OSOcean ScienceOSOcean Sci.1812-0792Copernicus PublicationsGöttingen, Germany10.5194/os-12-925-2016Observed and simulated full-depth ocean heat-content changes for 1970–2005ChengLijingchenglij@mail.iap.ac.cnhttps://orcid.org/0000-0002-9854-0392TrenberthKevin E.PalmerMatthew D.ZhuJiangAbrahamJohn P.International Center for Climate and Environment Sciences,
Institute of Atmospheric Physics, Chinese Academy of
Sciences, 100029, Beijing, ChinaNational Center for Atmospheric
Research, Boulder, CO, USAMet Office Hadley Centre, FitzRoy Road,
Exeter, EX1 3PB, UKSchool of Engineering, University of St. Thomas, St. Paul, MN, USAJohn Abraham (jpabraham@stthomas.edu) and Lijing Cheng (chenglij@mail.iap.ac.cn)26July20161249259354April20168April20167June20169July2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://os.copernicus.org/articles/12/925/2016/os-12-925-2016.htmlThe full text article is available as a PDF file from https://os.copernicus.org/articles/12/925/2016/os-12-925-2016.pdf
Greenhouse-gas emissions have created a planetary energy imbalance that is
primarily manifested by increasing ocean heat content (OHC). Updated
observational estimates of full-depth OHC change since 1970 are presented
that account for recent advancements in reducing observation errors and
biases. The full-depth OHC has increased by 0.74 [0.68,
0.80] × 1022 J yr-1 (0.46 Wm-2) and 1.22
[1.16–1.29] × 1022 J yr-1 (0.75 Wm-2) for
1970–2005 and 1992–2005, respectively, with a 5 to 95 % confidence
interval of the median. The CMIP5 models show large spread in OHC changes,
suggesting that some models are not state-of-the-art and require further
improvements. However, the ensemble median has excellent agreement with our
observational estimate: 0.68 [0.54–0.82] × 1022 J yr-1
(0.42 Wm-2) from 1970 to 2005 and 1.25
[1.10–1.41] × 1022 J yr-1 (0.77 Wm-2) from 1992
to 2005. These results increase confidence in both the observational and
model estimates to quantify and study changes in Earth's energy imbalance
over the historical period. We suggest that OHC be a fundamental metric for
climate model validation and evaluation, especially for forced changes
(decadal timescales).
Introduction
Since the beginning of the industrial revolution, increased emissions of
long-lived greenhouse gases such as carbon dioxide have resulted in an
accumulation of thermal energy in the climate system (Trenberth et al., 2014;
von Schuckmann et al., 2016) via the associated net energy imbalance at
Earth's top-of-atmosphere (TOA). It is estimated that more than 90 % of
the excess heat is stored in the ocean and is manifested by ocean warming
(Loeb et al., 2012; Balmaseda et al., 2013; Rhein et al., 2013; Trenberth et
al., 2014), i.e., an increase in global ocean heat content (OHC; Lyman et
al., 2010; Levitus et al., 2012; Abraham et al., 2013). Due to the ocean's
dominant role in the global energy storage changes, the rate of OHC change
provides a strong constraint on Earth's energy imbalance on interannual and
longer timescales (Palmer and McNeall, 2014; Trenberth, 2015). Numerous
efforts have been made to detect the historical OHC change (for example,
Levitus et al., 2005; Gouretski and Koltermann, 2007; Smith and Murphy, 2007;
Domingues et al., 2008; Palmer and Haines, 2009; Ishii and Kimoto, 2009;
Lyman et al., 2010; Levitus et al., 2012; Balmaseda et al., 2013; Cheng et
al., 2015a) and attribute causes to its variation (Palmer et al., 2009;
Gleckler et al., 2012). However, large uncertainties exist in OHC estimates
(Abraham et al., 2013; Balmaseda et al., 2013; Rhein et al., 2013), which can
confound our understanding of the changes in Earth's energy imbalance since
the 1970s.
Flexible-grid method. (a) shows the geographical
distribution of 700 m OHC in 1980 on each 1∘ by 1∘ grid,
showing good data coverage in the Northern Hemisphere and sparse data in the
Southern Hemisphere. To fill these data gaps by using the flexible-grid
method, OHC on each grid in the poorly sampled region (defined as the
Argo-Ship Area in CZ14) is calculated by averaging OHC on a large
latitude–longitude grid with sizes of 5∘ by 5∘, 5∘
by 10∘, 5∘ by 20∘, 1∘ by 40∘,
8∘ by 40∘, and 10∘ by 40∘ separately. The
resultant OHC distribution is shown from (b) to (g).
A major source of error in the historical in situ temperature data that
underpin OHC estimates are time-varying systematic biases in expendable
bathythermograph (XBT) temperature measurements (Gouretski and Koltermann,
2007; Lyman et al., 2010; Abraham et al., 2013). Numerous correction schemes
have been proposed to remove the time-varying XBT biases (Cheng et al.,
2015b), but these schemes vary in their formulation and performance. Hence,
the XBT community met in 2014 and made a series of recommendations on the
factors that should be accounted for when designing and implementing an XBT
bias-correction scheme (Cheng et al., 2015b). Only one bias-correction scheme
(Cheng et al., 2014) meets all of these recommendations, and has been shown
to correct the overall bias to less than 0.02 ∘C (for the 0–700 m
layer, less than 10 % of the total 0–700 m temperature change since
1970) and also reduce the spatio-temporal variation of bias.
Prior to 2004, observations of the upper ocean were predominantly confined
to the Northern Hemisphere and concentrated along major shipping routes; the
Southern Hemisphere is particularly poorly observed. In this century, the
advent of the Argo array of autonomous profiling floats (Roemmich et al., 2015; von Schuckmann et al., 2014) has
significantly increased ocean sampling to achieve near-global coverage for
the first time over the upper 1800 m since about 2005.
