Previous studies show that nonseasonal variations in global-mean sea level (GMSL) are significantly correlated with El Niño–Southern Oscillation (ENSO). However, it has remained unclear to what extent these ENSO-related GMSL fluctuations correspond to steric (i.e., density) or barystatic (mass) effects. Here we diagnose the GMSL budget for ENSO events observationally using data from profiling floats, satellite gravimetry, and radar altimetry during 2005–2015. Steric and barystatic effects make comparable contributions to the GMSL budget during ENSO, in contrast to previous interpretations based largely on hydrological models, which emphasize the barystatic component. The steric contributions reflect changes in global ocean heat content, centered on the Pacific. Distributions of ocean heat storage in the Pacific arise from a mix of diabatic and adiabatic effects. Results have implications for understanding the surface warming slowdown and demonstrate the usefulness of the Global Ocean Observing System for constraining Earth's hydrological cycle and radiation imbalance.
Sea level is an informative index of climate and serious concern for coastal communities. Hence, understanding the modern altimetry record is important from scientific and societal vantage points. The most apparent signals in the altimetric global-mean sea level (GMSL) data are the annual cycle and linear trend (e.g., Fig. 4 in Masters et al., 2012). In principle, these changes in the global ocean's water volume relate to the ocean's mass and its density, referred to as “barystatic” and “steric” sea level changes, respectively (e.g., Gregory et al., 2013; Leuliette, 2015). Past studies have successfully used in situ hydrography and satellite gravity data to assess ocean mass and density changes and to evaluate barystatic and steric effects on the annual cycle and the linear trend in GMSL (e.g., Lombard et al., 2007; Willis et al., 2008; Cazenave et al., 2009; Leuliette and Miller, 2009; Leuliette and Willis, 2011; Leuliette, 2014, 2015).
Although the annual cycle and linear trend are the most prominent signals in the record, altimeter data also evidence more subtle GMSL variations superimposed on those signals. In particular, it has long been reported that nonseasonal GMSL anomalies are significantly correlated with El Niño–Southern Oscillation (ENSO), such that the GMSL is anomalously positive during warm El Niño phases and anomalously negative during cool La Niña phases (Nerem et al., 1999, 2010; Chambers et al., 2002; Ngo-Duc et al., 2005; Landerer et al., 2008; Merrifield et al., 2009; Llovel et al., 2010, 2011; Boening et al., 2012; Cazenave et al., 2012, 2014; Meyssignac and Cazenave, 2012; Stammer et al., 2013; Fasullo et al., 2013; Haddad et al., 2013; Meyssignac et al., 2013; Calafat et al., 2014; Dieng et al., 2014, 2015; Pugh and Woodworth, 2014). Recent papers argue that ENSO-related GMSL changes are essentially of barystatic origin, related to changes in the hydrological cycle, and patterns of precipitation and evaporation (Llovel et al., 2011; Boening et al., 2012; Cazenave et al., 2012, 2014; Fasullo et al., 2013). However, these papers are based on either observations during an isolated event or correlation analysis of model output, and the extent to which barystatic or steric effects are responsible for ENSO-related GMSL fluctuations more generally has not been firmly established based on observations. In fact, conflicting accounts of the GMSL budget during ENSO events are given in the literature. For example, based on altimetry, sea-surface temperature data, and ocean model output, Nerem et al. (1999) reason that the anomalous GMSL rise during the 1997–1998 El Niño was due to thermal expansion of the upper ocean. In contrast, using altimetry and global hydrological models, Ngo-Duc et al. (2005), Llovel et al. (2011), and Cazenave et al. (2012) argue that this anomalous rise in GMSL was owing to an increase in global ocean mass. On the one hand, based on satellite data and in situ observations, Boening et al. (2012) and Fasullo et al. (2013) conclude that the anomalous fall in GMSL during the 2010–2011 La Niña was related to a decrease in global ocean mass. On the other hand, and based on very similar datasets, Dieng et al. (2014) conclude differently, finding that this anomalous GMSL fall was owing in approximately equal parts to barystatic and steric contributions.
