Single-Year and Double-Year El Niños

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Single-Year and Double-Year El Niños

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Compared with well documented and frequent occurrence of multi-year La Niña, double-year El Niño is less frequent and has not been well investigated. Both of them are a discrepancy from the cyclic behavior of the El Niño-Southern Oscillation and deserve investigation. During 1950-2021, 75% of El Niño events persist for one year, and 25% of them last for two years. Both central and eastern Pacific type El Niños occur in the single-year and double-year El Niños with various strengths. Compared with the single-year El Niños, the averaged warm water volume (WWV) is larger in the peak and declines much slower for the double-year El Niños, suggesting that a persistently recharged heat condition of the equatorial Pacific is a precondition for the emergence of a second-year El Niño. The faster decline of WWV in the single-year El Niños is associated with the in-phase decrease of its intraseasonal-interseasonal and interannual components, while the slower decline of WWV in the double-year El Niños is determined by the interannual component. In addition, the single-year and double-year El Niño may have different impacts on regional climate.

Asymmetry of ENSO duration

single-year and double-year El Niños

recharge and discharge processes

Accurate short-term climate prediction is a need for society. For the short-term climate prediction, El Niño-Southern Oscillation (ENSO) plays a crucial role and is the major source of the predictability of the global climate at seasonal-interannual time scales (National Research Council 2010; Hu et al. 2020a). ENSO is a quasi-periodic phenomenon with a reoccurrence every 2–7 years (McPhaden et al. 2021). Its growth and persistency are controlled by positive ocean-atmosphere feedbacks among the wind stress, sea surface temperature (SST), and thermocline depth fluctuations in the tropical Pacific (Bjerknes 1969), while its phase transition and decay are determined by a variety of negative feedbacks (Jin 1997; Wang 2018).

Although there have significant progress in understanding ENSO since Bjerknes (1969), unfortunately, successfully predicting ENSO is still a challenge sometimes (e.g., Barnston et al. 2012; Zhu et al. 2014; Hu et al. 2020b). Such challenge is mainly due to the no-cyclical feature of ENSO, which is linked to the asymmetry or diversity of ENSO (Choi et al. 2013; Timmermann et al. 2018). For example, on average, the amplitudes of SST anomalies in the eastern equatorial Pacific are larger in El Niño than in La Niña during their peaks. Spatially, major SST anomalies are in the eastern tropical Pacific in La Niña, while sometimes in the central and sometime in the eastern tropical Pacific in El Niño.

Another asymmetry is that the discharge associated with El Niño is stronger than the recharge linked to La Niña (Kessler 2002; Hu et al. 2017; Li et al. 2020; 2022a). Such asymmetry contributes to the asymmetry evolution of ENSO: both El Niño and La Niña are mostly followed by La Niña. As a result, double-dip La Niñas are observed very often, but it is seldom for two-year consecutive El Niño (Okumura and Deser 2010; Okumura et al. 2011; Choi et al. 2013; Hu et al. 2014; Kim and Yu 2021; Li et al. 2022a; Fig. 1). Based on the nonlinearity of the atmospheric processes (Hoerling et al. 1997) and asymmetry associated with the recharge and discharge process (Kessler 2002; Hu et al. 2017), different mechanisms have been proposed for the asymmetric duration between El Niño and La Niña. Okumura and Deser (2010) and Okumura et al (2011) emphasized the asymmetric role of the Indian Ocean in the ENSO cycle transition. During El Niño, delayed basin warming of the Indian Ocean induces anomalous surface easterly winds in the western equatorial Pacific, leading to the phase transition. During La Niña, the anomalous easterly wind forced by the reduced convection in the equatorial Pacific is stronger than the surface westerly wind induced by the Indian Ocean cooling because of the westward shift of the Pacific convection anomalies, prolonging the La Niña condition. Hu et al. (2014) proposed an ocean mechanism based on the asymmetric of recharge and discharge processes. They argued that the reflected cold water of previous La Niña from the southern American coast propagates westward as Rossby waves and interrupts the oceanic heat recharge process, leading to the emergence of a second-year La Niña.

