# The seasonal march of the equatorial Pacific upper-ocean and its El Niño variability

Florent Gasparin, Dean Roemmich

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## To cite this version:

Florent Gasparin, Dean Roemmich. The seasonal march of the equatorial Pacific upper-ocean and its El Niño variability. Progress in Oceanography, 2017, 156, pp.1-16. 10.1016/j.pocean.2017.05.010. hal-04886769

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https://hal.science/hal-04886769v1

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# The seasonal march of the equatorial Pacific upper-ocean and its El Niño variability

Florent Gasparin<sup>a,b,*</sup>, Dean Roemmich<sup>a</sup>

## Abstract

Based on two modern data sets, the climatological seasonal march of the upper-ocean is examined in the equatorial Pacific for the period 2004-2014, because of its large contribution to the total variance, its relationship to El Niño, and its unique equatorial wave phenomena. Argo provides a broadscale view of the equatorial Pacific upper-ocean based on subsurface temperature and salinity measurements for the period 2004-2015, and satellite altimetry provides synoptic observations of the sea surface height (SSH) for the period 1993-2015. Using either 11-year (1993-2003/2004-2014) time-series for averaging, the seasonal Rossby waves stands out clearly and eastward intraseasonal Kelvin wave propagation is strong enough in individual years to leave residuals in the 11-year averages, particularly but not exclusively, during El Niño onset years. The agreement of altimetric SSH minus Argo steric height (SH) residuals with GRACE ocean mass estimates confirms the scale-matching of *in situ* variability with that of satellite observations. Surface layer and subsurface thermohaline variations are both important in determining SH and SSH basin-wide patterns. The SH/SSH October-November maximum in the central-eastern Pacific is primarily due to a downward deflection of the thermocline (~20 m), causing a warm subsurface anomaly (>1°C), in response to the phasing of downwelling intraseasonal Kelvin and seasonal Rossby waves. Compared with the clima-

<footnote><sup>a</sup>Scripps Institution of Oceanography, University of California, 95000 Gilman Drive #
0230, La Jolla, USA</footnote>

<footnote><sup>b</sup>Now at Mercator Ocean, 10 Rue Hermès, 31520 Ramonville-Saint-Agne, France</footnote>

<footnote>*Corresponding author
Email address: fgasparin@mercator-ocean.fr (Florent Gasparin)</footnote>
tology, the stronger October-November maximum in the 2004-2014 El Niño composites is due to higher intraseasonal oscillations and interannual variability. Associated with these equatorial wave patterns along the thermocline, the western warm/fresh pool waters move zonally at interannual timescales through zonal wind stress and pressure gradient fluctuations, and cause substantial fresh (up to 0.6 psu) and warm (~1°C higher than the climatology) anomalies in the western-central Pacific surface-layer during the El Niño onset year, and of the opposite sign during the termination year. These El Niño-related patterns are then analyzed focusing on the case of the onset of the strong 2015/2016 episode, and are seen to be around two times larger than that in the 2004-2014 El Niño composites. The present work exploits the capabilities of Argo and altimetry to update and improve the description of the physical state of the equatorial Pacific upper-ocean, and provides a benchmark for assessing the accuracy of models in representing equatorial Pacific variability.

Keywords: elsarticle.cls, LATEX, Elsevier, template

2010 MSC: 00-01, 99-00

# 1. Introduction

The seasonal cycle in the equatorial Pacific is a prominent source of climate variability and is associated with a combination of processes at different spatial and temporal scales involving both ocean and atmosphere. Year-to-year fluctuations in the seasonal cycle are profoundly impacted by El Niño-Southern Oscillation (ENSO), the dominant interannual climate signal on Earth (Rasmusson and Carpenter, 1982). In recent decades, ENSO has stimulated the implementation of a major tropical Pacific observing system and its modeling counterpart, but despite significant progress having been made in the description and understanding of basin-wide atmospheric and oceanic variability, some basic relationships between seasonal and El Niño variations are not correctly reproduced by models (Guilyardi, 2006; Guilyardi et al., 2009). For example, coupled ocean-atmosphere general circulation models (CGCMs) can miss representing
the peak of El Niño in boreal winter, which is one of the most robust features of ENSO evolution (Guilyardi et al., 2009; Bellenger et al., 2014; Ham and Kug, 2014). In addition, seasonal forecasts of El Niño occurrence and amplitude remain a major research challenge as shown by the case of the 2014 year. Despite the presence of oceanic and atmospheric indicators in early 2014, apparently signaling the onset of a strong El Niño, El Niño never materialized that year (McPhaden, 2015; McPhaden et al., 2015). Issues in modeling and forecasting seasonal variations in the equatorial Pacific clearly highlight that striking shortcomings remain in our capability to represent and anticipate this phenomenon. There are however some indications that improved representation of seasonal variations of thermohaline stratification of the upper-ocean in models and forecasting systems will improve representation and prediction skills (e.g., An and Wang, 2001; Ham et al., 2013; Zhu et al., 2014). Thus, a refined description of the seasonal variations in the equatorial Pacific upper-ocean based on modern data sets is of significant importance for better understanding, modeling, and forecasting the tropical Pacific climate.

Over the past decades, extensive research has contributed to understanding the mechanisms responsible for seasonal variability in the equatorial Pacific upper-ocean. On the basis of expendable bathythermograph (XBT) observations, Meyers (1979a) analyzed the seasonal variations in the depth of the 14°C isotherm in the tropical Pacific and found westward propagation along the equator from the eastern to the western Pacific at a speed consistent with a linear equatorial Rossby wave. He suggested that this propagation is forced by seasonal variations of the zonal wind stress, with an oceanic response through local Ekman pumping. This finding was supported using a more extensive XBT data set by Kessler (1990). Lukas and Firing (1985) and then Kessler and McCreary (1993) showed the vertical propagation of these seasonal Rossby waves. Later, Yu and McPhaden (1999b) described, using more than 5 years of observations from the Tropical Atmosphere-Ocean (TAO) array, how the seasonal march of surface winds, zonal currents, SST, thermocline depth, and dynamic height were all dominated by wind-forced equatorial Kelvin and Rossby waves at the equa-
tor. In addition to the description of the unique equatorial wave phenomena, earlier work in the equatorial Pacific upper-ocean has also focused on seasonal fluctuations of heat and freshwater exchanges, which are of crucial importance in controlling basin-wide air-sea interactions at longer timescales (e.g., Yu and McPhaden, 1999a; Wang and McPhaden, 1999; Hasson et al., 2013).

