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A Comparison of the Variability and Changes in Global Ocean Heat Content
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from Multiple Objective Analysis Products During the Argo Period
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Xinfeng Liang1*, Chao Liu1, Rui M. Ponte2, Don P. Chambers3
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1. School of Marine Science and Policy, University of Delaware, Lewes, DE 19958, USA
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2. Atmospheric and Environmental Research, Lexington, MA, 02421, USA
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3. College of Marine Science, University of South Florida, St Petersburg, FL, 33701, USA
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* Corresponding author: Xinfeng Liang (xfliang@udel.edu)
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Manuscript (non-LaTeX) Click here to access/download;Manuscript (non-
LaTeX);Manuscript_Revision_Clean.docx
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Abstract
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Ocean heat content (OHC) is key to estimating the energy imbalance of the earth system. Over
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the past two decades, an increasing number of OHC studies were conducted using oceanic
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objective analysis (OA) products. Here we perform an intercomparison of OHC from eight OA
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products with a focus on their robust features and significant differences over the Argo period
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(2005-2019), when the most reliable global scale oceanic measurements are available. For the
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global ocean, robust warming in the upper 2000 m is confirmed. The 0-300 m layer shows the
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highest warming rate but is heavily modulated by interannual variability, particularly the El
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Niño–Southern Oscillation. The 300-700 m and 700-2000 m layers, on the other hand, show
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unabated warming. Regionally, the Southern Ocean and mid-latitude North Atlantic show a
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substantial OHC increase, and the subpolar North Atlantic displays an OHC decrease. A few
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apparent differences in OHC among the examined OA products were identified. In particular,
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temporal means of a few OA products that incorporated other ocean measurements besides Argo
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show a global-scale cooling difference, which is likely related to the baseline climatology fields
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used to generate those products. Large differences also appear in the interannual variability in the
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Southern Ocean and in the long-term trends in the subpolar North Atlantic. These differences
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remind us of the possibility of product-dependent conclusions on OHC variations. Caution is
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therefore warranted when using merely one OA product to conduct OHC studies, particularly in
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regions and on timescales that display significant differences.
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1 Introduction
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Over the past decades, about 93% of the accumulated energy imbalance of the earth system is
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stored in the ocean (e.g., Roemmich et al. 2015; Riser et al. 2016). Therefore, besides being one
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of the most important indicators of climate change, ocean heat content (OHC) provides essential
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constraints for estimating the global energy imbalance (Hansen et al. 2011; Trenberth et al. 2014;
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von Schuckmann et al. 2016; von Schuckmann et al. 2020). Many efforts have been made to
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unveil and understand the variability of global and regional OHC on various timescales. For the
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global ocean, previous studies show that OHC variations are strongly modulated by both
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anthropogenic forcing and natural climate variability (e.g., Lyman et al. 2010; Balmaseda et al.
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2013; Xie et al. 2015). In particular, the globally integrated OHC in the upper 300 m clearly
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responds to major El Niño–Southern Oscillation (ENSO) events (e.g., Domingues et al. 2008;
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Cheng et al. 2019). Below 300 m, the globally integrated OHC is less affected by temporal
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variability and shows clear trends. Regionally, OHC is heavily affected by lateral and vertical
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redistributions and displays different variability and change (e.g., Trenberth and Fasullo, 2013;
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Chen and Tung, 2014; von Schuckmann et al. 2016; Liang et al. 2017).
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Many previous studies on OHC variations are based on different types of ocean products,
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including objective analyses (OA), reanalyses and state estimates (Balmaseda et al. 2013; Chen
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and Tung 2014; Cheng et al. 2015, Wunsch and Heimbach, 2014). However, substantial
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uncertainties in various data products have been noted (e.g., Palmer et al., 2017; Wang et al.
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2018). A major source of the uncertainties is the temporally and spatially sparse historical
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observations of ocean temperature (Desbruyères et al. 2014). The situation has been dramatically
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improved with the deployment of the global Argo float array since the 2000s. The Argo program,
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which has become a major component of the present global ocean observing system, provided
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most of the present global-scale temperature and salinity measurements in the upper 2000 m
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(Roemmich et al. 2009, 2015, 2019).