Compared with well documented and frequent occurrence of multi-year La Niña, double-year El Niño is less frequent and has not been well investigated. Nevertheless, the multi-year durations of both El Niño and La Niña are a discrepancy from the cyclic behavior of the ENSO theory, such as the recharge and discharge oscillator (Jin 1997), thus deserves investigation. For example, (a) are there any differences in the SST and warm water volume (WWV) anomalous evolution between the single- and double-year El Niños? (b) What are the differences in different time sale variations between the single- and double-year El Niños? These are the focus of this work. The paper is organized as follows. After the introduction, the data and methods used in the analyses are described in section 2; section 3 shows the results; a summary with discussion is given in section 4.

SSTs are from HadISST1 with a 1^o x 1^o resolution (Rayner et al. 2003). To exclude the influence of the interdecadal variations and long-term trends and to focus on the interseasonal-interannual time scale feature of ENSO, the relative Niño3.4 index is adopted to represent ENSO, which is defined as the difference between the SST anomaly averaged in (5°S − 5°N, 170°W − 120°W; the Niño3.4 region) and the SST anomaly averaged in the whole tropics (0°-360°, 20°S-20°N) following van Oldenborgh et al. (2021). To have the comparable variance as the conventional Niño3.4 index, the relative Niño3.4 index is renormalized by multiplying by 1/(1 − F) with “F” the regression of the 20°S–20°N SST anomalies on the Niño3.4 index (van Oldenborgh et al. 2021).

ENSO year classification follows the NOAA Climate Prediction Center (CPC) (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php). An El Niño (a La Niña) is referred to that 3-month running mean Niño 3.4 index is above 0.5°C (blow − 0.5°C) with continuation for a minimum of 5 consecutive overlapping seasons. In the CPC definition, the conventional Niño 3.4 index is calculated with SST from ERSST.v5 (Huang et al. 2017) and climatology based on centered 30-year base periods updated every 5 years. All the El Niño (a La Niña) years during January 1950-December 2021 are listed in Table 1 according to NOAA/CPC’s classification. In the following analyses, year “0” refers to the development year, year “1” is the decay year, and year “2” represents the year after the decay year.

To distinguish the flavor of ENSO, the Niño3 and Niño4 indices are used, which are the SST anomalies averaged in (5°S − 5°N, 150°−90°W) and (5°S − 5°N, 160°E − 150°W), respectively.

The ocean subsurface heat condition along the equatorial Pacific is represented by the WWV index which is defined as the anomaly of the depth of oceanic 20^oC isotherm (D20) averaged over the region of 5^oS-5^oN, 120^oE-80^oW following Meinen and McPhaden (2000). D20 on a 1^ox1^o grid during January 1958-December 2016 is from the Ocean Reanalysis System 4 (ORAs4) of the European Centre for Medium-Range Weather Forecasts (ECMWF; Balmaseda et al. 2013) and during January 2017-December 2021 from the Global Ocean Data Assimilation System of NOAA (GODAS; Behringer 2007).

The atmospheric circulation is represented by surface pressure from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) reanalysis for the period from January 1949 to the present (Kalnay et al. 1996). The precipitation over land is analyzed using the data from Chen et al. (2002) which are derived from gauge observations from over 17,000 stations collected in the Global Historical Climatology Network and the Climate Anomaly Monitoring System datasets. The data span the period since January 1948 with a 1^ox1^o resolution.

To identify the contributions of different time scales to the intensity and evolution of ENSO recharge and discharge processes, Ensemble Empirical Mode Decomposition (EEMD) is adopted for decomposing the WWV index. EEMD is adaptive and derives optimal frequencies from the data itself, instead of using a priori “global” basis functions of rigid periods in Fourier transform-based time series analysis, which naturally separate components of different time scales (Wu and Huang 2009).