The lack of historical data coverage requires a gap-filling (or mapping)
strategy to infill the data gaps in order to estimate the global integral of
OHC. A pioneering study showed that an improved strategy for gap-filling
methods and corrections for XBT biases improved the consistency between
models and observations of upper 700 m OHC (Domingues et al., 2008). Owing
to sparse observations in the Southern Hemisphere, Durack et al. (2014)
explored this region as a primary source of underestimation of OHC trends
using climate models from the Coupled Model Intercomparison Project Phase 3/5
(CMIP 3/5; Meehl et al., 2007; Taylor et al., 2012). Cheng and Zhu (2014)
examined the observation system evolution in this century, identifying a
spurious signal from 2001 to 2003 in global OHC estimates due to inadequate
sampling of the Southern Hemisphere prior to Argo. Accordingly, these studies
imply that many past estimates likely underestimate the long-term trend.
The aim of this study is to use these improved XBT bias corrections and
gap-filling methods designed to minimize the impact of historical sampling
changes and to confront CMIP5 models with the state-of-the-art observational
estimates of OHC change. We note that the work presented here is broadly
similar to the recent study of Gleckler et al. (2016) and provides an
important independent verification of some of their key findings. However,
the present study also makes use of a larger number of CMIP5 models (24
compared to 15) and observation-based estimates of the 0–700 m ocean heat
content changes (8 compared to 3), including improved XBT bias corrections
and new mapping approaches. We are therefore able to more fully characterize
the uncertainties associated with CMIP5 models and place our new
observation-based estimates of OHC in the context of several previous
estimates (including those of Gleckler et al.). The paper is arranged as
follows. In Sect. 2 the data and methods are introduced. The various
observation-based OHC estimates used are discussed in Sect. 3.1; CMIP5 model
simulations are presented in Sect. 3.2. We summarize our findings in Sect. 4.
Data and methods
The new observation-based estimates of OHC presented here use the XBT
bias-correction scheme from Cheng et al. (2014) applied to the most recent
version of the World Ocean Database (WOD2013; Boyer et al., 2013). MBT bias
is corrected using the method provided in Ishii and Kimoto (2009). Because
the choice of reference climatology to compute anomalies can lead to errors
due to the sparseness and inhomogeneity of the historical ocean sampling
(Lyman and Johnson, 2014; Cheng and Zhu, 2015), it is preferable to use the
climatology that is constructed based on data with near-global data coverage
(Cheng and Zhu, 2015), i.e., during the recent years in the Argo period. In
this study, we use a climatology constructed for the period 2008–2012,
similar to Cheng and Zhu (2014) and Cheng et al. (2015a).
We apply two approaches to mapping the OHC data. The first (Cheng and Zhu,
2014; hereafter termed the CZ14 method) calculated annual mean OHC in
data-rich areas (defined as the Ship Area) and a linear OHC trend in
data-sparse regions (defined as the Argo-Ship Area). Then the two estimates
are summed to get the global OHC. The second approach is an extension to CZ14
that uses flexible grid sizes to retain greater spatial information while
ensuring an adequate number of observations in each grid box. OHC on each
1∘ by 1∘ grid in poorly sampled regions (Argo-Ship Area
defined in CZ14) is calculated by averaging OHCs over a large
latitude–longitude grid with sizes of 5∘ by 5∘, 5∘
by 10∘, 5∘ by 20∘, 2∘ by 40∘,
8∘ by 40∘, and 10∘ by 40∘ separately to
ensure that all regions have data coverage (Fig. 1). The gridded averaged
anomalies are then integrated to get global OHC. This method
(“flexible-grid” method hereafter) maintains the observed OHC in data-rich
regions without smoothing and provides a smooth OHC field in data-sparse
regions. This is appropriate for the Southern Hemisphere, where there is more
homogeneity, less land, and no boundary currents.
In addition to our new observation-based OHC estimates, we also present two
recent sets of estimates that make use of dynamical models. The first uses
climate model simulations (Durack et al., 2014) to adjust five of the
existing upper 700 m OHC estimates (Domingues et al., 2008; Durack and
Wijffels, 2010; Ishii and Kimoto, 2009; Levitus et al., 2012; Smith and
Murphy, 2007), which may have underestimated trends due to the very limited
data coverage in the Southern Hemisphere. In addition to the Durack et
al. (2014) global OHC adjustments that are based on comparing hemispheric
ratios of heat uptake in the CMIP5 models, it is desirable to also use other
estimates from independent studies. The second is the ORAS4 data set, which
is an ocean reanalysis product (Balmaseda et al., 2013). Ocean reanalyses
have the advantage of synthesizing a large number of different observations
into a dynamically consistent estimate of the historical ocean state and can
potentially provide greater physical insight into the mechanisms of OHC
change (Balmaseda et al., 2013; Palmer et al., 2015; Xue et al., 2012). The
five ensemble members in ORAS4 approximately represent the uncertainties in
the wind forcing, observation coverage, and the deep ocean. OHCs considered
within layers of 0–700, 700–2000, and 2000 m–bottom are all used in this
study.
Observational ocean heat content from 1970 to 2010. The 0–700 m
OHC is shown in red (flexible-grid method), pink (CZ14 method), and yellow
(ORAS4). Five adjusted OHCs presented in Durack et al. (2014) are shown as
dots, which are the OHC changes per 35 years. The 700–2000 m OHC is sourced
from NODC in green, and abyssal (2000 m–bottom) OHC is from Purkey and
Johnson (2010) and shown in black (the warming rate within 1970–1991 is
scaled to 3 times the linear trend in Purkey and Johnson, 2010). Full-depth
OHC time series are also presented in blue (flexible-grid method), dark
purple (CZ14 method), and light blue (ORAS4). All of the time series are
referred to a baseline OHC within the 3-year period: 1969–1971. The vertical
colored bars are 2-year intervals, starting when the event (volcano or El
Niño) began.
List of CMIP5 models and group names.