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Several groups have used the measurements from the Argo floats to produce gridded OA
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products (e.g., Hosoda et al. 2008; Roemmich and Gilson, 2009; Good et al. 2013; Li et al.
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2017). These products have been widely used by the oceanography and climate communities to
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address various ocean and climate questions (e.g., Cheng et al. 2015; Desbruyères et al. 2017).
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For example, previous OHC studies based on those products have revealed robust and rapid
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warming in the global ocean and provide constraints for estimating the Earth’s energy imbalance
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(e.g., Trenberth et al. 2016). Despite the fact that the Argo program provides the most abundant
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global-scale temperature and salinity measurements over the past 15 years, apparent differences
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among those products that are solely or primarily based on Argo products still exist (e.g.,
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Trenberth et al. 2016; Wang et al. 2017; Liu et al. 2020). These differences were generally
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attributed to the different baseline climatology and mapping methods used to produce those data
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products (Abraham et al. 2013; Cheng and Zhu, 2014, 2015; Boyer et al. 2016). However, as far
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as we are aware, a detailed examination of the differences in OHC among those OA products
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over the Argo period is still lacking.
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In this study, following Liu et al. (2020), a companion paper focusing on the salinity field, we
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examine the OHC variations during the Argo period (2005-2019) from a set of widely used OA
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products. In contrast to previous studies, we explicitly present and examine the differences
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among the selected OAs. The results, particularly the differences among the examined OAs, can
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serve as a useful reference for future OHC studies. The paper is organized as follows: the
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selected OAs and the comparison methods are described in Section 2. A thorough
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intercomparison of the spatial and temporal variations of the global OHC from the selected OA
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products during the Argo period (2005-2019) is presented in Sections 3, 4 and 5. The results are
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summarized and discussed in Section 6.
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2 Data and Methods
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2.1 Gridded Temperature Datasets
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Eight coarse resolution OA products were used in this study, including BOA global ocean Argo
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gridded dataset (BOA; Li et al. 2017), the Institute of Atmospheric Physics ocean gridded
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product (IAP; Cheng and Zhu, 2016), the Met Office EN4.2.1 (EN4; Good et al. 2013), gridded
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product from the International Pacific Research Center (IPRC), objective analyses from
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Meteorological Research Institute (Ishii; Ishii et al. 2017), MOAA-GPV from JAMSTEC
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(MOAA; Hosoda et al. 2008), Roemmich-Gilson Argo Climatology from the Scripps Institution
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of Oceanography (SIO; Roemmich and Gilson, 2009), and gridded product from National
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Centers for Environmental Information (NCEI; Boyer et al. 2005; Levitus et al. 2012). Most of
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the intercomparison conducted in this study is based on these eight coarse resolution OAs.
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Seven of the OAs provide monthly gridded fields; one (the NCEI product) supplies only 3-month
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averages. All have the same horizontal gridding of 1°x1°. Some of the selected products include
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other data sources, such as expendable bathythermograph (XBT) data and CTD data (Table 1).
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Consequently, some of them (i.e., EN4, IAP, Ishii and NCEI) have much longer temporal
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coverages than the purely Argo-based products. Although the depth range varies among the
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selected products, all of them cover the upper 2000 m of the global ocean, where Argo floats are
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the primary data source. Another main difference among the OAs stems from their various
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interpolation techniques (e.g., Stammer et al., 2020). More specifically, EN4, MOAA and SIO
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used objective analysis with different covariance functions and decorrelation radii; BOA further
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applied a refined Barnes successive method to improve its monthly data; Ishii and NCEI used bin
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weighted averages; and IAP used an ensemble optimal interpolation method combined with
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model simulations to provide first-guess climatology. Some detailed information about the
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selected OA products is summarize in Table 1.