From Table 1 and Fig. 1, we can see that most of the El Niño events persist for one year, a few of them last for two years, and no one for three years. In contrast, most La Niña events last for two years, and some of them persist for one year or three years. Numerically (Fig. 2, Table 1), 75% of El Niño events persist for one year, 25% for two years, while for La Niña events, 31% of them last for one year, 54% of them two years, and 15% of them three years. Here, 2020/22 La Niña is classified as a double-year La Niña, nevertheless, the tropical Pacific is still in a La Niña condition until May 2022, thus, 2020/22 La Niña may evolve into a triple-year La Niña. Such evolution differences between El Niño and La Niña are the well-known duration asymmetry of ENSO: El Niño events mostly persist for one year, while La Niña events favor to last for multiple years (Kessler 2002; Ohba and Ueda 2009; Okumura and Deser 2010; Okumura et al. 2011; Hu et al. 2014).

For single-year El Niños and based on the relative Niño3.4 index, the strengths vary from weak to extremely strong (Fig. 3a), and their average declines to below average in the spring of the decay year. While for double-year El Niños (Fig. 3b), the strengths of the two peaks in their averages are comparable, and all the first-year peaks of the relative Niño3.4index in each El Niño don’t exceed 2^oC (extreme El Niño). That is in contrast to La Niña. Hu et al. (2014) argued that a strong La Niña is favorable to be followed by a second-year La Niña, which, on average, is weaker than the first-year La Niña.

To identify the flavor of El Niño, i.e., central Pacific (CP) or eastern Pacific (EP) events, the difference between the Niño3 and Niño4 indices (Niño3 minus Niño4) is displayed (Kao and Yu 2009). When the difference is positive (negative), it is called EP (CP) El Niño. From Fig. 4, we can see both positive and negative differences present in the single-year and double-year El Niños, meaning that both CP and EP type El Niños occur in the single-year and double-year El Niños.

As a key indicator of the ENSO phase turnaround (Jin 1997; Hu et al. 2017), the WWV index shows apparent differences between the averages of the single-year and double-year El Niños. The WWV index decreases quickly since the peak of single-year El Niño and becomes negative since January of the decay year (Fig. 5a), consisting with the recharge and discharge oscillator paradigm (Jin 1997). While, compared with the single-year El Niños (Fig. 5a), for the double-year El Niños (Fig. 5d), their averaged WWV is larger in the peak, declines much slower, and reaches negative until January(2), suggesting that a persistently recharged heat condition of the equatorial Pacific is a precondition for the emergence of a second-year El Niño.

In addition to the impact of low-frequency variation and long-term trend on the ENSO (Yeo et al. 2016; Li et al. 2019), Li et al. (2022b) recently noted that La Niña decay in the boreal spring and early summer is mainly controlled by the intraseasonal-interseasonal variation. From Fig. 5b, c, e, and f, we can also see that both the intraseasonal-interseasonal and interannual components of the WWV index have appreciable differences in the averages between the single-year and double-year El Niños. For the single-year El Niño average (Fig. 5b, c), the intraseasonal-interseasonal and interannual components of the WWV index have an in-phase decline (discharging) since the decay of the El Niños, leading to the enhanced discharging of the equatorial Pacific (Fig. 5a). While, for the double-year El Niño average, there is a slightly recharging tendency after the decay of the first-year El Niños for the intraseasonal-interseasonal component (Fig. 5e). For the interannual component, compared with the single-year El Niños (Fig. 5c), the decrease of the WWV index in the double-year El Niños (Fig. 5f) is slower and the WWV index reaches negative in the late spring and early summer of year(1). As a result, the total WWV index decays slower and the equatorial Pacific is in the recharged phase until July of year(1) (Fig. 5d), unfavorable for the phase transition.

