An overview of decadal-scalesea surface temperature variability in theobservational record
Clara Deser, Adam Phillips

doi: 10.22498/pages.25.1.2

National Center for Atmospheric Research, Boulder, USA
Introduction
Due to their thermal and mechanical inertia, the oceans
play a key role in decadal-scale climate variability(DCV) and provide a potential source of initial-value
predictability for low-frequency climate fluctuations.
Characterizing oceanic DCV is challenging, however,
due to the limited duration of the observational record
combined with the sparse and irregular data coverage.
These constraints also hinder assessments of therobustness of the patterns and timescales of DCV, andunderstanding of the governing mechanisms. In this briefnote, we provide an overview of the main phenomenaof DCV in the historical sea surface temperature (SST)data record, discuss proposed interpretations andcausal mechanisms, and highlight outstanding researchquestions.

Figure 1: Distribution of sea surface temperature observations
from the International Comprehensive Ocean Atmosphere
Data Set. Maps show the percentage of months with at least
one measurement in a 2 degree latitude by 2 degree longitude
grid box during (a) 1870-1899, (b) 1920-1939, and (c) 19701989. Timeseries (1870-2015) show the percentage of gridboxes that have at least one observation per month within
the regions outlined in Fig. 1c. (d) North Pacific (20°-70°N,
110°E-100°W), (e) North Atlantic (0°-60°N, 80°W-0°W), and(f) Southern Ocean (50°-70°S, 0°W-360°E).

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SST data coverage
Our focus on SST is motivated by both practical andphysical considerations. On the practical side, the longest
ocean temperature records are measured near thesurface from ships-of-opportunity, starting with bucket
samples in the 19th and early 20th centuries followed
by engine-intake measurements (e.g., Woodruff et al.,2008). On the physical side, SSTs are the main agent ofcommunication between the atmosphere and the ocean,and thus represent a key quantity for probing DCV (for a
discussion of the upper-ocean mixed layer heat budget,
c.f. Deser et al., 2010).

Fig. 1 (left column) shows maps of SST data coveragebased on the International Comprehensive Ocean
Atmosphere Data Set (ICOADS) (Woodruff et al., 2008)
during three representative 20-year periods spanningthe late 19th and 20th centuries: 1870-1899, 1920-1939,
and 1970-1989. These maps show the percentage ofmonths with at least one measurement in a 2°latitude by
2° longitude grid box in the 20-year period indicated. We
note that the instrumental coverage falls off rapidly before
1870, and that satellites provide nearly global coverage
starting in the 1980s (see Woodruff et al., 2008 andDeser et al., 2010). The discrete outlines of commercial
shipping routes and their changes over time are readily
apparent, especially in the earlier time periods (Fig. 1).
Broadly speaking, the North Atlantic, western South
Atlantic, and northern Indian Ocean contain the highest
density of observations, with reasonable coverage backto approximately 1870. Data coverage in the North
Pacific is limited before about 1920, in the Tropics before
about 1960, and in the Southern Ocean before the adventof satellite remote sensing (Fig. 1). The uneven andchanging spatial coverage of SST measurements from
historical ship-based archives must be taken into accountin any analysis of DCV. Further information on the spatiotemporal coverage of other SST data sets is available atclimatedataguide.ucar.edu.
The main phenomena of DCV
In our view, there is no unique “best” approach todefining the main phenomena of DCV. Here, we adopt abasin-specific perspective, which has the advantage that
CLIVAR Exchanges No. 72, June 20172

any inter-basin linkages (including those lagged in time)
are not built-in to the analysis protocol. Similarly, we
analyze monthly data (lightly smoothed with a 3-point
running mean) so as to avoid artificially building in any
low-frequency behavior. In this regard, it is important
to bear in mind the null hypothesis that any low-passfiltered time series will exhibit DCV, but it need not bephysically meaningful (i.e., it may not be distinguishable
from a random process).

We use the NOAA Extended Reconstruction Sea Surface
Temperature, version 3b (ERSSTv3b) dataset, which
employs a statistical procedure on the ICOADS data tofill in missing grid boxes (Smith et al., 2008); other data
sets yield similar results (not shown). Following previous
studies, we subtract the global mean SST anomaly (SSTA)
from the SSTA at each grid box for each month and year
(hereafter, we use the nomenclature SSTA* to denote thisresidual from the global mean) unless noted otherwise.
This procedure is intended to remove any secular global
trends that may be associated with changes in externalradiative forcing such as those due to human-induced
increases in greenhouse gas concentrations and sulfate
aerosols accompanying fossil fuel burning. We shallreturn to the issue of how well this procedure achieves
its intended purpose in Section 5.

