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GEOPHYSICAL RESEARCH LETTERS, VOL. 37, L10701, doi:10.1029/2010GL043321, 2010

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ArticleTwentieth century tropical sea surface temperature
trends revisited
Clara Deser,1 Adam S. Phillips,1 and Michael A. Alexander2
Received 18 March 2010; revised 6 April 2010; accepted 9 April 2010; published 18 May 2010.

[ 1 ] This study compares the global distribution of 20th
century SST and marine air temperature trends from a wide
variety of data sets including un‐interpolated archives aswell as globally‐complete reconstructions. Apart from theeastern equatorial Pacific, all datasets show consistency intheir statistically significant trends, with warming
everywhere except the far northwestern Atlantic; the largest
warming trends are found in the middle latitudes of bothhemispheres. Two of the SST reconstructions exhibit
statistically significant cooling trends over the easternequatorial Pacific, in disagreement with the un‐interpolated
SST and marine air temperature datasets which show
statistically significant warming in this region. Twentiethcentury trends in tropical marine cloudiness, precipitationand SLP from independent data sets provide physicallyconsistent evidence for a reduction in the strength of theatmospheric Walker Circulation accompanied by aneastward shift of deep convection from the western to thecentral equatorial Pacific. Citation: Deser, C., A. S. Phillips,
and M. A. Alexander (2010), Twentieth century tropical sea surface
temperature trends revisited, Geophys. Res. Lett., 37, L10701,
doi:10.1029/2010GL043321.

1. Introduction[2] Sea surface temperature (SST), a fundamental physical parameter of the climate system, is well suited formonitoring climate change due to the oceans’ large thermal
inertia compared with that of the atmosphere and land.
Accurate determination of long‐term SST trends is hampered, however, by poor spatial and temporal sampling andinhomogeneous measurement practices [Hurrell andTrenberth, 1999; Rayner et al., 2009]. As a result, 20th
century SST trends are subject to considerable uncertainty,
limiting their physical interpretation and utility as verification for climate model simulations. This uncertainty is
especially evident in the tropical Pacific where even the signof the centennial trend is in question [Vecchi et al., 2008].
Given the influence of tropical Pacific SST anomalies on
climate worldwide, resolving these discrepancies remains animportant task.
[3] Previous studies have focused largely on SST trends
from reconstructed data sets. The purpose of this study is toprovide a broad assessment of 20th century global SST
trends by considering a wide variety of gridded data sources1

National Center for Atmospheric Research, Boulder, Colorado,USA.
2
Earth System Research Laboratory, NOAA, Boulder, Colorado,USA.
Copyright 2010 by the American Geophysical Union.
0094‐8276/10/2010GL043321

including un‐interpolated archives as well as globally complete reconstructions. The SST trends are compared withindependently measured night‐time marine air temperature(NMAT) trends for evidence of physical consistency. The
SST/NMAT trends over the tropical Pacific are further
evaluated in the context of trends in tropical cloudiness,precipitation, and sea level pressure.

2. Data and Methods[4] Global SST trends since 1900 are computed for 5
different datasets: Hadley Centre SST version 2 (HadSST2)[Rayner et al., 2006; Minobe and Maeda, 2005]; Hadley
Centre sea ice and SST version 1 (HadISST1) [Rayner et al.,2003]; National Oceanic and Atmospheric Administration
Extended Reconstructed SST version 3 (ERSSTv3b) [Smithet al., 2008]; and Kaplan Extended SST version 2 (Kaplanv2)[Kaplan et al., 1998]. HadSST2 and Minobe/Maeda (bothon a 2° × 2° latitude/longitude grid) are based on theInternational Comprehensive Atmosphere‐Ocean Data Set(ICOADS) and employ different quality‐control and bias
correction procedures; no “analysis” of the data is performed(e.g., no spatial or temporal smoothing or interpolation) andmissing grid boxes are not filled in. HadISST1 (1° × 1°),ERSSTv3b (2° × 2°), and Kaplanv2 (5° × 5°) are analysis
products which use different optimal statistical procedures tosmooth the data and fill in missing values; further information is given in the cited references.
[5] In addition to SST, we compute trends in night‐timemarine air temperatures (NMAT) from Meteorological Office
Historical Marine Air Temperature version 4 (MOHMAT4)[Rayner et al., 2003] and terrestrial air temperatures from
Hadley Centre/Climate Research Unit Temperature version 3variance‐adjusted (HadCRUT3v) [Brohan et al., 2006]. Bothdatasets are on a 5° × 5° grid, and like HadSST2 missing gridboxes are not filled in and no “analysis” of the data is performed. It should be noted that MOHMAT4, HadCRUTv3
and HadSST2 are independent in that they consist of measurements from different observational platforms or instruments (N. Rayner, personal communication, 2010). Over thetropics, we also compute trends in land station precipitationfrom Hulme et al. [1998], total cloud amount and sea levelpressure (SLP) from ICOADS version 2.4 [Woodruff et al.,2008], and SLP from Hadley Centre SLP version 2 [Allanand Ansell, 2006]. The Hulme (2.5° × 3.75°) and ICOADS(2° × 2°) data are neither smoothed nor interpolated, while
HadSLP2 (5° × 5°) is a globally complete optimal reconstruction based on terrestrial station records and marine
observations from ICOADS.
[6] We calculate linear trends from monthly anomalies
using the method of least‐squares, and assess their statisticalsignificance using a Student’s‐t test and taking into account