However, previously mentioned studies have been limited by spatial or temporal resolution of the available data sets, and have focused on the seasonal variations of the thermal structure due to a lack of salinity measurements. In the present work, seasonal fluctuations of the upper-ocean physical state are described, mostly based on two modern data sets. Argo provides a broadscale view of the equatorial Pacific upper-ocean based on subsurface temperature and salinity measurements for the period 2004-2015, and satellite altimetry provides complementary synoptic observations of the sea surface height (SSH) for the period 1993-2015. Note that the time period of the analysis coincides with the so-called hiatus in global warming, which had a significant imprint in the mean state (England et al., 2014) and seasonal cycle (Amaya et al., 2015) of the tropical Pacific.

The present work begins with a basic description of surface and subsurface temperature and salinity fields as seen in the Argo data set, including their mean and seasonal variations. Vertical sections of the upper-ocean above 400 dbar are presented because most of the processes involved in the ocean-atmosphere interactions at seasonal timescales operate in this layer. Next, an expanded description of the depth-integrated ocean fields, including the 0/2000 dbar steric height (SH) and the altimetric SSH is provided, and the SSH balance is examined in the equatorial Pacific. Then, the seasonal march during El Niño episodes is described using El Niño composites during the 2004-2014 period, and focusing in particular on the onset of the 2015/2016 El Niño. The representativeness of this fixed-era climatology, based on the 2004-2014 Argo record, is examined with the 22-year altimetric SSH time series. The present work exploits the capabilities of Argo and altimetry to update and improve the description of the physical state of the equatorial Pacific upper-ocean, and of its dynamical processes, on
a range of timescales that impact or interact with the seasonal variability.

## 2. Data sets

### 2.1. Temperature/salinity from Argo floats

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Temperature and salinity profiles from Argo floats were acquired from the Argo Global Data Assembly Center (GDAC) for the period January 2004 to February 2016. Low quality profiles are excluded after several quality control tests were performed (Roemmich and Gilson, 2009). More than 90% of the floats have a cycling period of 10 days, while the others are cycling every 7 days. Anomaly fields every 5 days are constructed on a 1°x1° grid following the procedures of Roemmich and Gilson (2009), with improvements in the optimal interpolation spatial covariance function and by including the time domain as described by Gasparin et al. (2015). The Argo Program has made a major contribution to subsurface ocean sampling in the tropical Pacific, including with the deployment of 41 Iridium-floats along the equator in the beginning of 2014. These new generation floats ("Sounding Oceanographic Lagrangian Observer"-II, SOLO-II) have a short surface time of 15 minutes (due to the Iridium 2-way communications), and minimal equatorial divergence (see Supplementary Figure S1 in Gasparin et al., 2015). The number of Argo profiles has more than doubled along the equatorial band, from around 150 profiles per month in 2012-2013 to around 350 profiles in 2014-2015 (Figure 1a), making the equatorial band the best sampled region of the tropical Pacific in 2015 (Figure 1b).

### 2.2. Remote sensing data sets

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Satellite altimeters provide SSH anomaly estimates, here from the AVISO delayed-mode merged product from October 1992 to December 2015. This consists of gridded SSH combining all available satellite altimeters. The 2014 SSALTO/Duacs product (DUACS 2014) with a 1/4°x1/4° spatial and daily temporal resolution is used in this study, and has been downloaded from the website www.aviso.altimetry.fr/duacs/. Anomalies from the 2004-2014 annual
mean are considered and then linearly de-trended. For consistency with Argo 5-day time-series, the SSH time-series is temporally (5-day) and spatially (1°x1°) smoothed using running-mean filtering.

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Surface winds and precipitation are estimated using daily ECMWF-Interim reanalysis winds (downloaded from the website http://data.ecmwf.int/data/) on a 0.75°x0.75° grid (Dee et al., 2011). Similarly to SSH, the surface wind time-series is temporally (5-day) and spatially (1°x1°) filtered.

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Gravity data from the Gravity Recovery and Climate Experiment (GRACE) mission provide monthly estimation of the ocean mass component during the Argo era. These data represent regional anomalies (the global mean of each time step has been removed). Field solutions are given in global spherical harmonic basis functions (Chambers and Bonin, 2012). Here, the latest release (RL05) from the *GeoForschungsZentrum* GFZ group is used on a 1° x 1° ocean grid (downloaded from the website http://grace.jpl.nasa.gov/data/mass/). The ocean mass component is expressed in terms of equivalent water height in cm. Note that our findings are independent of the chosen solutions (GFZ, *Center for Space Research* CSR or *Jet Propulsion Laboratory* JPL). As a second estimate, the recent JPL GRACE mascon solution, employing geophysical constraints (Watkins et al., 2015), is also used.

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### 3. Hydrography

Note that the “seasonal cycle” sometimes refers to the amplitude of the seasonal harmonic, or to the monthly averaging of fields. Here we are describing the average over many years of corresponding sequential 5-day segments of the year, considering that timescales shorter than a month could be important in the seasonal cycle. To avoid misinterpretation of these results, the term ‘seasonal march’ is used in preference to ‘seasonal cycle’. Confidence intervals for each 5-day segment of the seasonal march were obtained using the Student t-distribution, then averaged together to form a single estimate applicable to all 5-day segments. This assumes that year-to-year variations around the sample

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mean for a time-step were random and uncorrelated. Sampling error compos-
ites are zonally-averaged along the equatorial Pacific and are given as a single
interval for 95% confidence limits relevant to each seasonal march in Figs 4, 7,
11, 12 et 17.

3.1. *Temperature*

The equatorial Pacific displays distinctive vertical displacements of in the
mean temperature field due to the zonal tilting of the thermocline (Figure 2b).
This is the signature of direct atmospheric forcing by the mean trade winds in
the central equatorial Pacific (Fig2a), which push surface waters to the west.
Above a deep thermocline (~180 m), the warm pool is formed in the western
Pacific, while the eastern Pacific thermocline rises close to the surface (~50 m)
contributing to the cold tongue.

