Abstract

The climate over the north Indian Ocean (NIO) is one of the most dynamic in the world because of seasonally reversing monsoon winds. In this study, the climate of the NIO and the variability of its surface waves using the European Centre for Medium-Range Weather Forecasts (ECMWF) global atmospheric reanalysis product (ERA-Interim) for the period 1979–2012 are analyzed. Annual average significant wave height (SWH) of the NIO ranges from 1.5 to 2.5 m and the seasonal average is highest (3–3.5 m) during the monsoon period [June–September (JJAS)]. Swells propagating from the Southern Hemisphere are present in the NIO during the premonsoon [February–May (FMAM)] and postmonsoon [October–January (ONDJ)] periods. The waves are separated into wind seas and swells based on the wave energy statistical method. The results show that the NIO is swell dominated and that wind sea heights are lower compared to the swell heights. Higher wind sea and swell heights are observed during the monsoon in the western NIO because of strong cross-equatorial winds of the Somali (Findlater) jet. In the postmonsoon period, the eastern NIO shows a higher swell height than the western NIO shows. SWH shows an annual increasing trend in the western NIO. On a seasonal scale, the trends are increasing significantly in the monsoon compared to the postmonsoon period in a major part of the NIO, whereas the premonsoon period shows a decline in SWH. In the NIO, the monsoon is the dominant mode of variability and it covers 92% of the total variability. Wave climate is also influenced by the annual and interannual variability in monsoon wind and rainfall.

1. Introduction

Climate change, a topic that is widely discussed worldwide, certainly has an impact on the oceans. The Indian Ocean is the third largest ocean in the world and covers approximately 20% of the water on the earth’s surface (Rais 1987). The Northern Hemisphere of this basin, the north Indian Ocean (NIO), has a large influence on the scientific, industrial, engineering, and commercial grounds of the countries on its rim. The NIO has a decisive role in the climate of the countries surrounding it and influences the rainfall of the densely populated coastal areas of South Asia (Clark et al. 2000). The unique geography of the NIO (Fig. 1), being bounded to the north by the Asian continent, leads to a complex annual cycle associated with substantial seasonal reversals of the annual monsoon winds (Slingo et al. 2005). Wind waves are the prominent feature of the ocean surface and play a major role in planning activities in the open ocean and in coastal zones. Hence, a change in wave climate influences coastal and offshore activities. Waves in the NIO undergo large seasonal variations {monsoon [June–September (JJAS)], premonsoon [February–May (FMAM)] and postmonsoon [October–January (ONDJ)]} due to the reversal of the large-scale wind field over the NIO between the boreal summer and winter (Kumar et al. 2012). The Indian subcontinent divides the NIO into two semienclosed seas: the Arabian Sea (AS) and the Bay of Bengal (BoB). Both of these seas encroach onto the equatorial regime of the basin (Shankar and Shetye 2001). The NIO, particularly the BoB region, is an area generally prone to cyclone events, and these events can result in large interannual variations in wave parameters.

Many regional studies have been conducted on the change in wave climate, especially in the North Pacific and North Atlantic regions (Carter and Draper 1988; Allan and Komar 2000; Caires and Swail 2004; Gulev and Grigorieva 2004; Parise and Farina 2012; Vanem and Walker 2013). Simmonds and Keay (2002) observed that the increase in wave height in the North Atlantic and North Pacific is due to the increase in cyclone numbers and the corresponding increase in the mean rate of mechanical energy input into the oceans. Vethamony et al. (2000) studied the wave climate of the Indian Ocean using altimeter and model data for the period 1986–89. Young (1999) studied the seasonal variability of the global ocean wind and wave climatology based on a dataset spanning a period of 10 years obtained from a combination of satellite remote sensing and model predictions. Based on the satellite altimeter and scatterometer data, Chen et al. (2002) presented swell and wind sea climate over the global ocean. As a part of the global study, Young et al. (2011) examined long-term trends in significant wave height (SWH) in the NIO from 1985 to 2008 based on satellite altimeter data. Semedo et al. (2011) presented a global view of the wind sea, and swell climate and variability based on the 45-yr European Centre for Medium-Range Weather Forecasts (ECMWF) wave reanalysis product (ERA-40). Chempalayil et al. (2012) studied variations in wave characteristics during a 3-yr period at a shallow-water location in the eastern AS, and Shanas and Kumar (2014) examined changes in wind speed and SWH at this location for 34 years. Using altimeter data, E. D. Kumar et al. (2013) studied changes in SWH and wind speed over the Indian Ocean for the period 1993–2010 by categorizing the area into six zones. Recently, Shanas and Kumar (2015) analyzed trends in surface wind speed and SWH in the central BoB. However, in these studies, the wave climatology and the long-term variations for the entire NIO are not well documented, and the need remains for a detailed analysis of the regional wave climate of the NIO. Hence, we analyze the wave climate of the NIO and its variability over different seasons and years.