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L10701Figure 1. Twentieth century SST trends (°C per century) computed from monthly anomalies since 1900 for various data
sets as indicated. White grid boxes denote insufficient data, and gray boxes indicate trends that are not statistically significant at the 95% confidence level. See text for additional information.
serial autocorrelation based on the method of Zwiers andvon Storch [1995].

3. Results
3.1. Data Coverage[7] In order to discriminate between secular climatechange and naturally‐occurring multi‐decadal variability, it
is important to consider as long a period of record as possible. Seeking a balance between adequate data coverage(see Figure S1 in the auxiliary material) and length ofrecord, we have examined SST trends using a variety of startdates (1900, 1910 and 1920) and data sampling thresholds;
all of the results discussed below are robust to the different
choices.1 In the figures that follow, we show trend maps
based on the period 1900–2008 using a 3 month per decadethreshold; trends based on 1920–2008 using a 24 month per
decade threshold are shown in Figure S2 of the auxiliarymaterial. We emphasize that although our sampling crite1
Auxiliary materials are available in the HTML. doi:10.1029/
2010GL043321.

rion is lenient, we rely on additional factors such as regional
coherency (note that no additional spatial smoothing has
been applied to any of the data sets) and consistency withindependently measured marine air temperatures to assess
the reality of the SST trends.
3.2. Global SST Trends[8] The 20th century SST trend distributions from the
5 different data sets are compared in Figure 1 for the period
1900 to 2008 (2002 for Minobe/Maeda, the latest year
available), along with air temperature trends based on
HadCRUTv3 over land and MOHMAT4 over the oceans forthe period 1900 to 2005 (the latest year available). The SST
trends from the un‐interpolated HadSST2 and Minobe/
Maeda archives are similar, exhibiting positive values
everywhere except the western portion of the northern North
Atlantic. The largest warming trends (approximately 1.2–1.6°C
per century) occur directly east of the continents in thenorthern hemisphere, in the Southern Ocean and the eastern
tropical Atlantic. The eastern tropical Pacific warms byapproximately 0.8–1.0°C per century, similar in magnitude
to the tropical Indian Ocean and the central tropical Atlantic.
Trends in NMAT from the un‐interpolated MOHMAT4

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L10701Figure 2. Twentieth century tropical climate trends from a variety of data sources as indicated. White grid boxes denoteinsufficient data. See text for additional information.

dataset corroborate those in SST from HadSST2 andMinobe/Maeda, with generally similar large‐scale patterns
and amplitudes. The agreement between NMAT and SST,
physically related quantities from independent data sets,
provides strong support for the reality of their trends. Anotherimportant confirmation of the marine NMAT trends is theircoherence with independent air temperature trends overnearby land areas from HadCRUT3v. For example, theterrestrial warming over the islands of Indonesia and coastal
regions of Australia, South America, South Africa, North
America, Europe, and eastern Asia is remarkably similar in

amplitude to the air temperature increases over the adjacentoceanic regions (even the cooling at the southern tip ofGreenland agrees with the cooling over the far northAtlantic). There are a few isolated areas where the air temperature trends over land do not match those over nearby
maritime areas, for example Madagascar, the southeastern
United States, and northern Chile.
[9] The 3 reconstructed SST data sets (ERSSTv3b,
HadISST1, and Kaplanv2) exhibit broad similarity in theirtrend patterns and amplitudes, as well as overall agreementwith the un‐interpolated SST data sets, with the notable