Seasonal variations are first presented above 400 dbar in two-month aver-
aged anomalies from the annual mean in Figure 3. The seasonal patterns of
temperature are mainly observed in the eastern Pacific surface layer and at the
thermocline level (±2°C), but extending below it. For that reason, tempera-
ture anomalies are then presented in Figure 4 in time-longitude plots at the sea
surface and at the mean thermocline level. As mentioned previously, seasonal
variations of temperature are prominent in the eastern Pacific surface layer, with
a maximum in March-April (>2°C) and a minimum in September-October (<-
1.5°C), while the sun crosses the equator twice a year. This pronounced annual
pattern, rather than semi-annual, results from a combination of ocean dynam-
ics and ocean-atmosphere interactions, including impacts related to cloudiness
variations (Mitchell and Wallace, 1992; Kessler et al., 1998). In the western
Pacific warm pool region, semi-annual variability of small amplitude (less than
0.5°C) dominates, with maxima in May and November and minima in Febru-
ary and August. Seasonal fluctuations of the warm pool's eastern edge are
small (±500 km zonally), compared with interannual variations (several thou-
sand km), and mainly result from seasonal variations of zonal advection (Picaut
et al., 1996), mainly controlled by winds (Fig.4a).
At the thermocline level, subsurface variations are dominated by thermocline displacements (±10-15 m), with downward deflection inducing warm anomalies, and vice versa (Figure 3). These westward propagating anomalies are related to Rossby waves taking about a year for crossing the Pacific (Meyers, 1979a; Kessler and McCreary, 1993) (Figure 4). These waves are primarily generated by local Ekman pumping (Kessler, 1990; Kessler and McCreary, 1993), with a weakening of the trades in the eastern Pacific at the beginning of the year (Fig.4a), implying a negative temperature anomaly associated with upward thermocline displacement. Eastward propagating anomalies are found in the central-eastern Pacific, especially in November associated with the maximum vertical thermocline displacement (~20 m) causing a warm anomaly (>2°C). It is noteworthy that this warm anomaly at the thermocline depth does not reach the sea surface in the eastern Pacific in November-December (Figure 3f). This will be compared later in Section 5, in which seasonal variations are shown during El Niño years. The overall seasonality and spatial thermal structure provided by the Argo temperature fields are thus consistent with previous descriptions of the equatorial Pacific thermal fields (e.g., Kessler et al., 1996; Yu and McPhaden, 1999b; Johnson et al., 2002).

## 3.2. Salinity

The mean salinity field (Fig.2d) is also consistent with previous descriptions based on sparser data sets (e.g., Johnson et al., 2002), and mainly reflects the influence of heavy rainfall and wind-driven circulation. In the western Pacific, the warm pool associated with strong atmospheric convection and accompanying rainfall, exhibits a large excess of precipitation over evaporation (Fig. 2c), which in turn generates the fresh pool (Delcroix and Henin, 1991). In the east, high rainfall in the Intertropical Convergence Zone (ITCZ) and due to the "gap" winds across Central America (Xie et al., 2005) are responsible for the far eastern Pacific fresh pool (Alory et al., 2012). In the subsurface layers, the stronger subsurface salinity maximum in the western Pacific is mainly caused by the subduction of high salinity surface waters of the central-eastern Pacific under
the warm/fresh pool in association with the convergence of surface currents (Lukas and Lindstrom, 1991; Kuroda and McPhaden, 1993; Picaut et al., 1996; Vialard and Delecluse, 1998; Maes, 2008).

Because of the weak salinity stratification at the thermocline level, the seasonal thermocline displacement does not create substantial salinity anomalies as in temperature, and seasonal variations of salinity are mainly observed in the surface layer (Figure 4c). In the western and eastern Pacific, SSS anomalies (±0.2 psu), are generally positive during boreal spring and negative during boreal winter. This seasonal variability is related to precipitation changes (Fig. 4d), through the meridional position of the South Pacific Convergence Zone (SPCZ) and ITCZ (Fig.4d), and annual salinity advection of surface currents (North/South Countercurrents) (e.g., Bingham et al., 2010; Yu, 2011; Hasson et al., 2013).

### 3.3. Density

Density changes, due to either temperature or salinity, are shown in vertical sections in Figure 6 for May-June and November-December anomalies. The impact of salinity on density is estimated by subtracting density, with salinity replaced by the mean value, from density with the observed salinity included. This allows the quantification of temperature and salinity contributions on stratification, with the actual density changes being the sum of temperature and salinity changes. Seasonal temperature changes clearly dominate with resulting density changes (>0.3 kg m⁻³), especially in the eastern Pacific surface layer and at the thermocline level, with warm (cold) anomalies causing density to decrease (increase). Density changes due to the increasing/decreasing salinity are smaller, and are mostly in the surface layer. In the western Pacific surface layer, density changes due to salinity (>0.1 kg m⁻³) dominate, while in the eastern Pacific, they tend to partially offset the warming signal. Hence, while substantial density changes are caused by temperature in the subsurface layer and in the eastern Pacific, salinity dominates density changes in the western Pacific. This point is reinforced during El Niño years as shown in Section 5.
4. The concurrent 2004-2014 SH and SSH data sets

The quality and consistency of the present upper-ocean observing system for representing seasonal variability in the equatorial Pacific are assessed by attempting to close the SSH balance in the equatorial Pacific during the climatological seasonal march (CSM). Numerous recent studies have shown that we are now able to close the globally-averaged SSH balance using altimetry, Argo and GRACE data sets (e.g. Willis et al., 2008; Leuliette and Miller, 2009). However, this has not been demonstrated for the equatorial Pacific.

First, the 2004-2014 CSM from Argo SH (0/2000 dbar) is compared with altimetric SSH, considering the latter to approximate vertically integrated sub-surface density or temperature. In Figure 7, the time-longitude diagram of altimetric SSH exhibits large-scale westward phase propagation of anomalies, taking about a year from the eastern to the western boundary. These westward propagating anomalies (±5 cm), with a speed of around 0.5 m s⁻¹, are associated with annual first baroclinic mode Rossby waves, resulting from eastern boundary reflections of Kelvin waves and the modulation of easterly winds in the central-eastern Pacific (Chelton et al., 2003). Similar sections, presented along 5°N in Figure 8, exhibit higher amplitude than at the equator, confirming that this westward propagation is associated with annual Rossby waves. Those are broadly weaker in the beginning of the year and stronger in the second half of the year (Meyers, 1979a), and follow the seasonal march of the ITCZ in the meridional direction (closer to the equator in March, Mitchell and Wallace, 1992) (Figure 9). This pattern constitutes the main variability of the upper-ocean at seasonal timescales in the tropical Pacific and has been investigated in previous studies, based on oceanographic cruise data, XBT measurements, TAO/TRITON moorings, remote sensing data and numerical models (e.g. Meyers, 1979b,a; Kessler, 1990; Kessler and McCreary, 1993; Yu and McPhaden, 1999b; Wang et al., 2000; Chelton et al., 2003). These seasonal variations of altimetric SSH, mostly resulting from vertical displacements of the thermocline, produce an anomalously thick (high SH and SSH) and thin (low SH/SSH) sur-
face layer (Rebert et al., 1985).

At shorter timescales, three eastward propagating positive SSH anomalies, observed in July (+2 cm), August (+3 cm) and October (+6 cm) in the central-eastern Pacific, appear superimposed on the positive seasonal SSH anomaly in the second half of the year. They act on the stratification by amplifying, or reducing, the background seasonal variations. These intraseasonal bumps, related to first baroclinic mode Kelvin waves, are known to be mainly driven by the Madden-Julian Oscillation (MJO) and its associated westerly wind episodes (Kessler et al., 1995; Harrison and Vecchi, 1997; McPhaden et al., 1998; Lengaigne et al., 2003; Eisenman et al., 2005), but have been also identified in relation to convectively-coupled atmospheric Rossby waves (e.g. Puy et al., 2015). Here, they appear to be strong enough in individual years to leave residuals in the 11-year average, particularly in October-November.