2. Data and method

The present study is based on the global atmospheric reanalysis product of the ECMWF known as ERA-Interim (Dee et al. 2011). ERA-Interim is the latest global reanalysis product of ECMWF and is available from 1979 onward. Based on an improved atmospheric model and assimilation procedures used in ERA-40, ERA-Interim uses a two-way coupled atmosphere–wave model system. The Wave Model (WAM) has also received some improvements, including a revised formulation of the ocean wave dissipation scheme and the introduction of a new scheme to parameterize unresolved bathymetry (Bidlot et al. 2007). A four-dimensional variational data assimilation scheme is used in ERA-Interim.

B. P. Kumar et al. (2013) compared wind stress estimates from ERA-Interim against in situ observations in the tropical Indian Ocean and found that the data captured good temporal variability with better performance than other reanalysis data (the correlation coefficient was ~0.86). In the shallow waters of the eastern AS, Shanas and Kumar (2014) found that ERA-Interim SWH data showed good correspondence with the measured Waverider buoy data. For the central BoB, Shanas and Kumar (2015) found that ERA-Interim SWH data compared well with buoy data for mean values, but that ERA-Interim data underpredicted the measured SWH data up to 15% for high waves (SWH > 2.5 m). Durrant et al. (2013) found that a negative bias in modeled SWH has its origins primarily in the forcing; however, the reduction of systematic wind biases does not result in universal improvement in modeled SWH. In the present study, we use 34 years of SWH data (1979–2012) at 6-h temporal and 1° × 1° spatial resolutions. We also use ERA-Interim wind speed at a 10-m height of the same temporal and spatial resolutions to separate waves into wind seas and swells.

Generally, the sea state is either wind seas dominated or swells dominated. We carry out the separation of wind seas and swells using wind speed and SWH based on the method proposed by Chen et al. (2002). If U is the surface wind speed at 10 m above the sea surface, then the wind–wave relation for a fully developed sea is (Chen et al. 2002)

 
formula
 
formula

Chen et al. (2002) introduced two probability indices for quantifying the frequency of occurrence of swell and wind seas: Ps = Ns/N and Pw = Nw/N, where Ns and Nw are the number of swell-dominated and wind-sea-dominated events at the study area, respectively; N = Ns + Nw; and Ps and Pw are statistical descriptions of the proportion of swell and wind sea dominances, respectively. Using these statistical proportions, we can separate the wind sea and swell energy from the total energy of the sea state based on the method suggested by Jiang and Chen (2013). The equations for estimating wind sea height (Hw) and swell height (Hs) from SWH are

 
formula
 
formula

respectively. The assumptions are not strict and the statistical method does not work well for studies of short duration (such as 1 day). However, this method is helpful in obtaining a view of the wind sea and swell climatology.

Monthly average rainfall data provided by the Indian Institute of Tropical Meteorology (IITM) obtained from rain gauges in various part of India from 1979 to 2012 are also used in the study (available at http://www.tropmet.res.in/static_page.php?page_id=53). We obtain the wave climate trend based on the slope of the linear best-fit curve to the annual mean SWH and the seasonal mean SWH for 34 years. The statistical significance of trends in the SWH is estimated by applying Kendall’s tau–Sen test (Sen 1968; Burkey 2006). Empirical orthogonal function (EOF), wavelet, and cross-wavelet methods (Torrence and Compo 1998; Grinsted et al. 2004) are used to analyze the SWH, wind speed, and rainfall data.

To know how the wave height is influenced by the tropical cyclone (TC), we have considered the TC events in the AS for the period 1979–2012 using the position and intensity estimates reported in the JTWC best-track dataset (Chu et al. 2012): (i) tropical depressions to cyclonic storms (wind speed ~17–47 kt, 1 kt = 0.51 m s−1), (ii) severe cyclone category (wind speed of ~48–63 kt), and (iii) very severe cyclonic storms to supercyclones (wind speed > 64 kt). Also we analyzed the distribution of the locations where the maximum intensity of TC is observed and finally considered a box of latitude 17.5°–22.5°N and longitude 60°–65°E (box A in Fig. 1) to analyze the link between the severe cyclone events and SWH.

