Climate Dynamics

, Volume 49, Issue 3, pp 909–930 | Cite as

Ocean heat content variability and change in an ensemble of ocean reanalyses

  • M. D. PalmerEmail author
  • C. D. Roberts
  • M. Balmaseda
  • Y.-S. Chang
  • G. Chepurin
  • N. Ferry
  • Y. Fujii
  • S. A. Good
  • S. Guinehut
  • K. Haines
  • F. Hernandez
  • A. Köhl
  • T. Lee
  • M. J. Martin
  • S. Masina
  • S. Masuda
  • K. A. Peterson
  • A. Storto
  • T. Toyoda
  • M. Valdivieso
  • G. Vernieres
  • O. Wang
  • Y. Xue
Open Access


Accurate knowledge of the location and magnitude of ocean heat content (OHC) variability and change is essential for understanding the processes that govern decadal variations in surface temperature, quantifying changes in the planetary energy budget, and developing constraints on the transient climate response to external forcings. We present an overview of the temporal and spatial characteristics of OHC variability and change as represented by an ensemble of dynamical and statistical ocean reanalyses (ORAs). Spatial maps of the 0–300 m layer show large regions of the Pacific and Indian Oceans where the interannual variability of the ensemble mean exceeds ensemble spread, indicating that OHC variations are well-constrained by the available observations over the period 1993–2009. At deeper levels, the ORAs are less well-constrained by observations with the largest differences across the ensemble mostly associated with areas of high eddy kinetic energy, such as the Southern Ocean and boundary current regions. Spatial patterns of OHC change for the period 1997–2009 show good agreement in the upper 300 m and are characterized by a strong dipole pattern in the Pacific Ocean. There is less agreement in the patterns of change at deeper levels, potentially linked to differences in the representation of ocean dynamics, such as water mass formation processes. However, the Atlantic and Southern Oceans are regions in which many ORAs show widespread warming below 700 m over the period 1997–2009. Annual time series of global and hemispheric OHC change for 0–700 m show the largest spread for the data sparse Southern Hemisphere and a number of ORAs seem to be subject to large initialization ‘shock’ over the first few years. In agreement with previous studies, a number of ORAs exhibit enhanced ocean heat uptake below 300 and 700 m during the mid-1990s or early 2000s. The ORA ensemble mean (±1 standard deviation) of rolling 5-year trends in full-depth OHC shows a relatively steady heat uptake of approximately 0.9 ± 0.8 W m−2 (expressed relative to Earth’s surface area) between 1995 and 2002, which reduces to about 0.2 ± 0.6 W m−2 between 2004 and 2006, in qualitative agreement with recent analysis of Earth’s energy imbalance. There is a marked reduction in the ensemble spread of OHC trends below 300 m as the Argo profiling float observations become available in the early 2000s. In general, we suggest that ORAs should be treated with caution when employed to understand past ocean warming trends—especially when considering the deeper ocean where there is little in the way of observational constraints. The current work emphasizes the need to better observe the deep ocean, both for providing observational constraints for future ocean state estimation efforts and also to develop improved models and data assimilation methods.


Heat content Temperature Variability Climate change Global warming Energy budget Ocean reanalyses Ocean state estimation Ocean models Inter comparison Ocean Observations Data assimilation  

1 Introduction

Ocean reanalyses (ORAs) represent an important tool for understanding ocean variability and climate change (Lee et al. 2009) and underpin a number of forecast activities, such as operational oceanography and seasonal-to-decadal prediction (Rienecker et al. 2010). ORAs employ a variety of ocean general circulation models (OGCMs) and data assimilation schemes to synthesize a diverse network of available ocean observations in order to arrive at a dynamically consistent estimate of the historical ocean state. The nature of the underlying OGCM, data assimilation scheme and observations used varies, often according to the role for which the ORA is intended. For example, systems designed for near real-time forecasts of the ocean mesoscale tend to use higher resolution OGCMs and satellite altimeter data will feature strongly in the assimilation system. Conversely, systems that are used primarily for estimating the ocean state over the 20th Century will tend to use coarser resolution OGCMs and/or simpler assimilation schemes for reasons of computational expense. In addition, a number of products combine the available observations into spatially-complete gridded fields using purely statistical approaches, without the use of an OGCM.

Intercomparison of ORAs promotes insights into the performance of data assimilation systems, the underlying physical models, and adequacy of the ocean observing system to constrain key variables of interest, such as ocean heat content change (e.g. Carton and Santorelli 2008; Xue et al. 2012). Here we present a comparison of a number of state-of-the-art products as part of the Ocean Reanalysis Intercomparison Project (ORA-IP; Balmaseda et al. 2015), which is an international research collaboration led by the CLIVAR Global Synthesis and Observations Panel (GSOP) and the Global Ocean Data Assimilation Experiment (GODAE) community. The scope of ORA-IP includes various aspects of the ocean state, including the following: sea level; mixed layer depth; ocean salinity; surface fluxes and mass; heat and freshwater transports. The present work focuses on the representation of OHC variability and change as represented by an ensemble of ORAs (Table 1).
Table 1

Overview of the different ORAs used in this study




Control Method





1°/3° no model

Optimal interpolation (T/S/SST)

Guinehut et al. (2012), Mulet et al. (2012)




1°/2° MOM4 coupled


Saha et al. (2010), Xue et al. (2011)




1°/2° NEMO3.2


Storto et al. (2011)




1° MITgcm


Fukumori (2002)




1°/3° MOM4 coupled


Zhang et al. (2007), Chang et al. (2013)

6. EN3*


UK Met Office

1° no model

Optimal interpolation (T/S)

Ingleby and Huddleston (2007)



University of Hamburg

1 × 1°/3° MITgcm


Köhl (2015)



Mercator Océan

1°/4° NEMO3.1


Ferry et al. (2012)

9. GloSea5


UK Met Office

1°/4° NEMO3.2


Blockley et al. (2014)

10. GMAO



1°/2° MOM4p1


Rienecker et al. (2008)




1 × 1°/3° MOM3


Behringer (2007)

12. K7


1° MOM3


Masuda et al. (2010)

13. MOVE-C



0.3°–1° MRI.COM2 coupled


Fujii et al. (2009)




0.3°–1° MRI.COM3


Fujii et al. (2015)

15. MOVE-G2



0.3°–1° MRI.COM3


Toyoda et al. (2013)

16. NODC*



1° no model

Objective analysis (T/S)

Levitus et al. (2012)

17. ORAS4



1° NEMO3


Balmaseda et al. (2013a), Mogensen et al. (2012)

18. SODA


University of Maryland/Texas A&M University

0.4 × 1°/4° POP2.1


Carton and Giese (2008)

19. UR025.4


University of Reading

1°/4° NEMO3.2


Haines et al. (2012)

* An analysis product based on statistical mapping of observations that does not include a dynamical ocean model component

OHC is a key variable for initialization of seasonal-to-decadal predictions (Balmaseda et al. 2010; Dunstone and Smith 2010) and the rate of ocean heat uptake under anthropogenic climate change plays an important part in determining future global surface temperature and sea level rise (Kuhlbrodt and Gregory 2012). Improved understanding the processes that control OHC variability and change may offer the potential to reduce uncertainties in future climate change projections through application of suitable observational constraints (e.g. Stott and Forest 2007). Thus ORAs have an important role to play in development of improved forecasts on a range of timescales and in refining our understanding of future global and regional climate change.