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Two relatively high-resolution (¼°) OA climatologies, the World Ocean Atlas 2018 (WOA18)
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and the WOCE Argo-based ocean global climatology (WOCE Argo, Gouretski, 2018), were also
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used in this study. Both WOA18 and WOCE Argo cover pre-Argo period and thus use other data
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sources besides Argo. Because of their differences in temporal (multi-decadal climatology for
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WOA18, monthly climatology for WOCE Argo) and spatial resolution from the other products
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listed above, WOA18 and WOCE Argo were not used in generating the ensembles and
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intercomparison but in discussing the possible impacts of product resolution. Some further
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information about WOA18 and WOCE Argo is also provided in Table 1.
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2.2 Methods
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In this study, OHC within a certain layer was defined as:
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where is the temperature profile of seawater, and and are the density and heat capacity
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computed from the temperature, salinity and pressure based on the Thermodynamic Equation of
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Seawater - 2010 (TEOS-10); and are the lower and upper limits of the corresponding layer.
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Following previous studies (e.g., Liu et al. 2020), the analyzed layers for the intercomparison are
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0-300 m, 300-700 m and 700-2000 m. Since the vertical spacing is different among the examined
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products, our analyses focus more on the depth-averaged fields and their temporal and spatial
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variations.
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Ensemble mean and ensemble spread were used to examine the robustness of features revealed in
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selected OA products. Due to their different temporal intervals, monthly temperature fields from
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the seven monthly OA products were firstly averaged to match the 3-month resolution of NCEI.
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The ensemble values were then generated from the eight coarse-resolution OA products for their
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overlapping spatial coverage (65°N to 60°S) and time span (2005 through 2019). At each grid
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point, the ensemble mean was calculated as the mean of the eight OA products. The ensemble
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spread was defined as the standard deviation of the eight products and served as an indicator of
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the level of “disagreement” of the corresponding values from the selected OA products. For
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some quantities, a ratio of spread-to-mean was also provided to further quantify the
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corresponding uncertainty.
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In this study, we first calculated the temporal mean of OHC to present and examine its mean
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state in each of the three layers over the period 2005-2019. After that, the climatological annual
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cycle of OHC was calculated and removed from the OHC time series at each grid point. Since
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the focuses of this study are the low-frequency components, a yearly running-mean was also
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used to remove the subannual signals, which are likely non-physical in the examined OA
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products (Trenberth et al. 2016). The linear trend of OHC was computed with the least-squares
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regression, and its uncertainty was defined as twice the standard error (95% confidence), in
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which the reduced degrees of freedom were considered for error correction. The interannual
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variability was then defined as the detrended and low-pass filtered OHC time series.
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3 Spatial Patterns
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3.1 Time Mean
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The time mean of the ensemble mean of OHC and the spread of OA products around the
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ensemble mean over the Argo period are shown in Figure 1. Major well-known features appear,
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such as the Pacific Warm Pool in the 0-300 m and 300-700 m layers, and the relatively warm
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patches in the Indian Ocean and the North Atlantic in the 700-2000 m layer. The ensemble
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spread can be interpreted as a quantification of the uncertainty of the ensemble mean fields. The
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calculated ensemble spread (Fig. 1d-f) shows that the largest uncertainties appear along the
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major western boundary currents (e.g., Kuroshio and Gulf Stream) and the Antarctic
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Circumpolar Current (ACC) in all the examined layers. For the 0-300 m layer, large ensemble
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spreads also appear in the tropical regions as zonal bands. The spread-to-mean ratio (Fig. 1g-i)
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further depicts the relative relationship of the two terms described above. For all three layers, the
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overall uncertainty is very small since the spread-to-mean ratio is around or below 10-3 at nearly
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every grid point except a few regions where mesoscale variability is high (106 J m-2 of the spread
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to 109 J m-2 of the mean).
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Although its magnitude is small, it is still useful to examine sources of the ensemble spread,
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particularly in regions where relatively large ensemble spread appears. Differences in temporal
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mean OHC between each examined OA and the ensemble mean are displayed in Figure 2.