The literature thus paints a confusing picture. Clarifying the nature of ENSO-related GMSL variations is important for understanding the ocean's role in Earth's hydrological cycle and energy imbalance (e.g., Fasullo et al., 2013; Leuliette, 2015). Here we exploit the growing record length of the Global Ocean Observing System, analyzing satellite gravity, radar altimetry, and in situ hydrographic observations using linear estimation (regression) to elucidate observationally the nature of the altimetric GMSL budget for ENSO events.
We study GMSL records from four groups: AVISO (Ablain et al., 2009), Colorado (Nerem et al., 2010), NOAA (Leuliette and Scharroo, 2010), and CSIRO (Church and White, 2011). Time series derive from the reference altimetry missions (TOPEX/Poseidon, Jason-1, -2). The standard corrections (postglacial rebound, wet troposphere, inverted barometer) are made and a 60-day filter is used to remove a spurious 59-day signal (Masters et al., 2012). Time series are interpolated onto regular monthly intervals over 1993–2015 and we use the ensemble average across the interpolated records. A standard error (Table 1) is estimated based on variances in differences between time series (cf. Ponte and Dorandeu, 2003).
Results of OLS applied to altimetric GMSL (
Monthly Argo in situ temperature and salinity grids produced by Scripps
Institution of Oceanography (SIO) and International Pacific Research Center
(IPRC) are also employed. The grids are generated using objective analysis
applied to quality controlled float profiles (Roemmich and Gilson, 2009).
Fields span from 65
Monthly estimates of the barystatic sea level term based on retrievals from the Gravity Recovery and Climate Experiment (GRACE) (e.g., Tapley et al., 2004) are also considered. Values are from Release-05 data processed by the three main science data system centers at the University of Texas at Austin Center for Space Research (CSR; Bettadpur, 2012), the Jet Propulsion Laboratory (JPL; Watkins and Yuan, 2012), and the GeoForschungsZentrum Potsdam (GFZ; Dahle, 2013). These data are then postprocessed by Don P. Chambers at the University of South Florida following the methods detailed in Chambers and Bonin (2012) and Johnson and Chambers (2013). We consider the ensemble mean across the estimates, deriving an estimate of the standard error according to variances in the differences between series (Ponte and Dorandeu, 2003). To be overlapping with Argo, we consider the GRACE ocean mass data over 2005–2015.
Figure 1a shows nonseasonal anomalies of GMSL (i.e., annual cycle and trend removed) alongside the Multivariate ENSO Index (MEI) (Wolter and Timlin, 1998) over 2005–2015. As in earlier papers cited above, there is a tight relation between GMSL and MEI curves, such that the GMSL is higher during El Niño periods and lower during La Niña periods. The Pearson product-moment correlation coefficient (hereafter simply referred to as the correlation) between these two records (0.73) is significant at the 95 % confidence level and suggests that approximately half of the nonseasonal anomalous GMSL variance over this period corresponds to ENSO. More generally, we observe that correlation between the nonseasonal GMSL and MEI anomalies is significant for all other 11-year periods during the altimeter record, as well as for the entire 23-year altimetric record itself (not shown).
Monthly time series over 2005–2015 of
Nonseasonal GMSL anomalies from satellite altimetry data are consistent with the sum of barystatic and steric components from GRACE and Argo (Fig. 1b). The correlations between GMSL from GRACE and Argo and from altimetry (0.89), and between MEI and the sum of GRACE and Argo (0.67), are both significant. Correlation values between GRACE and the MEI (0.54; Fig. 1c) and Argo and the MEI (0.65; Fig. 1d) are also significant. In fact, all pairs of time series displayed in Fig. 1 are significantly correlated (not shown). These results suggest that GMSL fluctuations tied to ENSO and seen by satellite altimetry are independently corroborated by the other ocean observing platforms and that barystatic and steric terms both contribute to the significant relationship between GMSL and ENSO.