Modeling center (or group)Institute IDModel nameCommonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), AustraliaCSIRO-BOMACCESS1.0Beijing Climate Center, China Meteorological AdministrationBCCBCC-CSM1.1 BCC-CSM1.1(m)Canadian Centre for Climate Modelling and AnalysisCCCMACanESM2National Center for Atmospheric ResearchNCARCCSM4Community Earth System Model ContributorsNSF-DOE-NCARCESM1(FASTCHEM)Commonwealth Scientific and Industrial Research Organization in collaboration with Queensland Climate Change Centre of ExcellenceCSIRO-QCCCECSIRO-Mk3.6.0NOAA Geophysical Fluid Dynamics LaboratoryNOAA GFDLGFDL-CM3 GFDL-ESM2G GFDL-ESM2MNASA Goddard Institute for Space StudiesNASA GISSGISS-E2-RMet Office Hadley Centre (additional HadGEM2-ES realizations contributed by Instituto Nacional de Pesquisas Espaciais)MOHC (additional realizations by INPE)HadGEM2-CC HadGEM2-ESInstitut Pierre-Simon LaplaceIPSLIPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LRJapan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental StudiesMIROCMIROC-ESMAtmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and TechnologyMIROCMIROC5Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology)MPI-MMPI-ESM-MR MPI-ESM-LR MPI-ESM-PMeteorological Research InstituteMRIMRI-CGCM3Norwegian Climate CentreNCCNorESM1-M NorESM1-ME
OHC trends during the 1970–2005 period in observations and CMIP5
models. (a) 0–700 m. (b) Full depth. For models, the
histograms are the distribution of CMIP5 results, and the median of the CMIP5
multimodel results is shown in solid line, with the 5–95 % confidence
interval in dashed lines. For observations, we present the linear trends by
different studies: this study (both CZ14 and the flexible-grid method), five
estimates in Durack et al. (2014) after adjustment, and five ensembles of
ORAS4 reanalysis. The 5–95 % confidence intervals for observations are
shaded in light green. A quadratic fit to the entire pre-industrial control
run was used to correct the CMIP5 time series for model drift.
Combining our new OHC estimates with existing estimates provides an ensemble
of observation-based estimates of historical upper 0–700 m OHC changes, and
the spread is a simple measure of the observational uncertainty. Differences
across the ensemble arise not only from mapping methods, but also from the
choice of climatology, input data quality control procedures, and XBT
correction scheme (Palmer et al., 2010).
To arrive at estimates of full-depth OHC change, we adapted and adjusted the
Levitus et al. (2012) estimate for the 700–2000 m layer and, for the deeper
ocean for the period 1990–2010, we use information from Purkey and
Johnson (2010), which was also used in the IPCC-AR5 report (Rhein et al.,
2013). Prior to 1990, there is a larger uncertainty regarding the rate of
deep-ocean warming. Because the upper 700–2000 m oceans show an approximate
tripling of the heating rate from 1992 to 2005 compared to from 1970 to 1991
(as shown in Fig. 2, green curve), we assume a proportionate increase in heat
uptake in the deep-ocean (2000 m–bottom). An assumption has to be made here
because there are not sufficient observations below 2000 m. The upper oceans
are mostly controlled by the wind, but the deep oceans (i.e., 700 m–bottom)
are mainly controlled by the meridional overturning circulation. So the OHC
changes at 700–2000 m and 2000–bottom may share some similarities. For
uncertainty calculations, we use a lower bound of no deep-ocean warming prior
to 1992 and an upper bound of an unchanging linear trend from 1970 to 2005,
as assumed in Church et al. (2011). Because this is an important assumption,
it is valuable to assess the uncertainties involved. We show that the
difference of this lower and upper bound of the 700 m–bottom OHC change is
equal to ∼ 13 % (∼ 10 %) of the full-depth OHC change
during 1970–1991 (1970–2005), which indicates the maximum error induced by
this assumption. The ORAS4 data also provide estimates of OHC changes deeper
than 700 m. We estimate the uncertainty for the OHC changes below 700 m by
computing the standard error from the ensemble members of Levitus et
al. (2012), Purkey and Johnson (2010), and ORAS4 ensembles, and presenting
the 5–95 % confidence interval.
We compare our observation-based OHC ensemble with 24 CMIP5 model simulations
(Table 1) of historical OHC changes. Climate models suffer from so-called
“drift” (Sen Gupta et al., 2013; Hobbs et al., 2015), i.e., spurious
long-term trends arising due to the slow model adjustment to the initial
conditions and/or imperfect representation of the energy budget. This drift
can bias the long-term representation of the ocean temperature, especially in
deeper layers. Because there is no general consensus on how to correct for
climate drift in models, we applied two different drift correction strategies
by using available pre-industrial control (“piControl”) runs of 24 CMIP5
models. We applied both a linear and quadratic fit to the OHC time series of
pi-control runs for OHC0-700, 700–2000, and 2000–6000 m. The resulting
regression function is removed from the historical simulations for each
model. The two methods show nearly identical results (Tables 2 and 3) and we
present the results for quadratic drift correction as the basis of our
discussions.
To quantify the OHC changes for a given time period, we fit a linear trend.
An alternative method for calculating the OHC difference between the two ends
of a time series shows consistent results (compare Table 2 with Table 3). For
both observation-based OHC and CMIP5-OHC results, we calculate the median of
the ensemble to reduce the impact of outliers, together with the 5 to
95 % confidence interval of the median assuming that the values were
independently and randomly sampled from a population distributed according to
a Gaussian distribution. Therefore, the 5–95 % confidence interval is
± standard error × 2.10. The Student's t test is used to
examine the significance of the difference between observations and CMIP5
models.
Summary of ocean heat content change. Comparison of CMIP5 models and
observations. The medians with the 5–95 % confidence interval are
presented.
Summary of total ocean heat content change within 1970–2005 and
1992–2005 by using an alternative method to assess the long-term OHC change.
Here the total OHC changes based on observations are calculated by the
difference of OHC with 2004–2006 and OHC within 1969–1971 (1990–1992) for
the 1970–2005 (1992–2005) period to reduce the interannual temporal
variability. This is an alternative method to assess the OHC change in
addition to the linear trend in Table 2.
Figure 2 presents the observation-based 0–700 m OHC estimates by using the
methods listed in the previous section, after taking the Southern Hemisphere
sampling bias into account. The updated 0–700 m OHC estimate based on the
CZ14 method indicates a total upper ocean warming of approximately
21.0 × 1022 J, equal to a linear trend of
0.58 × 1022 J yr-1 (or 0.35 Wm-2, averaged over
the global surface area) from 1970 to 2005. The six individual flexible-grid
method estimates (based on six choices of grid size; Fig. 2) span a range of
0.52–0.58 × 1022 J yr-1 during the 1970–2005 period,
consistent with the CZ14 estimate. In addition, according to Durack et
al. (2014), the change in global 0–700 m OHC over the period 1970–2005
increased by 0.43–0.56 × 1022 J yr-1 (Fig. 2). One
estimate (Smith and Murphy, 2007), which shows much smaller values than the
others, is discounted, but including the Smith and Murphy (2007) value does
not impact our results, since we use the median rather than the mean. ORAS4
reanalysis shows a range of 0.49–0.53 × 1022 J yr-1 for
the 0–700 m OHC.
