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Global mean sea level rise during the recent warming hiatus from
satellite-based data
Luu Q.H. a,b*, Q. Wu c, P. Tkalich d, and G. Chen c,e
a Department of Computer Science and Software Engineering, Swinburne University of
Technology, Victoria, Australia; b School of Interdisciplinary Sciences, Vietnam National University, Hanoi, Vietnam;c Department of Ocean Technology, Ocean University of China, Qingdao, Shandong, China;d Tropical Marine Science Institute, National University of Singapore, Singapore;e Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National
Laboratory for Marine Science and Technology, Qingdao, Shandong, China;
*Corresponding author(s): [email protected]
Global mean sea level rise during the recent warming hiatus from
satellite-based data
Satellite remote sensing has provided an unprecedented opportunity to understand
the spatio-temporal change of the Earth’s climate system. In this study, we take
advantage of the oceanographic satellite-based data to examine the global mean
sea level rise during a transitional episode (1994–2003) referred to as the onset of
recent global warming hiatus. We remove the signals accounted for the El Niño-
Southern Oscillation, the Pacific Decadal Oscillation, and solar radiation using an
Empirical Orthogonal Function and multivariate regression analysis. The trend
estimates over the period 1993–2015 are significantly improved in accordance
with the reduction of uncertainty by a half. Our results associate the observed
deceleration of sea level rise during the onset with the climate oscillations. It
strengthens a conclusion deduced by an alternative approach using modelling,
whilst highlights the robustness of combining satellite-based datasets and climate
indices in a reliable statistical estimation.
Keywords: satellite observations; sea level anomaly; global warming
1. Introduction
Satellite remote sensing (SRS) has a wide range of applications in most Earth science
disciplines, from hydrology, geography, geology, and ecology to oceanography
(Cracknell and Varotsos, 2007; Hossain 2016). In understanding the climate change,
SRS has been becoming an indispensable element of climate system observations, e.g.
more than a half of Essential Climate Variables strongly relied on satellite observations,
thanks to its accuracy and nearly global coverage sampling across the Earth’s surface
(Nerem et al., 2010; Chen et al., 2010; Cracknell and Varotsos, 2011; Yang et al.,
2013). The SRS data can be integrated into climate models to simulate climate
dynamics and to project future scenarios, or be assimilated to reanalysis products and
observational datasets for uses in other direct and indirect applications (e.g., IPCC,
2013; Prakash et al., 2013; Zeng et al., 2015; Loew et al., 2017).
Being a visible manifestation of climate change, the global warming has been
evident since the late nineteen century from both, direct temperature observations and
complicated climate models. Over the period 1880–2012, the global mean surface
temperature rise was estimated at 0.85°C (IPCC, 2013). Albeit with divergence in the
regional trends, the warming tendency was observed virtually over the entire the Earth.
IPCC (2013) attributed the centennial warming mainly to the emissions of atmospheric
greenhouse gases. However, an unexpected slowdown in the surface temperature trend
was noticed in the 1998–2012 episode, which is often referred to as the recent global
warming hiatus. During this short period, the temperature escalation was only 0.05°C
per decade, which is 2–3 times weaker than projected (IPCC, 2013). Its existence casted
doubt on the argument that the intensification in greenhouse gas concentration is the
source of global warming through increasing the global temperature. Since the notion is
a key for understanding the mechanism of climate change (Meehl et al., 2012; IPCC,
2013), the apparent hiatus triggered extensive debates (Estrada et al., 2013; Rajaratnam
et al., 2015; Xie, 2015; Watson et al., 2016). In fact, a rigorous and comprehensive
understanding of the greenhouse contribution to climate change is unlikely available
(Kondratyev and Varotsos, 1995; Varotsos et al., 2014).
While much attention is paid to questioning the existence or elucidating the
mechanism of hiatus, there has been little focus to ascertain its signature on the ocean
(Cazenave et al., 2014). Being a tangible manifestation of climate change (Becker et al.,
2014), the sea level rise may give us a hint on the footprint of hiatus since it is
expectedly rising proportional to the temperature (Rahmstorf 2007; Vermeer and
Rahmstorf, 2009; Hay et al., 2015). Fingerprints of natural variability of the Earth’s
climate system are also immensely apparent in the variations of ocean surface (e.g.,
Church et al., 2005; Church et al., 2006; Prandi et al., 2009; Zhang and Church, 2012).