OHC variability and change is a particularly topical research area, given the strong scientific and wider media interest in the recent slowdown in surface temperature rise (e.g. Hawkins et al. 2014), often referred to as the global warming ‘hiatus’ (e.g. Trenberth and Fasullo 2013). Increased ocean heat uptake and the vertical re-arrangement of heat in the ocean have both been proposed as key mechanisms that have contributed to the ‘hiatus’. Heat re-arrangement has been linked primarily with the tropical Pacific (Meehl et al. 2011; England et al. 2014) but there is evidence that the higher latitudes may also have played a role (Chen and Tung 2014; Drijfhout et al. 2014; Roemmich et al. 2015).

ORAs provide an important resource for improving our understanding of the ocean’s role in modulating global surface temperature rise on interannual to decadal timescales. Analysis of climate model simulations has shown substantial vertical re-arrangement of ocean heat and highlighted the global ocean’s dominant role in Earth’s energy budget on annual-to-decadal timescales (Palmer et al. 2011; Palmer and McNeall 2014). Through combining the available ocean observations with OGCMs, ORAs may offer new insights into the processes of vertical heat re-arrangement and have also be used to derive estimates of Earth’s energy imbalance (Loeb et al. 2012; Trenberth et al. 2014; Smith et al. 2015).

Despite the recent development of the Argo network of profiling floats (Roemmich et al. 2009), historical observations of ocean temperature are sparse in time and space (Purkey and Johnson 2010; Desbruyères et al. 2014), often limited to a particular depth range, and may require correction for instrumental biases (Abraham et al. 2013). These issues mean that there are substantial—and difficult to quantify—uncertainties in our knowledge of ocean heat content change during the late twentieth and early twenty first centuries. One way to evaluate this uncertainty is using the spread of different reanalysis products that use ostensibly the same raw information, but different methodologies, to evaluate the same variable of interest. Previous studies have used similar ‘ensembles of opportunity’ to characterize the uncertainty in upper ocean heat content derived from statistical analyses (Lyman et al. 2010; Palmer et al. 2010; Abraham et al. 2013).

Estimates of the past ocean state are fundamentally limited by the availability of historical ocean profiles. Prior to the inception of the Argo array profiling floats in the early 2000s, reasonable ocean coverage is only afforded for the upper few hundred meters since the late 1960s (Roemmich et al. 2012; Lyman and Johnson 2014). As a result, many of the historical estimates of OHC variability and change have been limited to the upper 700 m or so (Lyman et al. 2010; Palmer et al. 2010; Abraham et al. 2013). In addition, the upper layers of the ocean are widely regarded to be the primary source of predictability for seasonal forecast systems, for example for initializing the tropical Pacific for ENSO forecasts (Xue et al. 2012). For these reasons, much of our analysis focuses ocean depth ≤700 m.

We present spatial patterns of depth-integrated temperature for the following: (i) the climatological time mean; (ii) the amplitude of the seasonal cycle; (iii) internannual variability; (iv) decadal and multi-decadal trends. We also compare zonal averages of (i) and (ii). The manuscript is organized as follows. In Sect. 2 we present an overview of the ORA datasets used. The pre-processing steps and analysis methods are presented in Sect. 3. The results are presented in Sects. 47 and cover the following aspects: the time-mean OHC and seasonal cycle; interannual variability; time series of OHC; and spatial trends of OHC. In Sect. 8 we present our closing discussion and summary.

2 Data

A total of 19 ORAs are used in the intercomparison presented here. These span a range of time periods and have a diverse set of data assimilation methods and underlying ocean model configurations (Table 1). While the majority of products include a dynamic OGCM, three of the products are based on statistical analysis of the observations (ARMOR3D, EN3 and NODC) and do not include a dynamic model component. Note that the ARMOR3D product only covers the upper 2000 m. For the purposes of this intercomparison the deeper levels use the NODC climatology and therefore the variability and change signals below 2000 m for ARMOR3D are very small, by construction. We use a version of the EN3 analysis that is based on profiles with expendable bathythermograph (XBT) corrections applied following table 1 of Wijffels et al. (2008). The NODC data have mechanical baythermograph (MBT) and XBT bias corrections applied as described in Levitus et al. (2012). All data providers carried out the computation of vertically-integrated temperature for a number of vertical layers (see Sect. 3) and interpolated the data onto a regular 1 × 1 degree latitude-longitude grid. To ensure consistency among the grids used, all data were subsequently post-processed using the Climate Data Operator bilinear re-mapping tool.

3 Methods

For the purposes of this intercomparison, the different production centers provided monthly-mean two-dimensional fields of depth-integrated potential temperature, 〈θ〉, from the surface to a number of fixed depths, defined as:
$$\left\langle\uptheta \right\rangle = \int {\uptheta\,({\text{x}},{\text{y}},{\text{z}})} \,{\text{dz}}$$
with units Celsius meters (Cm). Throughout much of the manuscript we use depth integrated temperature as basis of the intercomparison. An advantage of 〈θ〉 is that it has an intuitive physical interpretation. For example, a change in 〈θ〉 of 70 Cm over the 0–700 m layer is equivalent to a 0.1 C average change over the column. Annual time series of OHC are generated for each hemisphere and the globe by spatially integrating maps of 〈θ〉 and multiplying by reference values of seawater density (1025 kg m−3) and specific heat capacity (3985 J kg−1 K−1). These time series are converted to OHC anomalies by subtracting the mean value over a reference period. The integration depths specified for the intercomparison were: 0–100 m; 0–300 m; 0–700 m; 0–1500 m; 0–3000 m; 0–4000 m; and the full water column (0–bottom). For some products the closest model level was used for the integration, provided that this was within a few meters of the specified depth. The data are analyzed as a ‘true’ integral, meaning that the summation is inclusive of all ocean model grid boxes shallower than the lower bound of the integration. Thus the number of ocean grid points for each product is the same for all layer integrals and, for example, the 300–700 m layer can be reconstructed by simply subtracting the 0–300 m layer from the 0–700 m layer.