The collection of the different observational OHC estimates discussed above
(16 individual estimates) provides current best estimates of OHC, along with
an estimate of the uncertainty associated with the analytical methodology
(Fig. 3a). Although all OHC estimates are based on essentially the same
temperature profile database, they use four different methods, and hence
their differences give an indication of the uncertainty. The total OHC change
of the upper 700 m layer has increased by
0.55 × 1022 J yr-1 (0.34 Wm-2) from 1970 to 2005,
which is the median among all of the ensemble members, with a 5–95 %
confidence interval of 0.50–0.60 × 1022 J yr-1.
On the other hand, it is worthwhile noting that the comparison of CZ14,
flexible-grid method, and ORAS4 results shows inconsistencies in OHC changes
on interannual timescales (Fig. 2), indicating that the errors in OHC
estimates are still larger than the interannual variability, as shown in
Abraham et al. (2013). However, all of the estimates show the OHC decreases
after the major volcano eruptions: El Chichón in March–April 1982 and
Pinatubo in June 1991 (Fig. 2). The OHC change after the two volcano
eruptions is approximately assessed by subtracting the OHC 1 year before the
eruption from the OHC in the second year after eruption. It shows a 0–700 m
OHC decrease of ∼-2.67 [-3.28, -2.06] × 1022 J
after El Chichón and ∼-2.72 [-3.97,
-1.47] × 1022 J after Pinatubo, indicating the strong ocean
cooling. The negative radiative forcing to the ocean (and climate system) due
to the volcano eruption is probably the major reason for this decrease
(Church et al., 2005, Domingues et al., 2008; Balmaseda et al. 2013), but our
observational analyses can not exclude the possibility that the unforced
ocean variability (such as ENSO, Trenberth and Fasullo, 2012) and the insufficiency of data coverage
(which could induce spurious interannual OHC change) are fully or partly
responsible for the values calculated above, which requires more careful
model-based studies in the future. Moreover, it is also suggested that
volcanic eruptions can trigger an El Niño-like response in the ocean,
which is another possible explanation (Mann et al., 2005).
There is also an
indication of substantial heat discharge from the upper 700 m ocean
following the extreme 1997–1998 El Niño event (Balmaseda et al., 2013;
Roemmich and Gilson, 2011), with the CZ14 estimate showing a lesser response
than the other estimates partly due to their assumption of a linear long-term
change in the data-sparse region. This 0–700 m OHC decrease is
∼-2.73 [-3.27, -2.20] × 1022 J after the
1997–1998 El Niño averaging over all the products. The decrease is
calculated by the difference in OHC between 2000 and 1998 for ORAS4 and
between 2000 and 1999 for CZ14 and the flexible-grid method, since the latter
products appear to be a delayed response. The differences among the data sets
indicate the uncertainties of both gap-filling methods and the processes of
OHC redistribution during ENSO represented by re-analyses (ORAS4) in the
vertical in the Pacific Ocean and into the other ocean basins via atmosphere
teleconnections (Mayer et al., 2013).
For deeper ocean layers, we adopt the 700–2000 m ocean heat content
estimate from 1970 to 2005 in Levitus et al. (2012), where all of the
historical in situ data are objectively analyzed. According to Levitus et
al. (2012), the 700–2000 m ocean warmed by 0.12 × 1022
(0.17 × 1022) J yr-1 or 0.075 (0.106) Wm-2 over
the global surface since 1970 (1992). For the abyssal (2000 m–bottom) OHC
changes, according to the strategies provided in the Methods section, we
estimate a deep-ocean warming of 0.03 × 1022
(0–0.11 × 1022) J yr-1 or 0.02 (0–0.07) Wm-2
during the 1970–1991 period and 0.11 × 1022 J yr-1
(0.077 Wm-2) during 1992–2005. According to the two estimates at two
layers, the ocean warming rate deeper than 700 m is
0.15 × 1022 J yr-1 (0.090 Wm-2) during
1970–2005. However, as we discussed above, the traditional method from
Levitus et al. (2012) is likely to underestimate the long-term trend, and
this is also the case for the 700–2000 m estimate of OHC change. Hence it
is also valuable to use ORAS4, which provides alternative estimates of
700–2000 m/2000 m–bottom OHC changes and also provides an assessment of
the uncertainty. It is shown from the recent Ocean Reanalyses Intercomparison
Project (Palmer et al., 2015) that there remain large biases in the deeper
ocean, because there are limited 700 m (historical) data available, and
hence it is a challenge for assimilation to deliver information to the model
in those layers. ORAS4 shows the deeper 700 m–bottom ocean warming of
0.09–0.24 × 1022 J yr-1 (0.056–0.150 Wm-2) since
1970, indicating large uncertainties generally consistent with the previous
assessments based on Levitus et al. (2012) and Purkey and Johnson (2010).
By summing OHCs for the different layers 0–700, 700–2000, and
2000 m–bottom, the observation-based full-depth OHCs are obtained. All of
these results (Fig. 3b) indicate a range of full-depth ocean warming of
0.50–0.79 × 1022 J yr-1 (0.31–0.50 Wm-2) over
the 36-year period (1970–2005, again calculated by linear trend). The median
of the different estimates is 0.74 [0.68,
0.80] × 1022 J yr-1 (1.22
[1.16–1.29] × 1022 J yr-1) since 1970 (since 1992),
with the values in brackets representing the 5 and 95 % confidence
intervals of the median. This is equivalent to a global energy imbalance of
0.46 [0.42, 0.50] Wm-2 (0.75 [0.69, 0.81] Wm-2) averaged over
Earth's surface area since 1970 (1992). Furthermore, after the two major
volcano eruptions, the total OHC decrease is ∼-2.42 [-3.28,
-1.56] × 1022 J for El Chichón and ∼-3.19
[-4.92, -1.67] × 1022 J for Pinatubo. Following the major
1997–1998 El Niño event, the total OHC decreases by ∼-1.85
[-2.62, -1.10] × 1022 J. This indicates a substantial
rearrangement of heat from 0 to 700 m to the deeper ocean, since most
ensemble members show smaller full-depth heat loss than for the 0–700 m
layer.
