Influence of PDO and ENSO with Indian summer monsoon rainfall and its changing relationship before and after 1976 climate shift

doi:10.21203/rs.3.rs-1736675/v1

Download PDF

Research Article

Influence of PDO and ENSO with Indian summer monsoon rainfall and its changing relationship before and after 1976 climate shift

https://doi.org/10.21203/rs.3.rs-1736675/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

In this study, we investigated the possible relation between the Indian summer monsoon and the combination of the different phases of Pacific Decadal Oscillation (PDO) and El Niño Southern Oscillation (ENSO) before and after the climate shift in 1976. This study is carried out using IMD’s rainfall dataset, HadISST v1.1 dataset and twentieth century reanalysis dataset by comparing anomalies of the respective parameters from 1901 to 2020. It is found that when positive (negative) phases of PDO and El Niño (La Niña) co-occur, deficit (surplus) rainfall are likely to occur over entire India. SST signatures of both phenomena are evident in this context. However, when negative (positive) PDO and El Niño (La Niña) co-occur, the signal is mixed and it is unlikely that either surplus or deficit rainfall conditions will occur over entire India. SST signatures are disrupted and minimised. In other words, when ENSO and PDO are in (out of) phase they enhance (counteract) the conventional monsoon-ENSO relationship. After confirming climate shift in 1976, the study periods were further divided into pre and post climate shift periods based on Niño 3.4 index and PDO index and analysed their impact on the Indian summer monsoon rainfall. In the pre-shift example, in-phase conditions exhibit similar qualities to those described above. Rainfall patterns are more indicative of ENSO than PDO. In the post-shift situation, the positive anomaly of SST in the PDO and Niño region is significantly stronger than in the pre-shift phase. When compared to the pre-shift example, positive rainfall anomalies are amplified during positive PDO and El Niño, while negative PDO and La Niña show a weakening of positive rainfall anomalies. The out-of-phase condition has a balancing effect due to the counteracting impact, but with an increased positive anomaly of SST. In that combination, rainfall patterns with PDO characteristics rather than ENSO characteristics emerge. Low level wind anomalies and other circulation features are consistent with the above result.

Rainfall Variability

Pacific Decadal Oscillation

El Nino Southern Oscillation

Climate Shift

circulation pattern

The Indian summer monsoon rainfall (ISMR) from June to September has various socio-economic impacts across south Asia particularly over the Indian subcontinent (Webster et al., 1998; Gadgil and Gadgil, 2006; Rajeevan and Nanjundiah, 2009). This monsoon rainfall accounts for 70–90% annual rainfall over India (Shukla et al., 2016). The ISMR has large spatio-temporal variability, which can impact the economic growth of the country (Mooley and Parthasarathy, 1984; Kripalani et al., 2003) that plays a vital role in agriculture, reservoir management, hydrological power generations and many more, thus it controls the economy of India (Gadgil and kumar, 2006; Mertz et al., 2009; Saha and mooley., 1978). ISMR exhibits different temporal variabilities ranging from diurnal to multidecadal time scales in addition to its spatial heterogeneity (Krishnamurthy and Shukla, 2000; Rajeevan et al., 2010; Varikoden et al., 2010, 2012, Seetha et al., 2020; Reshma et al., 2021). Earlier studies also mentioned that the variabilities in ISMR can be due to the local forcing (Chakraborty et al., 2002; Nair et al., 2018; Shukla and Wallace, 1983) as well as by teleconnections like El Niño Southern Oscillations, Pacific decadal oscillations, Atlantic Multidecadal Oscillations (Varikoden and Preethi, 2013; Varikoden et al., 2015; Luo and Lau, 2017; Malik et al., 2017; Nair et al., 2018). The combinations of such teleconnections can also be a reason for these types of variabilities (Krishnamurthy and Krishnamurthy, 2014; Krishnamurthy and Krishnamurthy, 2017, Levine et al., 2017). There are studies mentioned about the empirical and dynamical relationships between ISMR and ENSO (Shukla and Paolino, 1983; Webster and Yang, 1992; Webster et al., 1998; Kumar et al., 1999; Varikoden et al., 2015; Seetha et al., 2020; Hrudya et al., 2020, 2021) and ISMR and PDO (Malik et al., 2017; Joseph, 2014; Malik and Brönnimann, 2018; Krishan and Sugi, 2003). Their combined effect always has greater importance, since both are happening in the Pacific Ocean (Mantua et al., 1997, Mantua and Hare, 2002; Rasmusson and Wallace, 1983).

The PDO and ENSO have a negative correlation with ISMR (Sikka and Gadgil, 1980; Rasmusson and Wallace, 1983; Krishnamurthy and Krishnamurthy, 2014; Krishnamurthy and Krishnamurthy, 2017, Krishnan and Sugi, 2003; Kumar et al., 1999; Sikka, 1980). The ENSO influenced the ISMR in the presence of PDO is based on the phases they coexist (Krishnamurthy and Krishnamurthy, 2014). It also determines rainfall pattern, strength and intensity (Krishnamurthy and Krishnamurthy, 2014; Krishnamurthy and Krishnamurthy, 2017).

Climate change refers to the change in the environmental conditions of the earth due to many internal and external factors (Crowley, 2000; Stern and Kaufmann, 2014) and it has become a global concern over the last few decades (Fisher et al., 2021; Baines and Folland., 2007). Alterations to the earth's atmosphere that occur over much longer periods—decades to millennia—are characterised as climate change.

The stepwise changes in climate variabilities of the climate system between different states in temporal domain is called climate shifts (Miller et al., 1994) and such shifts were noticed in the North Pacific between 1976 and 1977, and that is manifested in the time series of the PDO (Graham, 1994; Mantua and Hare, 2002), which ultimately changed the basic mechanisms of a phenomenon that happened in the Pacific Ocean (Meehl et al., 2009). Climate change and climate shift are having a different perspective. Their normal characteristics such as their ordinary SST range, precipitation range, etc, are also changed (Sabeerali et al., 2019). The climate shift has increased the Pacific Ocean SST by 0.75°C (Nitta and Yamada, 1989; Gaffen et al., 1991). Studies show that there exist dynamically consistent changes in the rainfall, SST, surface winds, and organised convection during the mid-1970s (Graham, 1994; Suhas et al., 2008; Sabeerali et al., 2012; Sahana et al., 2015; Kuttippurath et al., 2021). These changes can also be reflected on the relationship of ENSO and PDO on ISMR.