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L10701Figure 3. Monthly anomaly time series for selected regional tropical climate indices (see text for definitions). Blue andpurple denote SST (°C) from HadSST2 and HadISST1, respectively; orange denotes cloudiness (oktas); and green denotes
SLP (hPa). The top, middle and bottom sets of curves show the raw, 12‐month running mean, and 20‐year low‐pass filteredmonthly anomaly time series, respectively. Color coded numbers to the right of each set of curves denote correlation coefficients between each pair of indices (e.g., an orange and blue numeral gives the correlation between cloudiness and SST).

exception of the central and eastern equatorial Pacific. In thisregion, HadISST1 and Kaplanv2 exhibit weak but significant
cooling while ERSSTv3b shows significant warming, thelatter in agreement with HadSST2, Minobe/Maeda, and
MOHMAT4. The discrepancy between HadISST1 and
ERSST was highlighted by Vecchi et al. [2008] [see also
Hurrell and Trenberth, 1999].
3.3. Tropical Climate Trends[10] Trends in tropical marine cloudiness from ICOADS,
land station precipitation from Hulme, and SLP fromICOADS and HadSLP2r are shown in Figure 2; SST trends
from HadISST1 and HadSST2 are also shown for reference.
All trends are computed based on monthly anomalies
beginning in 1900 and ending in 2008 (2006 for ICOADS)
except for precipitation which uses a start date of 1920 due
to insufficient data coverage before that time and an end
date of 1996, the last year of data available. As in Figure 1,
no spatial smoothing has been applied to any of the trend
maps. Changes in observing practice may have caused
spurious increases in ICOADS cloudiness [Norris, 1999].
Following Deser and Phillips [2006], we account for these

artificial trends by removing the tropical (30°N–30°S) mean
cloudiness trend from each oceanic grid box. The resemblance of the spatial patterns of land station precipitation
and residual cloudiness trends provides strong evidence forthe spurious nature of the tropical mean cloudiness trend(Figure 2).
[11] The distribution of residual cloudiness trends exhibits
positive values (0.8–1.4 oktas per century) over the central
equatorial Pacific accompanied by negative values over thewestern equatorial Pacific (−0.4–−0.8 oktas per century).
This pattern is reminiscent of the cloudiness (and precipitation) changes that occur in association with El Nino events[Deser et al., 2004] and the 1976/77 climate regime “shift”[Deser and Phillips, 2006]. Negative cloudiness trends arealso found over the western Indian Ocean, the Pacific Inter‐
Tropical Convergence Zone (ITCZ) east of 135°W and thesubtropical eastern Pacific and Atlantic. Precipitation trends
are generally consistent with residual cloudiness trends inregions where the two data sets overlap: in particular, positive precipitation trends are found at island stations in thecentral equatorial Pacific between 160°E and 140°W, andnegative trends to the west between130°E and 160°E. SLP