Full-depth OHC by individual CMIP5 models and observations. The
observational OHC time series (black dashed) uses the CZ14 method
(0–700 m), Levitus et al. (2012) (700–2000 m), and Purkey and
Johnson (2010) (2000 m–bottom). The multimodel ensemble median is shown in
dashed curve. A quadratic fit to the entire pre-industrial control run was
used to correct the CMIP5 time series for model drift in the upper panel, and
the results for the linear fit are shown at the bottom.
Comparison of full-depth OHC change between observation and CMIP5
models (a) for two separate time periods, 1970–1991 (in blue bars)
and 1992–2005 (in red bars), and (b) for two vertical layers:
0–700 m (in red bars) and 700 m–bottom (in blue bars). The medians of the
observational total OHC changes are shown in solid lines, compared with the
model results in dashed lines. Their 5–95 % confidence intervals are
presented in error bars. The 5–95 % confidence intervals for
observations are also shaded in light red and light blue. A quadratic fit to
the entire pre-industrial control run was used to correct the CMIP5 time
series for model drift.
Climate model assessments
It is important to quantify the agreement of models, such as those in CMIP5
(Taylor et al., 2012; Durack et al., 2014; Gleckler et al., 2016), with
observations, both to validate the models and also to reconcile the
observations with expectations based on radiative forcing estimates.
Comparisons are made (Fig. 3a) between the updated OHC observations and a
24-member climate model ensemble from 1970 to 2005, which is the limit for
reasonable observational coverage (Lyman and Johnson, 2014) and is also
restricted to the end time of the CMIP5 model runs for historical simulations
(2005). Because the Durack et al. (2014) global OHC adjustments are partly
based on heat uptake in the CMIP5 models, they should not be used to then
evaluate the models. When removing Durack et al. (2014) estimates, the median
change within 1970–2005 is 0.56 × 1022 J yr-1 for
OHC0-700 m and 0.75 × 1022 J yr-1 for OHC0-700 m, both
of which are nearly identical to the results in Table 2, suggesting that
including Durack et al. (2014) does not influence the main conclusion of our
study.
The distribution of OHC0-700 m from the 24 models after a correction of
“climate drift” (see Methods) shows an ensemble median of 0.42
[0.32–0.51] × 1022 J yr-1 (0.26
[0.19–0.37] Wm-2) for the 1970–2005 time period and 0.89
[0.77–1.02] × 1022 J yr-1 (0.55
[0.48–0.64] Wm-2) for 1992–2005. The sensitivity of the results to
the climate drift correction is very small (within
0.03 × 1022 J yr-1) when two different climate drift
correction methods are applied (as shown in Tables 2 and 3 and Fig. 4). For
the 1970–2005 period, the median of the CMIP5 models is significantly
smaller than observations (0.55
[0.50–0.60] × 1022 J yr-1), indicating that the models
underestimate the upper 700 m OHC change since 1970. But within the
1992–2005 period, the median of the CMIP5 models falls into the confidence
interval of the existing observational estimates, indicating that the
ensemble median of models agrees very well with observational estimates in
the recent period.
For full-depth OHC, drift-corrected CMIP5 models show the total OHC change by
0.68 [0.54–0.82] × 1022 J yr-1 (0.42
[0.34–0.51] Wm-2) from 1970 to 2005 and 1.25
[1.10–1.41] × 1022 J yr-1 (0.77
[0.68–0.88] Wm-2) during 1992–2005 (Fig. 3). The CMIP5 ensemble
median again shows very good agreement with observations for both 1970–2005
(0.74 × 1022 J yr-1) and 1992–2005
(1.22 × 1022 J yr-1). The central estimates of
observation-based and CMIP5 OHC change are consistent within the estimated
uncertainty. The total OHC decrease after the two major volcano eruptions is
∼-0.60 [-0.81, -0.38] × 1022 J for El Chichón
and ∼-1.47 [-1.93, -1.00] × 1022 J for Pinatubo,
which are weaker than for observations.
Table 2 provides a summary of observed and simulated OHC change for different
time periods and depths. CMIP5 results are shown for the upper ocean, both
with linear and quadratic drift corrections. Within the drift-corrected CMIP5
models, the rate of ocean warming has nearly doubled since 1992 (Fig. 5,
Table 2): 0.56 [0.43, 0.68] × 1022 J yr-1 within
1970–1991 (∼ 0.35 [0.26, 0.43] Wm-2 over global surface)
compared to 1.25 [1.10, 1.41] × 1022 J yr-1 during
1992–2005 (∼ 0.77 [0.67, 0.87] Wm-2) for both the
drift-corrected CMIP5 ensembles, while for observations the corresponding
values are 0.61 [0.53, 0.69] × 1022 J yr-1 within
1970–1991 (∼ 0.38 [0.33, 0.43] Wm-2), and 1.22 [1.16,
1.29] × 1022 J yr-1 during 1992–2005 (∼ 0.75
[0.71, 0.80] Wm-2). This provides evidence of an acceleration of ocean
warming due to the increasing radiative forcing from rising greenhouse gases
and from the effects of volcanic eruptions near the intersection of those two
time periods (Myhre et al., 2013). This acceleration of ocean warming is also
found by a recent study (Gleckler et al., 2016).
Furthermore, the model ensemble median of full-depth OHC agrees well with
observations but significantly underestimates the OHC change in the upper
700 m (Fig. 5b), yet OHC changes for 700–6000 m in the models are likely
to overestimate the warming rate prior to 1990. Together these are indicators
that the models might be too diffusive and that the vertical distribution of
heat may not be correct, as suggested by previous studies (Forest et al.,
2008; Kuhlbrodt and Gregory, 2012).