There are studies on the variabilities of ISMR and its association with ENSO and PDO, however, the combined influence of ENSO and PDO on the ISMR variability and its changes in the recent decades are yet to be studied systematically. The probability of changes occurring after the climate shift of 1976/77 has also not been investigated. Therefore, the present study explores the influence of ENSO and PDO on ISMR before and after the climate shift. The changes in the recent decades and their linked dynamics are carried out on the atmospheric, and oceanic parameters to bring out the variability of ISMR by the influence of ENSO and PDO in different combinations. In section 2, we briefly explained the datasets and methods used in this study. Variabilities that happened to SST, rainfall pattern and circulation features due to the combination of ENSO and PDO are explained in section 3. Section 4 depicts the conclusions of the study.

2.1 Data

We have used long period daily gridded rainfall data set over India from India Meteorological Department (IMD) with high spatial resolution (0.25 X 0.25 latitude - longitude) from 1901 to 2020 (Rajeevan et al., 2008; Rajeevan and Bhate., 2009; Pai et al., 2014). This data set was prepared by running basic quality control over 6955 rain gauge stations in India and missing data is replaced from neighbouring rain gauge measurements. The climatological and variability aspects of rainfall over India produced from this data set were comparable to the existing gridded daily rainfall data sets. Furthermore, due to its higher spatial resolution and the higher density of rainfall stations used in its development, the spatial rainfall distribution, such as heavy rainfall areas in the orographic regions of the west coast and over northeast, low rainfall in the leeward side of the Western Ghats was more realistic and better presented in IMD gridded dataset.

Sea Surface Temperature (SST) studies are carried out using monthly SST data from HadISST version 1.1 (https://www.metoffice.gov.uk/hadobs/hadisst), which has a spatial resolution of 1^o longitude X 1^o latitude (Reyner et al., 2003). It is reconstructed using a two stage reduced space optimal interpolation procedure, followed by superposition of quality improved gridded observations onto the reconstructions to restore local detail (Reyner et al.,2003). Additionally, the zonal and meridional circulations are also used in this study, which are obtained from twentieth century reanalysis version 3 (20CRv3) provided by National Oceanic and Atmospheric Administration - Cooperative Institute for Research in Environmental Sciences (https://psl.noaa.gov/data/gridded/data.20thC_ReanV3.pressure.html). It has 1^o latitude × 1^o longitude horizontal spatial resolution with 28 vertical pressure levels (1000–1 hPa) on a monthly time scale (Slivinski et al., 2019). The 20CR ensemble is the first to span over 100 years of sub-daily global atmospheric conditions. This gives the best estimate of the weather at any given location and time, as well as an estimate of its confidence and uncertainty. The version 3 system (NOAA-CIRES-DOE 20CRv3) employs improved data assimilation methods, such as an adaptive inflation algorithm, a newer, higher-resolution forecast model that specifies dry air mass, and assimilates a larger set of pressure observations.

For selecting years of different phases of PDO and ENSO, the PDO Index and Niño 3.4 index was taken. PDO index which is known as the leading principal component of monthly sea surface temperature variability of North Pacific Ocean (Deser et al., 2016) and Niño 3.4 index which is defined as the average SSTs of the region in between 5^oN-5^oS, 120^oW-170^oW in the Pacific Ocean, (Trenberth, 2020) was obtained from the website https://www.cpc.ncep.noaa.gov/data/ and https://www.ncdc.noaa.gov/teleconnections of National Oceanic and Atmospheric Administration – Cooperative Institute for Research in Environmental Sciences (NOAA–CIRES), respectively.

2.2 Methods used in the analysis

The study period (1901 to 2020) was classified into four quadrants based on the Niño 3.4 and PDO indices. The quadrants were selected as per the orientation of the cartesian coordinate system. The quadrant 1 (3) is the combination of the warm (cold) phase of PDO and El Nino (La Nina). The quadrant 2 (4) is the combination of the cold (warm) phase of PDO and El Nino (La Nina). In this category, the PDO and ENSO are in phase in the quadrants 1 and 3, whereas they are out-of-phase in 2 and 4 quadrants. In this study, the threshold value for considering the Niño and PDO are considered more than 60% of their standard deviation. Warm phase is considered if indices > 0.36 for ENSO and > 0.55 for PDO, while the cold phase is defined as when the value is < -0.36 for ENSO and < -0.55 for PDO.

However, to study the changes of climate shift, the study period was subdivided into pre-shift (1930–1976) and post-shift (1977–2020) periods. The pre and post periods were categorised into four quadrants as done earlier. However, we modified the above criteria of 60% of the standard deviation to 25% of the standard deviation of the respective indices for incorporating more cases in all the four quadrants before and after the 1976/1977 periods.

Interannual variations of PDO and Niño 3.4 index are used to identify variation of PDO and ENSO within the study period. The effect of PDO and ENSO on the precipitation patterns of Indian monsoon summer rainfall is studied using correlations between these indices and ISMR. A clear signature of climate shift was noticed in 1976/77 period from the interannual variability of both the indices. Spatial anomaly is used to compare differences in the pattern of parameters like precipitation, SST, zonal and meridional wind, stream function, velocity potential, that happened before and after climate shift.

3.1 Climatology of ISMR and SST

The climatological features of rainfall in the Indian subcontinent and SST in the Indo-Pacific domain during the summer monsoon season (June to September) for the period of 1901 to 2020 are given in Fig. 1. Figure 1a depicts high rainfall over the windward side of the Western Ghats and the northeastern regions of the country, where the monsoon rainfall exceeds 16 mm day^− 1. Over central India, the rainfall is about 10 mm day^− 1 and it is less than 5 mm day^− 1 over southeast, north and northwest Indian regions. The Western Ghats in the west coast region receives high rainfall, with an average annual rainfall of about 3800 mm year^− 1 in the windward side (Rao., 1976). North eastern region also gets high amounts of rainfall compared to the north and central part of India. Rain shadow region of south India receives less rainfall during the monsoon season.