For the same period of observation, Argo SH (Figure 7b) shows essentially the same characteristics as altimetric SSH, with the quasi-annual westward propagation and the intraseasonal eastward propagation that is strongest near the end of the year. Compared with altimetric SSH, Argo SH is somewhat noisier, presumably due to the coarser *in situ* sampling. Recent enhancements in the equatorial Argo coverage have improved the representation of these basin-wide features (Gasparin et al., 2015). To further compare Argo SH and altimetric SSH estimates, the CSMs from both data sets, zonally-averaged in the central equatorial Pacific (170°W-120°W, 2°S-2°N), are shown in Figure 7c. Seasonal variations are well-observed in both fields, with a minimum in February (-4 cm) and a maximum in October (5 cm), as well as intraseasonal oscillations which strongly modulate these larger-scale anomalies such as in October (from 0 to 6 cm in a month). Argo SH and altimetric SSH thus show similar patterns in the equatorial Pacific and provide more details of the seasonal march than previous observations, including intraseasonal Kelvin waves embedded in the CSM such as the October-November peak.

In spite of the strong similarities noted above, SSH anomalies appear to be lower than Argo SH values between January and June (~-1.4 cm), and higher
from July to December (~1.4 cm). These differences could reflect the seasonal
contribution of deep SH (below 2000 dbar) and/or mass-related components,
both of which are not included in the 0/2000 dbar upper-ocean steric component
of Argo SH, but are in SSH. To identify the differences between SSH and SH, we
subtract the Argo SH anomaly from the altimetric SSH anomaly and consider
the CSM of these differences over the equatorial Pacific. The main question
is whether we are able to close the SSH balance on seasonal timescales in the
equatorial Pacific. In Figure 10a, SSH minus SH residuals reveal a clear seasonal
march, which appears remarkably zonally consistent across the basin. The zonal
average of these residuals shows a seasonal cycle with amplitude of 1.5 cm, with
a minimum in March and a maximum in September (Figure 10b). In order
to account for a portion of the deep SH (below 2000 dbar), a simple linear
regression coefficient γ (SSH~γSH), as in Gasparin et al. (2015), is determined
using Argo SH and the co-located altimetric SSH (blue line in Figure 10b), but
this estimation represents only ±0.1 cm. Deep Argo floats, which will provide a
direct estimate of the deep steric component below 2000 dbar, should test this
finding in the coming years.

Seasonal variations of the mass-related component are estimated using the
2004-2014 GRACE data. GRACE represents the regional anomaly - the global
mean having been removed in this product at each time step (http://grace.jpl.nasa.gov/data/get-
data/monthly-mass-grids-ocean/). An annual harmonic of 8.5 mm amplitude
with maximum in mid-October is then added for including global mean an-
nual variations (Chambers et al., 2004; Williams et al., 2014). As noted by
Willis et al. (2008) or Rietbroek et al. (2009), variations of the ocean mass
are due to several land/sea mass exchanges including river runoff, evapora-
tion/precipitation, and ice sheet thawing. Seasonal variations of ocean mass,
averaged across the equatorial Pacific, are shown in Figure 10b (red crosses),
and appear in good agreement with the SSH minus SH residuals. Note that
the GRACE mascon solution is slightly larger than the conventional GRACE
solution. The RMS difference between SSH and 0/2000 dbar SH CSMs, zonally-
averaged across the equatorial Pacific, is 1.0 cm, and 0.9 cm when the deep SH
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estimation is included along with the 0/2000 dbar SH. The inclusion of the ocean mass component with SH estimation reduces the RMS difference to 0.3 cm.

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The mass changes are the residual of the horizontal mass transport divergence and the surface fluxes. The presence of heavy rainfall in the equatorial region raises the question of how the mass contribution due to local variations in E-P is accounted for in the SSH budget. By comparing E-P and mass variations in the equatorial box (2S-2N; 130E-80W), local annual variations in E-P (~2mm/day) appear to be 5 times larger than mass variations (~0.4mm/day). Thus, E-P annual variations are probably balanced by annual variations of meridional transports plus continental inputs. Further investigations are needed to determine mass contribution of surface versus horizontal contributions, which will require a refinement of volume transport budget in the equatorial band.

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To conclude, despite their dynamical difference, the SSH and SH fields are consistent, and present clear similarities in seasonal and intraseasonal anomalies in the equatorial Pacific seasonal march such as the October-November intraseasonal peak in the central-eastern Pacific. The agreement of SSH minus SH residuals with ocean mass confirms the small contribution of deep SH in the equatorial Pacific, and reveals that we are now able to close the SSH balance in the equatorial Pacific on intraseasonal and seasonal timescales, with a 3 mm-residual.

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# 5. The seasonal march during El Niño years

## 5.1. The 2004-2014 El Niño composites

The occurrence of the peak of El Niño during the boreal winter season is one of the most robust features of El Niño evolution (e.g., Guilyardi, 2006; Ham et al., 2013). This motivates a comparison of the CSM in the equatorial Pacific upper-ocean during El Niño years, with that of the climatology. This is done here (i) by computing El Niño composites for the period 2004-2014 (including 2004/2005, 2006/2007, 2009/2010 episodes), and (ii) by analyzing the case of the onset of the strong 2015/2016 episode. El Niño composites are each computed

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for a 2-year period ‘0000-0001’. The year ‘0000’ is the onset year of El Niño, while the year ‘0001’ is the termination year. Each time-step of the ‘0000-0001’ period is the average of the corresponding time-step in the three 2-year El Niño episodes. To the degree that the variability between composites (±5 cm) tends to be smaller than the composite anomalies (±10 cm), the El Niño composites for the 2004-2014 period can be considered as representative of El Niño episodes for the period 2004-2014.

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Figure 11 shows the seasonal marches of the 0/2000 dbar SH and altimet-
ric SSH in the El Niño composites. The two data sets present similar basin-
wide variations, dominated by a large-scale zonal seesaw centered around 160°E
(Alory and Delcroix, 2002), with positive anomalies mostly observed during the
onset year (up to 16 cm) in the central-eastern Pacific and negative anomalies
(around -10 cm) during the termination year. In the western Pacific, anomalies
are of the opposite sign. These variations reflect upward and downward de-
flections of the thermocline, respectively associated with negative and positive
SH or SSH anomalies, and result from surface wind variations at interannual
timescales. Superposed on these basin-wide variations, eastward propagating
anomalies mark the presence of intraseasonal Kelvin waves. These intrasea-
sonal propagating features, resulting from western Pacific winds (Figure 12a),
have been identified as one of the key factors for the onset of El Niño (e.g.,
Kessler et al., 1995; Eisenman et al., 2005; Menkes et al., 2014; Hu et al., 2014;
Fedorov et al., 2014; Lian et al., 2014; Chen et al., 2015), and appear to con-
tribute to the SH and SSH maximum in October-November of the onset year,
similarly to that observed in the climatology.