Although the comparison between the observational and CMIP5 full-depth OHC
results in an insignificant difference, CMIP5 models show a large spread
(Figs. 3, 4, and 5), indicating that there are still large uncertainties in
model simulations of Earth's energy budget (Flato et al., 2013). The spread
of CMIP5 models far exceeds the estimated observational uncertainty in the
OHC changes, even for the upper 0–700 m where the model drift is expected
to be less important compared to the deeper layer. There are two groups of
models: seven models calculate a small upper 700 m ocean warming of less
than 0.3 × 1022 J yr-1 over 1970–2005; the other group
shows a 0–700 m ocean warming of
0.3–0.75 × 1022 J yr-1 (Fig. 3a). The first group also
shows a much smaller full-depth OHC increase of less than
0.35 × 1022 J yr-1 than the second:
0.35–1.05 × 1022 J yr-1 over 1970–2005 (Fig. 3b). The
second group shows better agreement with observational estimates. The models
with smaller values should be treated with caution in future analyses. The
reasons why the models have large divergence are still an actively studied
issue. Frölicher et al. (2015) discussed the large range of model results
and attributed a contribution of this to the differences in indirect
aerosols. Additionally, CMIP5 has been missing post-2000 volcanic eruptions
in these simulations as discussed in Glecker et al. (2016), but this effect
is shown to be small and less than 0.1 Wm-2, as indicated in Trenberth
et al. (2014).
Furthermore, the OHC for models shows a non-Gaussian distribution (Fig. 3),
potentially challenging our method of the use of Gaussian estimations for the
confidence levels. However, there is no a priori reason for the statistics to
be non-Gaussian, other than that there is a small sample and the likelihood
that there are some outliers. The non-Gaussian nature of the distribution
(Fig. 3) may be partly due to the small sample size. The use of the median
reduces the impact of outliers and then enables us to use the standard
deviation to characterize the spread.
Summary
This study presents new estimates of observed OHC change since 1970 based on
improved mapping methods and XBT bias corrections. Our results suggest that
previous IPCC-AR5 observational estimates of a 0–700 m OHC change of
∼ 0.26 Wm-2 may be too low, typically by about ∼ 25 %
compared to our findings here (∼ 0.35 Wm-2), supporting the
conclusions of Durack et al. (2014) based on somewhat different constraints.
Our estimates of full-depth OHC change show remarkably good agreement with
the CMIP5 ensemble median response during 1970–2005 and give us confidence
that the climate models are not systematically biased in their simulation of
historical variations in Earth's energy imbalance over this period.
The present work demonstrates how improvements in OHC estimation methods have
led to a greater degree of consistency with climate model simulations of
long-term changes in Earth's energy budget. In turn this allows an evaluation
of the models and suggests that some may not be credible. Further work is
needed to understand the spatial patterns of ocean heat uptake and TOA
changes over the historical past as a means of assessing potential model
deficiencies in key processes. Since 93 % of the energy of global warming
is stored in the ocean, our observation-based results indicate that the ocean
component of Earth's heat imbalance is ∼ 0.38 [0.33, 0.43] Wm-2
from 1970 to 1991 and ∼ 0.75 [0.71, 0.80] Wm-2 from 1992 to 2005.
With 0.07 Wm-2 for the other components (Trenberth et al. 2014), the
implied average energy imbalance is 0.46 [0.40, 0.52] Wm-2 after 1970
and 0.82 [0.76, 0.88] Wm-2 after 1992. For the period 1970–2005, our
new value is about 15 % larger than the central estimate of Rhein et
al. (2013) over the same period, and could have important implications for
closure of the sea-level budget.
Data availability
The CZ14 data and CMIP5 OHCs are available at
http://159.226.119.60/cheng/ and data access to ORAS4 OHC is available
at
https://climatedataguide.ucar.edu/climate-data/oras4-ecmwf-ocean-reanalysis-and-derived-ocean-heat-content.
Acknowledgements
Lijing Cheng and Jiang Zhu are supported by National Natural Science
Foundation of China (41506029) and Chinese Academy of Sciences' project
“Western Pacific Ocean System: Structure, Dynamics and Consequences”
(XDA11010405). Matthew D. Palmer is supported by the Joint UK DECC/Defra Met
Office Hadley Centre Climate Programme (GA01101) and his work represents a
contribution to Natural Environment Research Council DEEP-C project
NE/K005480/1. Kevin E. Trenberth is supported by DOE grant DE-SC0012711. NCAR
is sponsored by the National Science Foundation. We thank NOAA/NODC, who made
the observational ocean temperature data set available. We acknowledge the
World Climate Research Programme's Working Group on Coupled Modelling, which
is responsible for CMIP, and we thank the climate modeling groups (listed in
Table 1 of this paper) for producing and making available their model output.
For CMIP the US Department of Energy's Program for Climate Model Diagnosis
and Intercomparison provides coordinating support and led development of
software infrastructure in partnership with the Global Organization for Earth
System Science Portals. Edited by: M.
Hecht Reviewed by: M. Balmaseda and one anonymous referee
References
Abraham, J. P., Reseghetti, F., Baringer, M., Boyer, T., Cheng, L., Church,
J., Domingues, C., Fasullo, J. T., Gilson, J., Goni, G., Good, S., Gorman,
J. M., Gouretski, V., Ishii, M., Johnson, G. C., Kizu, S., Lyman, J.,
MacDonald, A., Minkowycz, W. J., Moffitt, S. E., Palmer, M., Piola, A.,
Trenberth, K. E., Velicogna, I., Wijffels, S., and Willis, J.: A review of
global ocean temperature observations: implications for ocean heat content
estimates and climate change, Rev. Geophys., 51, 450–483, 2013.Balmaseda, M. A., Trenberth, K. E., and Källén, E.: Distinctive
climate signals in reanalysis of global ocean heat content, Geophys. Res.
Lett., 40, 1–6, 10.1002/grl.50382, 2013.
Boyer, T. P., Antonov, J. I., Baranova, O. K., Boyer, T. P., Coleman, C. L.,
Garcia, H. E., Grodsky, A. I., Johnson, D. R., Locarnini, R. A., Mishonov,
A. V., Reagan, J. R., Sazama, C. L., Seidov, D., Smolyar, I., Yarosh, E. S.,
and
Reagan, J. R.: World Ocean Database 2013, edited by: Levitus, S. and
Mishonov, A., NOAA Atlas NESDIS 72, 209 pp., 2013.