The single-year and double-year El Niños may have different impacts on the global climate. As an example, Fig. 6 shows the composites of SST (shading over oceans), land precipitation (shading over land), and surface pressure (contours) anomalies in the winter of the mature phase of single-year El Niños (represented by S_D(0)JF(1)), and in the winter of the mature phase of the 1st and 2nd years of the double-year El Niños (represented by D_ D(0)JF(1) and D_D(1)JF(2), respectively). Although the broad-scale patterns are similar for the composites (Fig. 6a-c), there are a lot of detailed differences in their amplitudes and regional distributions. For example, the SST anomalies in the eastern tropical Pacific are more significant in S_D(0)JF(1) (Fig. 6a) than in D_ D(0)JF(1) (Fig. 6b) and D_D(1)JF(2) (Fig. 6c), and it is the opposite in the tropical North Atlantic Ocean. In the Indian Ocean, the SST anomalies are more significant in S_D(0)JF(1) (Fig. 6a) and D_D(1)JF(2) (Fig. 6c) than in D_ D(0)JF(1) (Fig. 6b) The surface pressure anomalies also display appreciable differences among the three composites in the regions including the high-latitudes of Eurasia, the North Pacific Ocean, and the North Atlantic Ocean. For the land precipitation anomaly composites in southeastern China, it is above normal in S_D(0)JF(1) and D_D(1)JF(2), and below normal in D_ D(0)JF(1). In the central North America, the precipitation is significantly wet in S_D(0)JF(1) and D_ D(0)JF(1), and near average in D_D(1)JF(2).

Compared with the single-year El Niños, the averaged WWV index is larger in the peak and declines much slower for the double-year El Niños, suggesting that a persistently recharged heat condition of the equatorial Pacific is a precondition for the emergence of a second-year El Niño. The faster decline of the WWV index in the single-year El Niños is associated with the in-phase decrease of its intraseasonal-interseasonal and interannual components, while the slower decline of the WWV index in the double-year El Niños is determined by the interannual component.

Each El Niño or La Niña are unique, implying that in addition to the interference among different time scales, the interactions between the tropics and extratropics are an important contributor to the ENSO diversity and complication. For instance, Kim and Yu (2021) argued that El Niño events initiated by subtropical Pacific atmosphere-ocean coupling show larger uncertainty in their decay evolution patterns than those initiated by tropical Pacific atmosphere-ocean coupling. The onset location of subtropical Pacific-onset El Niño, which interacts with the eastern edge of the western Pacific warm pool, is a key factor controlling its decay evolution. When the onset is located east (west) of 155°E, the event has a strong tendency to reverse (maintain) its phase, leading to cyclic (multiyear) evolution. Recently, Ding et al. (2022) hypothesized a connection between the North Pacific Oscillation (NPO) and multi-year El Niños. The NPO during boreal winter can trigger a CP El Niño during the subsequent winter. Then the CP Niño re-energizes the NPO through exciting atmospheric teleconnections to the extratropics, which re-triggers another El Niño event in the following winter. Thus, extra-tropical influence may also be an important contributor to the different durations of El Niños.

Last, we must make clear that in addition to potential physics differences, the differences in the composites and averages might be partially due to the sampling, since the sample sizes are small, especially for the double-year El Niños. Nevertheless, it is necessary to further investigate the physics in the single-year and double-year El Niños, and their different impacts on regional climate and the associated mechanisms, which benefits the seasonal-interannual climate prediction.

Acknowledgments:

The authors declare no financial conflict of interest. This work was supported by the National Natural Science Foundation of China (41630424, 41875119, 41930967, and 41661144007).

Data availability statement:

The analysis and reanalysis data used in this work are available through Kalnay et al. (1996), Chen et al. (2002), Rayner et al. (2003), Balmaseda et al. (2013), Behringer (2007), Li et al. (2022b), and also online: NCEP/NCAR reanalysis from https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html, precipitation from https://psl.noaa.gov/data/gridded/data.precl.html, HadISST from http://hadobs.metoffice.com/hadisst/data/download.html, ORAs4 D20 from https://climatedataguide.ucar.edu/climate-data/oras4-ecmwf-ocean-reanalysis-and-derived-ocean-heat-content, and GODAS D20 from https://www.esrl.noaa.gov/psd/data/gridded/data.godas.html, respectively, or contact us via [email protected].