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SSH minus SH residuals, zonally-averaged across the equatorial Pacific, are shown for the seasonal marches during El Niño years in Figure 11c (black line). Residuals, including deep SH (below 2000 dbar) and mass-related components, have higher amplitude than the climatology (Figure 7b) during the El Niño onset year, and lower amplitude during the termination year. In order to distinguish the deep SH from the mass-related components, a portion of the deep SH (below 2000 dbar) is removed from the SSH minus SH residuals by using a simple linear

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regression (blue line), similarly to the climatology. This suggests that the deep
SH is mostly positive during the onset of El Niño, and on average equal to 0.3
cm, with a maximum in November of around 1 cm. During the termination year,
the deep SH regression estimate is negative and on average is equal to -0.3 cm.
The mass-related component is estimated using GRACE data (red crosses), and
do not shown differences with respect to the climatology. The RMS difference
between the SSH and the 0/2000 dbar SH is estimated at 1.25 cm, decreasing
to 0.5 cm when the deep SH and the ocean mass estimates are included along
with the 0/2000 dbar SH. This remaining residual of the SSH balance, which is
slightly higher than the 0.3 cm climatological residual, is mostly attributable to
a fewer number of years considered in the 3-year El Niño composites compared
with the 12-year mean climatology.

To further explore basin-wide variations of the upper-ocean, the amplitude
and depth dependence of these seasonal variations during the evolution of El
Niño are examined through description of the thermohaline structure captured
in the Argo record. SST and temperature at the thermocline level during El
Niño are shown in Figure 12b,c. At the thermocline depth, seasonal variations
of temperature during the El Niño onset years present similar west-east contrast
to that observed in the climatology, but with greater amplitude. Cold anomalies
observed in the central-eastern Pacific and warm anomalies in the western
Pacific during January-February and March-April are two times stronger than
the climatology. This results from a steeper thermocline associated with the
strengthening of the trades prior to an El Niño episode (Wyrtki, 1975). These
intensified winds push the warm pool further west, resulting in a smaller surface
warming in the eastern Pacific (~1.5°C) than that observed in the climatology
(~2°C). In May-June and July-August, conditions are similar to climatological
conditions, and correspond to the time of the year when thermocline slope is
the closest to its mean position. With the smallest year-to-year upper-ocean
variability, this period corresponds to the "spring predictability barrier". However,
this is around the period when the atmosphere is expected to be the most
sensitive to oceanic variations (Spencer, 2004).
Temperature anomalies in September-October and November-December of El Niño onset years are significant in the surface and subsurface layers, with the highest magnitude along the thermocline. Warm anomalies (up to 5°C) in the central-eastern Pacific are caused by a downward deflection of the thermocline, with a maximum of more than 40 m in November-December. These warm anomalies propagate eastward, reflecting the occurrence of Kelvin waves, and almost reach the surface in the eastern Pacific as suggested by the weaker cold anomaly at the surface compared with the climatology. Associated with these subsurface fluctuations, anomalies in the surface layer are affected by zonal advection. At this time of the year, the eastern edge of the warm pool is located at its easternmost position, around 20° of longitude further east than its climatological position. In addition, a warm anomaly in the central Pacific surface layer is moving eastward, an extension of the eastward displacement of the warm pool. The location of this warm surface anomaly in the central Pacific is typically a feature of *Central Pacific* El Niño (Kug et al., 2009; Kao and Yu, 2009). This has been confirmed by McPhaden et al. (2011), who compared thermocline depth between El Niño composites for the periods 1980-1999 and 2000-2010. They showed that the 2000-2010 El Niños, characterized by a pre-dominance of *Central Pacific* El Niño variability, had a shallower thermocline, but were also located further west compared with the 1980-1999 El Niños, which were predominantly of the *Eastern Pacific* El Niño variety. They also suggested that the primary source of anomalous surface warming in the eastern Pacific was the zonal advection during *Central Pacific* El Niño, while processes related to the thermocline depth displacement were dominant during *Eastern Pacific* El Niños.

Similarly to the climatology, salinity anomalies during El Niño have small amplitude along the thermocline due to the weak haline stratification at this level, which is thus relatively insensitive to thermocline displacement (not shown). The most prominent pattern is observed at the surface in the western Pacific around 170°E, with fresh anomalies (up to 0.6 psu), mostly resulting from the zonal displacements of the convective cell and the SSS front at the eastern edge
of the fresh pool (up to 20° of longitude) (Figure 12e,d). This strong freshwater anomaly dominates density anomaly in the western Pacific 0-100m surface layer (Figure 13). In the eastern Pacific, seasonal changes are only slightly higher than that of the climatology.

## 5.2. The case of the onset year of the 2015/2016 El Niño

Here, the upper-ocean conditions during the onset year of the 2015/2016 El Niño are examined and compare with the earlier 2004-2014 El Niño composites. This also allows the assessment of the capability of the present integrated observing system for near-real-time monitoring of the upper-ocean during the evolution of the 2015/2016 El Niño. In Figure 14, SH and SSH anomalies, relative to the 2004-2014 mean, are presented for the year 2015, with positive values in the central-eastern Pacific for almost the entire year. In general, the 2015 SH/SSH anomalies are stronger than that in the 2004-2014 El Niño composites, such as in the central-eastern Pacific during the October-November maximum peaking at 25 cm in 2015 versus 15 cm in the 2004-2014 El Niño composites. This results from a stronger downward deflection of the thermocline in 2015 (up to 60 m). In addition, the 2015 SH/SSH maximum is located around 10° of longitude further east than in the composites. This demonstrates that the El Niño-related 2015 changes of the upper-ocean were particularly strong due to a steeper thermocline slope, compared with the 2004-2014 El Niño composites. The comparison of the 2015 SH anomaly with the 1997 reveals that the maximum is however weaker in 2015 than in 1997 (when the SSH anomaly was up to 35 cm) and located around 10° of longitude further west (not shown).

In order to assess how the SSH is balanced during the 2015 event, the SSH minus SH residuals are shown in Figure 14c. Residuals are similar to that of the climatology (±2.5 cm), but include a significant deep SH contribution from the regression estimate (~1.1 cm) compared with a small estimate in the El Niño composites (0.3 cm). The residuals of the SSH balance, including the deep SH and mass-related components, is 0.7 cm RMS. These remaining residuals in the SSH balance are very small compared with the amplitude of the
El Niño variations during the year (more than 25 cm). This shows a very good consistency between SH and SSH for near-real-time representation of basin-wide variations in the equatorial Pacific upper-ocean.