Monthly climatologies for each product are computed for the period 1993–2009, with the exception of MOVECORE (see Sect. 2), for which we use the period 1993–2007. These climatologies are used in two aspects of the intercomparison. Firstly, the time-average for each grid box is computed to provide a comparison of the mean state of the period 1993–2009. Secondly, the amplitude of the climatological seasonal cycle is computed for each grid box by simply subtracting the minimum monthly value from the maximum monthly value. The remainder of the analysis presented here is carried out using annual mean values of 〈θ〉.

Maps of interannual variability are computed for the period 1993–2009 (except MOVECORE, which uses 1993–2007). The variability for each ORA is computed as the standard deviation of annual values for each grid box after removing a linear trend. Lastly, spatial maps of 〈θ〉 change are computed by fitting a linear trend to each grid box over the periods 1970–2009 and 1997–2009, with the latter period intended to characterize ocean heat content changes during the surface warming ‘hiatus’ period.

In our analysis of interannual variability (calculated for each product following the removal of ORA-specific climatologies), we follow the ‘signal-to-noise’ definition of Xue et al. (2012). Here, the ‘signal’ (S) is defined as the temporal standard deviation of the ensemble mean over a specified period. The ‘noise’ (N) is defined as the temporal average of the standard deviation of ensemble spread over the same period. This definition of S/N is thus a measure of the average spread across the ensemble relative to the signal of the ensemble mean. Areas where S/N < 1 can be interpreted as regions where differences in the underlying ocean models tend to dominate over the ability of the available observations and data assimilation schemes to constrain the ORA solutions. In the other spatial map comparisons presented here we often show the ensemble mean (M), the ensemble standard devidation (SD) and the ratio of the two (M/SD) to provide an indication of the spread and level of agreement among ORAs.

4 Time-mean and amplitude of the seasonal cycle

The first aspect of our intercomparison is the time-mean 〈θ〉 over the 0–700 m layer for the period 1993–2009 (Figs. 1, 2). The largest differences across the ensemble are mostly associated with areas of high eddy kinetic energy (EKE), such as the Southern Ocean and boundary current regions. The representation of high EKE regions depends strongly on the resolution of the underlying OGCM and also the extent to which the ORA solution is constrained by satellite altimeter data, which can impact subsurface temperature and OHC (Lea et al. 2014). Not all ORAs assimilate satellite altimeter data (Table 1) and those that do may differ according to any errors in the mean dynamic topography (MDT) used in the assimilation (Haines et al. 2011). Differences in MDT and both hydrography and altimeter assimilation methods are particularly important in the Southern Ocean, and may explain the large spread and positive/negative dipoles exhibited in that region. Differences in the North Atlantic sector are likely associated with differing representation of the Atlantic meridional overturning circulation and the associated heat transport (Pohlmann et al. 2013; Valdivieso et al. 2015). While model resolution affects the simulation of the AMOC, studies have shown that the mean value can be reasonably well represented in ¼ degree models (Roberts et al. 2013), although the associated heat transports are too weak (Haines et al. 2013).
Fig. 1

Anomaly of climatological depth-integrated temperature for the 0–700 m layer (Celsius meters) for each ORA relative to the ensemble mean. Also shown is the ensemble mean, standard deviation and signal-to-noise ratio. All values are computed for the period 1993–2009, except for MOVECORE (1993–2007)

Fig. 2

Zonally averaged anomaly of 0–700 m depth-integrated temperature, relative to the ensemble mean, for individual ORAs. All curves are shown in each panel and plotted in grey or according to the legend. A common data mask has been applied to all products. All values are computed for the period 1993–2009, except for MOVECORE (1993–2007)

Many of the analyses show a widespread cool or warm bias, relative to the ensemble mean (e.g. SODA, GloSea). Disentangling the roles of differing ocean vertical mixing, ocean circulation and surface forcings in determining these biases is a challenging problem and requires further and detailed analysis. Errors in both momentum and heat fluxes have a role here (Lee et al. 2013; Valdivieso et al. 2015) and consideration of mixed layer depths may also offer additional insights (Toyoda et al. 2015). The representation of vertical mixing in ocean models remains an area that requires particular attention due to the different mixing schemes, related parameters and the often poorly quantified effects of numerical mixing (e.g. Buchard et al. 2008). Regionally, the departures from the ensemble mean can exceed ±500 Cm, or 0.7 C in terms of the depth-averaged temperature (Fig. 1). However, the zonal averages show that all analyses are generally within ±350 Cm (or 0.5 C) of the ensemble mean between 60S and 60N (Fig. 2). The increased spread north of 60N may be indicative of differing representation of the Arctic marginal ice zones and may be exacerbated by the reduction of ocean grid points at these latitudes.

The amplitude of the seasonal cycle of 〈θ〉 over the 0–700 m layer is well constrained by the ORA ensemble over the vast majority of the ocean (Fig. 3). The largest differences among the ORAs are often associated with areas of high EKE, as well as the North-west Pacific and central Indian Ocean. There is no obvious relationship between the individual ORAs representation of the ocean mean-state and the amplitude of the seasonal cycle, as seen by comparing Figs. 1 and 3. The zonal average plots show a similar morphology among the ORAs for the seasonal cycle amplitude (Fig. 4), with a characteristic off-equatorial double-peak and maxima around 40N/S—corresponding to the latitude of maximum wind stress curl and seasonal changes in Ekman divergence. Differences in the locations of the peaks at about 40N/S are therefore likely indicative of different wind-driven circulations among the ORAs. In addition, the MOVE products show another peak around 65–70N, in the region of the northern hemisphere marginal ice zone. Generally, the MOVE products show a larger amplitude seasonal cycle than the other ORAs and particularly pronounced peaks at 40N/S. Differences across the ensemble can arise from the representation of ocean vertical mixing, surface fluxes and also the data assimilation.
Fig. 3

Top row ensemble mean (M); ensemble standard deviation (SD); and the ratio of the two (M/SD) for the amplitude of the seasonal cycle over the 0–700 m layer. Lower panels the difference between each ORA and the ensemble mean. All values are computed for the period 1993–2009, except for MOVECORE (1993–2007)

Fig. 4

Zonally averaged amplitude of the seasonal cycle in the 0–700 m layer for individual ORAs. All curves are shown in each panel and plotted in grey or according to the legend. A common data mask has been applied to all products. All values are computed for the period 1993–2009, except for MOVECORE (1993–2007)