Balmaseda MA, Mogensen K, Weaver AT (2013) Evaluation of the ECMWF ocean reanalysis system ORAS4. Q J R Meteorol Soc 139:1132–1161. doi: 10.1002/qj.2063
Barnston AG, Tippett MK, L’Heureux ML, Li S, DeWitt DG (2012) Skill of real-time seasonal ENSO model predictions during 2002–2011 — Is our capability increasing? Bull Amer Meteor Soc 93(5):631–651. doi: 10.1175/BAMS-D-11-00111.1
Behringer DW (2007) : The Global Ocean Data Assimilation System (GODAS) at NCEP. Preprints, 11th Symp. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, San Antonio, TX, Amer. Meteor. Soc., 3.3. [Available online at http://ams.confex.com/ams/87ANNUAL/techprogram/paper_119541.htm]
Chen M, Xie P, Janowiak JE, Arkin PA (2002) Global land precipitation: A 50-yr monthly analysis based on gauge observations. J Hydrometeor 3:249–266
Choi K, Vecchi GA, Wittenberg AT (2013) ENSO transition, duration, and amplitude asymmetries: Role of the nonlinear wind stress coupling in a conceptual model. J Clim 26(23):9462–9476
Ding R, Tseng Y-h, Di Lorenzo E, Shi L, Li J, Yu J-Y, Wang C, Sun C, Luo J-J, Ha K-J, Hu Z-Z, Li F (2022) : Multi-year El Niño events tied to the North Pacific Oscillation. Nature-Communications (revised)
Hoerling M, Kumar A, Zhong M (1997) El Niño, La Niña, and the nonlinearity of their teleconnections. J Clim 10:1769–1786
Hu Z-Z, Kumar A, Xue Y, Jha B (2014) Why were some La Niñas followed by another La Niña? Clim Dyn 42(3–4):1029–1042. doi: 10.1007/s00382-013-1917-3
Hu Z-Z, Kumar A, Huang B, Zhu J, Zhang R-H, Jin F-F (2017) Asymmetric evolution of El Niño and La Niña: The recharge/discharge processes and role of the off-equatorial sea surface height anomaly. Clim Dyn 49(7–8):2737–2748. doi: 10.1007/s00382-016-3498-4
Hu Z-Z, Kumar A, Jha B, Huang B (2020a) How much of monthly mean precipitation variability over global land is associated with SST anomalies? Clim Dyn 54(1–2):701–712. doi: 10.1007/s00382-019-05023-5
Hu Z-Z, Kumar A, Huang B, Zhu J, L'Heureux M, McPhaden MJ, Yu J-Y (2020b) The interdecadal shift of ENSO properties in 1999/2000: A review. J Clim 33(11):4441–4462. doi: 10.1175/JCLI-D-19-0316.1
Huang B, Thorne PW, Banzon VF, Boyer T, Chepurin G, Lawrimore JH, Menne MJ, Smith TM, Vose RS, Zhang H-M (2017) Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5), Upgrades, validations, and intercomparisons. J Clim 30(20):8179–8205. doi: 10.1175/JCLI-D-16-0836.1
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Amer Meteor Soc 77:437–471
Kao H-Y, Yu J-Y (2009) Contrasting eastern-Pacific and central-Pacific types of ENSO. J Clim 22:615–632. doi: 10.1175/2008JCLI2309.1
Kessler WS (2002) Is ENSO a cycle or a series of events? Geophys Res Lett 29(23):2125. doi: 10.1029/2002GL015924
Kim J-W, Yu J-Y (2021) Evolution of subtropical Pacific-onset El Niño: How its onset location controls its decay evolution. J Geophys Res 48 e2020GL091345. doi: 10.1029/2020GL091345
Li X, Hu Z-Z, Huang B (2019) Contributions of atmosphere-ocean interaction and low-frequency variation to intensity of strong El Niño events since 1979. J Clim 32(5):1381–1394. doi: 10.1175/JCLI-D-18-0209.1
Li X, Hu Z-Z, Zhao S, Ding R, Zhang B (2022a) : The asymmetry of the tropical Pacific thermocline fluctuation associated with ENSO recharge and discharge (in preparing)