To further investigate upper-ocean conditions during 2015, November through December temperature and salinity anomalies, with respect to the 2004-2014 mean, are presented in Figure 16a,b. The pattern of temperature along the equatorial Pacific is that of a dipole, signaling the El Niño fluctuation of the thermocline slope, similarly to that in the El Niño composites. The maximum temperature anomaly, reaching 7°C, is found in the eastern Pacific. A cold anomaly in the western Pacific confirms that these anomalies are induced by a steeper thermocline slope than that in the 2004-2014 El Niño composites. Temperature at the thermocline level reveals that the west-east contrast is observed throughout almost the entire year. The SST field is warmer by about 3°C than the 2004-2014 mean, (and more than 2°C higher than the El Niño composites).

As in the temperature field, a prominent pattern of salinity is observed in 2015, but within the surface layer. This fresh pool extends to about 100 dbar, with a large fresh anomaly (<-0.8 psu) occurring between 170°E and 160°W. In Figure 16c, it appears that this fresh anomaly significantly decreases the density in the surface layer by around -1.5 kg/m³. Resulting from a combination of zonal advection and precipitation, the 2015 fresh pool has been identified as contributing up to half, and occasionally more, of the SH anomaly in the western Pacific, and to the eastward displacement of the SSS front through the zonal pressure gradient (Gasparin and Roemmich, 2016).

# 6. Representativeness: SSH during 2004-2014 versus 1993-2003

The occurrence of intraseasonal variability in the 2004-2014 climatology and in the 2004-2014 El Niño composites raises the question of the adequacy of an 11-year time-series such as Argo for representing the long-term mean seasonal march. Based on the longer altimetric SSH time-series, the 2004-2014 CSM, previously described, is compared with the 1993-2003 CSM in Figure 17. For
the 1993-2003 period, the CSM exhibits similar broad general patterns as for
the period 2004-2014, such as the westward propagating annual Rossby waves,
but with a slightly higher amplitude (±4 cm). The intraseasonal eastward prop-
agation feature observed in late-year is also present in the 1993-2003 CSM, but
with longer intraseasonal timescales, and located farther to the east in the 1993-
2003 CSM than in the 2004-2014 CSM. These particular characteristics reflect
seasonal fluctuations of zonal wind stress, which show positive intraseasonal
anomaly around the dateline in October (Fig. 17a,b). Differences observed
in the two periods can be explained by longer-term variability associated with
ENSO characteristics and decadal timescales (Dewitte et al., 2008). However,
further investigations are required to understand whether the decadal variabil-
ity may interact with intraseasonal pulses having different timescales. Thus,
although differences are found between the 2004-2014 and 1993-2003 CSMs, the
general basin-wide patterns described in the 2004-2014 CSM are also observed
in the 1993-2003 CSM.

At first glance, the observed differences, in the 1993-2003 CSM compared
with the 2004-2014 CSM, are related to the occurrence of the strong 1997-1998
El Niño episode during the 1993-2003 period, referred to as the “El Niño of the
century” (McPhaden, 1999). In order to assess how individual strong episodes
impact on the CSMs, the altimetric SSH is averaged in the central equatorial
Pacific for the two 11-year CSMs, and without including the strongest El Niño
of each period, respectively the 1997/98 and 2009/10 episodes (Figure 17c,d).
The close similarity between both estimations, including or not the strongest
El Niño (RMS differences estimated at 0.6 cm), demonstrates that the main
patterns observed in the 11-year CSMs do not reflect only the direct impacts
of the strongest El Niño episodes. More surprising, the intraseasonal patterns
observed in late-year are still apparent when the strongest El Niño episodes
are removed, suggesting that these late-year patterns are quasi-independent of
individual strong episodes. This is a striking point because the amplitude of
the 1997/1998 El Niño episode, phase-locked with the seasonal march, could
suggest a priori a strong impact on the 11-year CSM.
To further investigate the effects of El Niño on the representation of the seasonal march, the three El Niño episodes of each period are removed from the 11-year CSMs (Figure 18). Compared with the 11-year CSM in Figure 17, similar patterns are observed and notably the intraseasonal eastward propagation in late-year during both periods, which even conserves the specific characteristics of each period. Thus, similarities between the 11-year CSM, and without including El Niño years, shows that the late-year intraseasonal variability cannot be totally attributable to a bias due to El Niño. This statement is confirmed using the 30-yr long moored time series from the TAO/TRITON array (not shown). Moreover, the stronger seasonal annual Rossby wave amplitude observed during the 1993-2003 CSM, and without including El Niño years, does not only reflect interannual variability, but also longer-term variability, as suggested by McPhaden et al. (2011). With a RMS difference between seasonal variations of the 11-year CSMs, and without including El Niño years, estimated at 1.5 cm for both periods, it is seen that ENSO years modulate the amplitude of the CSM, but do not significantly change its general features.

Thus, clear similarities are observed during the 11-year CSMs from two different periods. The late-year intraseasonal peaking of SSH appears as a persistent feature of the seasonal march. The maximum of altimetric SSH occurs in October-November during both 11-year CSMs, and is still observed when El Niño years are removed from the 11-year CSMs, suggesting that these intraseasonal variations are mostly an expression of the mean seasonal march instead of directly influenced by El Niño features. Differences between the 11-year CSMs, and without including El Niño, show that El Niño mostly impacts the amplitude of the CSM features, and to a lesser degree, their occurrence. This highlights that, although interannual and longer-term variability influence the CSM, an 11-year time-series provides a consistent picture of the upper-ocean seasonal march in the equatorial Pacific.
## 7. Summary and discussion

Using more than 22 years of altimetry and more than 12 years of Argo data, a detailed description of the upper-ocean seasonal march is provided in the equatorial Pacific at 5-day temporal resolution. For the period 2004-2014, Argo SH and altimetric SSH agree well in representing climatological variations, which show clear intraseasonal and seasonal basin-scale propagation of similar amplitude (±5 cm). The most striking feature is the presence of a clear eastward propagating SSH/SH maximum in October-November, resulting from the superposition of a downwelling eastward intraseasonal Kelvin wave on a downwelling westward propagating seasonal Rossby wave. At this time of the year, cold anomalies (around -1.5°C) are observed in the eastern Pacific surface waters, while warm anomalies (>2°C) are found in the subsurface layers due to the downward deflection of the thermocline of around 20 m caused by equatorial waves.

The RMS difference of the SSH and SH CSMs is around 1.5 cm, and is mostly attributable to the mass component of SSH. The deep SH (below 2000 dbar), estimated with a linear regression of the 0/2000 dbar SH with SSH, represents only ±0.1 cm, assuming that the deep component variability is phased with that of the upper part. Even though our deep estimation is consistent with other terms in the sea surface height balance, this assumption needs to be confirmed due to the vertically-propagating feature of equatorial waves (Kessler and McCreary, 1993). This should be achieved through the ongoing development of the deep ocean observing system, which will provide a direct estimation of this deep SH component in the coming decade (Johnson et al., 2015).