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Global-scale differences appear in many of the OA products. In particular, EN4 (Fig. 2g-i) shows
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widespread negative differences from the ensemble mean in all the three layers. Similar patterns
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appear in the 0-300 m layer results from IAP, Ishii and NCEI, which all have longer time spans
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and utilize a lot of historical observations in the pre-Argo period. As previous studies (e.g.,
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Boyer et al. 2016; Wang et al. 2017) suggested, the uncertainty of the time mean OHC estimates
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is likely related to the choice of the “first guess” climatological field as well as the mapping
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methods used to generate the OA products. For the case of EN4, its difference is most likely due
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to its choice of the baseline climatology, which represents the average temperature field over a
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multidecadal period before Argo (Good et al. 2013). Due to the general warming trend over the
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past decades, the mean temperature during the Argo period will be higher than that for any pre-
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Argo period; using the pre-Argo climatology could potentially cause OHC estimates to lower
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values during the Argo period. Compared to the ensemble mean, the EN4 shows a difference of -
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0.5°C in the 0-300 m layer and -0.2°C in the deeper layers. In addition to the global-scale
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differences, other interesting patterns exist. For instance, IPRC displays various zonally banded
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structures (Fig. 2j), which are likely associated with the altimetry SSH data used to adjust regular
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grids; shifts of surface currents between different time periods and consequently the SSH
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patterns would cause such banded structure. Also, consistent with the ensemble spread (Fig. 1d-
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f), many of the examined OA products show noticeable differences associated with major ocean
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currents.
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3.2 Interannual Variability
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The robust and inconsistent features of interannual variability of OHC were investigated by
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examining the distribution of its amplitude, defined as its temporal standard deviation (Figs. 3 &
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4). The strongest interannual variability is associated with the major ocean currents in all the
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examined layers and appears in the tropical ocean in the upper 300 m (Fig. 3a-c). The ensemble
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spreads of the amplitude of the interannual variability in OHC (Fig. 3d-f) display similar patterns
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to the amplitude of interannual variability. More specifically, large spread appears in the
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Kuroshio region, the Gulf Stream, and the ACC in all the examined layers. In the upper 300 m,
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large spread also appears in the tropical regions, such as the Eastern Tropical Pacific and the
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Western Tropical Atlantic Ocean. Most regions mentioned above are usually associated with
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high mesoscale activity (Dong et al. 2014; Thomas et al. 2016), and the way impacts of
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mesoscale eddies are incorporated in different products could be a reason for the large
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differences in those regions. In contrast to the amplitudes and spreads of the interannual
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variability in OHC, the patterns of the spread-to-mean ratio are largely different (Fig. 3g-i).
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While the spread-to-mean ratio of the interannual variability is mostly below 0.5 in each of
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examined layers, some areas with larger values do appear in the Indian sector of the Southern
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Ocean, particularly in the subsurface layers. Based on values of this ratio, the uncertainty of
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interannual variability in OHC is clearly larger than that of the time mean (Fig. 1g-i).
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The plausible sources of the ensemble spread of the amplitude of interannual variability in OHC
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were further investigated. Figure 4 presents the differences of the amplitude of interannual OHC
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variability between each OA product and the ensemble mean. The most notable feature is the
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differences along major ocean currents. In particular, the interannual variability from BOA is
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about 50% stronger than the others in regions like the ACC and western boundary currents (Fig.
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4b&c). EN4, IPRC and SIO also show similar patterns in the subsurface layers, but to a lesser
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extent. IAP, on the other hand, shows clear negative differences along the major ocean currents.
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Since the associated ensemble members in each group include both Argo-only and Argo-
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included products (see Table 1), the source of such differences is likely the interpolation method
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(Boyer et al. 2016) rather than bias from certain measurements. Caution is warranted when using
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OA products to explore the OHC interannual variability in those regions of strong currents,
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particularly for conclusions that depend on the magnitude of interannual variability.
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3.3 Trend
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The 15-year linear trends of the globally and regionally integrated OHC in different layers were
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calculated (Fig.5). Globally, the trend varies from 3.6 (BOA) to 4.1 (EN4) × 1021 J yr-1 in the 0-
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300 m layer, 1.5 (IPRC) to 2.1 (BOA) × 1021 J yr-1 in the 300-700 m layer, 3.1 (IAP) to 4.4
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(EN4) × 1021 J yr-1 in the 700-2000 m (Fig. 5). These estimated trends marginally overlap within
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the uncertainty of 2σ except for EN4 in the 700-2000 m layer. All the OA products show
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significant warming trends in all the three layers (Fig. 5).
