Local climate impacts of dipole-like sea surface temperature oscillations in the Southern Hemisphere

Dipole phenomena in ocean-atmospheric variability such as the Indian Ocean Dipole have been recognized as important factors that greatly affect local climates. This study presents evidence of two dipole modes in sea surface temperature anomaly (SSTA) over high latitude Southern Hemisphere (one in South Pacific and one in South Indian Ocean), identified using empirical orthogonal functions and cross-correlation analysis. These dipole modes have interannual periodicity, which is also explored for their seasonal variability and modes. Herein, a dipole mode is defined as a quasi-periodic oscillation between positive and negative phases in the various climate proxies, though predominantly in SST, which is supported by the signal’s synchronized relationship with atmospheric variability (as recorded by pressure and wind records). In addition, the dipole modes have a clear synchronization relationship to local precipitation records, which is described in this paper. For this purpose, an index to represent the time-dependent evolution of each dipole mode and to better define and understand the teleconnections of the dipole modes with other climate variables was defined. The findings described here provide a more precise and unique understanding of the globally distributed SSTA teleconnections and climate’s synchronized dynamics than that has currently been studied. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). doi: 10.2166/wcc.2020.113 ://iwaponline.com/jwcc/article-pdf/12/2/311/866007/jwc0120311.pdf Jeseung Oh Innovative Growth Department, Korea Agency for Infrastructure Technology Advancement, Anyang, Gyeonggi 14066, Korea Yong Jung (corresponding author) Department of Civil and Environmental Engineering, Wonkwang University, Iksan, Jeonbuk 54538, Korea E-mail: yong_jung@wku.ac.kr


INTRODUCTION
The variability of the Earth's climate system is intricately tied to the energy transfer between the ocean and atmosphere. In this paper, dipole patterns of this transfer via the analysis of the sea surface temperature (SST), sea level pressure (SLP), and horizontal wind (U-wind) were determined using both the empirical orthogonal function (EOF) and cross-correlation analysis. SSTs, specifically SSTAs, are an important proxy for tracking larger scale climate change due to the strong effects of temperature differences at the ocean-atmosphere boundary on the heat and moisture exchange between ocean and atmosphere, each of which can be visualized as a major climatic oscillator. Small deviations in this surface exchange can have a widespread effect in both the atmospheric circulation and global weather patterns (Ahrens ). In addition, SST records are relatively long and accurate climate records, as they predate satellite monitoring, so that when selecting a proxy to judge regional climate, this particular set of proxies is a logical option, though not the only one.
One of the earlier works to use the term dipole in this context (Servain )   Oceans were identified using EOF and cross-correlation analysis and the seasonality of the dipoles were discussed.
The synchronized relationship between the ocean dipole modes and atmospheric variability is also discussed, following the definition of synchronization first established by Huygens (Pikovsky et al. ) and extended to the climate system by Rial (). It is possible to identify stable phase and frequency locking in the two climate dipoles that this paper presents, satisfying the definition of synchronization. In addition, the dipole oscillators seen in modern climate dynamics may be the result of synchronization that can be suggested.
The rest of this paper is organized as follows: in the section 'Data and methods', the dataset and methods used to gain a better understanding of the two identified dipoles were described. The section 'Results' details the results; specifically, the proxy analyses surrounding the two synchronized SSTA oscillations are detailed. The section 'Discussion' discusses the differences between the newly identified dipole modes and previously published climate phenomena that share certain characteristics with our dipole modes, specifically the Antarctic Circumpolar Wave (ACW), to emphasize the uniqueness of the results presented here.
The conclusion will be followed by a summary of the paper's main findings and suggestions for future avenues of research using the methods introduced here.

DATA AND METHODS
For the SST data in this study, two SST datasets from different organizations were employed; specifically, the Monthly gridded mean SLP and U-wind (zonal wind) datasets are also used in this study to assess how the dipole modes communicate with atmospheric variables.
These datasets were drawn from the 20th Century Reanalysis data (Compo et al. ) provided by the NOAA, which is available at 2 by 2 latitude-longitude grid resolution from January 1871 to December 2011.
In addition, regional precipitation data from the Global Precipitation Climatology Centre (GPCC, 1901(GPCC, -2010 and A high-pass filter is also applied to remove long-term trends, specifically longer than 8 years. The oscillating period of 2-7 years was focused, which is commensurate with the period of the strongest SST oscillation, ENSO. Prepared data were analyzed by EOF which decomposes the dataset for finding time series and spatial patterns on the data in terms of orthogonal basis functions. Figure 1 summarizes the preprocessing procedure of SST, MSLP, and U-wind for EOF.

RESULTS
Two dipole-like oscillations are identified by the first EOF modes over the South Pacific (South Pacific Dipole  center). This figure shows the defined dipole mode indices very well explain SSTA around the selected dipole centers. And, it also shows that the dipole modes are not limited to a few grid points. In order to ensure this is not an artifact or serendipity of the dataset, we also employed a different dataset, GISST and it is shown in Figure S1     SLPA between the two poles, as is expected given the hemisphere and pressure differences. It is also important to note that while this synchronized, oscillating system is persistent throughout the available proxy records, it is significantly weakened for the period of July-September or during the Austral winter (see Figures 6 and 7).