Climate fluctuations strengthen or weaken the annual variations of sea level by an
increment that may overwhelm the marginal contribution of the global warming (Zhang
and Church, 2012; Trenberth, 2015; Luu et al., 2015). In fact, trend aliasing induced by
natural variability can amount up to 80% of the total sea level rise rate at some regions,
such as in the west of the Philippines during the period 1993–2009, when in the middle
and the east Pacific Ocean it rose three times faster than the global rate (Zhang and
Church, 2012; Peyser et al., 2016). Hence, recent studies (Han et al., 2010; Calafat and
Chambers, 2013; Cazenave et al., 2014; Yi et al., 2015; Visser et al., 2015; Watson,
2016) suggested that short-term natural oscillations should be removed from the
observed long-term signals to reveal underlying sea level trends linked to the global
warming.
In the paper, we take advantage of standardised SRS-based datasets to revisit the
responses of global mean sea level to the alleged hiatus. To capture the transitional
formation of the phenomenon and to preserve the reliability of statistical estimations,
we uncover the rates masked by natural variability by discounting its dominant modes
from sea level signals after an Empirical Orthogonal Function analysis. We then remove
climate and non-climate data from the observed sea level change (1993–2015), which
excludes artifacts arising from numerical modelling parameterizations, and discuss the
trends during the onset period (1994–2003).
2. Data and method
2.1. Sea level data and climatic indices
We adopt the latest monthly mean sea level data from two SRS-based datasets. The
main analysis is conducted with the Archiving, Validation and Interpretation of Satellite
Oceanographic (AVISO) product (http://www.aviso.altimetry.fr/en/data.html) for the
period 1993–2015 at the high resolution of 1/4°×1/4°, which integrates outputs from
Saral, Cryosat-2, Jason-1 and 2, T/P, Envisat, GFO, ERS-1 and 2 and Geosat satellites.
Another SRS-based data (https://www.cmar.csiro.au/sealevel/sl_data_cmar.html) used
for cross-validation are provided by Commonwealth Scientific and Industrial Research
Organization (CSIRO) which combined measurements from TOPEX/Poseidon, Jason-1,
and Jason-2/OSTM satellites in a coarser resolution (1°×1°). We removed seasonal
cycles from both datasets, which are then subjected to a 5-month moving average
smoothing as suggested by Church and Zhang (2012). Data corrected for the glacial
isostatic adjustment (GIA) and inverse barometer (IB) are used in the analysis and
discussion.
Dominant climate indices having high correlation with sea level variability are
employed to derive the trend. They consist of the Pacific Decadal Oscillation (PDO),
the El Niño–Southern Oscillation (ENSO), the Central Pacific ENSO (CP-ENSO) and
the total solar irradiance (TSI). The monthly PDO index from 1854 was archived from
Extended Reconstructed Sea Surface Temperature delivered by the National Centers for
Environmental Information (https://www.ncdc.noaa.gov/teleconnections/pdo/). To
depict ENSO, we adopt the Multivariate ENSO Index (MEI,
http://www.esrl.noaa.gov/psd/enso/mei) provided by Wolter and Timlin (2011). The
CP-ENSO is another climate fluctuation alternative to the regular ENSO
(http://www.ess.uci.edu/~yu/2OSC/), which has a stronger teleconnection with the
southern Indian Ocean (Yu and Kao, 2007; Kao and Yu, 2009). Lastly, TSI represents
the influence of the Sun on the Earth’s climate system (Ridley et al., 2014), and is
prepared by Frohlich (2000) over the time span 1978–2015 at the Physikalisch
Meteorologisches Observatorium Davos of the World Radiation Center
(https://www.pmodwrc.ch/pmod.php?topic=tsi/composite/SolarConstant). We applied
the successive 25-month and 37-month moving averages to PDO and TSI as suggested
by Zhang and Church (2012) to yield corresponding decadal indices (namely, D1 and
D2, respectively); while the interannual and shorter-timescale ones (I1, I2, and I3)
related to ENSO and CP-ENSO are derived by subtracting the averages from respective
original time-series. Due to this smoothing, all data used are truncated to the common
period (1993–2012).
2.2. Principal modes of sea level variability and trend estimations
Empirical Orthogonal Function (EOF) analysis is employed to detect the principal
modes of sea level variability which are thereafter correlated to well-established climate
phenomena. Bearing in mind that the long-term sea level variability can be smeared up
by the strong short-term signal, we pay attention to both dominant decadal and
interannual components. The low-passed subset is deduced from successive 25- and 37-
month moving averages of the AVISO data, which is sufficient to resolve the decadal
contribution (Zhang and Church, 2012). The interannual and shorter timescale
component is deduced by removing the derived low-pass filter from the original data.