In Fig. 1b, the equatorial Pacific Ocean experiences high SST greater than 27^o C, which is concentrated over the western side between 120^o E and 160^o E. Eastern side experiences relatively low SST between 21^o C and 25^o C. SST decreases towards higher latitudes beyond 20^o N. The equatorial Indian Ocean shows SST more than 27^o C. The PDO region is comparatively cooler than the Niño region. The spatial distribution of rainfall and SST varies considerably during the occurrence of ENSO and PDO, moreover the co-occurrence of both the events can amplify the modification in their spatial pattern. Hence a detailed understanding of the variability in different space time domains and relationship of PDO and ENSO with ISMR is needed.

3.2 Interannual variation and relationship of PDO and ENSO

Interannual variations between PDO and ENSO along with linear trend for the period before and after 1976 (1930–1975 and 1976–2020) for a period from 1901 to 2020 during southwest monsoon are given in Fig. 2a. PDO and ENSO interannual variations have witnessed major alterations before and after the 1970s. The standard deviation of Niño 3.4 index is 0.60 and PDO index is 0.92, both are significant at more than 99% confidence level. In the decadal variations, before the 1970s (pre-shift), the phase transitions of PDO took 15–20 years but after the 1970s (post-shift), only 12–15 years were taken for it. In recent years after the 1970s, the warm (cold) phase of PDO tends to occur with the warm (cold) phase of ENSO. Earlier years showed a less intense ENSO and PDO where magnitude of indices were in between − 1.5 and + 1.5. But recent years have phases with high intensity which are varying in between − 2.5 to + 2.5. During 1930 to 1975, interannual variations of PDO index and Niño 3.4 index are inhomogeneous. Occurrence of El Niño (La Niña) is not coinciding with positive PDO (negative PDO). Intensity of ENSO is smaller ( in between − 1 to + 1) than that of PDO (in between − 2 to + 2). During 1976 to 2020, the occurrence of El Niño (La Niña) coincided with the occurrence of positive PDO (negative PDO). Both indices have similar intensities (ranging from − 3 to + 3) and interannual fluctuations. These observable changes indicate that a climate shift occurred in 1976, which is consistent with previous studies (Graham 1994; Chaluvadi et al., 2021, Miller et al., 1994). It should be noticed that the ENSO trend has changed from negative to positive (trends are not significant), however the PDO trend has shown significant changes in their trend values (from − 0.29 to -0.01). Global warming and abrupt changes in SST conditions can be a reason for the changes occurring in the prevailing climatic conditions (Philander and Philander, 2008; Schuldt et al., 2011).

Figure 2b is showing the regression analysis of the Niño 3.4 index and PDO index during the southwest monsoon from 1901 to 2020. In the Fig. 2b, the first quadrant represents (warm) positive PDO (pPDO) and El Niño phases (positive values for PDO index and Niño 3.4 index), second quadrant represents (cold) negative PDO (nPDO) and El Niño phases (negative values for PDO index and positive values for Niño 3.4 index), third quadrant represents nPDO and La Niña (negative values for PDO index and Niño 3.4 index) and fourth quadrant represents pPDO and La Niña (positive values for PDO index and negative values for Niño 3.4 index). There exist years having warm (cold) phases of PDO and ENSO. Mixed cases are also seen but the number of occurrences is less. Regression line is showing a positive relationship. Points are staggered and the linear correlation coefficient is 0.5 and regression coefficient of linear relation is 0.3 which denotes an in-phase relationship. Even though they are having one to one relation, lots of staggered points are there which gives an importance to study its properties on quadrant basis.

3.3 Combined effect of PDO and ENSO on SST and ISMR anomalies

To substantiate the relation between ISMR and combination of PDO and ENSO, composites of summer monsoon seasonal anomalies of rainfall and SST are shown in Fig. 3 and Fig. 4, respectively. The number of pPDO and El Niño, pPDO and La Niña, nPDO and El Niño and nPDO and La Niña years included in the composite analysis are 32, 27, 21 and 39, respectively. The nPDO and La Niña composite of SST is shown in Fig. 3a indicating negative SST anomalies in the eastern part along the west coast of North America and stronger positive SST anomalies in the central and western parts of the north Pacific. This gives strong nPDO signatures. A pattern of severe negative anomalies exists in the eastern and central equatorial Pacific, with a neutral situation in the western Pacific indicating La Niña signatures. The pPDO and El Niño composite (Fig. 3c) shows a pattern similar to the nPDO and La Niña composite but with SST anomalies of opposite signs. Figure 3c shows positive SST anomalies in the eastern Pacific and negative SST anomalies in the North western and central Pacific. The equatorial Pacific is having positive anomalies of SST in the eastern side and weak negative anomalies in the western side. Both of the above mentioned in-phase examples, the Indian Ocean appears to be a mirror image of the equatorial Pacific Ocean.

Figure 3b shows the SST anomalies of nPDO and El Niño. It has positive anomalies in the equatorial eastern Pacific and weak negative anomalies in the western Pacific, consistent with El Niño conditions. Positive anomalies in the north Pacific with slightly weak negative anomalies of SST consistent with the nPDO phase. However, the anomalies are weak compared to the in-phase composites. Thus, the nPDO phase and El Niño counteract each other to minimise their proper signals. Similar to the case of pPDO and La Niña composite in Fig. 3d, which shows negative anomalies in the equatorial eastern Pacific as well as northwest Pacific and weak positive anomalies in northeast Pacific. In the case of out-of-phase examples mentioned above, the Indian Ocean is a mirror image of the equatorial Pacific, just as in in-phase ones, but with different spatial distribution and homogeneity.

The previously described SST features influence southwest monsoon rainfall patterns. Spatial distribution of rainfall anomalies of southwest monsoon during different combinations of PDO and ENSO in Fig. 4 is used to analyse this concept. The negative PDO and La Niña composite of rainfall shows positive anomalies of rainfall all over India except the North eastern region (Fig. 4a). Higher anomalies (> 3 mm day^− 1) are observed in the northern portion of the Konkan coast. This enhanced flood condition over almost the entire India indicates that La Niña and nPDO, both of which produce flood conditions over India, are complementing each other. pPDO and El Niño composite (Fig. 4c) shows below normal rainfall patterns over central India and western coast of the country (Krishnan et al., 2003; Kanamitsu and Krishnamurti, 1978; Krishnamurthy and Goswami, 2000). However the Northeastern region is showing positive anomalies of rainfall. This enhanced signature in the rainfall anomalies seems to be the result of pPDO and El Niño, both associated with negative rainfall anomalies over India, complementing each other. Figure 4b shows nPDO and El Niño composite of rainfall in which India is experiencing a combination of drought and neutral conditions, with a domination of drought conditions. In a pPDO and La Niña composite (Fig. 4d), floods occur in the north and central regions of India, whereas negative rainfall anomalies occur in the southern India and western regions of Gujarat. Hence out-of-phase cases having a mixed pattern of rainfall with ENSO domination.