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trends from ICOADS are generally positive over the tropicalIndian Ocean and western Pacific, and a mixture of negativeand positive values over the eastern Tropical Pacific. The
smoother HadSLP2r trends corroborate the large‐scale pattern evident in ICOADS, and are indicative of a weakeningof the SLP gradient between the eastern Pacific and theIndian Ocean/West Pacific. Collectively, the independent
trends in marine cloudiness, precipitation and SLP provide
physically consistent evidence for a reduction in the strengthof the atmospheric Walker Circulation accompanied by aneastward shift in convection from the western to the central
equatorial Pacific.
[12] Based on the maps shown in Figure 2, we formed thefollowing regional time series: eastern equatorial Pacific
SST (1°N–1°S, 170°W–90°W) from HadSST2 (a narrow
equatorial box is used for later comparison with HadISST1,
but similar results are obtained for 5°N–5°S); central
equatorial Pacific cloudiness (6°N–12°S, 165°E–150°W)
minus central north Pacific cloudiness (18°N–6°N, 165°E–
150°W) from ICOADS; and Indian Ocean/West Pacific SLP(20°N–20°S, 30°E–150°E) minus eastern Pacific SLP(20°N–20°S, 180°–70°W) from ICOADS. These indices are
not sensitive to the precise definitions of the regions except
for cloudiness, which exhibits some differences in the firstfew decades of the 20th century (not shown); similar regions
were used for characterizing Pacific multi‐decadal variability by Deser et al. [2004]. These indices are displayed in
Figure 3 using various degrees of temporal smoothing toemphasize different aspects of the variability.
[13] Although the raw monthly anomaly time series
exhibit considerable high frequency noise, they are coherent
on interannual and longer time scales and exhibit prominent
ENSO variability throughout the record. The main periodsof disagreement occur in association with high levels ofnoise due to paucity of data especially during the first two
decades and World War II. The correlation coefficients
between each pair of indices (marked directly on Figure 3)
are all significant at the 99% level: the 0.66 correlation
between the SST and SLP records is particularly noteworthy
given the large number of data points and lack of temporalsmoothing. There is also strong correspondence among the
12‐month running mean records, with 95% significant correlations of 0.61–0.91. The 20‐year low‐pass filtered records
also exhibit high correlations (0.86–0.95) reflecting consistency in their multi‐decadal fluctuations as well as in theiroverall upward trends. Note that the trends constitute only asmall fraction of the total variability of the raw monthly
anomaly time series and less than half of the variability ontime scales longer than 20 years. Strong correspondence
between two canonical SLP and SST records of ENSO dating back to 1877 was shown by Bunge and Clarke [2009]
using more sophisticated analysis techniques.
[14] The disagreement in sign between the trends in
HadSST2 and HadISST1 in the eastern equatorial Pacific is
readily apparent by comparing their 20‐year low‐pass filtered records (Figure 3). Note that the two SST indices agree
well on inter‐annual and multi‐decadal timescales (correlation coefficients of 0.95 for detrended 12‐month runningmeans and 0.90 for detrended 20yr lowpass filtered data):
only their long term trends differ. The correlations between
the 20‐yr lowpass filtered equatorial Pacific SST index
based on HadISST1 and the cloudiness and SLP records
shown in Figure 3 (0.38 and 0.50, respectively) are sub-L10701stantially lower than those based on HadSST2 (0.86 and
0.95, respectively).

4. Summary and Discussion[15] We have evaluated 20th century (1900 to approximately 2008) SST trends from a variety of data sources
including un‐interpolated archives as well as globally complete reconstructions. Statistically significant SST trends
from the two un‐interpolated datasets (HadSST2 and Minobe/
Maeda) are positive everywhere except the northern North
Atlantic, with magnitudes approximately 0.4–1.0°C per century in the tropics and subtropics and 1.2–1.6°C per century at
higher latitudes. These SST trends are corroborated by independently measured NMAT trends from the un‐interpolated
MOHMAT4 dataset and by terrestrial air temperature trends
at coastal locations from the un‐interpolated HadCRUT3v
archive. SST trends from the reconstructed datasets(ERSSTv3b, HadISST1 and Kaplanv2) are generally similar
to those from the un‐interpolated archives with the notable
exception of the tropical Pacific which exhibits cooling in
HadISST1 and Kaplanv2.
[16] The intensified warming trends off the east coasts ofChina and northern North America and over the northern
North Pacific may be related to the relatively shallow
bathymetry and associated ocean mixed layer depths assuggested by Xie et al. [2002]. In addition, advection ofcontinental air temperature trends by the prevailing westerlies
and a reduction in cloud cover (not shown) may play a role.
The cooling trend directly south of Greenland may be related
to the century‐long upward trend in the North Atlantic
Oscillation via the associated increase in wind speed andresulting heat loss from the ocean surface. Enhanced warming
over the Southern Ocean may be due in part to a decrease incloud cover (not shown). Alternatively, the pattern of coolingin the far north Atlantic coupled with warming in theSouthern Ocean may be a signature of a weakened oceanic
thermohaline circulation in response to global warming.
Further work is needed to assess the mechanisms responsible
for the spatial distribution of the 20th century SST trends.
[17] The HadISST1 and Kaplanv2 reconstructions disagree with ERSSTv3b and the un‐interpolated datasets on
the sign of the SST trend in the eastern equatorial Pacific.
Independent NMAT measurements show a warming trend in
this region that is very close in magnitude to that from
HadSST2 (0.36°C compared to 0.35°C per century). Given
that marine air temperature and SST anomalies are physically constrained by surface energy exchange, the agreement
between NMAT and HadSST2 provides strong support for
the reality of the warming trend in the eastern equatorial
Pacific. Though the trends in HadISST1 and Kaplanv2 overthe eastern equatorial Pacific appear to be erroneous, theirinterannual to decadal variations are in agreement with thosein HadSST2 and NMAT (correlation coefficients of 0.95 and
0.92, respectively, based on detrended 12‐month runningmean anomalies for the region 1°N–1°S, 170°W–90°W).
Our results have implications for the design of atmosphericmodel experiments forced with observed SST trends. In
particular, specifying the 20th century trend component from
HadISST1 or Kaplanv2 in the eastern tropical Pacific may
lead to an unrealistic atmospheric circulation response.
[18] Centennial trends in tropical marine cloudiness, precipitation, and SLP from independent data sources were also