From the analysis, it was found in the decadal subset that the first two modes account
for 61.2% of the total variance. The most dominant mode (EOF1-D) explains 38.8% of
the signals and is highly correlated (correlation coefficient r=0.92) with the
corresponding low-passed filtered PDO; while the second mode (EOF2-D) is strongly
tied (r=0.78) to TSI. Meanwhile, three leading dominant modes in the interannual
subset are overwhelmed by signals related to ENSO which account for 38.6% total
variance. Explaining 25.9% total signals, the largest principal mode (EOF1-I) is tightly
connected (r=0.94) to the ENSO, while the second pattern (EOF2-I) bears a congruency
(r=0.90) with the lagged ENSO (shifted by 7 months). The remaining dominant mode is
linked to the CP-ENSO (r=0.56). By utilizing the EOF analysis with more dominant
components of natural variability, we extend the works of Zhang and Church (2012) to
better estimate sea level trends.
The fitting has been used intensively to separate the sea level rise rate unrelated
to global warming (Visser et al., 2015). In our multiple variable linear regression
(MVLR) analysis, we assume that sea level change (h) is proportional to temporal states
determined decadal and interannual climate indices and the trend (a) linked to the
global warming. Its relationship is expressed in the formula:
h ( t )=a t +∑k=1
2
dk Dk+∑k=1
3
ik I k+b+ε
(1)
where t is the time (month), Dk (k=1,2) and I k (k=1,2,3) are the above-defined
(normalized) decadal and interannual indices, dk , ik and b are the fitted coefficients, and
ε is the error (considered as random noise). In case the trend is uncorrected for the
climate variability, coefficients related to climate indices (dk,ik) are excluded, and the
MVLR is deducible to the single variable linear regression (SVLR) to compute only the
so-called apparent trend. We apply MVLR to remove climate signals in either a spatial
(i.e., two-dimensional) set of time-series of the regional sea level (RSL) or execute the
correction on the sole time-series of the global mean sea level (GMSL). The global
mean time-series derived with area weights from those both approaches, being named
correspondingly the RA (the global average of RSL) and the GA, show similar temporal
pattern but slightly different mean rates (Figure 3a).
Statistical estimations at 95% confidence interval are computed using a two-
tailed Student’s t-test. To resolve the autocorrelation problem arising from the original
least square fitting, we applied the first-order Auto-Regressive and first-order Moving-
Average (ARMA(1,1)) model suggested by Foster and Brown (2015), which correcting
the underestimation in the commonly used first-order autoregressive AR(1) models.
3. Global mean sea level trend and the signature of hiatus
3.1. Synoptic regional and global mean sea level trends
Spatial patterns of RSL trends derived from SRS-based observations are displayed in
Figure 1, which are consistent with the previous study of Zhang and Church (2012). In
the Pacific Ocean, the apparent trends computed from AVISO data (Figure 1a) consist
of significantly high RSL rise rates (>12 mm year-1) founded in the western tropical area
(20°S–20°N; 120°E–175°E). At its eastern basin, small apparent trends (-2–2 mm year-
1) extend from 30°S to 40°N latitudes, being the most prominent along the northern side
of the equator (5°N–20°N); while a parallelogram structure is also detected around
(5°S–18°N; 160°E–180°E). Corrected for the natural variability (Figure 1b), the areas
having highly confident rates have enlarged by 70%, seizing the central tropical region
of the Pacific Ocean. Our results thus bear a high similarity in terms of basic patterns
and magnitudes with the sea level footprints derived earlier by Zhang and Church
(2012).
The monthly change of GMSL records starting in 1993 is shown in Figure 2a.
The corrected GMSL in both averaging approaches (GA and RA) are similar, except for
few discrepancies (<4 mm) in the years 1997, 2008 and 2011. Meanwhile, the
differences between apparent GMSL and the corrected one are year-to-year and strongly
linked to the variability (Figures 2b, 2c, and 2d). The apparent GMSL lowered (~3–6
mm) in 1996, 2007–2008 and 2009–2010, while it was higher (by ~2–5 mm) in
1997/1998 (Figure 2a). They are often attributed to ENSO and PDO (Cazenave et al.,
2012; Meyssignac et al., 2012; Hamlington et al., 2013). For instance, a positive
anomaly (~5 mm) of apparent GMSL with respect to the corrected level was
experienced during the strong El Niño event in 1997/1998 (Figure 2b). It is consistent
with the finding of Cazenave et al. (2012) who pointed this to the mass change in the
tropical Pacific Ocean. Such results imply that our corrected sea level represents
effectively historic patterns and agrees well with previous regional findings.