3. Results

a. Wave climatology of the NIO

1) SWH climatology

Figure 2 displays the annual mean SWH of the NIO and three seasonal SWH means. The range of the annual average SWH in the deep waters of the NIO is within 1.5–2.5 m (Fig. 2a). The annual average SWH is higher (~2–2.5 m) off the southeast coast of Sri Lanka, the west side of the Andaman and Nicobar Islands, and the northeast coast of Africa. The annual average SWH in the coastal regions of the west and northeast coasts of India is 1–1.5 m. However, the annual average SWH is less than 1 m in the coastal region of the southeast coast of India due to the sheltering effect of the Sri Lankan landmass.

The seasonal climatology of the SWH in the premonsoon period indicates that the waves over the NIO become low in height due to a lower wind speed over the basin. The NIO is calm with a SWH ranging from 0.5 to 1.5 m (Fig. 2b). During this time, the southwestern and southeastern NIO show higher SWHs compared to other regions. The SWH is decreasing from south to north and southwest to northeast in the eastern and western NIO, respectively. During the Asian summer monsoon, due to the strong cross-equatorial winds of the Findlater (Somali) jet (Findlater 1969), wave heights are high in the AS. Consequently, the maximum average wave height (3–3.5 m) for the NIO during a monsoon period occurs in the western AS as a result of the strong southwesterly winds caused by the East African highlands (Slingo et al. 2005). These southwesterly winds spread and wane in the northeast direction (Slingo et al. 2005), as do the waves (Fig. 2c). Hence, high waves reach the northern part of the west coast of India before they reach the southern part.

A study by Johnson et al. (2012) observed that higher swells are present at the northern part of the NIO along the eastern AS compared to the southern part. The monsoon wind in the NIO splits into two branches from the southern part of the Indian subcontinent: (i) the AS branch and (ii) the BoB branch. The monsoon winds in the BoB are weaker than the monsoon winds in the AS. The BoB branch generates surface waves in the southern BoB, but these waves do not reach the major part of the southeast coast due to the sheltering of the Sri Lankan island. The lower wave height in the western BoB compared to the wave height in the eastern AS during the monsoon season is not only due to weaker BoB winds in the monsoon but also the sheltering effect of Sri Lanka.

Semedo et al. (2011) noted that the SWH seasonal mean maxima in the Southern Hemisphere are located in the Southern Ocean Indian sector. Extreme wave conditions occur at high southern latitudes due to the massive amounts of kinetic energy deposited into the Southern Ocean associated particularly with the transient meteorological systems (Simmonds et al. 2005). Young (1999) discussed the important role played by the intense wave-generation systems of the Southern Ocean and confirmed it to be consistently the roughest ocean on earth. Alves (2006) reported that the waves generated along the Southern Ocean Storm Belt have a considerable impact on the global wave climate due to swell propagation. The wave height in the NIO during the postmonsoon period is higher than it is in the premonsoon. During the postmonsoon period, high waves (Fig. 2d) are observed in the southern NIO with a maximum SWH (~2–2.5 m) in the BoB rather than in the AS (~1.5–2 m) due to the closure effect of the Maldives landmass to the Southern Ocean swells. Because of northeast monsoon winds, the wave height in the BoB is higher than that in the AS during the postmonsoon period (Johnson et al. 2013a,b).

The spatial distribution of yearly and seasonal standard deviation (SD) of SWH from 1979 to 2012 is shown in the right panel of Fig. 2. The annual SD shows that it is less than 0.05 m in most of the region (Fig. 2e). The annual SD maximum is 0.07 m in the western AS at the northeast coast of Africa and it spread toward the north through the western NIO. During the premonsoon, the SD is less than 0.1 m in most of the region and higher values are observed in the southwestern region of the AS (Fig. 2f). In the NIO, a higher SD is observed during monsoon compared to other seasons (Fig. 2g). During this period, the western and northern AS shows a higher SD with the northern AS having a slightly higher value (~0.16 m) than the western AS. During this time the BoB region also shows a higher value (~0.11 m) compared to other seasons, but even during the monsoon, the south to central-east coast of India shows a very small SD. This low SD could be due to the sheltering effect of the Sri Lankan landmass. The Sri Lankan landmass has a major role in the wave climate of a major part of the eastern shelf seas of India. During the postmonsoon period, a higher SD is observed in the southeast of the NIO and in the western AS (Fig. 2h).