To consider the GMSL budget related to ENSO more formally, we use linear
estimation, namely ordinary least squares (OLS). We model the data as linear
combinations of decadal trend, annual cycle, and MEI regressors,
simultaneously solving for the regression coefficients for all predictors by
minimizing the residual. This particular form of linear regression is
motivated by previous studies referenced in the Introduction. (Indeed, the
regression explains
Table 1 shows results of this OLS procedure applied to altimetry, GRACE, and
Argo. All quoted values are 90 % confidence intervals as described in
Appendix B. (Since they are not our focus here, we defer discussion of
results for the annual cycle and linear trend to Appendix C.) Per unit MEI
change, altimetric GMSL changes by
The OLS regression coefficients demonstrate that steric and barystatic
effects generally make comparable contributions to the ENSO-related GMSL
changes over the study period. Judging from Monte Carlo simulations performed
using values in Table 1 (see Appendix D), it is as likely as not
(33–66 % likelihood) that barystatic effects are responsible for
45–58 % of the sum of barystatic and steric contributions to GMSL
variations linked to ENSO, and very unlikely (
The thick black curve is the likelihood that the barystatic contribution to ENSO-related GMSL changes will exceed a certain fraction of the sum of barystatic and steric terms based on Monte Carlo runs, where the steric term is evaluated based on the average of the SIO and IPRC gridded data products. The thin dark gray (light gray) curve is that same likelihood but with the steric term assessed using only the SIO (IPRC) product.
Regional distributions of ENSO-related terrestrial water storage, which are ultimately coupled to the barystatic contributions to GMSL fluctuations through mass conservation, are explored in past papers (Llovel et al., 2011; Boening et al., 2012; Phillips et al., 2012; Fasullo et al., 2013; de Linage et al., 2013; Eicker et al., 2016); they are not revisited here. However, ENSO-related GMSL behavior owing to steric effects is not as well understood. The steric contributions to the GMSL fluctuations related to ENSO arise from changes in ocean heat content. Arguments based on mass conservation (Munk, 2003) suggest that any global steric contributions resulting from salinity changes would be exceedingly small. To elucidate ocean heat content changes potentially contributing to GMSL changes related to ENSO, we apply the OLS method to Argo vertical potential temperature profiles, averaging horizontally over the global ocean as well as individual ocean basins (Fig. 3).
Coefficients of regressions of Argo potential temperature on the MEI
(
There is significant warming of the global ocean's surface waters (0–100 m)
and cooling within its main thermocline (130–320 m) during El Niño
periods. Marginally significant warming also occurs at some intermediate
depths (600–650 m). On the whole, the global upper ocean (0–2000 m) gains
Given only the Argo data, one cannot unambiguously assess heat budgets for
the various layers over the different basins. One possible interpretation is
that net Pacific Ocean heat storage is owing to local surface heat exchanges
with the atmosphere. This interpretation assumes no contributions from the
deep ocean (
Hypothesized Pacific heat budget during El Niño events. The blue
blocks are the ocean surface (0–110 m), main thermocline (120–380 m), and
intermediate water (400–2000 m) layers. The red arrows are heat exchanges
between the ocean layers or with the overlying atmosphere. Black values are
either the total ocean heat storage within the layers as given by Argo data
or the required heat exchanged between them under the stated assumptions of
no transports between ocean basins and no contributions from the deep
(
Previous studies suggest that both the global ocean and climate system lose heat during El Niño events (e.g., Roemmich and Gilson, 2011; Loeb et al., 2012; Trenberth et al., 2014). This would appear to conflict with our finding that the ocean is warmer during El Niños. However, the discrepancy is only apparent, since we consider ocean heat content and those past studies focus on the ocean heat content tendency (i.e., its rate of change). Moreover, scrutinizing visual examination of the earlier results (e.g., Fig. 8 in Trenberth et al., 2014) suggests that there is a phase lag between ENSO and the heat content tendency, such that warming precedes El Niño peaks and cooling follows peaks. This would be fully consistent with our findings, and those of von Schuckmann et al. (2014), who show a negative global ocean heat content anomaly during the 2010–2011 La Niña. Future studies should investigate in closer detail the coherence between variations in ocean heat content and ENSO.
Nonseasonal anomalies of GMSL (black) and MEI (shading) over 1993–2015.