5 Interannual variability

ORA estimates of interannual variability over the 0–300 m layer (Fig. 5) show a S/N ratio >1 over most of the North Atlantic, Pacific and Indian Oceans. The South Atlantic and Southern Ocean typically show S/N values <1, due to weak signal and a combination of weak signal and relatively large ensemble noise, respectively. Again, areas of high EKE emerge as the regions of largest ensemble spread. Interannual variability in the products without an OGCM (i.e. ARMOR3D and EN3) is generally lower than the other ORAs, particularly in the Southern Ocean where observations are sparse and mesoscale variability is high.
Fig. 5

Interannual variability of depth-integrated temperature for the 0–300 m layer (Celsius meters) for each ORA, computed as the temporal standard deviation of annual mean values for each grid box after removing a linear trend. White areas indicate where no data are available. Also shown is the ensemble average temporal standard deviation (‘signal’, S), the temporal average of the ensemble standard deviation (‘noise’, N), and the ratio of the two (S/N). The ensemble values (top row) are computed for each grid box based on all available data. All values computed for 1993–2009 except for MOVECORE (1993–2007)

For the 300–700 m layer, we see a dramatic reduction in S/N ratio for the ensemble (Fig. 6). Areas where S/N > 1 are still found throughout large regions of the Pacific, but limited to isolated areas in the tropical Indian Ocean and the Northeast Atlantic. Compared to the 0–300 m layer, the change in S/N appears to be dominated by a reduction in the signal, with the noise having a similar spatial pattern and magnitude.
Fig. 6

Same as Fig. 5, but for the 300–700 m layer

The comparison of interannual variability for the 700–6000 m layer (Fig. 7) shows that there is very little agreement in the deep ocean such that S/N < 1 in almost all locations. We note that the products without an OGCM (ARMOR3D and EN3) generally have lower variability than the other ORAs. This is not surprising, as for the majority of this depth range, these products are unconstrained by observations and relax to climatology. In contrast, the ORAs that include an OGCM component are free to generate variability that is consistent with the model dynamics, even if it is not constrained by in situ observations. Some models show particularly large variability in the Southern Ocean and North Atlantic (e.g. GECCO, GODAS), but it is not clear whether this can be considered true interannual variability, or whether it is related to drift associated with long-timescale adjustments in the deep ocean. We also note that there appears to be a relationship between the regions of high signal and high noise for this depth range. The patterns show a strong resemblance to the distribution of mode waters (Hanawa and Talley 2001) and also include regions associated with open ocean convection and deep water formation (Marshall and Schott 1999). The implication is that the different ORAs vary in their representation of these water formation processes, owing to different surface forcings and representation of vertical mixing, as discussed in the previous section.
Fig. 7

Same as Fig. 5, but for the 700–6000 m layer

6 Time series of OHC change

Similar to previous studies (Lyman et al. 2010; Palmer et al. 2010; Abraham et al. 2013), ORA time series of OHC change for the 0–700 m vary in a number of aspects, including: interannual variations; the estimated response to the major volcanic eruptions in 1963, 1982 and 1991; decadal and multi-decadal trends (Fig. 8). The range of trends and spread among the analyses for the global ocean is similar to the statistical products presented in the IPCC 5th Assessment report (Rhein et al. 2013). A number of products appear to show large initialization or spin-up ‘shock’—i.e. an initial and rapid change in OHC in the first few years of the time series. Separating the time series by hemisphere illustrates the larger spread in the Southern Hemisphere—consistent with the lack of observations over this domain.
Fig. 8

Time series of ocean heat content change, relative to a 1993–2007 baseline. All curves are shown in each panel and plotted in grey or according to the legend. To aid comparison the NODC product has been highlighted in black in all panels

Many ORAs qualitatively support the findings of Balmaseda et al. (2013b); exhibiting enhanced ocean heat uptake below 300 m from the mid-1990s or early 2000s (Fig. 9a, b). This includes both of the statistical analyses (ARMOR3D and EN3), showing that an OGCM is not required to capture this behavior. Balmaseda et al. (2013b) suggested that the additional heat uptake below 300 m simulated by ORAS4 was associated with changes in the Pacific Decadal Oscillation and also a weakening of the AMOC. However, it remains unclear how much of this enhanced deep ocean heat uptake might be explained as a sampling artifact. Cheng and Zhu (2014) showed that the dramatic improvement in ocean sampling during the initiation of Argo resulted in an artificial jump in OHC over 0–700 m during 2001–2003. Given the lack of historical in situ ocean observations south of about 20S, it seems plausible that much of the increase in deep ocean heat uptake demonstrated by the ORAs during the 2000s may result from Argo suddenly ‘revealing’ a longer-term Southern Ocean warming signal (Roemmich et al. 2015).
Fig. 9

Time series of ocean heat content change for a range of depth intervals, computed to a baseline period of 1948–1993 for illustrative purposes. The dashed grey lines indicate the equivalent heating rate (W m−2) computed for Earth’s surface area

Some of the full depth OHC time series show very large rates of change—exceeding 3 W m−2 globally during some periods—and such changes cannot be reconciled with satellite-based reconstructions of Earth’s energy imbalance (Allan et al. 2014) or the global sea level budget (Church et al. 2011). CFSR (Fig. 9a) shows very some large, almost oscillatory, changes in OHC below 700 m that may be linked to previously documented discontinuities in deep ocean temperature and salinity (Xue et al. 2011). For the MOVE products (Fig. 9b), the large rates of OHC change are a known feature related to the use of vertical empirical orthogonal functions in those products.