2) Swell and wind sea climatology

The annual and seasonal climatology of wind seas and swells in the NIO are shown in Fig. 3. It is clear from the left panel of Fig. 3 that swell SWH is always higher than wind sea SWH (right panel of Fig. 3). The annual climatology of wind seas and swells indicates that the waves in the NIO are dominated by swells (Fig. 3a). The annual average of the wind sea height is less than 1 m in a major part of the NIO. Wind sea height of more than 1 m is observed in the south at the eastern entrance of the Gulf of Aden (off the northeast coast of Africa). In seasonal climatology, wind seas are very low during the premonsoon season (Fig. 3b). During this time, swells are in the range of 1–1.5 m and swell height decreases from south to north in the NIO. Semedo et al. (2011) and Johnson et al. (2013b) found that the swell propagating from the Southern Hemisphere reaches the west coast of India during the premonsoon period. Because of the calm condition (low wind seas) of the NIO during the premonsoon period, the propagation of swells from the Southern Hemisphere is more visible. These swells are absent during the monsoon period due to turbulence in the NIO created by strong winds (Johnson et al. 2013b). Chen et al. (2002) observed an enhancement of the wind sea generation in the AS in the boreal summer [June–August (JJA)] as a consequence of the Asian monsoon. Semedo et al. (2011) also reported that the NIO, with the exception of the summer monsoon period (JJA), is the most swell-dominated area of the world’s oceans. In the eastern AS, the average period of swells in the monsoon is less than that during the other two seasons (Kumar et al. 2012) because these swells are generated within the western NIO (Fig. 3c) and reach the west coast of India from the southwest direction. The swells in the western NIO are due to a higher southwest wind condition in this region (average wind speed of ~11–12 m s−1). Because of this higher wind speed, a wind sea height greater than 2 m is observed only during the monsoon period in the western NIO. Higher winds in this region pump energy mostly into shorter (high frequency), slowly moving waves. These waves transfer this energy across the continuous spectrum of waves of all scales toward longer (lower frequency) components, thus allowing the longer (lower frequency) waves to grow by means of nonlinear interaction (Babanin 2011). The generated swells reach the west coast of India.

In the BoB region, the southern BoB shows a higher swell height than other regions due to the propagation of swells from the south Indian Ocean. Chen et al. (2002) identified the southern BoB as one of the major swell pools in the world’s oceans. A surface wind speed (not shown) that is higher during the postmonsoon period than in the premonsoon period generates relatively high waves in the NIO during the postmonsoon (Fig. 3d). During this period, comparatively higher swell and wind sea heights are observed on the northeastern coast of Africa, but this wave system is weak during the premonsoon period. Johnson et al. (2013b) observed that waves with a peak wave period of 8–13 s reaches the central west coast of India during the postmonsoon period from the southwest direction, whereas the occurrence of these waves is much less during the premonsoon season.

b. Variability of wave height

1) Wave trend analysis

The yearly and seasonal trends of SWH in the NIO are shown in Fig. 4. Compared to the seasonal cycle, the linear trend in the SWH is relatively small. The trend of SWH with a significance level greater than 90% is only shown in Fig. 4. The western part of the NIO shows a yearly increasing trend (Fig. 4a), and in this region, the eastern entrance of the Gulf of Aden and the western equatorial Indian Ocean show the maximum value (~0.4 cm yr−1). The western Indian Ocean shows an increasing wave height at the rate of 0.1–0.4 cm yr−1. The SWH in the waters of two semienclosed basins, the Red Sea and the Persian Gulf, also shows this increasing trend. These regions are important for the transport of cargo vessels carrying oils and goods. Hence, an increase in wave height can affect marine transport in this region. In a small area near the east coast of India, a decreasing trend of SWH is observed.

We compared our estimated SWH trend with that reported by Young et al. (2011). Young et al. (2011) reported that the long-term trend of SWH in the NIO was in the range of 0%–0.75% (0–0.75 cm yr−1) during the period 1985–2008. During the same period, the present study shows a similar increasing trend in most of the regions. Some parts of the NIO show a decreasing trend similar to the trend observed in Young et al. (2011), but regionally a slight difference is observed in both studies. For example, our study shows a decreasing trend in a small area of the central east coast of India, whereas the study of Young et al. (2011) shows a small increasing trend in that region.