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While the globally integrated OHC continually increases during the examined period, substantial
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regional differences were observed (Figs. 5&6). Figure 6 shows spatial patterns of the linear
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trend of OHC over 2005-2019. As numerous prior studies have found (e.g., Gille 2008, Böning
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et al. 2008), substantial warming appears in the Southern Ocean in all layers. For the Pacific
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Ocean, a clear ENSO like structure appears in the 0-300 m and 300-700 m layers, with warming
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in the eastern tropical Pacific and cooling in the western tropical Pacific. However, it should be
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noted that trends in a large portion of the region displaying ENSO like feature are not statistically
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significant. Also, a warming pattern dominates the 700-2000 m layer in the Pacific. For the
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North Atlantic, the OHC trend shows a clear dipole pattern in all three layers, with cooling in the
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subpolar region and warming in the subtropical region. In contrast, the South Atlantic is
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dominated by a warming trend, particularly in the 300-700 m and 700-2000 m layers.
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The ensemble spread of the OHC trends is presented in Fig. 6d-f. Similar to the results of the
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time mean and interannual variability, large spreads of the OHC trend are associated with major
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ocean currents (i.e., Kuroshio, Gulf Stream and the ACC). The differences in OHC trend
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between each OA and their ensemble mean (Fig. 7) confirm again that most of the differences
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occur in the most mesoscale-dynamic regions in the global ocean. But, in contrast to results of
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the time mean and interannual variability, differences of the OHC trends show much clearer
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relatively short-scale features, particularly in the upper 300 m, highlighting the challenges on
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interpretation of regional OHC changes based on one-degree OA products.
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The trends of the horizontally averaged OHC were also examined (Fig. 8a-b). Overall, the global
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ocean shows widespread warming trend over 2005-2019 in the entire upper 2000 m. The largest
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trend is observed within the top 50 m (around 3 × 1019 J yr-1) but with large uncertainty, and then
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rapidly decreases to 0.5-0.7 × 1019 J yr-1 around 150 m. Below 150 m, the warming rate
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decreases slowly with depth in all examined products. The warming trends are overall significant
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except between 150-250 m where the 95% confidence interval cross zero. The trends of the
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zonally averaged OHC shows a number of similar patterns in all examined layers (Fig. 8c-e),
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such as cooling in the northern subpolar region and warming south of 30oS although some are
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not significantly different from zero. Combining with Figure 6, we can see that the subpolar
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cooling in the Northern Hemisphere comes from the Atlantic. For regions between 30oS and
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30oN, the upper 300 m (Fig. 8c) shows substantial differences from the layers below. In
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particularly, a cooling trend appears around 10oN in the 0-300 m layer, which is very likely
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ENSO related, but disappears in the layers below. Again, this indicates the calculation of global
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OHC trend in the upper ocean will be heavily affected by ENSO events that occurred during the
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examined period.
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Figure 8 also shows a few notable differences in the vertical and meridional structures of the
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long-term global OHC trends. Vertically, EN4 consistently shows stronger warming than other
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products; NCEI displays a small and suspicious signal around 800 m; and IPRC and SIO show
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similar and weaker warming between 300 m and about 1000 m. Meridionally, the most evident
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differences appear in the northern subpolar region (Fig. 8c-f). More specifically, IPRC, BOA,
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MOAA and SIO show strong cooling both in the top 300 m and below, but IAP, EN4, Ishii and
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NCEI show much weaker cooling or even warming trend in the northern subpolar region. Since
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the two groups are largely different in their data sources, i.e., with or without other
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measurements besides Argo, the use of other ocean measurements in the high latitude regions
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could be the reason for the substantial differences. Cautions should be taken when describing and
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interpreting OHC changes in regions displaying significant differences, particularly the subpolar
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Atlantic Ocean and subsurface ocean.