3.4 Comparison of SST and rainfall using pure conditions

To further substantiate the relation between monsoon and ENSO and to understand such relation relative to PDO, composites of summer monsoon seasonal anomaly of rainfall and SST of pure ENSO and PDO cases are shown in Fig. 5 and Fig. 6, respectively. Figure 5a illustrates La Niña conditions with a negative SST anomaly in the eastern and central equatorial Pacific and weak positive anomalies in the western equatorial Pacific. The Indian Ocean is likewise witnessing a negative SST anomaly, particularly in the Arabian Sea. Rainfall composite during La Niña period (Fig. 6a) exhibits an above-normal precipitation pattern across India, with the exception of the central and eastern parts, which show below-normal precipitation. El Niño is causing a horseshoe pattern of positive SST anomalies in the central and eastern equatorial Pacific and negative anomalies in the western equatorial Pacific (Fig. 5b). The positive SST anomaly can also be detected in the western Indian Ocean. Rainfall composite during El Niño (Fig. 6b) depicts the drought situation in India. A positive SST anomaly can be evident in the northeastern Pacific during a warm or pPDO (Fig. 5c), but a significant negative SST anomaly can be detected in the central and western Pacific. A positive SST anomaly can also be found across the Indian Ocean. Rainfall composite of pPDO (Fig. 6c) indicating a minor precipitation deficit. A negative SST anomaly exists in the northeastern Pacific during nPDO (Fig. 5d), while positive SST anomalies exist in the central and western Pacific. The Indian Ocean is experiencing neutral SST conditions which leads to the above normal rainfall pattern in the rainfall composite of nPDO (Fig. 6d).

Drought conditions are experienced when the pPDO and El Niño is present. Drought severity is greater in El Niño situations than in pPDO. Flooding is the result of both a nPDO and La Niña. As noted previously, the La Niña situation causes more positive rainfall anomalies than negative PDO situations. It can be concluded that the ISMR has a negative correlation with PDO and ENSO. Spatial correlation of ISMR with PDO and Niño 3.4 indices can further substantiate this result. Figure 7 is the spatial correlation coefficient of Indian summer monsoon rainfall with PDO and Niño 3.4 indices during southwest monsoon. Contours represent regions with 95% significance level.

The two indices show regionally varying relationships with ISMR. The correlation is significant over most parts of India, except in the northeast, where the correlation is insignificantly out-of-phase as noted in earlier studies (e.g., Guhathakurta and Rajeevan, 2008; Varikoden and Babu 2015; Nair et al., 2018; Shukla and Mooley 1987). The correlation values are more significant in the Niño 3.4 index than PDO index. In some parts of India correlation of ISMR with PDO index is becoming positive. The ENSO (Fig. 8b) is having a strong negative correlation with ISMR compared to PDO. Most of the regions are having a significance of more than 95% confidence level. This can be identified using contours drawn in the figure. When comparing years with a combination of phases and pure ones, it is noted that if they are in-phase, strong and well defined SST features of both events occurring in the site. When they are out-of-phase, their intensity drops and various places experience neutral conditions. The horseshoe pattern of SST, which is an important feature of the ENSO SST pattern, is well characterised in in-phase conditions but not in out-of-phase conditions. Rainfall intensifies (weakens) when both are in warm (cold) phase, ie, during in-phase conditions. In the out-of-phase scenario, ENSO patterns are more prominent than PDO patterns.

3.5 Climate shift and change in the behaviour of teleconnections

Climate shifts in the Pacific Ocean have a significant impact on the phenomena that occur there (meehl et al., 2009; Mantua and Hare, 2002). During the shift basic characteristics and properties of all the phenomena in the Pacific Ocean changed (Graham, 1994). Considering the changes in the Pacific ocean, It is assumed that phenomena like ENSO and PDO, which are produced by the SST and SLP variations in the Pacific Ocean can also be changed along with this.

To recognize changes in the SST anomalies of the Pacific Ocean under distinct ENSO and PDO phase combinations, spatial distribution of SST (^oC) anomalies during PDO and ENSO were drawn for pre-shift and post-shift (Fig. 8). Negative SST anomalies can be detected in the northwestern and central Pacific during pPDO and El Niño situations (Fig. 8a), with positive anomalies near the western coast of north America. Positive anomalies can also be seen in the central and eastern equatorial Pacific, while negative anomalies can be seen in the western equatorial Pacific. Positive anomalies detected around the western coast of North America, as well as in the eastern and central Pacific. However, the same relationship exists in the post-shift period with higher magnitudes (Fig. 8e). Negative anomalies in the western Pacific region are diminishing. The Indian Ocean has negative anomalies and neutral temperatures before the shift, but positive anomalies after the shift.

Negative SST anomalies can be detected in the northwestern and central Pacific during positive PDO and El Niño situations (Fig. 8b), with positive anomalies near the western coast of North America. Positive anomalies can also be seen in the central and eastern equatorial Pacific, while negative anomalies can be seen in the western equatorial Pacific. Positive anomalies are found around the western coast of North America, as well as the eastern and central Pacific, become stronger after the shift (Fig. 8f). Negative anomalies in the western Pacific region are diminishing. The Indian Ocean has negative anomalies and neutral temperatures in the pre-shift condition, however, it turned to positive anomalies during the post-shift period.

In nPDO and El Niño conditions (Fig. 8c), positive SST anomalies in the north Pacific with a weak negative anomaly around it show nPDO characteristics. The horseshoe pattern of positive SST anomalies across central and eastern Pacific with weak negative anomalies at the western Pacific shows El Niño signature. However, the anomalies are weak compared to the in-phase composites. Thus, the nPDO phase and El Niño counteract each other to minimise their proper signals. El Niño characters are visible compared to PDO. The Indian Ocean is having a negative SST anomaly. In the post-shift phase (Fig. 8g), a stronger positive anomaly of SST is observed in the central and western Pacific, with a weaker negative anomaly along north America's western coast. Positive anomaly is present in the equatorial Pacific, reducing El Niño signatures which depicts clear dominance of PDO characteristics. Figure 8d, which shows nPDO and La Niña, has negative SST anomalies over the western coast of North America and positive SST anomalies along the western and central Pacific, both are having strong PDO signatures. Negative SST over the equatorial Pacific is a strong indicator of La Niña. SST traces of both events are observed. Positive anomalies are evident everywhere over the Oceanic region after the shift (Fig. 8h), with the exception of the eastern side of the Pacific, which has weak negative SST anomalies. Both phenomena's signature traits are missing, despite the increased positive anomaly of SST. The Indian Ocean is having negative SST anomalies in pre-shift however positive anomalies in post-shift.