Two transects of width 10° of longitude and centered on 65.25° and 90°E were calculated from 53.25°S to 20.25°N, with the purpose of examining the annual mean SWH trends in Indian Ocean. The trend of SWH decreases from south to north along both transects (from 0.86 to 0.04 cm yr−1 along 65.25°E and from 0.79 to −0.05 cm yr−1 along 90°E) except at the northernmost location. The northern location at both transects indicated a higher increasing trend (Fig. 5). The statistical significance levels of the trends at locations 1, 4, 5, 7, and 11–14 are above 95%, at locations 6, 8, and 10 the levels are above 90%, at location 3 they are above 86%, and at locations 2 and 9 the levels are insignificant. Since the waves in the eastern AS are mainly the swells arriving from the Southern Ocean (Semedo et al. 2011; Johnson et al. 2013b), the increase in SWH in the NIO is due to the increase in swells in the Southern Ocean. Hemer et al. (2008) reported that the wave events are likely to increase in magnitude in the southern coast of Australia due to intensification of the storms. Hemer et al. (2010) observed increasing trend in SWH for the Indian Ocean sector of the Southern Ocean.

During the premonsoon, a large area in the northeastern AS, the northwestern BoB, and the southern region of the AS shows a decreasing SWH (Fig. 4b). The region near the Andaman and Nicobar Islands also shows a decreasing SWH. However, during the monsoon, the SWH shows a strong increasing trend in almost all areas (Fig. 4c). It has increased more than 1 cm yr−1 in the northern AS and is at the highest value this season. The second maximum is observed in the western equatorial Indian Ocean. During this season, the eastern NIO and the northern BoB show an increasing trend. In the postmonsoon period, a strong increasing trend in SWH is observed in the southwestern NIO (Fig. 4d). A greater increasing trend is observed in the eastern entrance of the Gulf of Aden (>0.5 cm yr−1). The region around Sri Lanka (the southern region of the Indian subcontinent) shows an increasing SWH in the range of 0.2–0.3 cm yr−1.

To explore the influence of local winds on the waves, the trend of wind speed is examined (Figs. 4e–h). The western AS shows a positive trend in wind speed similar to the trend of SWH. Similar to the observation of Shanas and Kumar (2014), the annual mean wind speed shows a statistically significant decreasing trend of 1–2 cm s−1 yr−1 in the central eastern AS.

2) Modes of variability of SWH

The EOF method is used in this section to represent different modes of variability of SWH in the NIO. We have carried out the analysis using the monthly average SWH and wind speed. The first EOF mode of SWH [EOF1 (SWH)] covers 92% of the total variability in NIO (Fig. 6a) and all other modes are degenerate (~8%). EOF1 (SWH) exhibits the maximum value at the western part of the study area and it mainly spreads and wanes toward the north and northeast. A weak branch of it enters the BoB through the southern part of India. The first mode of variability of wind speed is shown in Fig. 6b and it covers 70% of the total variability. Since the waves are mainly generated in the ocean surface by the wind stress acting on water and this stress is directly proportional to the square of the wind speed, the climatology of the square of the wind speed for the monsoon months from 1979 to 2012 is presented in Fig. 6d. Figures 6a and 6b show the same pattern as Fig. 6d and hence we can say that the first EOF mode of SWH and wind speed in the NIO is due to the monsoon wind. The BoB branch of the monsoon is also visible in these figures.

The correlation of principal component time series of SWH [PC1 (SWH)] and that of wind speed [PC1 (WS)] are 0.94 with a significance level above 95%. The PC1 (SWH) and PC1 (WS) are shown in Fig. 6c, which shows the annual oscillation of the monsoon. From the above-mentioned factors, it is clear that the wave climate variability in the NIO is influenced by the monsoon wind. Modes of monthly anomaly of SWH are shown in Fig. 6e. From the figure it is clear that strong negative variability in AS is similar to the EOF1 of SWH and wind speed. It is spreading and waning in the northwest direction. The EOF1 of SWH anomaly covers 51% of the total variability of the SWH anomaly in the NIO. The PC1 of the first mode of the SWH and wind speed anomaly is shown in Fig. 6f. The correlation between wind speed and SWH is 0.54 with a significance level greater than 99%. From the time series of the EOF1 of SWH anomaly (Fig. 6f), it can be seen that some extreme events during the monsoon period occur from 1979 to 2012.