We define the main phenomena of DCV for each basin
separately as follows. North Pacific (NPAC): leading
principal component (PC) time series of monthly SSTA*
over the domain (20°-70°N, 110°E-100°W) following
Mantua et al. (1997). North Atlantic (NATL): time seriesof SSTA* averaged over the domain (0°-60°N, 80°W-0°W)
following Trenberth and Shea (2006). Southern Ocean(SO): time series of SSTA (not SSTA*) averaged overthe domain (50°-70°S, 0°W-360°E) following Fan et al.
(2014). We have inverted the SO time series to facilitatecomparison with NPAC and NATL. These regions are
outlined in Fig. 1c. To obtain the global-scale patterns
associated with each time series, we regress SSTA* (SSTA
for the case of the SO) at each grid box on the standardized
index time series.

Before showing the spatial patterns of DCV, we return tothe issue of data coverage. The right-hand panels of Fig. 1
show time series of data coverage in each region defined
above, represented as the percentage of grid boxes thathave at least one observation in each month. Consistent
with the data coverage maps, the NPAC region shows>50% of grid boxes with at least one observation starting
around 1920 except during the 1940s (Fig. 1d). The NATL
region shows >50% of grid boxes present since about
1885 except for the World Wars and around 1900 (Fig.
1e). Finally, coverage in the SO region is always <40%,
and <10% before 1950 (Fig. 1f). A large seasonal cycle
is evident in the SO region, with peak coverage during
summer (Fan et al., 2014). In view of these results, wechoose to show the NPAC and NATL time series starting
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in 1890 (mindful of the reduced coverage in NPAC before1920), and the SO record starting in 1950. However, theglobal regression maps are all based on the period since1950 to accommodate the lack of data over the Southern
Ocean (and to a lesser extent, the Tropical Pacific) before
that time.
The three SSTA* patterns show a great deal of similarity
in their global structures, despite that they are basedon different index regions. For example, NPAC showsa pan-Indo/Pacific pattern with symmetry about theequator, reminiscent of the low-frequency “tail” of
ENSO (also termed the “Pacific Decadal Oscillation PDO
or “Inter-Decadal Pacific Oscillation IPO) (Zhang et al.,
1997; Power et al., 1999; Vimont, 2005; Newman et al.,2016). It also features linkages to the Atlantic in theform of alternating polarities with latitude, with positive
values over the northern North Atlantic, and negativevalues over the Pacific sector of the Southern Ocean(Fig. 2a). NATL exhibits an out-of-phase relationshipbetween SSTA* in the North and South Atlantic, distinct
from that based on NPAC (Fig. 2b). However, it shares
the same SSTA* polarity over the northern NATL
and the same PDO-like structure, albeit with weakermagnitude, as that based on NPAC, and it shows negativevalues throughout the Southern Ocean (Fig. 2b).

Figure 2: Spatial and temporal characteristics of sea surface
temperature anomaly (SSTA) variability in selected ocean
basins. (Left column) Global SSTA* regression maps (degreesC) based on the (a) leading principal component of North
Pacific SSTA*, (b) North Atlantic SSTA*, and (c) invertedSouthern Ocean SSTA. All indices were standardized prior tocomputing the regression maps. Index regions are outlined byblack boxes. (Right column) Standardized 3-month runningmean time series (1880-2015) of the (a) leading principal
component of North Pacific SSTA*, (b) North Atlantic SSTA*,
and (c) inverted Southern Ocean SSTA. Asterisk indicatesthat the global mean SSTA was removed prior to computing
the time series and regression maps.

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Finally, the pattern based on the (inverted) SO index isvery similar to that based on NATL, except for the sign
over the northern tropical Atlantic (Fig. 2c).