To investigate variations of ISMR and to identify differences in ISMR pattern before and after climate shift, spatial distribution of rainfall (mm day^− 1) anomalies during PDO and ENSO periods before climate shift and after climate shift is shown in Fig. 9. In the positive PDO and El Niño case, the pre-shift condition (Fig. 9a) is showing a negative anomaly of rainfall with some positive rainfall anomaly western coast and northern parts of India. whereas the aftershift (Fig. 9e) is showing a more intensive negative anomaly of rainfall. pPDO and La Niña case is having positive rainfall anomalies favourable for La Niña condition in the pre-shift period (Fig. 9b), whereas negative rainfall anomalies which is a favourable condition for positive PDO are identified in post-shift (Fig. 9f). During nPDO and El Niño, negative anomalies of rainfall (El Niño favourable situation) can be seen in the pre-shift case (Fig. 9c), whereas positive anomalies (nPDO favourable situation) can be seen in the after shift phase (Fig. 9g). nPDO and La Niña show an increased amount of rainfall anomaly in pre-shift (Fig. 9d) but during post-shift (Fig. 9h), positive rainfall anomaly weakens and can see some negative rainfall anomalies in north eastern and central portions of India. Thus the analysis shows that during in-phase conditions, SST anomalies for ENSO and PDO combinations are showing their individual SST signatures. Warm (cold) phase has characteristics of El Niño (La Niña) and pPDO (nPDO) in pre-shift. In post-shift, negative (positive) anomalies become weaker (stronger). Rainfall decreases (increases) when warm (cold) phases coexist during pre-shift. After the climate shift, negative (positive) anomalies of rainfall become stronger (weaker). Out-of-phase conditions counteract each other and minimise their characteristic SST signals. But ENSO features are prominent during the pre-shift and PDO features are prominent during after shift. Rainfall patterns are coinciding with this result.

3.6 Enhancement of changes using dynamical parameters

Understanding the low level circulation anomalies helps us to identify the regions where the circulation pattern is favourable for rainfall. Figure 10 shows circulation anomalies at 850 hPa during the pre-shift and the post-shift periods. In pPDO and El Niño conditions, the pre-shift case (Fig. 10a) shows the westerly component of easterly trade winds strengthens over the central Pacific region. This strengthening of westerly components modifies the ENSO - monsoon relationship. Anomalous westerlies are also observed at 20°N in the Pacific Ocean, which indicates the weakening trade winds due to the high SST/ low SLP produced by pPDO and El Nino conditions. This combined effect decreases rainfall in India. The divergence pattern of the wind vector which leads to descending motion can also be attributed to decreased rainfall. Figure 10e shows the post-shift condition of pPDO and El Niño, decreased intensity of anomalous westerlies is seen in both Niño and PDO regions. The divergence pattern of wind vectors is prominent over India which further decreases rainfall than that of the pre-shift condition. The pPDO and La Niña during the pre-shift situation (Fig. 10b), show a convergence of the wind, thereby enhanced rainfall over India. Easterlies are prominent in the Niño region and the Indian Ocean region. Weakened easterlies originated from the western side of the Niño region due to the lower SST and higher SLP due to the climatic conditions caused by the La Niña. In the post-shift case (Fig. 10f), there is divergence all over central India, and strong easterlies are evident, decreasing rainfall. The rainfall is further weakened by westerlies across the Indian Ocean.

The pre-shift (Fig. 10c) has weak anomalous westerlies over the Indian region, which strengthen the prevalent trade winds over the Indian subcontinent that leads to deficit rainfall during nPDO and El Niño conditions. During the post-shift period (Fig. 10g), strong westerlies led to convergence over the Indian regions, enhancing convection and thereby enhancing rainfall over the Indian subcontinent. In nPDO and La Niña conditions (Fig. 10d), a convergence is observed over India in association with very weak easterlies in the western Pacific. This convergence weakens during the post-shift situation (Fig. 10h), which weakens the rainfall. Strong easterlies are observed in the western central Pacific region along with strong westerlies in the central eastern and north Pacific regions. Analysis of wind vectors are consistent with the results obtained from the analysis of SST and rainfall.

Further analysis of velocity potential and stream function is done to understand large scale features of horizontal circulation at 850 hPa levels (Fig. 11). During the pre-shift case (Fig. 11a), a tendency of divergence across India is noticed that reduces rainfall over there. The PDO region is also showing higher convergence and thereby ascending motion of air. In the post-shift period (Fig. 11e), a positive anomaly of velocity potential intensified, increasing the descending motion through an increase of divergence over India and decreasing rainfall. In the pre-shift phase of pPDO and La Niña (Fig. 11b), intensified convergence over India increases the amount of rainfall. In the post-shift (Fig. 11f) phase, there is a divergence, which reduces rainfall. Intensified divergence patterns are observed in between 20^o N and 40^o N in the post-shift period due to the increase in the temperature at that region after the shift.

During the nPDO and El Niño, the divergence pattern caused by the relocation of the convergence centre towards the Indian Ocean resulted in deficit rainfall over India in the pre-shift case (Fig. 11c). The post-shift case (Fig. 11g) shows a small convergence over north India, causing an above normal rainfall. Increased convergence near the Indian subcontinent enhances positive rainfall anomalies in the pre-shift situation of nPDO and La Niña (Fig. 11d). But the post-shift situation (Fig. 11h) shows a slightly divergent pattern over India, which weakens the positive rainfall anomaly. Thus in-phase conditions show an increased positive (negative) rainfall anomalies when warm (cold) phases of PDO and ENSO coexist. In the case of out-of-phase conditions, the rainfall pattern shows similar to the rainfall pattern of ENSO during the pre-shift case; however, the rainfall pattern shows as that of the PDO pattern during the post-shift period.