3.2. Signature of hiatus in global mean sea level observations
To examine the signature of hiatus, we first estimate the recent evolution of the apparent
GMSL rise rates. The rates are calculated for different time spans, each of which starts
in a given year but altogether ends in 2012 (Figure 3a). It shows that there is a gradual
but hardly noticeable reduction in the apparent sea level rise rate from 3.18±0.29 mm
year-1 for the period 1994–2012 to the 2.98±1.53 mm year-1 in the more recent episode
2003–2012. Yet, there was no spike or discontinuity in sea level trend experienced in
either year 1997, 1999 or 2001. Although ocean volume is tied to the mean temperature
change mainly through two mechanisms (i.e., thermal expansion of seawater associated
with the increase in ocean heat content, and the melt of ice sheets and glaciers due to the
warmer atmosphere), the response of sea level to an abrupt change might be slower. For
volcano eruptions, aerosols injected into the stratosphere caused a scattering of
incoming solar radiation and a rapid cooling of the atmosphere, subsequently leading to
a rapid drop of the GMSL up to 5 mm in the case of the Mt Pinatubo incident in 1991
(Church et al., 2005). The fastest sea level drops are often observed between 6 to 18
months after the eruption events (Church et al., 2005). The manifestation of a slowdown
in sea level rise was probably faster, i.e., happening no later than 2002, since the
temperate variations in the hiatus may not undergo a process as complicated as in the
volcano eruption to influence the water mass. Taken this rule of thumb into account, we
observe marginal deceleration rate 0.7% per year in the apparent sea level during the
onset (1994–2003), but no abrupt change was visible in this era.
The alleged corresponding hiatus in sea level might be nevertheless masked by
the natural variability. By subtracting the climate fluctuations, we obtained the corrected
GMSL rise rate with the MVLR (Figure 3a). In the GA approach, the revised rate for
the period 1994–2012 is 3.32±0.14 mm year-1. It is almost unchanged for the latest era
2003–2012 with the rate of 3.28±0.68 mm year-1. In the RA approach, the global rates
become uniformly lower by about 0.08 mm year-1 on average. The corrected ones are
3.24±0.17 mm year-1 for the years 1994–2012, and 3.20±0.86 mm year-1 for 2003–2012.
In all suspicious years (1997, 1999 and 2001), there is no evidence of any abrupt
slowdown (>0.1mm year-1) in sea level rise. The corrected GMSL rise rates computed
by the MVLR method in the both approaches are similar around the crests being about
3.20–3.30 mm year-1 since 1993. They are higher than the apparent GMSL rate
regressed using the SVLR method by an amount of 0.10–0.35 mm year-1 during the
onset. The increasing gaps between them are mainly induced by the oblique tendency
caused by the combined strengthening of recent negative phase of PDO (Figure 2c) and
the intensification of ENSO events (Figure 2b), same as indicated earlier (Zhang and
Church, 2012; Hamlington et al., 2013; Kosaka and Xie, 2013; England et al., 2014;
Watanabe et al., 2014; Maher et al., 2014; Meehl et al., 2014; Trenberth, 2015). Again
we found that there is no sudden drop (>3%) of sea level tendency observed during the
entire onset period.
The uncertainty range remains relatively high in the SVLR analysis of the
AVISO data. For instance, the confidence level can be as large as ±55% of the estimate
of a trend for the recent 10 years (2003–2012). Thanks to the improvement in signal-to-
noise ratio, the introduction of natural variability into the MVLR regression reduced the
uncertainty down to ±20–30% of the overall trend during the same period. Between
1994 and 2003, longer time-series lead to the shorter (i.e. better) the confidence interval,
linked by a relationship that follows a logarithm-like fold. The enhancement in
estimation, on the other hand, is also attributed to the application of ARMA(1,1)
method, which helps to resolve the underestimated standard errors in the ordinary least
square fitting technique.