The vertical structure of ocean temperature changes during ENSO events found here (Fig. 3) has implications for understanding which ocean regions and depth levels contributed to the recent “surface warming slowdown”, which some partly relate to the dominant La Niña phase of the 2000s relative to the 1990s (Kosaka and Xie, 2013; Cazenave et al., 2014; England et al., 2014; Risbey et al, 2014). Nieves et al. (2015) determine that the slowdown was caused by a decadal shift in Indo-Pacific heating; they show that the Pacific Ocean above 100 m cooled while the Indian Ocean between 100 and 300 m warmed from the 1990s to the 2000s, but that the rate of global ocean heat storage above 1500 m did not change during that time. Our results (Fig. 3) suggest that cooling of the surface Pacific between the 2 decades is consistent with phasing of ENSO, but subsurface Indian warming and lack of net ocean warming or cooling are not, hinting that processes unrelated to ENSO also contributed to the surface warming slowdown, consonant with papers showing an important role for the Interdecadal Pacific Oscillation (Meehl et al., 2013; Trenberth and Fasullo, 2013; Steinman et al., 2015; Fyfe et al., 2016).
In this study, SIO and IPRC Argo datasets were considered. While reflected in
the standard errors, differences between these two products are apparent. For
example, while both curves evidence an overall increase from the beginning of
2011 to the middle of 2015, the SIO and IPRC global steric height series
diverge thereafter, with IPRC turning down and decreasing, and SIO continuing
to rise through the latter half of 2015 (Fig. 1d). These global differences
stem from regional discrepancies (Fig. 5). Nonseasonal steric height patterns
over the global ocean from SIO and IPRC from July to December 2015 are
generally similar, but manifest clear discrepancies in the North Pacific,
such that SIO shows more negative values than IPRC near the Equator towards
the west, and more positive values over the tropics more broadly (Fig. 5c).
Differences between the datasets could be due to different data sources,
vertical resolution, or processing strategies, and more detailed future
studies should more definitively attribute such discrepancies. Results shown
in Llovel et al. (2014) attest to similar differences between SIO and IPRC
datasets with regard to the global steric height trend over 2005–2013. Our
qualitative conclusions are robust to such quantitative differences
between the Argo datasets; for example, employing either SIO or IPRC only,
the GMSL budget related to ENSO closes (not shown), and it is unlikely
(
Finally, nonseasonal anomalous GMSL was considerably higher during the 2014–2015 El Niño than during the 1997–1998 El Niño (Fig. 6), which is noteworthy because these two El Niño events were comparable in amplitude. (In addition to the distinct axis limits, Figs. 1a and 6 differ in that the removed linear trend and annual cycle are estimated for 2005–2015 in the former and 1993–2015 in the latter.) This could suggest that the relationship between GMSL and ENSO is a complicated function of time period and frequency band, in which case the results presented here apply strictly to the study period. However, it could also suggest that other climate modes (e.g., Pacific Decadal Oscillation, e.g., Hamlington et al., 2016) exert an influence on GMSL that has yet to be discussed.
It has long been known that nonseasonal variations in global-mean sea level (GMSL) are correlated with El Niño–Southern Oscillation (ENSO), but the nature of such GMSL fluctuations tied to ENSO, whether steric or barystatic, has remained unclear. We used linear estimation to consider a decade's worth of altimetry, GRACE, and Argo data processed by different research centers, thus clarifying the nature of the GMSL balance related to ENSO. Fluctuations in ENSO, GMSL, and barystatic and steric terms are significantly correlated (Fig. 1). Barystatic and steric components render comparable contributions to GMSL changes during ENSO events (Table 1). The steric contributions reflect ocean heat storage across various depths in the Pacific Ocean (Fig. 3). We offered a heuristic interpretation of the Pacific heat budget during ENSO periods in terms of diabatic exchanges at the sea surface and adiabatic redistributions within the ocean interior (Fig. 4), but more work is needed in the future to diagnose more definitively the relative contributions of surface fluxes, interbasin exchanges, vertical transports, and the deep ocean to the heat budgets. More work is also needed to understand differences between gridded Argo datasets (Fig. 5) and to determine why the anomalous GMSL response to ENSO was apparently much stronger during the 2014–2015 El Niño than during the 1997–1998 El Niño (Fig. 6). Our results corroborate previous suggestions made based on models (Landerer et al., 2008) or observations during an isolated event (Dieng et al., 2014, 2015) that steric contributions to ENSO-related GMSL fluctuations are not negligible relative to barystatic contributions. These findings also have implications more generally for understanding the ocean's role in the planet's radiation imbalance and hydrological cycle.
