June–October of various El-Niño (Red)/La-Niña (Green) years

Fig. 7

The absolute dynamic topography as observed from the altimeters with direction of geostrophic current overlaid on it for El-Niño year of 1997 and La-Niña year of 1999

Fig. 8

The heat content (integrated from surface to 700 m) averaged over the south-eastern equatorial Indian Ocean (90°–100°E and 0–7°s) during various strong El-Niño/La-Niña years

Fig. 9

The meridional heat transport (700 m) from equatorial Indian Ocean to Bay of Bengal during strong El-Niño of 1997 (left panel)/La-Niña of 1999 (right panel)

Hence, during La-Niña phase the western pacific warms up from month of June onwards and this warming continues till December. The ocean heat from western Pacific finds its way into BoB via southern and equatorial Indian Ocean during La-Niña phase. This heat has positive impact on the tropical cyclo-genesis over BoB particularly during the post monsoon season starting from September to December. Apart from several atmospheric and oceanic factors favorable for cyclone/storm formation over BoB, one should carefully consider the heat entrainment from Pacific to Indian Ocean that can manifest itself as one of the crucial parameters in boosting the cyclonic activities over BoB.

4 Conclusion

The El-Niño and La-Niña events modulate the cyclonic activities of BoB during post monsoon months. Many studies have shown various atmospheric parameters responsible for it. This study shows the vital role of ocean in such a modulation. It has been found that during the La-Niña years ACE of post monsoon cyclonic season is much more for BoB. This is primarily because BoB gets additional heat energy during La-Niña from the western Pacific that finds its way to BoB. It is found that western Pacific is warmer during La –Niña as compared to El-Niño phase. A warmer water mass is prominent near the channel connecting Indian and Pacific Ocean situated over the northern part of Australia. This enhances the zonal heat transport from western Pacific to Southern Indian Ocean during La-Niña years as compared to El-Niño years. This additional heat accumulating in south Indian Ocean is well observed by altimeters as well, since a positive absolute dynamic height is a proxy for larger heat content. The additional heat strengthens the geostrophic component of ocean currents propagating the heat into east equatorial Indian Ocean. Presence of well known Wyrtki Jet during September–October carries the heat further eastward and after encountering the Sumatra coast it branches into BoB. Apart from several other atmospheric factors as depicted by previous studies, this additional boost of heat is definitely worth considering for the increased cyclonic activities during La-Niña events.

Notes

Acknowledgements

Authors would like to thank the Director, SAC and Deputy Director (EPSA) for their great support and encouragement for this work. This work would not be complete without the data provided by ISRO, AVISO, JTWC, NOAA and ECMWF.

References

  1. Ali MM, Jagadeesh PSV, Jain S (2007) Effects of eddies and dynamic topography on the Bay of Bengal cyclone intensity. EOS Trans AGU 88(8):93–95CrossRefGoogle Scholar
  2. Ali MM, Kashyap T, Nagamani PV (2013) Use of sea surface temperature for cyclone intensity prediction needs a relook. EOS 94:117CrossRefGoogle Scholar
  3. Balmaseda MA, Mogensen K, Weaver A (2013) Evaluation of ECMWF ocean reanalysis system ORAS4. QJRMS 139(674):1132–1161CrossRefGoogle Scholar
  4. Cabanes C, Grouazel A, von Schuckmann K, Hamon M, Turpin V, Coatanoan C, Paris F, Guinehut S, Boone C, Ferry N, Boyer Montégut B, Carval T, Reverdin G, Pouliquen S, Traon P (2013) The cora dataset: validation and diagnostics of in situ ocean temperature and salinity measurements. Ocean Sci 9:1–18. doi: 10.5194/os-9-1-2013 CrossRefGoogle Scholar
  5. Felton CS, Subrahmaniyam B, Murthy VSN (2013) ENSO—modulated cyclogenesis over the Bay of Bengal. J Clim 26:9806–9817CrossRefGoogle Scholar
  6. Girishkumar MS, Thanga Prakash VP, Ravichandran M (2015) Influence of Pacific Decadal Oscillation on the relationship between ENSO and tropical cyclone activity in the Bay of Bengal during October–December. Clim Dyn Clim Dyn 44:3469–3479CrossRefGoogle Scholar
  7. Goni GJ, Trinanes JA (2003) Ocean thermal structure monitoring could aid in the intensity forecast of tropical cyclones. EOS Trans AGU 84(51):573CrossRefGoogle Scholar
  8. Goswami BN, Ajayamohan RS, Xavier PK, Sengupta D (2003) Clustering of synoptic activity by Indian summer monsoon intraseasonal oscillations. Geophys Res Lett 30:1431. doi: 10.1029/2002GL016734 Google Scholar
  9. Häkkinen S, Rhines PB, Worthen DL (2016) Warming of global ocean: spatial structures and water mass trends. J Clim 29:4949–4963CrossRefGoogle Scholar
  10. Kikuchi K, Wang B (2010) Formation of tropical cyclones in the northern Indian Ocean associated with two types of tropical intraseasonal oscillation modes. J Meteorol Soc Jpn 88:475–496CrossRefGoogle Scholar
  11. Li Z, Yu W, Li T, Murty VSN, Tangang F (2013) Bimodal character of cyclone climatology in the Bay of Bengal modulated by monsoon seasonal cycle. J Clim 26:1033–1046CrossRefGoogle Scholar
  12. Nagura M, McPhaden MJ (2008) The dynamics of zonal current variations in the central equatorial Indian Ocean. Geophys Res Lett 35:L23603. doi: 10.1029/2008GL035961 CrossRefGoogle Scholar
  13. Shay LK, Goni GJ, Black PG (2000) Effects of a warm oceanic feature on Hurricane Opal. Mon Weather Rev 128(5):1366–1383CrossRefGoogle Scholar
  14. Sikhakolli R, Sharma R, Kumar R, Gohil BS, Sarkar A, Prasad KVSR, Basu S (2013) Improved determination of Indian Ocean surface currents using satellite data. Rem Sens Lett 4:335–343CrossRefGoogle Scholar
  15. Wyrtki K (1973) An equatorial jet in the Indian Ocean. Science 181:262–264. doi: 10.1126/science.181.4096.262 CrossRefGoogle Scholar
  16. Yanase W, Satoh M, Taniguchi H, Fujinami H (2012) Seasonal and intraseasonal modulation of tropical cyclogenesis environment over the Bay of Bengal during the extended summer monsoon. J Clim 25:2914–2930CrossRefGoogle Scholar
  17. Yuan DL, Han WQ (2006) Roles of equatorial waves and western boundary reflection in the seasonal circulation of the equatorial Indian Ocean. J Phys Oceanogr 36(5):930–944. doi: 10.1175/JPO2905.1 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Space Applications CentreIndian Space Research OrganizationAhmedabadIndia
  2. 2.Center for Ocean-Atmospheric Prediction StudiesFlorida State UniversityTallahasseeUSA

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