The NPAC, NATL and SO index time series are remarkably
similar during their period of overlap (1950-2015), withpronounced decadal-scale variability evident even in
3-month running mean data (Figs. 2d-f). In particular,
each record swings from positive to negative and backto positive during 1950-2015, with a suggestion that
NATL leads NPAC and SO by 10-20 years. Before 1950,
NPAC shows less prominent decadal-scale variability anda weaker relationship with NATL than after 1950. We
refrain from quantifying these statements due to the low
number of degrees of freedom associated with such shortrecords of DCV.

Causes of DCV
The substantial degree of commonality in the global-scalepatterns associated with Pacific, Atlantic and Southern
Ocean DCV, combined with the fact that they are based on
short records and sparse sampling, make it challenging
to identify these phenomena robustly in terms of spatial
and temporal character. These constraints also make it
difficult to assess whether they arise from distinctive
dynamical processes operating on decadal time scales
and/or whether they are best viewed as manifestations ofa “random walk” process or processes (see also Newman
et al., 2016). The concept of global-scale SSTA “hypermodes” (Dommenget and Latif, 2008; Dommenget, 2010;
Clement et al., 2011) has been advanced to account forthe similarity of global-scale SSTA patterns regardlessof how they originate. This concept relies on the notionthat the lower the frequency, the more global the pattern,
due to the interplay between SSTA and the large-scaleatmospheric circulation.
These issues highlight the need for a combined approachbased on observations, paleo-climate records andmodeling to delineate robust phenomena of DCV andto understand their causes. Indeed, modeling studies
based on thousands of years of simulation for robust
statistics suggest that Atlantic DCV and its globalscale teleconnections may originate from the mutualinteraction of the oceanic Atlantic Meridional Overturning
Circulation (AMOC) and the large-scale atmosphericcirculation in the form of the North Atlantic Oscillation(NAO) (Delworth et al., 2016; Delworth and Zeng, 2016;
Ruprich-Robert et al., 2016), although the mechanisms
continue to be under investigation. Similar conclusions
arise for Southern Ocean DCV (Latif et al, 2013; Latif etal., 2015; Zhang et al., 2016). Finally, Pacific DCV may
reflect a combination of stochastic processes, eachwith a different decorrelation time scale and regional
emphasis arising from dynamical and thermodynamic
air-sea interaction (Clement et al., 2011; Okumura, 2013;
Newman et al., 2016).
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Internal vs. externally-forced DCV
As mentioned above, DCV is traditionally identified
as the residual from the global mean SSTA; the latterinterpreted as the secular fingerprint of human-induced
climate change. However, human-induced climate change
is not spatially uniform (Xie et al., 2010) and thus theremoval of the global-mean SSTA may fall short of its
intended purpose. The validity of this approach can betested with large initial-condition ensembles of historicalsimulations with comprehensive coupled climate
models, such as 40-member Community Earth System
Model Large Ensemble (CESM-LE) (Kay et al., 2015).
The CESM-LE has been used to isolate externally-forced
and internally-generated components of simulated
NATL DCV, with implications for observed NATL DCV(Tandon and Kushner, 2015; Murphy et al., 2016). Thesestudies indicate that a significant portion of NATL DCV
since 1920 may be externally-forced, and that empiricalmethodologies used to separate these components inthe single observational record may be inadequate.
Further work is needed to evaluate the efficacy of
empirical approaches for separating forced and internal
components of DCV, not only in the NATL but in otherocean basins as well. These approaches include linear(or other forms of) detrending, removal of the globalmean, optimal fingerprinting (Ting et al., 2009; Ting etal., 2014), pattern scaling (Hoerling et al., 2011; Bichet etal., 2015), and Empirical Ensemble Mode Decomposition(Wu et al., 2011).

Concluding remarks
We have presented a brief overview of the main
phenomena of DCV based on simple analyses of observed
SST over the historical record using a basin-specificapproach. Our results indicate that DCV is apparent evenin unfiltered seasonal data, and that DCV in the North
Atlantic, North Pacific and Southern Ocean share many
characteristics including their global-scale patterns andchronologies, especially since 1950. Results are more
ambiguous before that time. Given the shortness of theobservational record relative to the time scales of interest,
we believe that DCV is best viewed in terms of a casestudy approach rather than as a robust and stationary
statistical characterization. Long (thousands of years)
model control simulations provide an effective tool forassessing the robustness and global-scale linkages of
DCV, provided the model has a credible representationof the relevant processes governing DCV. Finally, an
outstanding research question is the extent to which DCV
is externally-forced vs. internally-generated.
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