To identify the influence of interannual variability of monsoon on the NIO wave climate, we used the PC1 (SWH) and that of wind speed with monthly average rainfall data for the central west coast of India (the Goa and Konkan regions) and all India. In the case of the central west coast of India, the annual rainfall is predominantly during the monsoon. For all-India rainfall data, the monsoon contributes 60%–90% of annual rainfall (Ramesh Kumar and Prabhu Dessai 2004). The yearly oscillation of monsoon and wave climate is well known, so that in this study we focused on the interannual oscillation of the monsoon and its relation to SWH. Here, we selected one box in central AS (10°–15°N, 60°–65°E, box B in Fig. 1), where higher variability of SWH is observed in EOF1 (SWH). The correlation of monthly and spatially averaged SWH over box B in central AS (Fig. 1) with rainfall over the central west coast and all over the India are high (a correlation coefficient of 0.93 and 0.91, respectively; both are statistically significant at 99%). Time series plot of monsoon mean SWH and wind speed within box B in central AS and monthly average rainfall data during the monsoon in the central west coast of India indicate that in some years the increase/decrease in SWH is linked to rainfall (Fig. 7). But in some years (e.g., 1986, 1994, 2001, and 2012) even though the SWH was high, a similar increase in rainfall was not observed and also when the rainfall was high, a lower SWH is observed in some years (e.g., 1985, 1987, 1997, 2000, and 2011). The monsoon mean SWH has a correlation with the monsoon mean wind speed (a correlation coefficient of 0.77). The study shows that a simple correlation or lag correlation analysis is insufficient for understanding the influence of interannual oscillations of the monsoon on SWH.

To remove the oscillations in the period range of one year and less than one year, the time series data are low-pass filtered using a Butterworth filter. These filtered and nonfiltered data are analyzed with wavelet and wavelet coherence analysis (Torrence and Compo 1998; Grinsted et al. 2004) and are shown in Figs. 8 and 9, respectively. In the case of SWH, we can see two bands of oscillations: one in the 4–6-yr band and the other one in the 7–10-yr band (Fig. 8a). Among these two bands, the 7–10-yr band is continuous, whereas the 4–6-yr band is observed until 1997 and after that it shifted gradually to the 5–10-yr band and became a combined band. A similar pattern of oscillations is observed in the case of PC1 of surface wind (Fig. 8b) and monthly average rainfall on the central west coast (Fig. 8c) of India and also all over India (Fig. 8d). The rainfall data of the central west coast region show a larger similarity with PC1 of SWH than compared to the all-India rainfall, because the all-India rainfall is influenced by the northeast monsoon [October–December (OND)] also (Kripalani and Kumar 2004).

By separating the modes of SWH using EOF, we found that the monsoon covers 92% as the first mode of variability. The phase relation between the SWH within box B (Fig. 1) and rainfall in the central west coast of India (monsoon rainfall) is identified with the help of cross-wavelet coherence analysis (Grinsted et al. 2004). Figure 9 shows the cross wavelet of SWH in spatial and monthly averaged within the box with monthly rainfall in the central west coast of India. From the figure, it is clearly visible that the SWH and monsoon rainfall are related with phase angle 0°, indicating that in a yearly cycle both of them are in phase. In the case of the interannual cycle, we already discussed the two bands of oscillation that were observed from filtered data and this band is also visible here. Among this phase angle, the 7–10-yr band is 0° as a 1-yr band, but phase angle 50° is observed in the case of the 4–6-yr band up to the year 1997. The 50° upward phase angle indicates that the monsoon rainfall leads the SWH by a 50° phase for this period.

c. Influence of tropical cyclones on wave height over the NIO

A detailed climatology of TCs in the AS is reported by Evan and Camargo (2011). Evan et al. (2011) reported an increase in intensification of tropical cyclones during the premonsoon season. For the study period, we have observed around 60 TCs over the NIO of which (i) 50% are observed as tropical depression to cyclonic storms (wind speed of 17–47 kt), (ii) 23% come under the severe cyclone category (wind speed of 48–63 kt), and (iii) 27% range from very severe cyclonic storms to supercyclones (wind speed > 64 kt) (Fig. 10a).