In order to compare large scale circulation features at lower level (850 hPa) between pre- and post-shift periods, anomalies of stream function are given in Fig. 12. During pPDO and El Niño during the pre-shift (Fig. 12a), Above the Indian subcontinent, a positive anomaly in stream function was seen, resulting in anticyclonic circulation and descending motion. As a result, the amount of rainfall falls. In the post-shift (Fig. 12e) case, positive anomalies shift towards the Arabian sea which inhibits convection hence further decreases the rainfall. In the pre-shift case (Fig. 12b), pPDO and La Niña shows a feeble negative anomaly of stream function over India, which increases rainfall as per property of La Niña but post-shift (Fig. 12f) shows just the opposite, which decreases the rainfall pattern as in pPDO. In the nPDO and El Niño pre-shift case (Fig. 12c), a positive anomaly of stream function causes the rainfall to decrease as in the case of pure El Niño. These positive anomalies result in the descending motion and anticyclonic circulation over India, which further suppresses the rainfall. But after shift (Fig. 12g) shows a negative anomaly of stream function over India with increased rainfall by the counterclockwise circulation pattern and ascending motion of air as in the pure PDO rainfall pattern. The nPDO and La Niña (Fig. 12d) case is showing negative anomalies with enhanced rainfall in the pre-shift but post-shift shows a weak positive anomaly (Fig. 12h), and it can be attributed to deficit rainfall. In general, the results of the previous analysis are also consistent with SST and rainfall observations, as well as dynamical characteristics like horizontal wind velocity and velocity potential.

This study has investigated the characteristics of SST during the simultaneous occurrences of pPDO and El Niño, nPDO and El Niño, pPDO and La Niña, and nPDO and La Niña. The impact of these various combinations on rainfall patterns before and after climate change is also investigated. Following confirmation of effects of the shift, an inquiry into alterations in the circulation pattern was conducted. An association between the decadal and interannual variability of the North and equatorial Pacific SST and the Indian summer monsoon rainfall has been established through correlation and composite analyses. The PDO index exhibits significant negative correlation with the ISMR, similar to the relation between ISMR and Niño 3.4 index. However, the ISMR–PDO correlation is slightly less than that between ISMR and Niño 3.4 index. In order to confirm the year of climate shift, time series of PDO index and Niño 3.4 index were drawn. Significant changes in the intensity and pattern of occurrences of the phases of PDO and ENSO were observed before and after 1976 hence 1976 is selected as the year of climate shift.

When positive (negative) phases of PDO and El Niño (La Niña) co-occur, more intense droughts (floods) are likely to occur over entire India. However, when negative (positive) PDO and El Niño (La Niña) co-occur, the signal is mixed and it is unlikely that either severe drought or severe flood conditions will occur over entire India. In other words, when ENSO and PDO are in (out of) phase they enhance (counteract) the conventional monsoon-ENSO relationship. When years were divided into pre-shift and post-shift periods, the attributes acquired during comprehensive analysis were completely altered. In the pre-shift example, in-phase conditions exhibit similar qualities to those described above; if both are in the warm (cold) phase, rainfall decreases (increases) more than the ambient condition. SST signatures during the in-phase situations exhibit the qualities of both the phenomena. Rainfall patterns are more indicative of ENSO than PDO. The rainfall pattern is determined by the El Niño or La Niña conditions existing in that combination.

In the post-shift situation, the positive anomaly of SST in the PDO and Niño region is significantly stronger than in the pre-shift phase. When compared to the pre-shift example, the in-phase condition shows an amplification of negative rainfall anomalies during positive PDO and El Niño and a weakening of positive rainfall anomalies during negative PDO and La Niña. Because of the counteracting impact, the out-of-phase condition has a neutralising effect, but with an increased positive anomaly of SST. Rainfall patterns exhibiting PDO traits rather than ENSO features occur in that combination. The wind anomaly pattern, stream function and velocity potential studies are all mostly consistent with the same mode of variation.

Acknowledgements:

The second author is thankful to the Director, Indian Institute of Tropical Meteorology (IITM) and to the Executive Director, Centre for Climate Change Research, IITM for encouragement. All other authors are grateful to CUSAT for providing the necessary facilities. We also thank India Meteorological Department (IMD) for the rainfall data, 20th century reanalysis and HadISST data products. The CCCR, IITM is fully funded by the Ministry of Earth Sciences (MoES), Govt. of India.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Anupama K Xavier and Hamza Varikoden. The first draft of the manuscript was written by Anupama K Xavier and all authors commented and on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability

The datasets generated during and/or analysed during the current study are available in the https://www.metoffice.gov.uk/hadobs/hadisst, https://psl.noaa.gov/data/gridded/data.20thC_ReanV3.pressure.html, and http://www.imdpune.gov.in/Clim_Pred_LRF_New websites.