Data used in this study are available from the sources detailed in Appendix Table E1. Matlab code for processing these data and creating the figures here are available from the first author upon request.
Let us regard the altimetric GMSL record (or any other data series for that
matter)
All values derived from OLS regression quoted in the main text, shown in
Fig. 3, and given in Table 1, are 90 % confidence intervals. These
intervals are determined as follows. First, to account for goodness of fit,
we compute the OLS standard errors, adjusting values according to the
effective degrees of freedom, as above. Second, to account for uncertainty in
the data, we propagate the standard errors in the data based on the OLS
estimator and the usual procedures for uncertainty propagation
(e.g., Thomson and Emery, 2014),
Here we briefly consider the GMSL budget for the annual cycle and the linear
trend. These cases have been discussed before in many previous investigations
(e.g., Leuliette, 2015, and references therein), and are discussed here
mainly for the sake of completeness. Altimetry gives a GMSL trend over
2005–2015 of
The amplitude of the GMSL annual cycle from altimetry is very similar to that from the sum of GRACE and Argo (Table 1). Also, we notice that the barystatic and steric annual cycles are roughly in antiphase, which leads to a GMSL annual cycle that is smaller in amplitude than the barystatic annual cycle. This feature has been noted and discussed in numerous previous studies (e.g., Leuliette and Miller, 2009). However, we note that, due to a slight phase difference between GMSL from altimetry and from GRACE and Argo (Table 1), there is actually a statistically significant residual in the annual cycle. While this is not made explicit in previous studies, it is implicit; for example, Leuliette and Miller (2009) show a similar difference in GMSL phase between altimetry and the sum of Argo and GRACE. It is not immediately obvious what is responsible for this discrepancy, and it is beyond our scope to explore the issue in depth. However, we hypothesize that it is due to sampling errors in the observing system, namely the fact that Argo does not sample at high latitudes or, probably more importantly, on shallow continental shelf seas.
We evaluate what the likelihood is that the barystatic sea level term
contributes more to ENSO-related GMSL fluctuations than the steric sea level
term. We make this evaluation probabilistically, performing
100 000 iterations of drawing two values, each one drawn from a separate
Student
The AVISO (Archiving, Validation, and Interpretation of Satellite Oceanographic data service) data were downloaded from the AVISO website (Table E1). The data are based on reference missions (Ocean Topography Experiment (TOPEX)/Poseidon and Jason series) with inverted barometer correction applied, the seasonal signal retained, and glacial isostatic adjustment applied.
The CSIRO (Commonwealth Scientific and Industrial Research Organisation) data were downloaded from the CSIRO website (Table E1). The version of the data used here had the inverse barometer and glacial isostatic adjustment corrections applied and the seasonal signals not removed (“jb_iby_srn_gtn_giy”). A 60-day smoothing was used to reduce a spurious 59-day cycle in the data related to alias of the ocean tides.
The Colorado data were downloaded from the Colorado sea level website (Table E1). The data version is version_2016rel2. A 60-day boxcar filter was also applied to the data.
The NOAA (National Oceanic and Atmospheric Administration) data were
downloaded from the NOAA website (Table E1). The product used here is based
on TOPEX/Poseidon and Jason series data with the seasonal signals retained.
A 60-day smoothing was applied to these data and a trend of
0.3 mm yr
The SIO Argo data were downloaded from the SIO website (Table E1). We used the 2004–2014 climatologies with the provided monthly extensions through February 2016.
The IPRC gridded data fields were downloaded from the IPRC website (see Table E1).
The GRACE data were downloaded from Don P. Chambers' Dropbox folder (Table E1). Data gaps and missing months in these time series were filled based on cubic interpolation.
MEI values were downloaded from the NOAA ENSO website (Table E1).
Locations and sources of the data used here. Websites accessible as of 2 June 2016.
Support for this research came from NASA grants NNX14AJ51G and NNH16CT00C. Helpful conversations with Steve Nerem, Rui Ponte, Don Chambers, and John Gilson are acknowledged. Two anonymous reviewers made valuable comments and suggestions, especially with respect to comparing the Argo datasets. The providers of the datasets are formally acknowledged in Appendix E and Table E1. Edited by: M. Hoppema Reviewed by: two anonymous referees