Li X, Hu Z-Z, Tseng Y-h, Liu Y, Liang P: 2022b: A Historical Perspective of the La Niña Event in 2020/21.J. Geophys. Res., 127 (7), e2021JD035546, doi: 10.1029/2021JD035546
McPhaden MJ, Santoso A, Cai W (2021) : El Niño Southern Oscillation in a Changing Climate. Geophysical Monograph 253, American Geophysical Union, Wiley, 1-506, doi: 10.1002/9781119548164
Meinen CS, McPhaden MJ (2000) Observations of warm water volume changes in the equatorial Pacific and their relationship to El Niño and La Niña. J Clim 13:3551–3559. doi: 10.1175/1520-0442
National Research Council (2010) : Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, 192 PP., ISBN-10: 0-309-15183-X, the National Academies Press, Washington, D. C., USA
Ohba M, Ueda H (2009) Role of nonlinear atmospheric response to SST on the asymmetric transition process of ENSO. J Clim 22:177–192
Okumura YM, Deser C (2010) Asymmetry in the duration of El Niño and La Niña. J Clim 23:5826–5843
Okumura YM, Ohba M, Deser C, Ueda H (2011) A proposed mechanism for the asymmetric duration of El Niño and La Niña. J Clim 24:3822–3829. doi: 10.1175/2011JCLI3999.1
Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108(D14):4407. doi: 10.1029/2002JD002670
Timmermann A et al (2018) El Niño–Southern Oscillation complexity. Nature 559(7715):535–545. doi: 10.1038/s41586-018-0252-6
van Oldenborgh GJ, Hendon H, Stockdale T, L'Heureux M, de Perez EC, Singh R, van Aalst M (2021) Defining El Niño indices in a warming climate. Environ Res Lett 16(4):044003. doi: 10.1088/1748-9326/abe9ed
Wang C (2001) A unified oscillator model for the El Niño-Southern Oscillation. J Clim 14:98–115
Wu Z, Huang NE (2009) Ensemble Empirical Mode Decomposition: a noise- assisted data analysis method. Adv Adapt Data Anal 1:1–41. doi: 10.1142/S1793536909000047
Yeo SR, Yeh SW, Kim KY, Kim WM (2016) The role of low frequency variation in the manifestation of warming trend and ENSO amplitude. Clim Dyn 49(4):1197–1213. doi: 10.1007/s00382-016-3376-0
Zhu J, Kumar A, Huang B, Balmaseda MA, Hu Z-Z, Marx L, Kinter JL III (2016) The role of off-equatorial surface temperature anomalies in the 2014 El Niño prediction. Sci Rep 6:19677. doi: 10.1038/srep19677
Yeo, S. R., S. W. Yeh, K. Y. Kim, and W. M. Kim, 2016: The role of low frequency variation in the manifestation of warming trend and ENSO amplitude. Clim. Dyn., 49 (4), 1197–1213, doi: 10.1007/s00382-016-3376-0.
Zhu, J., A. Kumar, B. Huang, M. A. Balmaseda, Z.-Z. Hu, L. Marx, and J. L. Kinter III, 2016: The role of off-equatorial surface temperature anomalies in the 2014 El Niño prediction. Sci. Rep., 6, 19677, doi: 10.1038/srep19677.

Table 1: Single-, double-, and triple-year El Niños and La Niñas during 1950-2021 according to the definition of NOAA/CPC (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php).

	El Niños	*La Niñas*
Single-Year	1951/52, 1953/54, 1963/64, 1965/66, 1972/73, 1979/80, 1982/83,1991/92, 1994/95, 1997/98, 2002/03, 2004/05, 2006/07, 2009/10, 2018/19	1964/65, 1988/89, 1995/96, 2005/06
Double-Year	1957/59, 1968/70, 1976/78, 1986/88, 2014/16	1954/56, 1970/72, 1983/85, 2007/09, 2010/12, 2016/18, 2020/22
Triple-Year		1973/76, 1998/2001

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Single-Year and Double-Year El Niños

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1. Introduction

2. Data And Methods

3. Results

4. Summary And Discussion

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