Since the rate of OHC change is the quantity of primary interest in the context of the understanding Earth’s energy imbalance, we fit rolling 5-year linear trends to each the ORA time series, following Smith et al. (2015). These time series are used to generate an ORA ensemble mean and standard deviation, for the various depth levels, integrated over the globe and each hemisphere with rates of OHC change expressed in W m−2 relative to the surface area of the Earth (Fig. 10). We omit CFSR and GODAS due to their large initialization ‘shock’ (Fig. 8) and also MOVECORE because the simulation terminates in 2007, resulting in a 15-member ORA ensemble for the period 1993–2009.
Fig. 10

5-year rolling trends of ocean heat content change over various depth layers for: the Globe (black); Northern Hemisphere (red); and Southern Hemisphere (blue). The solid lines show the ensemble mean with shaded regions indicating ±1 standard deviation. Changes are expressed as equivalent heating rate, in W m−2, relative to Earth’s surface area. Trends plotted relative to the mid-point of each 5-year period. Results are based on 15 ORAs as described in the text

As one would expect, the 0–300 m layer shows a relatively narrow spread, particularly for the Northern Hemisphere (Fig. 10). It is interesting to note that the Northern and Southern Hemisphere appear to be anti-correlated in these upper layers and this could be related to changes in the AMOC and associated changes in inter-hemispheric heat transport. The magnitude of mean global OHC change for the 0–300 m layer peaks at about 0.5 W m−2. The 300–700 m layer shows similar rates and sign of OHC change in both hemispheres, but a much larger spread in the Southern Hemisphere. Peak rates of the mean global OHC change are about 0.4 W m−2 for this layer. Below 700 m the mean global rate of OHC change is dominated by the Southern Hemisphere with the peak rate around 1997/1998. OHC integrated over the full column (Fig. 10, 0–6000 m) shows a fairly steady mean global rate of 0.9 ± 0.8 W m−2 for the period 1995–2002 that drops to 0.2 ± 0.6 W m−2 for the period 2004–2006 (error bars indicate 1 standard deviation of ensemble mean), in qualitative agreement with the findings of Smith et al. (2015). However, there is no evidence in our ORA ensemble mean of the ‘spike’ in OHC trends reported by Smith et al. in the early 2000s.

In terms of the ORA ensemble standard deviation, the spread in systematically less in the Northern Hemisphere than the Southern Hemisphere. This is probably a combination of both the better observational coverage and the lesser ocean volume north of the equator. There is a tendency towards reduced spread for all series over time, but this is not monotonic. Although the data here are limited to 2009, we appear to see the impact of Argo observations becoming available in the sharp reduction in ORA ensemble spread for the Southern Hemisphere below 300 m after about 2004 (Fig. 10).

7 Spatial patterns of OHC change

For the ORAs with data available back to 1970 (EN3, GECCO, ORAS4, SODA), we calculate linear trends in OHC for three layers (0–300, 300–700, and 700 m to bottom). For the upper 300 m (Fig. 11), the large scale pattern of warming is well-captured by the available ORAs, even in wide areas of the Southern Ocean, and there is agreement on the enhanced warmth of the North Atlantic sub-polar gyre relative to the global oceans. The equatorial Pacific and Indian oceans are areas where the ORA ensemble standard deviation exceeds the mean trend. This lack of agreement could be related to the large interannual variability in these regions associated with ENSO which will promote larger spread in the long-term trends through ‘end point’ effects. For 300–700 m (Fig. 12), the agreement across ORAs is substantially reduced, although there are some still some areas (e.g. North Atlantic) where the signals appear to be common across models. It is also surprising that some signals in the Southern Ocean appear to be consistent across products. However, the number of ORAs available for this comparison is limited (N = 4) so we should treat such evidence of ‘agreement’ across products with caution. For the 700 m-bottom layer (Fig. 13), the ensemble mean and spread across the ensemble is dominated by the very large signals in the GECCO product: this ORA exhibits a strong warming in the Atlantic and Sourthern Ocean and a cooling in the Indo-Pacific. Given the absence of strong observational constraints for this depth range during this period, it seems likely that these signals are unrepresentative of the real ocean and probably related to adjustment of the OGCM.
Fig. 11

Trends in 0–300 m vertically-integrated temperature (Celsius meters per year) for the period 1970–2009. White areas indicate where no data are available. Also shown is the ensemble mean trend (M), the standard deviation of ensemble trends (SD), and the ratio of the two (M/SD). The ensemble values (top row) are computed for each grid box based on all available data

Fig. 12

Same as Fig. 11, but for the 300–700 m layer

Fig. 13

Same as Fig. 11, but for the 700–6000 m layer

To evaluate the level of agreement in the spatial patterns of heat uptake and heat rearrangement by the ocean during the recent slow-down in global warming, we calculate spatial trends in OHC for the period 1997–2009. For the upper 300 m (Fig. 14), ORAs show agreement in a dipole pattern of warming in the West Pacific and cooling in the East Pacific. This is consistent with previous studies documenting increased sea surface heights and ocean temperatures in the West Pacific warm pool associated with the negative phase of the Pacific Decadal Oscillation (PDO) and a sustained increase in the Pacific trade winds (e.g. England et al. 2014). Although there is some agreement for a pattern of warming in the tropical and north Atlantic, there are large areas where the ORA ensemble spread exceeds the ensemble mean, which is perhaps surprising given that it is one of the best-observed regions of the global oceans. This may be indicative of the influence of dynamic processes, which are likely to vary among ORAs, as discussed in previous sections. Noise across the ensemble is largest in areas of ocean fronts, and there is very little agreement in the sign or magnitude trends in the Southern Ocean, likely reflecting the relative lack of observations in this region.
Fig. 14

Trends in 0–300 m vertically-integrated temperature (Celsius meters per year) for the period 1997–2009. White areas indicate where no data are available. Also shown is the ensemble mean trend (M), the standard deviation of ensemble trends (SD), and the ratio of the two (M/SD). The ensemble values (top row) are computed for each grid box based on all available data

For 300–700 m depth range (Fig. 15), agreement across the ensemble is limited to selected regions of sub-tropical convergence in the West Pacific and the North Atlantic sub-polar gyre. Ensemble spread is largest in the Southern Ocean, with products showing no agreement on whether there has been warming or cooling in this layer during this period.
Fig. 15

Same as Fig. 14, but for the 300–700 m layer

For the deepest layer (700 m to bottom, Fig. 16), there is virtually no agreement across ORAs for OHC trends during 1997–2009. Although the ensemble mean shows a particularly strong warming in the North Atlantic and more widespread warming in the Southern Ocean, there is no consensus on the magnitude of change. Perhaps notably, both ARMOR3D and EN3 (products that do not include a dynamical ocean model) exhibit warming signals in these regions. Warming of the North Atlantic sub-polar gyre is qualitatively consistent with previous studies that have linked such changes with a reduction in densities and a decline of the AMOC (e.g. Robson et al. 2014; Roberts et al. 2014).
Fig. 16

Same as Fig. 14, but for the 700–6000 m layer

8 Summary

We have presented a comparison of the representation of OHC variability and change as estimated by 16 state-of-the-art ORAs, focusing on five main aspects: (i) the time-mean state during 1993–2009; (ii) the amplitude of the seasonal cycle during 1993–2009; (iii) interannual variability during 1993–2009; (iv) global and hemispheric time series; (v) spatial patterns of change for 1970–2009 and 1997–2009.