Southern Ocean and Indian Ocean regions
SIDO is identified from the first EOF mode over the southern Indian Ocean basin and shows that the NW-SE pattern is similar to SPDO (Figure 2(b)). This dipole spans a region from within the south of the Indian Ocean (∼40 S and ∼85 E) to below Australia (∼60 S and ∼115 E). Importantly, the correlation coefficient analysis between SIDO DMI and SSTA (Figure 3) shows that SIDO variability accounts for the majority of variation over the high latitudes of the southern Indian Ocean.
Again, a second SSTA dataset (from GISTEMP) was used to show the robustness of this analysis to the dataset used.
However, in this case, the month-by-month analysis shows that the dipole mode remains strong in all individual months, with the exception of December when it seems to die out almost entirely. The complete seasonal variations of this dipole are detailed in Figure 9.
Following the precedent set in analyzing the previous dipole mode, the DMI time series was compared to SLP and U-wind for a better understanding of the set of mechanisms responsible for its dynamics. Once again, a strong connection is seen with both (Figures 10(a) and 10(b)).
When a positive dipole mode is seen in the DMI (warming over the NW and cooling over the SE), a high-pressure (positive) SLPA forms between the two centers of the dipole, while a negative anomaly is formed near Antarctica.
This pressure system correlates with the westerly wind seen along the 60 S over the SE pole. Due to the nature of vortex behavior and the presence of high SLP, this corresponds to easterly winds over the NW pole of the oscillator. These ocean-atmospheric relationships are relatively weakened in August-October (Figures 11 and 12), and this might be caused by smaller heat exchange during the Austral winter.
Further, the SIDO DMI dynamics are strongly teleconnected with precipitation over NW Australia, especially during positive anomaly periods. This is seen in the precipitation composition during the time of strong dipole occurrence (Figures 8(c) and 8(d)). This suggests the ability to predict some aspects of Australian rainfall through the knowledge of the DMI dynamics, especially with other knowledge about monsoon behavior in the Indian Ocean, but does not necessarily support the idea of causality between the two systems. However, it is likely that the two systems are teleconnected.
It is also worthwhile to consider SIDO's relationship to SPDO, especially given the earlier suggestion that the two might simply be part of the ACW. The comparison of the two DMIs shows that this is highly unlikely, as they are not correlated at all. This implies that the two dipoles do not share most dynamic behaviors or periods, though they do have similar superficial structures with their NW-SE patterns.  This may suggest a pumping structure either driving the wave or resulting from the wave, but does demonstrate that the two teleconnection structures may both be active, with the dipoles providing a potential underlying structure for the wave. However, it is reassuring that the dipole behavior previously mentioned is not a simple wave powered by the ACC due to the dynamics noted in Figure 13. The anomalies noted as dipoles in this paper are not smoothly transitioning around the ACC, but rather clearly oscillating at two distinct positions without a direct connection, requiring that they are synchronized through more intermediate fied, which took into account direct forcing from ENSO but no forcing from the PDO, in order to introduce a lag in connection with ENSO. The modified model is as follows: wherein Y is the SPDO DMI, i represents the time step, a and b are forcing amplitude parameters which will be iterated over to find the best fit between the model and the data, and ξ is the random noise variable. Then, correlation coefficients were calculated between observed DISP DMI and DMI generated from the model to inspect the impacts of El Niño on the SPDO. Figure S3 shows that while some correlation between model and data can be achieved with a minimal influence from the internal oscillation dynamics of the system or ENSO 3.4, in order to best fit the data or even create a significant correlation coefficient, the two forcing parameters are required to be near one. This shows a definite interaction between SPDO and ENSO, but also clearly demonstrates the independence of the internal oscillations of the SPDO system.

CONCLUSIONS
This paper has clearly identified new, synchronized systems of connected SST variations through the use of EOF analysis, correlation coefficients, and the corresponding DMIs of each new dipole. While these results may not be entirely unrelated to previously discovered teleconnection patterns, they are also not replications of these patterns. This work demonstrates a method of the teleconnection analysis that is independent of spatial dataset restrictions and that is capable of providing insight into teleconnection patterns based on proxy networks. Though this paper shows only one pair of SST dipoles with their corresponding ranges of proxy and time interdependencies, other potential dipoles were investigated in an effort to characterize the larger dynamics leading to these stable, interannual teleconnections. Finding patterns here may provide insight into the combined role that the physical and temporal factors play in forming and sustaining these synchronized oscillations.
For a complete picture of the dynamics, it will be important to perform the comparable analysis on as many climate proxies as possible as well as on as many identifiable dipoles as possible, in the hope of defining what set of conditions must be true in order to create a sustained synchronization of the climate on the sub-decadal scale.