Baines, P. G., and Folland, C. K. (2007). Evidence for a Rapid Global Climate Shift across the Late 1960s. Journal of Climate 20, 12, 2721-2744, available from: <https://doi.org/10.1175/JCLI4177.1> [Accessed 28 May 2021]
Chakraborty, A., Nanjundiah, R.S. and Srinivasan, J., (2002). Role of Asian and African orography in Indian summer monsoon. Geophysical research letters, 29(20), pp.50-1.
Chaluvadi, R., Varikoden, H., Mujumdar, M. and Ingle, S.T., (2021). Variability of West Pacific subtropical high and its potential importance to the Indian summer monsoon rainfall. International Journal of Climatology.
Crowley, T.J., (2000). Causes of climate change over the past 1000 years. Science, 289(5477), pp.270-277.
Deser, C., Trenberth, K. and Staff, N.C.F.A.R., (2016). The Climate Data Guide: Pacific Decadal Oscillation(PDO): Definition and Indices. NC f. A. Research, Ed.
Fischer, E.M., Sippel, S. and Knutti, R., (2021). Increasing probability of record-shattering climate extremes. Nature Climate Change, 11(8), pp.689-695.
Gadgil, S. and Gadgil, S., (2006). The Indian monsoon, GDP and agriculture. Economic and political weekly, pp.4887-4895.
Gadgil, S. and Rupa Kumar, K., (2006). The Asian monsoon—agriculture and economy. In The Asian Monsoon (pp. 651-683). Springer, Berlin, Heidelberg.
Gaffen, D.J., Barnett, T.P. and Elliott, W.P., (1991). Space and time scales of global tropospheric moisture. Journal of Climate, 4(10), pp.989-1008.
Graham, N.E., (1994). Decadal-scale climate variability in the tropical and North Pacific during the 1970s and 1980s: Observations and model results. Climate Dynamics, 10(3), pp.135-162.
Guhathakurta, P. and Rajeevan, M., (2008). Trends in the rainfall pattern over India. International Journal of Climatology: A Journal of the Royal Meteorological Society, 28(11), pp.1453-1469.
Hrudya, P.H., Varikoden, H., Vishnu, R. and Kuttippurath, J., (2020). Changes in ENSO-monsoon relations from early to recent decades during onset, peak and withdrawal phases of Indian summer monsoon. Climate Dynamics, 55(5), pp.1457-1471.
Hrudya, P.H., Varikoden, H. and Vishnu, R., (2021). Regional variabilities of rainfall and convective parameters during the summer monsoon period: their linkage with El Niño Southern Oscillation. Meteorology and Atmospheric Physics, pp.1-10.
Joseph, P.V., (2014). Role of ocean in the variability of Indian summer monsoon rainfall. The Earth's Hydrological Cycle, pp.723-738.
Kanamitsu, M. and Krishnamurti, T.N., (1978). Northern summer tropical circulations during drought and normal rainfall months. Monthly Weather Review, 106(3), pp.331-347.
Kripalani, R.H., Kulkarni, A., Sabade, S.S. and Khandekar, M.L., (2003). Indian monsoon variability in a global warming scenario. Natural hazards, 29(2), pp.189-206.
Krishnamurthy, V. and Goswami, B.N., (2000). Indian monsoon–ENSO relationship on interdecadal timescale. Journal of climate, 13(3), pp.579-595.
Krishnamurthy, L. and Krishnamurthy, V.J.C.D., (2014). Influence of PDO on South Asian summer monsoon and monsoon–ENSO relation. Climate Dynamics, 42(9-10), pp.2397-2410.
Krishnamurthy, L. and Krishnamurthy, V., (2017). Indian monsoon's relation with the decadal part of PDO in observations and NCAR CCSM4. International Journal of Climatology, 37(4), pp.1824-1833
Krishnamurthy, V. and Shukla, J., (2000). Intraseasonal and interannual variability of rainfall over India. Journal of climate, 13(24), pp.4366-4377..
Krishnan, R. and Sugi, M., (2003). Pacific decadal oscillation and variability of the Indian summer monsoon rainfall. Climate Dynamics, 21(3-4), pp.233-242.
Kumar, K.K., Rajagopalan, B. and Cane, M.A., (1999). On the weakening relationship between the Indian monsoon and ENSO. Science, 284(5423), pp.2156-2159.
Kuttippurath, J., Murasingh, S., Stott, P.A., Sarojini, B.B., Jha, M.K., Kumar, P., Nair, P.J., Varikoden, H., Raj, S., Francis, P.A. and Pandey, P.C., (2021). Observed rainfall changes in the past century (1901–2019) over the wettest place on Earth. Environmental Research Letters, 16(2), p.024018.
Levine, A.F., McPhaden, M.J. and Frierson, D.M., (2017). The impact of the AMO on multidecadal ENSO variability. Geophysical Research Letters, 44(8), pp.3877-3886.
Luo, M. and Lau, N.C., (2020). Summer heat extremes in northern continents linked to developing ENSO events. Environmental Research Letters, 15(7), p.074042
Malik, A., Brönnimann, S., Stickler, A., Raible, C.C., Muthers, S., Anet, J., Rozanov, E. and Schmutz, W., (2017). Decadal to multi-decadal scale variability of Indian summer monsoon rainfall in the coupled ocean-atmosphere-chemistry climate model SOCOL-MPIOM. Climate dynamics, 49(9), pp.3551-3572.
Malik, A. and Brönnimann, S., (2018). Factors affecting the inter-annual to centennial timescale variability of Indian summer monsoon rainfall. climate Dynamics, 50(11), pp.4347-4364.
Mantua, N.J., Hare, S.R., Zhang, Y., Wallace, J.M. and Francis, R.C., (1997). A Pacific interdecadal climate oscillation with impacts on salmon production. Bulletin of the american Meteorological Society, 78(6), pp.1069-1080.
Mantua, N.J. and Hare, S.R., (2002). The Pacific decadal oscillation. Journal of oceanography, 58(1), pp.35-44.
Meehl, G.A., Hu, A. and Santer, B.D., (2009). The mid-1970s climate shift in the Pacific and the relative roles of forced versus inherent decadal variability. Journal of Climate, 22(3), pp.780-792.
Mertz, O., Padoch, C., Fox, J., Cramb, R.A., Leisz, S.J., Lam, N.T. and Vien, T.D., (2009). Swidden change in Southeast Asia: understanding causes and consequences. Human Ecology, 37(3), pp.259-264.
Miller, A.J., Cayan, D.R., Barnett, T.P., Graham, N.E. and Oberhuber, J.M., (1994). The 1976-77 climate shift of the Pacific Ocean. Oceanography, 7(1), pp.21-26.
Mooley, D.A., Parthasarathy, B. and Munot, A.A., (1984). Large-scale droughts over India and their impact on agricultural production. Mausam, 35(3), pp.265-265.
Nair, P.J., Chakraborty, A., Varikoden, H., Francis, P.A. and Kuttippurath, J., (2018). The local and global climate forcings induced inhomogeneity of Indian rainfall. Scientific reports, 8(1), pp.1-12.
Nitta, T. and Yamada, S., (1989). Recent warming of tropical sea surface temperature and its relationship to the Northern Hemisphere circulation. Journal of the Meteorological Society of Japan. Ser. II, 67(3), pp.375-383.