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evaluated. These additional climate parameters were shown
to exhibit physically consistent trends consisting of a
weakening of the zonal SLP gradient between the eastern
Pacific and the Indian Ocean/western Pacific accompanied
by an eastward shift of cloudiness and precipitation from thewestern to the central equatorial Pacific. The causal relationship between the tropical Pacific SST and atmosphericcirculation trends is open to interpretation. On the one hand, aweakening of the atmospheric Walker Circulation is expected
to occur in response to increasing GHG concentrations, evenwith a zonally uniform warming of the tropical oceans, as
discussed by Vecchi and Soden [2007]. In this view, the roleof tropical ocean dynamics is to reduce the amplitude of boththe Walker Circulation change and the warming in the easternequatorial Pacific. Alternatively, the physical paradigm for
ENSO holds that a weakening of the zonal SLP gradient (e.g.,the Walker Circulation) is accompanied by a weakening of
the zonal SST gradient (more warming in the eastern compared to the western equatorial Pacific) due to the Bjerknes
feedback mechanism. Both mechanisms could contribute tothe observed centennial trends documented in this study.
[19] Recently, Karnauskas et al. [2009] reported that the
HadISST1, Kaplanv2 and ERSSTv3b reconstructions all
exhibit a strengthening of the east‐to‐west SST gradientacross the equatorial Pacific during the 20th century, despite
disagreeing on the sign of the SST trend in east. However,
only the month of September showed a statistically significant strengthening for ERSSTv3b, compared to June–
January (July–January) for HadISST1 (Kaplanv2). Using
data from all months of the year, we find no evidence for astatistically significant strengthening of the zonal gradient in
ERSSTv3b, HadSST2, Minobe/Maeda, or MOHMAT4.
Further, we emphasize that data coverage constraints make it
difficult to accurately determine the 20th century trend in thezonal SST gradient across the equatorial Pacific (Figure S1 ofthe auxiliary material), and indeed no such determination ispossible when a 20% threshold (24 months per decade) isused for computing the trends since 1920 (Figure S2 of theauxiliary material).
[20] Characterizing the pattern and amplitude of SST
trends over the past century remains a challenge due toobservational uncertainties associated with limited data
sampling, changing measurement techniques and analysis
procedures. Thus, there is a continuing need for refining andimproving the development of homogeneous gridded SST
data sets and associated globally‐complete reconstructions
for climate change research following the recommendationsof Rayner et al. [2009].
[21] Acknowledgments. We thank Lucia Bunge, Allan Clarke, Matt
Newman, Yuko Okumura, Nick Rayner and Kevin Trenberth for useful discussions during the course of this work. We also appreciate the commentsof two anonymous reviewers on a previous draft.

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M. A. Alexander, Earth System Research Laboratory, NOAA, 325
Broadway, Boulder, CO 80305, USA.
C. Deser and A. S. Phillips, National Center for Atmospheric Research,
PO Box 3000, Boulder, CO 80307, USA. (cdeser@ucar.edu)

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