To examine the sensitivity related to the dataset used, we compared the
estimations with another source of SRS data provided by CSIRO, which is different in
terms of the number of satellites involved, the processing technique, and the spatial
resolution. Figure 1c shows the regional patterns of sea level change over 1994–2012
derived from the CSIRO data in which the natural variability is completely removed.
Their footprints and scales are identical, except that the CSIRO features look smoother
(Figure 1c) over the entire globe (as its resolution is 16 times coarser) and its rate is
smaller (Figure 3b). The transient variability of trend slightly meanders between 3.05–
3.35 mm year-1 in the period 1994–2003, but its overall feature is highly similar to the
AVISO where no statistically significant slowdown is spotted (Figure 3b). Since both
dataset CSIRO and AVISO shared similar patterns, we can extend the sensitivity tests
using prepared CSIRO data in order to inspect the impact of IB and GIA corrections on
the existence of slowdown. It is found that when the IB effect is not taken into account,
the GMSL rise rate increased from 3.25±0.29 mm year-1 for the period 1994–2012 to
3.40±0.82 mm year-1 over 2013–2012 with a gradual upward in trend. On the other
hand, the trends uncorrected for the GIA also have an accelerating tendency with a
marginally lower rate of 2.65±0.24 mm year-1 for the same years.
4. Discussion and conclusion
The sea level change is driven by a mixture of global (anthropogenic) trends and
regional (natural) variabilities. This study re-examines the GMSL trends in which the
climatic oscillations associated with ENSO, PDO and solar radiation are removed from
the recorded sea level signals. Thanks to the latest SRS-based measurements over the
period 1993–2015, we can analyze the GMSL changes during the onset of global
warming hiatus (1994–2003). It is found that despite corrected regional sea level trends
are non-uniform, the GMSL hardly exhibit any slowdown (>1mm year-1) during the
onset. Subtraction of climate variability from the recorded signal reduces trend
uncertainty by half.
Non-climatic factors have been known to influence significantly the long-term
sea level trend, including the contribution of terrestrial waters (Chao et al., 2008; Llovel
et al., 2011). We further roughly assess the influence of one of its major contributions,
the artificial reservoir water impoundment, to the sea level change by supplementing it
to the corrected GMSL (Figure 2a) and the tendency (Figure 3a). It is found that the
modified GMSL rise rates become slightly faster the period (1994–2012), reaching 3.58
mm year-1 and 3.50 mm year-1 for GA and RA approaches, respectively (Figure 2a).
However, the trend tendency is unlikely to change significantly (>5%) in the long-term
(Figure 3a).
In a recent report, Canezave et al. (2014) corrected for natural variability by
combing various sources of model outputs and data. They took advantage of the Gravity
Recovery and Climate Experience (GRACE) measurement to capture the variations in
ocean mass and terrestrial water storage for the period 2003–2011 and supplemented the
missing data over 1993–2003 by outputs combined from a hydrological model and a
thermal expansion dataset processed from Argo floats. Interestingly, their computed
rates are highly consistent with our estimates by marginal differences of only 2–5%
(which is mainly due to slightly different compared periods), rejecting the slowdown of
sea level rise. The similarity is understandable, since variations in ocean mass and
terrestrial water storage are modulated by dominant climate fluctuations (Llovel et al.
2011; Canezave et al., 2014; Becker et al., 2014). On the other hand, as the GMSL rise
is camouflaged by such variability, its apparent rate is expectedly higher after the onset
when dominant climate drivers reverse its phase. This anticipation was recently
confirmed by Yi et al. (2017) who compared the different apparent rates and budgets of
recent GMSL rise.
While the global mean temperature and sea level regularly rose in tandem
(Rahmstorf 2007; Wu et al., 2017), the relationship between them is stochastic, and
undeterminable out of the context of sophisticated Earth’s climate system. Influential
factors are pointed to thermal expansion of ocean waters, decreasing land water storage
and land ice melting (Yi et al., 2017). Continuing efforts are needed to give deeper
insights, for instance, into how the deceleration of temperature increase impacts the
global mean sea level rise, and to which extent its influence is weakened by the melts of
ice and terrestrial water storage during the onset.
In summary, our results are in favour of associating the deceleration of apparent
sea level rise during the onset of hiatus with the natural variability. Instead of relying on
complicated modelling computations, this study highlights an independent and efficient
approach that directly explores satellite-based datasets within a reliable statistical
estimation to understand the changing climate.