Pai, D.S., Sridhar, L., Rajeevan, M., Sreejith, O.P., Satbhai, N.S. and Mukhopadhyay, B., (2014). Development of a new high spatial resolution (0.25× 0.25) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam, 65(1), pp.1-18.
Philander, G. and Philander, S.G., (2008). Encyclopaedia of global warming and climate change: AE (Vol. 1). Sage.
Rajeevan, M., Bhate, J. and Jaswal, A.K., (2008). Analysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data. Geophysical research letters, 35(18).
Rajeevan, M. and Bhate, J., (2009). A high resolution daily gridded rainfall dataset (1971–2005) for mesoscale meteorological studies. Current Science, pp.558-562.
Rajeevan, M. and Nanjundiah, R.S., (2009). Coupled model simulations of twentieth century climate of the Indian summer monsoon. Platinum jubilee special volume of the Indian Academy of Sciences, pp.537-568.
Rajeevan, M., Gadgil, S. and Bhate, J., (2010). Active and break spells of the Indian summer monsoon. Journal of earth system science, 119(3), pp.229-247.
Rao, Y.P., (1976). Southwest monsoon, meteorological monograph. India Meteorological Department, New Delhi, 366.
Rasmusson, E.M. and Wallace, J.M., (1983). Meteorological aspects of the El Nino/southern oscillation. Science, 222(4629), pp.1195-1202.
Rayner, N.A.A., Parker, D.E., Horton, E.B., Folland, C.K., Alexander, L.V., Rowell, D.P., Kent, E.C. and Kaplan, A., (2003). Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. Journal of Geophysical Research: Atmospheres, 108(D14).
Reshma, T., Varikoden, H. & Babu, C.A. Observed Changes in Indian Summer Monsoon Rainfall at Different Intensity Bins during the Past 118 Years over Five Homogeneous Regions. Pure Appl. Geophys. 178, 3655–3672 (2021). https://doi.org/10.1007/s00024-021-02826-8
Sabeerali C T, Rao S A, Ajayamohan R S and Murtugudde R (2012) On the relationship between Indian summer monsoon withdrawal and Indo-Pacific SST anomalies before and after 1976/1977 climate shift Clim. Dyn. 39 841–59
Saha, K.R. and Mooley, D.A., (1978). Fluctuations of monsoon rainfall and crop production. In Climatic Change and Food Production; International Symposium.
Sahana A S, Ghosh S, Ganguly A and Murtugudde R (2015) Shift in Indian summer monsoon onset during 1976/1977 Environ. Res. Lett. 10 054006
Schuldt, J.P., Konrath, S.H. and Schwarz, N., (2011). “Global warming” or “climate change”? Whether the planet is warming depends on question wording. Public opinion quarterly, 75(1), pp.115-124.
Seetha, C.J., Varikoden, H., Babu, C.A. and Kuttippurath, J., (2020). Significant changes in the ENSO-monsoon relationship and associated circulation features on multidecadal timescale. Climate Dynamics, 54(3), pp.1491-1506.
Sikka, D.R., (1980). Some aspects of the large scale fluctuations of summer monsoon rainfall over India in relation to fluctuations in the planetary and regional scale circulation parameters. Proceedings of the Indian Academy of Sciences-Earth and Planetary Sciences, 89(2), pp.179-195.
Sikka, D.R. and Gadgil, S., (1980). On the maximum cloud zone and the ITCZ over Indian longitudes during the southwest monsoon. Monthly Weather Review, 108(11), pp.1840-1853.
Slivinski, L.C., Compo, G.P., Whitaker, J.S., Sardeshmukh, P.D., Giese, B.S., McColl, C., Allan, R., Yin, X., Vose, R., Titchner, H. and Kennedy, J., (2019). Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Quarterly Journal of the Royal Meteorological Society, 145(724), pp.2876-2908.
Shukla, J. and Paolino, D.A., (1983). The Southern Oscillation and long-range forecasting of the summer monsoon rainfall over India. Monthly Weather Review, 111(9), pp.1830-1837.
Shukla, J. and Mooley, D.A., (1987). Empirical prediction of the summer monsoon rainfall over India. Monthly Weather Review, 115(3), pp.695-704.
Shukla, J. and Wallace, J.M., (1983). Numerical simulation of the atmospheric response to equatorial Pacific sea surface temperature anomalies. Journal of the Atmospheric Sciences, 40(7), pp.1613-1630.
Shukla, R.P. and Huang, B., (2016). Mean state and interannual variability of the Indian summer monsoon simulation by NCEP CFSv2. Climate Dynamics, 46(11), pp.3845-3864.
Suhas E and Goswami B N (2008) Regime shift in Indian summer monsoon climatological intraseasonal oscillations Geophys. Res. Lett. 35 L20703
Stern, D.I. and Kaufmann, R.K., (2014). Anthropogenic and natural causes of climate change. Climatic change, 122(1), pp.257-269.
Trenberth, K.E., (2020). ENSO in the Global Climate System. El Niño Southern Oscillation in a Changing Climate, pp.21-37.
Varikoden, H., Samah, A.A. and Babu, C.A., (2010). Spatial and temporal characteristics of rain intensity in the peninsular Malaysia using TRMM rain rate. Journal of hydrology, 387(3-4), pp.312-319.
Varikoden, H., Preethi, B. and Revadekar, J.V., (2012). Diurnal and spatial variation of Indian summer monsoon rainfall using tropical rainfall measuring mission rain rate. Journal of Hydrology, 475, pp.248-258.
Varikoden, H. and Babu, C.A., (2015). Indian summer monsoon rainfall and its relation with SST in the equatorial Atlantic and Pacific Oceans. International Journal of Climatology, 35(6), pp.1192-1200.
Webster, P.J. and Yang, S., (1992). Monsoon and ENSO: Selectively interactive systems. Quarterly Journal of the Royal Meteorological Society, 118(507), pp.877-926.
Webster, P.J., Magana, V.O., Palmer, T.N., Shukla, J., Tomas, R.A., Yanai, M.U. and Yasunari, T., (1998). Monsoons: Processes, predictability, and the prospects for prediction. Journal of Geophysical Research: Oceans, 103(C7), pp.14451-14510.

Download PDF

Version 1

posted

You are reading this latest preprint version

Influence of PDO and ENSO with Indian summer monsoon rainfall and its changing relationship before and after 1976 climate shift

Status:

Version 1

Abstract

Figures

1. Introduction

2. Data And Methodology

2.1 Data

2.2 Methods used in the analysis

3. Results And Discussions

3.1 Climatology of ISMR and SST

3.2 Interannual variation and relationship of PDO and ENSO

3.3 Combined effect of PDO and ENSO on SST and ISMR anomalies

3.4 Comparison of SST and rainfall using pure conditions

3.5 Climate shift and change in the behaviour of teleconnections

3.6 Enhancement of changes using dynamical parameters

4. Conclusions

Declarations

Data Availability

References

Status:

Version 1