For the same period of observation, the seasonal march of El Niño composites resembles the climatology, with stronger SH/SSH amplitude during the onset year of El Niño, mainly associated with higher intraseasonal oscillations in late-year. This remarkably strong variability is seen in thermocline displacement, with a maximum amplitude of more than 60 m in October-November during the onset of El Niño. The downward deflection of the thermocline causes warm sub-
surface anomalies reaching 4-5°C along the thermocline in the central-eastern Pacific. This vertical displacement warming competes with the eastward displacement of warm pool waters of around 20° of longitude to reduce the late-year seasonal cooling in the central-eastern Pacific (around -1°C). The description of the onset of the 2015/2016 El Niño shows that the amplitude of these upper-ocean fluctuations are almost doubled in 2015 compared with the composites. A 7°C warm anomaly is observed at the thermocline level resulting from a deeper thermocline (80 m). In the surface layer, the eastward displacement of the warm pool is located as far as 140°W in November 2015, and all the central-eastern Pacific surface layer is warmer (2°C). The unusual warm anomalies are significantly associated with a large fresh pool, with anomalies lower than -1 psu in the western Pacific (Gasparin and Roemmich, 2016).

It is important to note that, although interannual and longer-term variability influence the CSM, an 11-year time-series provides a consistent picture of the upper-ocean seasonal march in the equatorial Pacific. The comparison of SSH seasonal marches during 1993-2003 and 2004-2014 periods highlights that the climatological patterns are broadly consistent between the two periods, with the occurrence of intraseasonal Kelvin waves in October-November during both 11-year CSMs. The main patterns observed in the two CSMs are only partly influenced by the enhanced CSM during El Niño, since removing El Niño years conserves the main equatorial wave characteristics of both periods. In fact, the long-term variability, which has been identified as contributing to El Niño diversity (McPhaden et al., 2011), is also seen to influence the characteristics of the 11-year CSM, peaking farther to the west in the 2004-2014 CSM than for the 1993-2003 period (Lee and McPhaden, 2010).

Additionally, the description and understanding of salinity-related processes and their implication in ENSO evolution are still challenging, although the number of salinity measurements has been significantly increased thanks to Argo since 2000's. Unlike the unusual 2015 "fresh pool" conditions, the uncertainties in each term of the freshwater budget do not allow precise assessment of the freshwater budget at seasonal timescale due to the reduced amplitude. Some
improvement in the salinity fields as well as in the other terms of the balance
are thus crucial to attempt to close the seasonal march of the freshwater budget.

This study benefits from a pilot experiment carried out in the equatorial
Pacific in 2014, which has doubled the number of Argo floats in this region. It
reinforces the finding that intraseasonal variability is critical for the description
and better understanding of oceanic ENSO-related processes such as thermo-
cline propagation and surface layer advection. Some discussion in the frame-
work of the Tropical Pacific Observing System 2020 (see the first report of
TPOS 2020, tpos2020.org/first-report/) is underway to extend this enhanced
coverage to all the tropical Pacific. As an important element of discussions, de-
sign studies are assessing the performance of different configurations of TPOS
using both statistical and modeling experiments. Questions of how the future
integrated observing system will resolve intraseasonal variability are central to
continuing and improving the array for better understanding of equatorial wave
propagation and near-surface processes in the eastern equatorial Pacific.

In conclusion, a decade of Argo and two decades of altimetry have provided
the equatorial Pacific CSM with improved temporal and spatial resolution, and
assessment of the representativeness of the 11-year Argo data set for representing
climatological variations. The occurrence of intraseasonal variability at the end
of the year, preferentially during El Niño episodes, highlights the requirement
of considering a range of timescales for characterizing and modeling seasonal
variations in the El Niño context. The ongoing development of the deep-ocean
observing system, and the evolution of its upper part, will improve the scale-
matching of *in situ* and remote observations, and will provide an accurate full
picture of the ocean, which is crucial for near-real time monitoring of El Niño-
related processes.
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## acknowledgment

Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www.argo.ucsd.edu, http://argo.jcommops.org). Participation by D.R. and F.G. were supported through NOAA Grant NA10OAR4320156, and also by the Disaster Relief Appropriations Act of 2013 (P.L. 113-2), which funded NOAA research grants NA14OAR4830216 and NA14OAR4830302, and NASA Grant NNX13AE2G (Ocean Surface Topography). The statements, findings, conclusions, and recommendations herein are those of the authors and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration or the Department of Commerce. F.G. was additionally supported by a postdoctoral fellowship of the Scripps Institution of Oceanography. The TAO/TRITON Ar-ray is maintained through a multinational partnership involving institutions in the United States (NOAA), Japan (JAMSTEC), Taiwan (NTU), and France (IRD) (http://www.pmel.noaa.gov/tao/). The altimeter products were pro-duced by Ssalto/Duacs and distributed by Aviso with support from CNES (http://www.aviso.altimetry.fr/duacs/). We thank J. Sprintall and Bruce Cor-nuelle for their valuable comments and L. Lehmann for assistance in some pro-cessing. Graphics were produced using the visualization program FERRET, a product of NOAA's Pacific Marine Environmental Laboratory. GRACE ocean data were processed by Don P. Chambers, supported by the NASA MEaSURES Program, and are available at http://grace.jpl.nasa.gov.
Figure 1: (a) Number of Argo profiles in each 3° x 3° box in the 10°S-10°N band during the year 2015. (b) Number of Argo profiles in the equatorial band (1.5°S-1.5°N) in each month, January 2004-February 2016.
Figure 2: Annual mean along the equator (1°S-1°N) of (a,c) zonal wind (in m/s), and precipitation (in mm/day), and (b,d) Argo temperature (in °C), and Argo salinity (in psu) above 400 dbar. The thick black line on (b,d) indicates the mean pycnocline depth (σ=1025 kg/m³).

Figure 3: Seasonal fluctuations of Argo temperature along the equator (1°S-1°N) above 400 dbar. The full black line indicates the pycnocline depth (σ=1025 kg/m³), and the full red line indicates the warm pool edge (29°C-isotherm), with dashed lines corresponding to the annual mean positions. Anomalies, from the 2004-2014 annual mean, are averaged every two months. Units are °C.

Figure 4: Seasonal fluctuations along the equator of a) zonal wind (in m/s), b) SST (in °C), c) temperature at the mean pycnocline depth (σ=1025 kg/m³), d) precipitation (mm/day), and e) SSS (in psu). The white line in (b) and the red line in (e) indicate the SST 29°C-isotherm and the 34.8 psu-isohaline, respectively. The annual mean zonally-averaged 95% confidence intervals, which are estimated for each individual 5-day time step of the seasonal march based on Student t-distribution, are 0.6°C, 0.1°C, and 0.05 psu, respectively for b), c) and e). Anomalies are from the 2004-2014 annual mean.