Acknowledgments
There are no financial conflicts of interests for all authors. We thank the editor and two anonymous reviewers for useful comments and suggestions. G.C. and Q.W. were supported by N.F.S. of China grants no. 41331172, U1406404 and G.C.A.S.I.P. grant no. 61361136001, GASI-03-01-01-09.
ReferencesBecker, M., M. Karpytchev and S. Lennartz-Sassinek. 2014. “Long-term sea level trends: Natural or
anthropogenic?” Geophysical Research Letters 41:5571–5580, doi:10.1002/2014GL061027.
Calafat, F.M. and D.P. Chambers. 2013. “Quantifying recent acceleration in sea level unrelated to internal
climate variability.” Geophysical Research Letters 40:3661–3666, doi:10.1002/grl.50731.
Cazenave, A., O. Henry, S. Munier, T. Delcroix, A.L. Gordon, B. Meyssignac, W. Llovel, H. Palanisamy
and M. Becker. 2012. “Estimating ENSO influence on the global mean sea level, 1993–2010”
Marine Geodesy 35(sup1):82–97, doi:10.1080/01490419.2012.718209.
Cazenave, A., H.B. Dieng, B. Meyssignac, K. von Schuckmann, B. Decharme and E. Berthier. 2014.
“The rate of sea-level rise.” Nature Climate Change 4:58–361, doi:10.1038/nclimate2159.
Chao, B.F., Y.H. Wu and Y.S. Li. 2008. “Impact of artificial reservoir water impoundment on global sea
level.” Science 320(5873):212–214, doi:10.1126/science.1154580.
Chen, G., Z. Wang, C. Qian, C. Lv and Y. Han. 2010. “Seasonal-to-decadal modes of global sea level
variability derived from merged altimeter data.” Remote Sensing of the Environment 114:2524–
2535, doi:10.1016/j.rse.2010.05.028.
Church, J.A., N.J White and J.M. Arblaster. 2005. “Significant decadal-scale impact of volcanic eruptions
on sea level and ocean heat content.” Nature 438:74–77, doi:10.1038/nature04237.
Cracknell, A.P. and C.A. Varotsos. 2007. “Editorial and cover: Fifty years after the first artificial satellite:
from Sputnik 1 to ENVISAT”, International Journal of Remote Sensing, 28(10):2071-2072,
doi:10.1080/01431160701347147.
Cracknell, A.P. and C.A. Varotsos. 2011. “New aspects of global climate-dynamics research and remote
sensing”, International Journal of Remote Sensing, 32(3):579-600,
doi:10.1080/01431161.2010.517807.
England, M.H., S. McGregor, P. Spence, G.A. Meehl, A. Timmermann, W. Cai, A.S. Gupta, M.J.
McPhaden, A. Purich and A. Santoso. 2014. “Recent intensification of wind-driven circulation in
the Pacific and the ongoing warming hiatus, Nature Climate Change 4:222–227 (2014)
doi:10.1038/nclimate2106.
Estrada, F., P. Perron and B. Martínez-Lopez. 2013. “Statistically derived contributions of diverse human
influences to twentieth-century temperature changes.” Nature Geoscience 6:1050–1055,
doi:10.1038/ngeo1999.
Frohlich, C. 2000. “Observations of irradiance variability”, Space Science Review, 94:15–24.
Foster, G., and P.T. Brown. 2015. “Time and tide: analysis of sea level time series.” Climate Dynamics
45(1–2):291-308, doi:10.1007/s00382-014-2224-3.
Han, W., G.A. Meehl, B. Rajagopalan, J.T. Fasullo, A. Hu, J. Lin, W.G. Large, J. Wang, X.W. Quan,
L.L. Trenary, A. Wallcraft, T. Shinoda and S. Yeager. 2010. “Patterns of Indian Ocean sea-level
change in a warming climate.” Nature Geoscience 3:546–550, doi:10.1038/ngeo901.
Hamlington, B.D., R.R. Leben, M.W. Strassburg, R.S. Nerem, and K.-Y. Kim. 2013. “Contribution of the
Pacific Decadal Oscillation to global mean sea level trends.” Geophysical Research Letters
40:5171–5175, doi:10.1002/grl.50950.
Hossain, F. 2016. Earth Science Satellite Applications: Current and Future Prospects, Springer: Switzerland, doi:10.1007/978-3-319-33438-7.
IPCC. 2013. “Climate Change 2013: the Physical Science Basis. Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change”, Eds: T. F.
Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex
and P.M. Midgley, Cambridge University Press, Cambridge, United Kingdom and New York,
NY, USA.