If we consider the maximum intensity of TCs during the whole study period, there is a clear intensification of stronger events after 1996 (Fig. 10b). Evan et al. (2011) reported that the early development of the monsoon may be caused by enhanced land–ocean thermal contrast between the Asian landmass and the equatorial Indian Ocean, which can reinforce the northward pressure gradients that in turn strengthen the monsoon and associated cyclonic shear vorticity. We considered the distribution of the locations where the maximum intensity of TCs is observed in the AS and found that the occurrences of stronger events, such as severe cyclones and supercyclones of maximum intensity, are mostly in the area covering 17.5°–22.5°N, 60°–65°E. Hence, we have considered a box area (latitude 17.5°–22.5°N and longitude 60°–65°E; box A in Fig. 1) to analyze the link between the severe cyclone events and SWH. The analysis showed about 16 cyclones passed through the box and out of which the most severe events were selected for the study. The details of the cyclones’ date and their maximum intensity locations are presented in Table 1.

The selected cyclone events are during 1993, 2007, and 2010 and the strongest was the Supercyclone Gonu, which formed in early June 2007. We have extracted the time series data of SWH for each year at the location where the maximum cyclone intensity is observed. The encircled peaks in SWH (Fig. 11b) in all 3 years are due to the cyclone activity, and the SWH and wind speed observed during the maximum cyclone intensification for the study domain are presented in rows 3 and 4 of Fig. 11. An increase in wind speed and associated wave height is observed all throughout the events. The unusual intensification of SWH is directly related to the TC activity. As the TC moves the local wind speed becomes higher, which could generate higher wind seas. About 90% of the premonsoon TCs in the AS occur from mid-May to mid-June, during which the mean vertical wind shear over the TC intensification zone increases approximately from 12 to 25 m s−1 (Evan et al. 2011). Evan et al. (2011) also reported that the lifetime maximum intensification date of TCs also increased during the recent decade to around 13 days, confirmed with a higher confidence level. Since the intensity of cyclones increased and mostly occurred in latitudes greater than 15°N, the trend of SWH is higher in the northeastern part of the NIO (Figs. 4a and 5).

4. Summary and conclusions

The climatology and variability of surface waves in the NIO region is investigated using ERA-Interim data. This study focuses on the period 1979–2012 and includes the analysis of significant wave height, swell, and wind sea height. Wind sea and swells are separated from the wave data using the wave energy statistical method. The variability of the SWH for 34 years is studied by trend analysis and EOF analysis, and the role of tropical cyclones on the SWH variability is also examined. In the trend analysis, the western NIO shows a strong increasing trend and the increase in wave height in this area can affect the eastern coastal region of Africa. A higher annual increasing is observed in the western equatorial NIO and east of the eastern entrance of the Gulf of Aden. An annual decreasing SWH is observed in a small area along the east coast of India. During the premonsoon period, the NIO is relatively calm and shows a decreasing trend in SWH. A higher increasing SWH is observed during the monsoon period. The northern NIO shows an increasing trend of greater than 1 cm yr−1. The swells from the Southern Hemisphere are present in the NIO during the pre- and postmonsoons. The increasing and decreasing SWH in the NIO depends on the Southern Hemispheric swells and local wind system. The intermediate period waves generated off the northeast coast of Africa are propagated in a northeast direction and are observed in the eastern Arabian Sea. The EOF analysis indicates that the SWH variance is strongly dominated by the first mode and is associated with the monsoon wind in the NIO. A higher variability in the SWH is observed in the western AS region and is spreading and waning in the northeast direction. The unusual intensification of SWH in the NIO is directly related to the tropical cyclone activity. And also there is a clear intensification of stronger events after 1996. Since the intensity of cyclones is increasing, it will have much influence on the seasonal characteristics of SWH in the region. Wave climatology and trends in the NIO require further investigation in order to determine the role of the Southern Ocean swell in their modulation and the influence of interannual variation in the monsoon on SWH.

Acknowledgments

We thank the director of the National Institute of Oceanography, Goa, India, for providing encouragement in carrying out the study. We thank the Indian Institute of Tropical Meteorology, Pune, India, for providing the rainfall data. We thank the two reviewers for their critical comments and the suggestions for additional studies, which improved the scientific content of the paper. This work forms part of the doctoral thesis of the first author.

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Footnotes

*

National Institute of Oceanography Contribution Number 5744.