Figure 5: Seasonal fluctuations of Argo salinity along the equator (1°S-1°N) above 400 dbar. The full black line indicates the pycnocline depth (σ=1025 kg/m³), and the full red line indicates the fresh pool edge (34.8 psu-isohaline), with dashed lines corresponding to the annual mean positions. Anomalies, from the 2004-2014 annual mean, are averaged every two months. Units are psu.

Figure 6: Density anomalies, from the 2004-2014 annual mean, due to (a,b) temperature and (c,d) salinity above 400 dbar along the equator (1°S-1°N). Anomalies are averaged in (a,c) May-June and (b,d) November-December. The black lines indicate the pycnocline depth (σ=1025 kg/m³), and the red lines indicate the warm pool edge, with the dashed lines corresponding to the annual means. Units are kg/m³.
Figure 7: Seasonal fluctuations of (a) altimetric SSH and (b) Argo SH along the equator (1°S-1°N). In (c), altimetric SSH (in black) and Argo SH (in red) are zonally-averaged in the central Pacific (170°W-120°W). The annual mean zonally-averaged 95% confidence interval, which is estimated for each individual 5-day time step of the seasonal march based on Student t-distribution, is around 3 cm for both data sets. Anomalies are from the 2004-2014 annual mean. Units are cm.

Figure 8: As in Figure 7, but at 5°N (4°N-6°N).

Figure 9: Seasonal fluctuations of zonal wind stress (a) along the equator and (b) at 5°N. Anomalies are from the 2004-2014 annual mean. Units are 10⁻² N m⁻².

Figure 10: Seasonal fluctuations of ‘SSH minus SH’ residuals, ‘Fig. 7a minus Fig. 7b’, for (a) the overall equatorial Pacific and (b) zonally-averaged across the equatorial Pacific (black line). In (b), residuals, including the deep SH component (below 2000 dbar), are estimated from a linear regression using Argo SH and the co-located altimetric SSH. The mass changes are estimated from (i) conventional GRACE regional anomalies plus the global mean mass (red crosses, see text), and (ii) the JPL mascon version of GRACE (red bullets). Mass is expressed as equivalent water thickness. Anomalies are from the 2004-2014 annual mean. Units are cm.
Figure 11: Seasonal fluctuations during El Niño years for the 2004-2014 period (2004/05, 2006/07, 2009/10 episodes) along the equator (1°S-1°N) from (a) altimetric SSH, and (b) Argo SH. In (c), residuals, including the deep SH component (below 2000 dbar), are estimated from a linear regression using Argo SH and the co-located altimetric SSH (blue line). The mass changes (red crosses) are estimated from GRACE regional anomalies plus the global mean mass (see text), expressed as equivalent water thickness. The ‘0000’ and ‘0001’ years are associated with the onset and termination years, respectively. Each time-step of the "0000-0001" period is the average of the corresponding time-step of each 2-year El Niño episode. The annual mean zonally-averaged 95% confidence interval, which is estimated for each individual 5-day time step of the seasonal march based on Student t-distribution, is around 5 cm for both data sets. Anomalies are from the 2004-2014 annual mean. Units are cm.

Figure 12: As in Fig. 4, but during El Niño composites onset years (2004, 2006, 2009). The white line in (b) and the red line in (e) indicate the SST 29°C-isotherm and the 34.8 psu-isohaline, respectively, and the dashed lines correspond to the climatological positions. The annual mean zonally-averaged 95% confidence intervals, which are estimated for each individual 5-day time step of the seasonal march based on Student t-distribution, are 0.52°C, 0.24°C, and 0.08 psu, respectively for b), c) and e). Anomalies are from the 2004-2014 annual mean.

Figure 13: November-December anomalies, from the 2004-2014 annual mean, during El Niño composites onset years (2004, 2006, 2009) along the equator (1°S-1°N) of (a) temperature, (b) salinity and (c) density due to salinity variations. The full black line indicates the pycnocline depth ($\sigma=1025 \text{ kg/m}^3$), and the dashed black line is the annual mean position. Units are °C, psu and kg/m$^3$, respectively.
Figure 14: As in Figure 11, but during the year 2015. Anomalies are from the 2004-2014 annual mean.

Figure 15: As in Fig. 4, but during the year 2015. Anomalies are from the 2004-2014 annual mean.

Figure 16: As in Fig. 13, but during the year 2015. Anomalies are from the 2004-2014 annual mean.
Figure 17: Seasonal fluctuations of zonal wind stress (in $10^{-2}$ N m$^{-2}$) and altimetric SSH (in cm) along the equator (1°S-1°N) for (a,c) the period 1993-2003, and (b,d) the period 2004-2014. The lower panels are the zonal average of SSH in the central Pacific (170°W-120°W) for the 11-year CSM (full lines), and without considering the 1997/98 and 2009/10 episodes (dashed lines). Anomalies are from each 11-year annual mean. The annual mean zonally-averaged 95% confidence intervals, which are estimated for each individual 5-day time step of the seasonal march based on Student t-distribution, are 5 cm for the period 1993-2003 (c) and 3 cm for the period 2004-2014 (d).

Figure 18: Seasonal fluctuations of altimetric SSH along the equator (1°S-1°N) without considering the El Niño years (onset and termination years) for (a) the period 1993-2003 (years 1993, 1996, 1999, 2000, 2001 are considered), and (b) the period 2004-2014 (years 2008, 2011, 2012, 2013, 2014 are considered). Units are cm.
<figure><img src="image_3.png" /></figure>

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a) 2004-2015 MEAN ZONAL WIND

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b) 2004-2015 MEAN TEMPERATURE

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c) 2004-2015 MEAN PRECIPITATION

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d) 2004-2015 MEAN SALINITY

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2004-2014 MEAN ANNUAL CYCLE - Equator

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2004-2014 MEAN ANNUAL CYCLE - 5N

a) Altimetric SSH

<figure><img src="image_26.png" /></figure>

b) Argo SH

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c) 170W-120W

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Zonal wind stress

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Zonal wind stress

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a) SSH (2004-2014)

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b) Argo SH (2004-2014)

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c) SSH minus SH

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a) El Nino onset Zonal wind

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d) El Nino onset Precip.

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b) El Nino onset Argo SST

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c) El Nino onset pycno. T°

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e) El Nino onset Argo SSS

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2015

a)

SH 2015

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2015

b)

SSH 2015

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c)

SSH minus SH

<figure><img src="image_46.png" /></figure>

(cm)

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(cm)
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