The time-mean state and amplitude of the seasonal cycle for the 0–700 layer are generally well-constrained among the ensemble, but regional differences may warrant further investigation. We find that interannual variability for the 0–300 m can be resolved by the ORA ensemble over the majority of the Indo-Pacific and much of the Atlantic. However, the signal-to-noise ratio rapidly diminishes as one considers deeper layers.

Global time series of OHC show similar rates of change and ensemble spread to previous studies based only on statistical estimates. Comparison of the hemispheric OHC time series illustrates that the majority of the spread among analyses originates in the Southern Hemisphere. Several ORAs, including those that do not include an OGCM are in qualitative agreement about an increase in ocean heat uptake below 300 m from the mid 1990s or early 2000s. However, a number appear to suffer from large initialization shock and/or drifts in the deep ocean (where this is little observational constraint on the OGCM solution) so caution should be taken when using such products to estimate the global ocean heat inventory.

Spatial patterns of OHC change for the 0–300 m layer over the period 1970–2009 show large areas of agreement (ensemble mean trend > standard deviation of trends), based on the four analyses included here. While this area is reduced for the 300–700 m layer there remains some agreement on large-scale regions of warming in the Atlantic, Pacific and Southern Oceans. For the 700–6000 m layer only isolated regions of the Southern Ocean and central Atlantic show trends that are resolved by the ensemble spread.

The spatial trends of OHC for the 1997–2009 period generally show large areas of agreement for the 0–300 m, with the Pacific characterized by an east–west dipole. At deeper levels there is little or no agreement among the ensemble. However, the ensemble mean trends for the 700–6000 m layer show the largest warming trends in the North Atlantic sub-polar gyre, north Indian Ocean and Southern Ocean.



We would like to thank two anonymous reviewers for providing helpful comments on an earlier version of this manuscript. This work is a result of collaboration among the CLIVAR Global Synthesis and Observations Panel and the GODAE Ocean View communities. This work was partly funded by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101) and represents a Met Office contribution to the Natural Environment Research Council DEEP-C project NE/K005480/1.

Compliance with ethical standards

Conflict of interest

The authors declare no conflicts of interest. All authors have given consent to the submission.