Cheng, L. and Zhu, J.: Artifacts in variations of ocean heat content
induced by the observation system changes, Geophys. Res. Lett., 20,
7276–7283, 2014.
Cheng, L. and Zhu, J.: Influences of the choice of climatology on ocean
heat content estimation, J. Atmos. Ocean. Technol., 32, 388–394, 2015.
Cheng, L., Zhu, J., Cowley, R., Boyer, T., and Wijffels, S.: Time, probe type
and temperature variable bias corrections to historical expendable
bathythermograph observations, J. Atmos. Ocean. Technol., 31, 1793–1825,
2014.Cheng, L., Zhu, J., and Abraham, J.: Global upper ocean heat content
estimation: recent progress and the remaining challenges, Atmos. Ocean. Sci.
Lett., 8, 333–338,
10.3878/AOSL20150031, 2015a.Cheng, L., Abraham, J., Goni, G., Boyer, T., Wijffels, S., Cowley, R.,
Gouretski, V., Reseghetti, F., Kizu, S. Dong, S., Bringas, S., Goes, M.,
Houpert, L., Sprintall, J., and Zhu, J.: XBT Science: assessment of instrumental
biases and errors, B. Am. Meteorol. Soc., 97, 924–933, 10.1175/BAMS-D-15-00031.1, 2015b.
Church, J. A., White, N. J., and Arblaster, J. M.: Significant decadal-scale
impact of volcanic eruptions on sea level and ocean heat content, Nature,
438, 74–77, 2005.
Church, J. A., White, N. J., Konikow, L. F., Domingues, C. M., Cogley, J. G.,
Rignot, E., Gregory, J. M., van den Broeke, M. R., Monaghan, A. J., and
Velicogna, I.: Revisiting the Earth's sea-level and energy budgets from 1961
to 2008, Geophys. Res. Lett., 38, L18601–L18608, 2011.
Domingues, C. M., Church, J. A., White, N. J., Gleckler, P. J., Wijffels, S.
E., Barker, P. M., and Dunn, J. R.: Improved estimates of upper-ocean warming
and multi-decadal sea-level rise, Nature, 453, 1090–U1096, 2008.
Durack, P. and Wijffels, S.: Fifty-year trends in global ocean salinities
and their relationship to broad-Scale warming, J. Climate, 23, 4342–4362,
2010.Durack, P., Gleckler, P. J., Landerer, F., and Taylor, K. E.: Quantifying
underestimates of long-term upper-ocean warming, Nature Climate Change, 4,
999–1005, 10.1038/NCLIMATE2389, 2014.
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., Cox, P., Driouech, F., Emori, S.,
Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason C., and Rummukainen, M.: Evaluation of climate models,
in: Climate Change 2013:
The Physical Science Basis, Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, edited
by: Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge,
United Kingdom and New York, NY, USA, 777–787, 2013.Forest, C. E., Stone, P. H., and Sokolov, A. P.: Constraining climate model
parameters from observed 20th century changes, Tellus A, 60, 911–920,
10.1111/j.1600-0870.2008.00346.x, 2008.
Frölicher, T., Sarmiento, J. L., Paynter, D. J., Dunne, J. P., Krasting,
J. P., and Winton, M.: Dominance of the Southern Ocean in Anthropogenic
Carbon and Heat Uptake in CMIP5 Models, J. Climate, 28, 862–886, 2015.
Gleckler, P. J., Santer, B. D., Domingues, C. M., Pierce, D. W., Barnett, T.
P., Church, J. A., Taylor, K. E., AchutaRao, K. M., Boyer, T. P., Ishii, M.,
and
Caldwell, P. M.: Human-induced global ocean warming on multidecadal
timescales, Nature Climate Change, 2, 524–529, 2012.Gleckler, P. J., Durack, P. J., Stouffer, R. J., Johnson, G. C., and Forest,
C. E.: Industrial-era global ocean heat uptake doubles in recent decades,
Nature Climate Change, 6, 394–398, 10.1038/nclimate2915, 2016.Gouretski, V. and Koltermann, K. P.: How much is the ocean really warming?,
Geophys. Res. Lett., 34, L01610, 10.1029/2006GL027834, 2007.Hobbs, W., Palmer, M., and Monselesan, D.: An energy conservation analysis
of ocean drift in the CMIP5 global coupled models, J. Climate, 29, 1639–1653,
10.1175/JCLI-D-15-0477.1, 2015.
Ishii, M. and Kimoto, M.: Reevaluation of historical ocean heat content
variations with time-varying XBT and MBT depth bias corrections, J.
Oceanogr., 65, 287–299, 2009.
Ishii, M., Kimoto, M., and Kachi, M.: Historical ocean subsurface temperature
analysis with error estimates, Mon. Weather Rev., 131, 51–73, 2003.Kuhlbrodt, T. and Gregory, J. M.: Ocean heat uptake and its consequences
for the magnitude of sea level rise and climate change, Geophys. Res. Lett.,
39, L18608, 10.1029/2012GL052952, 2012.Levitus, S., Antonov, J., and Boyer, T.: Warming of the worldocean,
1955–2003, Geophys. Res. Lett., 32, L02604, 10.1029/2004GL021592, 2005.Levitus, S., Antonov, J. I., Boyer, T. P., Baranova, O. K., Garcia, H. E.,
Locarnini, R. A., Mishonov, A. V., Reagan, J. R., Seidov, D., Yarosh, E. S.,
and Zweng, M. M.: World ocean heat content and thermosteric sea level change
(0–2000 m), 1955–2010, Geophys. Res. Lett., 39, L10603, 10.1029/2012GL051106, 2012.
Loeb, N. G., Lyman, J. M., Johnson, G. C., Allan, R. P., Doelling, D. R.,
Wong, T., Soden, B. J., and Stephens, G. L.: Observed changes in
top-of-the-atmosphere radiation and upper-ocean heating consistent within
uncertainty, Nat. Geosci. 5, 110–113, 2012.
Lyman, J. and Johnson, G.: Estimating global ocean heat content changes in
the upper 1800 m since 1950 and the influence of climatology choice, J. Climate, 27, 1945–1957, 2014.
Lyman, J. M., Good, S. A., Gouretski, V. V., Ishii, M., Johnson, G. C.,
Palmer, M. D., Smith, D. M., and Willis, J. K.: Robust warming of the global
upper ocean, Nature, 465, 334–337, 2010.