Kao, H.Y. and J.Y. Yu. 2009. “Contrasting eastern-Pacific and central-Pacific types of ENSO.” Journal
of Climate 22:615–632, doi:10.1175/2008JCLI2309.1.
Kondratyev, K. YA. and C. Varotsos. 1995. “Atmospheric greenhouse effect in the context of global
climate change”, Il Nuovo Cimento, 18C(2):123–151.
Kosaka, Y. and S.P. Xie. 2013. “Recent global-warming hiatus tied to equatorial Pacific surface cooling.”
Nature 501:403–407, doi:10.1038/nature12534.
Llovel, W., M., Becker, A. Cazenave, S. Jevrejeva, R. Alkama, B. Decharme and B. Beckley. 2011.
“Terrestrial waters and sea level variations on interannual time scale.” Global and Planetary
Change 75(1):76-82, doi:10.1016/j.gloplacha.2010.10.008.
Luu, Q.H., P. Tkalich and T.W. Tay. 2015. “Sea level trend and variability around Peninsular Malaysia.”
Ocean Science 11:617–628, doi:10.5194/os-11-617-2015.
Maher, N., A.S. Gupta and M.H. England. 2014. “Drivers of decadal hiatus periods in the 20th and 21st
centuries.” Geophysical Research Letters 41:5978–5986, doi:10.1002/2014gl060527.
Meehl, G.A., A. Hu, C. Tebaldi, J.M. Arblaster, W.M. Washington, H. Teng, B.M. Sanderson, T. Ault,
W.G. Strand and J.B. White. 2012. “Relative outcomes of climate change mitigation related to
global temperature versus sea-level rise, Nature Climate Change 2:576–580,
doi:10.1038/nclimate1529.
Meehl, G.A., H. Teng and J.M. Arblaster. 2014. “Climate model simulations of the observed early-2000s
hiatus of global warming, Nature Climate Change 4:898–902, doi:10.1038/nclimate2357.
Meyssignac, B., D. Salas y Melia, M. Becker, W. Llovel and A. Cazenave. 2012. “Tropical Pacific spatial
trend patterns in observed sea level: internal variability and/or anthropogenic signature?”
Climate of the Past 8:787–802, doi:10.5194/cp-8-787-2012.
Nerem, R.S., D.P. Chambers , C. Choe and G.T. Mitchum. 2010. “Estimating mean sea level change from
the TOPEX and Jason altimeter missions.” Marine Geodesy 33(S1):435–446,
doi:10.1080/01490419.2010.491031.
Rahmstorf, S. 2007. “A semi-empirical approach to projecting future sea level rise”. Science,
315(5810):368–370.
Rajaratnam, B., J. Romano, M. Tsiang, N.S. Diffenbaugh. 2015. “Debunking the climate hiatus.”
Climatic Change 133:129–140, doi:10.1007/s10584-015-1495-y.
Peyser, C.E., J. Yin, F.W. Landerer and J.E. Cole. 2016. “Pacific sea level rise patterns and global surface
temperature variability.” Geophysical Research Letters 43:8662–8669,
doi:10.1002/2016gl069401.
Ridley, D.A., S. Solomon, J.E. Barnes, V.D. Burlakov, T. Deshler, S.I. Dolgii, A.B. Herber, T. Nagai,
R.R. Neely, A.V. Nevzorov, C. Ritter, T. Sakai, B. D. Santer, M. Sato, A. Schmidt, O. Uchino,
and J.P. Vernier. 2014. “Total volcanic stratospheric aerosol optical depths and implications for
global climate change.” Geophysical Research Letters 41:7763–7769,
doi:10.1002/2014GL061541.
Prakash, S., P. Prakash and M. Ravichandran. 2013. “Can oxycline depth be estimated using sea level
anomaly (SLA) in the northern Indian Ocean?” Remote Sensing Letters 4(11):1097-1106,
doi:10.1080/2150704X.2013.842284.
Prandi, P., A. Cazenave and M. Becker. 2009. “Is coastal mean sea level rising faster than the global
mean? A comparison between tide gauges and satellite altimetry over 1993–2007.” Geophysical
Research Letters 36:L05602, doi:10.1029/2008GL036564.
Trenberth, K.E. 2015. “Has there been a hiatus?” Science 349(6249):691–694.