  1. Abraham JP et al (2013) Monitoring systems of global ocean heat content and the implications for climate change, a review. Rev Geophys. doi: 10.1002/rog.20022 Google Scholar
  2. Allan RP et al (2014) Changes in global net radiative imbalance 1985–2012. Geophys Res Lett 41:5588–5597. doi: 10.1002/2014GL060962 CrossRefGoogle Scholar
  3. Balmaseda M et al (2010) Initialization for seasonal and decadal forecasts. In: Hall J, Harrison DE Stammer D (eds) Proceedings of OceanObs’09: sustained ocean observations and information for society, vol 2, Venice, Italy, 21–25 September 2009, ESA Publication WPP-306. doi: 10.5270/OceanObs09.cwp.02
  4. Balmaseda MA, Mogensen K, Weaver AT (2013a) Evaluation of the ECMWF ocean reanalysis system ORAS4. Q J R Meteorol Soc 139:1132–1161CrossRefGoogle Scholar
  5. Balmaseda MA, Trenberth KE, Källén E (2013b) Distinctive climate signals in reanalysis of global ocean heat content. Geophys Res Lett 40:1754–1759. doi: 10.1002/grl.50382 CrossRefGoogle Scholar
  6. Balmaseda MA et al (2015) The Ocean Reanalyses Intercomparison Project (ORA-IP). J Oper Oceanogr 7:81–99Google Scholar
  7. Behringer DW (2007) The global ocean data assimilation system at NCEP. Preprints, 11th Symposium on integrated observing and assimilation systems for atmosphere, oceans, and land surface, San Antonio, TX, Amer. Meteor. Soc., 3.3.
  8. Blockley EW et al (2014) Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new Global FOAM forecasts. Geosci Model Dev 7:2613–2638. doi: 10.5194/gmd-7-2613-2014 CrossRefGoogle Scholar
  9. Buchard H et al (2008) Observational and numerical modeling methods for quantifying coastal ocean turbulence and mixing. Prog Oceanogr 76(4):399–442CrossRefGoogle Scholar
  10. Carton JA, Giese BS (2008) A reanalysis of ocean climate using simple ocean data assimilation (SODA). Mon Weather Rev 136:2999–3017. doi: 10.1175/2007MWR1978.1 CrossRefGoogle Scholar
  11. Carton JA, Santorelli A (2008) Global decadal upper-ocean heat content as viewed in nine analyses. J Clim 21:6015–6035CrossRefGoogle Scholar
  12. Chang Y-S, Zhang S, Rosati A, Delworth TL, Stern WF (2013) An assessment of oceanic variability for 1960–2010 from the GFDL ensemble coupled data assimilation. Clim Dyn 40(3–4):775–803. doi: 10.1007/s00382-012-1412-2 CrossRefGoogle Scholar
  13. Chen X, Tung K-K (2014) Varying planetary heat sink led to global-warming slowdown and acceleration. Science 345:897–903CrossRefGoogle Scholar
  14. Cheng L, Zhu J (2014) Artifacts in variations of ocean heat content induced by the observation system changes. Geophys Res Lett 41:7276–7283. doi: 10.1002/2014GL061881 CrossRefGoogle Scholar
  15. Church JA et al (2011) Revisiting the Earth’s sea-level and energy budgets from 1961 to 2008. Geophys Res Lett 38:L18601. doi: 10.1029/2011GL048794 CrossRefGoogle Scholar
  16. Desbruyères DG et al (2014) Full-depth temperature trends in the northeastern Atlantic through the early 21st century. Geophys Res Lett 41:7971–7979. doi: 10.1002/2014GL061844 CrossRefGoogle Scholar
  17. Drijfhout SS, Blaker AT, Josey SA, Nurser AJG, Sinha B, Balmaseda MA (2014) Surface warming hiatus caused by increased heat uptake across multiple ocean basins. Geophys Res Lett 41:7868–7874. doi: 10.1002/2014GL061456 CrossRefGoogle Scholar
  18. Dunstone NJ, Smith DM (2010) Impact of atmosphere and sub-surface ocean data on decadal climate prediction. Geophys Res Lett 37:L02709. doi: 10.1029/2009GL041609 CrossRefGoogle Scholar
  19. England MH et al (2014) Recent intensification of wind-driven circulation in the pacific and the ongoing warming hiatus. Nat Clim Change 4:222–227CrossRefGoogle Scholar
  20. Ferry N et al (2012) GLORYS2V1 global ocean reanalysis of the altimetric era (1993–2009) at meso scale. Mercat Ocean Q Newsl 44:28–39.
  21. Fujii Y et al (2009) Coupled climate simulation by constraining ocean fields in a coupled model with ocean data. J Clim 22:5541–5557CrossRefGoogle Scholar
  22. Fujii Y, Tsujino H, Toyoda T, Nakano H (2015) Enhancement of the southward return flow of the Atlantic Meridional Overturning Circulation by data assimilation and its influence in an assimilative ocean simulation forced by CORE-II atmospheric forcing. Clim Dyn. doi: 10.1007/s00382-015-2780-1 Google Scholar
  23. Fukumori I (2002) A partitioned Kalman filter and smoother. Mon Weather Rev 130:1370–1383CrossRefGoogle Scholar
  24. Guinehut S, Dhomps A-L, Larnicol G, Le Traon P-Y (2012) High resolution 3D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci 8:845–857. doi: 10.5194/os-8-845-2012 CrossRefGoogle Scholar
  25. Haines K et al (2011) An ocean modelling and assimilation guide to using GOCE geoid products. Ocean Sci 7:151–164. doi: 10.5194/os-7-151-2011 CrossRefGoogle Scholar
  26. Haines K, Valdivieso M, Zuo H, Stepanov VN (2012) Transports and budgets in a 1/4° global ocean reanalysis 1989–2010. Ocean Sci 8:333–344. doi: 10.5194/os-8-333-2012.002/qj.2063 CrossRefGoogle Scholar
  27. Haines K, Stepanov VN, Valdivieso M, Zuo H (2013) Atlantic meridional heat transports in two ocean reanalyses evaluated against the RAPID array. Geophys Res Lett 40:343–348. doi: 10.1029/2012GL054581 CrossRefGoogle Scholar
  28. Hanawa K, Talley LD (2001) Mode waters. In: Siedler G, Church J (eds) Ocean circulation and climate. International geophysics series. Academic Press, New York, pp 373–386Google Scholar
  29. Hawkins E, Edwards T, McNeall D (2014) Pause for thought. Nat Clim Change 4:154–156CrossRefGoogle Scholar
  30. Ingleby B, Huddleston M (2007) Quality control of ocean temperature and salinity profiles—historical and real-time data. J Mar Syst 65:158–175. doi: 10.1016/j.jmarsys.2005.11.019   CrossRefGoogle Scholar
  31. Köhl A (2015) Evaluation of the GECCO2 ocean synthesis: transports of volume, heat and freshwater in the Atlantic. Q J R Meteorol Soc 141:166–181. doi: 10.1002/qj.2347 CrossRefGoogle Scholar
  32. Kuhlbrodt T, Gregory JM (2012) Ocean heat uptake and its consequences for the magnitude of sea level rise and climate change. Geophys Res Lett 39:L18608. doi: 10.1029/2012GL052952 CrossRefGoogle Scholar
  33. Lea DJ, Martin MJ, Oke PR (2014) Demonstrating the complementarity of observations in an operational ocean forecasting system. Q J R Meteorol Soc 140:2037–2049. doi: 10.1002/qj.2281 CrossRefGoogle Scholar
  34. Lee T et al (2009) Ocean state estimation for climate research. Oceanography 22:160–167CrossRefGoogle Scholar
  35. Lee T et al (2013) Evaluation of CMIP3 and CMIP5 wind stress climatology using satellite measurements and atmospheric reanalysis products. J Clim 26:5810–5826. doi: 10.1175/JCLI-D-12-00591.1 CrossRefGoogle Scholar
  36. Levitus S et al (2012) World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010. Geophys Res Lett 39:L10603. doi: 10.1029/2012GL051106 CrossRefGoogle Scholar
  37. Loeb NG et al (2012) Observed changes in top-of-the-atmosphere radiation and upper-ocean heating consistent within uncertainty. Nat Geosci 5:110–113. doi: 10.1038/NGEO1375 CrossRefGoogle Scholar
  38. Lyman J, Johnson GC (2014) Estimating global ocean heat content changes in the upper 1800 m since 1950 and the influence of climatology choice. J Clim 27:1946–1958. doi: 10.1175/JCLI-D-12-00752.1 CrossRefGoogle Scholar
  39. Lyman JM et al (2010) Robust warming of the global upper ocean. Nature 465:334–337. doi: 10.1038/nature09043 CrossRefGoogle Scholar
  40. Marshall J, Schott F (1999) Open-ocean convection: observations, theory, and models. Rev Geophys 37(1):1–64CrossRefGoogle Scholar
  41. Masuda S et al (2010) Simulated rapid warming of abyssal North Pacific waters. Science 329:319–322. doi: 10.1126/science.1188703 CrossRefGoogle Scholar
  42. Meehl GA et al (2011) Model-based evidence of deep-ocean heat uptake during surface-temperature hiatus periods. Nat Clim Change 1:360–364CrossRefGoogle Scholar
  43. Mogensen K, Balmaseda MA, Weaver AT (2012) The NEMOVAR ocean data assimilation system as implemented in the ECMWF ocean analysis for system 4. Tech memo 668. ECMWF, Reading, UKGoogle Scholar
  44. Mulet S et al (2012) A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in situ measurements. Deep Sea Res II 77–80:70–81. doi: 10.1016/j.dsr2.2012.04.012 CrossRefGoogle Scholar
  45. Palmer M et al (2010) Future observations for monitoring global ocean heat content. In: Hall J, Harrison DE, Stammer D (eds) Proceedings of OceanObs’09: sustained ocean observations and information for society, vol 2, Venice, Italy, 21–25 September 2009, ESA Publication WPP-306. doi: 10.5270/OceanObs09.cwp.68
  46. Palmer MD, McNeall DJ (2014) Internal variability of Earth’s energy budget simulated by CMIP5 climate models. Environ Res Lett. doi: 10.1088/1748-9326/9/3/034016 Google Scholar
  47. Palmer MD, McNeall DJ, Dunstone NJ (2011) Importance of the deep ocean for estimating decadal changes in Earth’s radiation balance. Geophys Res Lett. doi: 10.1029/2011GL047835 Google Scholar
  48. Pohlmann HD et al (2013) Predictability of the mid-latitude Atlantic meridional overturning circulation in a multi-model system. Clim Dyn 41:775–785. doi: 10.1007/s00382-013-1663-6 CrossRefGoogle Scholar
  49. Purkey SG, Johnson GC (2010) Warming of global abyssal and deep Southern Ocean waters Between the 1990s and 2000s: contributions to global heat and sea level rise budgets. J Clim 23:6336–6351. doi: 10.1175/2010JCLI3682.1 CrossRefGoogle Scholar
  50. Rienecker M et al (2008) The GEOS-5 data assimilation system—documentation of versions 5.0.1, 5.1.0, and 5.2.0. NASA/TM-2008-104606, vol 27Google Scholar
  51. Rienecker M et al (2010) Synthesis and assimilation systems—essential adjuncts to the global ocean observing system. In: Hall J, Harrison DE, Stammer D (eds) Proceedings of OceanObs’09: sustained ocean observations and information for society, vol 1, Venice, Italy, 21–25 September 2009, ESA Publication WPP-306. doi: 10.5270/OceanObs09.pp.31
  52. Rhein M et al (2013) Observations: ocean. In: Climate change 2013: the physical science basis. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
  53. Roberts CD et al (2013) Atmosphere drives recent interannual variability of the Atlantic meridional overturning circulation at 26.5°N. Geophys Res Lett 40:5164–5170. doi: 10.1002/grl.50930 CrossRefGoogle Scholar
  54. Roberts CD, Jackson LJ, McNeall D (2014) Is the 2004-2012 reduction of the Atlantic meridional overturning circulation significant? Geophys Res Lett 41:3204–3210. doi: 10.1002/2014GL059473 CrossRefGoogle Scholar
  55. Robson J, Hodson D, Hawkins E, Sutton R (2014) Atlantic overturning in decline? Nat Geosci. doi: 10.1038/ngeo2050 Google Scholar
  56. Roemmich D et al (2009) The Argo Program: observing the global ocean with profiling floats. Oceanography 22:34–43. doi: 10.5670/oceanog.2009.36 CrossRefGoogle Scholar
  57. Roemmich D, Gould WJ, Gilson J (2012) 135 years of global ocean warming between the Challenger expedition and the Argo Programme. Nat Clim Change 2:425–428. doi: 10.1038/nclimate1461 CrossRefGoogle Scholar
  58. Roemmich D et al (2015) Unabated planetary warming and its ocean structure since 2006. Nat Clim Change. doi: 10.1038/nclimate2514 Google Scholar
  59. Saha S et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057CrossRefGoogle Scholar
  60. Smith D et al (2015) Earth’s energy imbalance since 1960 in observations and CMIP5 models. Geophys Res Lett. doi: 10.1002/2014GL062669 Google Scholar
  61. Storto A, Dobricic S, Masina S, Di Pietro P (2011) Assimilating along-track altimetric observations through local hydrostatic adjustments in a global ocean reanalysis system. Mon Weather Rev 139:738–754CrossRefGoogle Scholar
  62. Stott PA, Forest CE (2007) Ensemble climate predictions using climate models and observational constraints. Philos Trans R Soc 365:2029–2052CrossRefGoogle Scholar
  63. Toyoda T et al (2013) Improved analysis of the seasonal-interannual fieldsby a global ocean data assimilation system. Theor Appl Mech Japan 61:31–48. doi: 10.11345/nctam.61.31 Google Scholar
  64. Toyoda T et al (2015) Intercomparison and validation of the mixed layer depth fields of global ocean syntheses/reanalyses. Clim Dyn. doi: 10.1007/s00382-015-2637-7 Google Scholar
  65. Trenberth KE, Fasullo JT (2013) An apparent hiatus in global warming? Earth’s Future 1:19–32. doi: 10.1002/2013EF000165 CrossRefGoogle Scholar
  66. Trenberth KE, Fasullo JT, Balmaseda MA (2014) Earth’s energy imbalance. J Clim 27:3129–3144. doi: 10.1175/JCLI-D-13-00294 CrossRefGoogle Scholar
  67. Valdivieso M et al (2015) An assessment of air-sea heat fluxes from ocean and coupled reanalyses. Clim Dyn (submitted)Google Scholar
  68. Wijffels SE et al (2008) Changing expendable bathythermograph fall-rates and their Impact on estimates of thermosteric sea level rise. J Clim 21:5657–5672CrossRefGoogle Scholar
  69. Xue Y et al (2011) An assessment of oceanic variability in the NCEP climate forecast system reanalysis. Clim Dyn 37:2511–2539CrossRefGoogle Scholar
  70. Xue Y et al (2012) A comparative analysis of upper ocean heat content variability from an ensemble of operational ocean reanalyses. J Clim 25:6905–6929CrossRefGoogle Scholar
  71. Zhang S et al (2007) System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon Weather Rev. doi: 10.1175/MWR3466.1 Google Scholar