Mann, M. E., Cane, M. A., Zebiak, S. E., and Clement, A.: Volcanic and solar
forcing of the tropical pacific over the past 1000 years, J. Climate,
18, 447–456, 2005.Mayer, M. K., Trenberth, K. E., Haimberger, L., and Fasullo, J. T.: The
response of tropical atmospheric energy budgets to ENSO, J. Climate, 26,
4710–4724, 10.1175/JCLI-D-12-00681.1, 2013.
Meehl, G. A., Covey, C., Delworth, T., Mojib, L., McAvaney, B., Mitchell, J.
F. B., Stouffer, R. J., and Taylor, K. E.: The WCRP CMIP3 multimodel dataset:
a new era in climate change research, B. Am. Meteorol. Soc., 88, 1383–1394,
2007.
Myhre, G., Shindell, D., Bréon, F. M., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J. F.,
Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and Natural Radiative Forcing, in: Climate
Change 2013: The Physical Science Basis, Contribution of Working Group I to
the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
edited by: Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 659–740, 2013.
Palmer, M. D. and Haines, K.: Estimating oceanic heat content change using
isotherms, J. Climate, 22, 4953–4969, 2009.
Palmer, M., Antonov, J., Barker, P., Bindoff, N., Boyer, T., Carson, M.,
Domingues, C. M., Gille, S., Gleckler, P., Good, S., Gouretski, V., Guinehut,
S., Haines, K., Harrison, D. E., Ishii, M., Johnson, G. C., Levitus, S.,
Lozier, M. S., Lyman, J. M., Meijers, A., von Schuckmann, K., Smith, D.,
Wijffels, S., and Gouretski, V.: Future observations for monitoring
globalocean heat content, in Proceedings of the OceanObs' 09: Sustained Ocean
Observations and Information for Society Conference, September 2009, edited
by: Hall, J., Harrison, D. E., and Stammer, D., 2, 21–25, ESA Publication
WPP-306, Venice, Italy, 2010.Palmer, M. D. and McNeall, D. J.: Internal variability of Earth's energy
budget simulated by CMIP5 climate models, Environ. Res. Lett., 9,
034016,
10.1088/1748-9326/9/3/034016, 2014.Palmer, M. D., Good, S. A., Haines, K., Rayner, N. A., and Stott, P. A.: A
new perspective on warming of the global oceans, Geophys. Res. Lett., 36,
L20709,
10.1029/2009GL039491, 2009.Palmer, M. D., Roberts, C. D., Balmaseda, M., Chang, Y.-S., Chepurin, G.,
Ferry, N., Fujii, Y., Good, S. A., Guinehut, S., Haines, K., Hernandez, F.,
Köhl, A., Lee, T., Martin, M. J., Masina, S., Masuda, S., Peterson, K.
A., Storto, A., Toyoda, T., Valdivieso, M., Vernieres, G., Wang, O., and Xue,
Y.: Ocean heat content variability and change in an ensemble of ocean
reanalyses, Clim. Dynam., 1-22, 10.1007/s00382-015-2801-0, 2015.Purkey, S. and Johnson, G.: Warming of global abyssal and deep southern
ocean waters between the 1990s and 2000s: contributions to global heat and
sea level rise budgets, J. Climate, 23, 6336–6351, 2010.
Rhein, M., Rintoul, S. R., Aoki, S., Campos, E., Chambers, D., Feely, R. A., Gulev, S., Johnson, G. C., Josey, S. A., Kostianoy, A.,
Mauritzen, C., Roemmich, D., Talley, L. D., and Wang F.: Observations: Ocean, in: Climate Change 2013: The Physical
Science Basis, Contribution of Working Group I to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change, edited by:
Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P., Cambridge Univ. Press, Cambridge, UK, and
New York, 261–265, 2013.Roemmich, D. and Gilson, J.: The global ocean imprint of ENSO, Geophys.
Res. Lett., 38, L13606, 10.1029/2011GL047992, 2011.Roemmich, D., Church, J., Gilson, J., Monselesan, D., Sutton, P., and
Wijffels, S.: Unabated planetary warming and its ocean structure since 2006,
Nature Climate Change, 5, 240–245, 10.1038/nclimate2513, 2015.
Sen Gupta, A., Jourdain, N. C., Brown, J. N., and Monselesan, D.: Climate
drift in the CMIP5 Models, J. Climate, 26, 8597–8615, 2013.Smith, D. M. and Murphy, J. M.: An objective ocean temperature and salinity
analysis using covariances from a global climate model, J. Geophys. Res.
Oceans, 11, C02022, 10.1029/2005JC003172, 2007.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of cmip5 and
the experiment design, B. Am. Meteorol. Soc., 93, 485–498, 2012.
Trenberth, K., Fasullo, J., and Balmaseda, M.: Earth's energy imbalance, J. Climate, 27, 3129–3144, 2014.Trenberth, K. E.: Has there been a hiatus?, Science, 349, 691–692,
10.1126/science.aac9225, 2015.
Trenberth, K. E. and Fasullo, J. T.: Tracking earth's energy: from El
Niño to global warming, Surv. Geophys., 33, 413–426, 2012.von Schuckmann, K., Sallée, J.-B., Chambers, D., Le Traon, P.-Y., Cabanes, C., Gaillard, F.,
Speich, S., and Hamon, M.: Consistency of the current global ocean observing systems from an Argo
perspective, Ocean Sci., 10, 547–557, 10.5194/os-10-547-2014, 2014.
von Schuckmann, K., Palmer, M. D., Trenberth, K. E., Cazenave, A., Chambers,
D., Champollion, N., Hansen, J., Josey, S. A., Loeb, N., Mathieu, P.-P.,
Meyssignac, B., and Wild, M.: An imperative to monitor Earth's energy imbalance,
Nature Climate Change, 6, 138–144, 2016.
Xue, Y., Balmaseda, M. A., Boyer, T., Ferry, N., Good, S., Ishikawa, I.,
Kumar, A., Rienecker, M., Rosati, A., and Yin, Y.: A comparative analysis of
upper ocean heat content variability from an ensemble of operational ocean
reanalyses, J. Climate, 25, 6905–6929, 2012.