Varotsos, A.C., & C.L.E. Franzke, M.N. Efstathiou and A.G. Degermendzhi. 2014. “Evidence for two
abrupt warming events of SST in the last century”, Theoretical and Applied Climatology
116:51–60, doi:10.1007/s00704-013-0935-8.
Visser, H., S. Dangendorf and A.C. Petersen. 2015. “A review of trend models applied to sea level data
with reference to the ‘acceleration-deceleration debate.’” Journal of Geophysical Research:
Oceans 120:3873–3895, doi:10.1002/ 2015JC010716.
Watanabe, M., H. Shiogama, H. Tatebe, M. Hayashi, M. Ishii and M. Kimoto. 2014. “Contribution of
natural decadal variability to global warming acceleration and hiatus, Nature Climate Change
4:893–897, doi:10.1038/nclimate2355.
Watson, P.J. 2016. “A new perspective on global mean sea level (GMSL) acceleration.” Geophysical
Research Letters 43:6478–6484, doi:10.1002/2016gl069653.
Wolter, K. and M.S. Timlin. 2011. “El Niño/Southern Oscillation behaviour since 1871 as diagnosed in
an extended multivariate ENSO index (MEI.ext).” International Journal of Climate 31:1074–
1087, doi:10.1002/joc.2336.
Wu, Q., Q.H. Luu, P. Tkalich and G. Chen. 2017. “An improved empirical dynamic control system model
of global mean sea level rise and surface temperature change.” Theoretical and Applied
Climatology, doi:10.1007/s00704-017-2039-3.
Xie, S.P. 2015. “Oceanography: leading the hiatus research surge”, Nature Climate Change 6, 345–346,
doi:10.1038/nclimate2973
Yang, J., P. Gong, R. Fu, M. Zhang, J. Chen, S. Liang, B. Xu, J. Shi, and R. Dickinson. 2013. “The role
of satellite remote sensing in climate change studies.” Nature Climate Change 3:875-883,
doi:10.1038/nclimate1908
Yi, S., W. Sun, K. Heki and A. Qian. 2015. “An increase in the rate of global mean sea level since 2010.”
Geophysical Research Letters 42:3998-4006, doi:10.1002/2015gl063902.
Yi, S., Heki, K., and Qian, A. 2017. “Acceleration in the global mean sealevel rise: 2005–2015.”
Geophysical Research Letters, 44:11905–11913, doi:10.1002/2017GL076129.
Yu, J.Y. and H.Y. Kao. 2007. “Decadal changes of ENSO persistence barrier in SST and ocean heat
content indices: 1958–2001.” Journal of Geophysical Research 112:D13106,
doi:10.1029/2006JD007654
Zeng, X., Y. Li, R. He & Y. Yin. 2015. “Clustering of Loop Current patterns based on the satellite-
observed sea surface height and self-organizing map.” Remote Sensing Letters, 6(1):11-19,
doi:10.1080/2150704X.2014.998347.
Zhang X. and J.A. Church. 2012. “Sea level trends, interannual and decadal variability in the Pacific
Ocean.” Geophysical Research Letters 39:L21701, doi:10.1029/2012GL053240.
Figure 1. Regional sea level trends deriving from satellite altimetry data for the period
1994–2012: (a) SVLR for AVISO dataset; (b) MVLR for AVISO; and (c) MVLR for
CSIRO dataset. Stippling indicates trend exceeding 95% confidence level. (d)
Meridional average sea level trends derived from AVISO data for different periods all
ending in the year 2012 with starting in 1994 (red), 1998 (green) and 2002 (blue). The
dashed lines show trends from SVLR, while the solid lines present rates derived by
applying MVLR on the RSL data.
Figure 2. (a) GMSL averaged monthly over the period 1993 to 2013 from AVISO data
under different unadjusted and unadjusted experiments. Monthly variability of
normalized climate indices: (b) high-passed MEI; (c) low-passed PDO; and (d) low-
passed TSI.
Figure 3. Transient variations of the GMSL rise rates over different periods, starting
from a year in between 1994 and 2003 and ending in the year 2012 derived from: (a)
AVISO data and (b) CSIRO data. For the CSIRO data, the pure trend is computed using
SVLR in the EXP1 case; while with EXP2 and EXP3 experiments, trends are corrected
for natural variability using the RA and GA approaches, respectively. Both EXP4 and
EXP5 experiments stem from EXP2, except that the IB effect is not considered in EXP4
while the GIA correction is not applied in EXP5. Vertical bars represent the 95%
confidence intervals.