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© The Author(s) 2015

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • M. D. Palmer
    • 1
    Email author
  • C. D. Roberts
    • 1
  • M. Balmaseda
    • 2
  • Y.-S. Chang
    • 3
  • G. Chepurin
    • 4
  • N. Ferry
    • 5
  • Y. Fujii
    • 6
  • S. A. Good
    • 1
  • S. Guinehut
    • 7
  • K. Haines
    • 8
  • F. Hernandez
    • 5
    • 9
  • A. Köhl
    • 10
  • T. Lee
    • 11
  • M. J. Martin
    • 1
  • S. Masina
    • 12
    • 13
  • S. Masuda
    • 14
  • K. A. Peterson
    • 1
  • A. Storto
    • 12
  • T. Toyoda
    • 6
  • M. Valdivieso
    • 8
  • G. Vernieres
    • 15
  • O. Wang
    • 11
  • Y. Xue
    • 16
  1. 1.Met Office Hadley CentreExeterUK
  2. 2.European Centre for Medium-Range Weather Forecasting (ECMWF)ReadingUK
  3. 3.Geophysical Fluid Dynamics Laboratory (GFDL)Princeton UniversityPrincetonUSA
  4. 4.University of MarylandCollege ParkUSA
  5. 5.Mercator OcéanRamonville Saint AgneFrance
  6. 6.Meteorological Research Institute (MRI)Japan Meteorological AgencyTsukubaJapan
  7. 7.CLS Space Oceanography DivisionRamonville Saint AgneFrance
  8. 8.University of ReadingReadingUK
  9. 9.Institut de Recherche pour le Développement (IRD)ToulouseFrance
  10. 10.University of HamburgHamburgGermany
  11. 11.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA
  12. 12.Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC)BolognaItaly
  13. 13.Istituto Nazionale di Geofisica e Vulcanologia (INGV)BolognaItaly
  14. 14.Research and Development Center for Global ChangeJapan Agency for Marine-Earth Science and TechnologyYokosukaJapan
  15. 15.Global Ocean and Assimilation Office (GMAO)NASAWashingtonUSA
  16. 16.NCEP/NOAA Climate Prediction CenterCollege ParkUSA

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