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4 Temporal Patterns
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4.1 Time Series
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Time series of the globally integrated OHC (after removing the annual cycles) in each examined
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layer are presented in Figure 9. In general, temporal variations of OHC are consistent among the
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examined OA products. The integrated OHC in different layers show variations on different
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timescales, ranging from interannual variations to decadal trends. Regarding the interannual
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variations, ENSO is the dominant climate mode (Balmaseda et al. 2013; Trenberth et al. 2014).
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The warm and cold ENSO events since 2005 are marked as color bands in Figure 9. The OHC,
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particularly in the upper 300 m layer, generally decreases (increases) during warm (cool) ENSO
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events, likely related to the energy exchange at the sea surface (Trenberth et al. 2002; Mayer et
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al. 2014). On the decadal scale, robust warming occurs in all three layers. Our calculation shows
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that the 0-300 m and the 700-2000 m layer each accounts for about 40% of the OHC changes in
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the entire upper 2000 m, and the 300-700 m layer makes up the other 20%.
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We also calculated the 5-year rolling trends of OHC in each layer following Smith et al. (2015).
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By doing so, we highlight the differences among the OHC variations from the selected OAs (Fig.
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10). All 5-year trends in the 0-300 m layer are positive and below 9 × 1021 J yr-1. For the 300-700
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m and 700-2000 m layers, most of the 5-year trends are positive except a few years at the
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beginning and end of the examined period. The most apparent differences among the examined
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OA products appear in the 0-300 m layer, where much larger trends are estimated from SIO
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between 2013 and 2016, indicating much faster warming in SIO over that period. Further
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examination reveals that these differences (see Fig. 12) are mostly from the top 200 m and are
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likely due to the uncertainties related to the unusual ENSO events in the most recent decade
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(Kim et al. 2011; Hu and Fedorov, 2019). Better agreement is achieved for the 300-700 and 700-
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2000 m layers, in which the strongest five-year warming trend appears following the 2010/2011
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La Niña event.
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4.2 Time Evolution as Functions of Depth and Latitude
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More information on temporal variations of the OHC over the global ocean can be obtained from
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its time evolution as a function of depth (Figs. 11 and 12) and of latitude (Figs. 13 & 14). Figure
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11a presents the time evolution of vertical profiles of the horizontally integrated OHC anomaly
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(relative to the time means). The OHC anomaly in the upper ocean is strongly correlated with the
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Oceanic Niño Index, with a correlation coefficient around 0.7 in the top 100 m and -0.6 at 200 m.
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The opposite signs of the correlation coefficients at different depths indicate that in addition to
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the air-sea heat exchanges modulated by ENSO (e.g., Trenberth et al. 2005; Roemmich and
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Gilson 2011; Cheng et al. 2015, 2019), the vertical redistribution of heat, which is associated
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with the vertical movement of the thermocline, likely contributes to the upper-ocean temperature
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changes as well. The ocean below 300 m shows a broad warming that decreases with depth and
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generally has a much weaker correlation with the Oceanic Niño Index. We also examined the
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evolution of the spread of OHC anomaly (Fig. 11b,d), which is generally small except the
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beginning years of the examined period (spread-to-mean ratio around 0.5) and decreases with
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increasing depth. The large spread at the beginning of the Argo period is likely due to the smaller
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number of Argo floats.
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The differences in temporal variations of the OHC profiles between each OA product and the
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ensemble mean are shown in Figure 12. The difference from the ensemble mean ranges about
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half of the mean OHC in Figure 11, and varies distinctively across the examined products. For
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instance, EN4 displays stronger temporal variations than the ensemble mean for its differences
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present the same pattern to the ensemble mean (cf. Fig. 11a & c and Fig. 12 e & f). On the
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contrary, differences of SIO present an opposite pattern to the ensemble mean and therefore
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likely show overall weaker temporal variability (cf. Fig. 11a & c, and Fig. 12 m & n). The other
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products also present small differences on interannual to decadal timescales. In particular, NCEI
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shows some differences around 800 m, which is warmer before 2010 and cooler after, causing
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the “spike” in Figure 8b. Overall, while the ENSO signal in the upper ocean and the widespread
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warming along the entire water column are robust, the observed differences imply that the
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detailed vertical structures are represented slightly differently by the different OA products,
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which can also be inferred from Figure 8b.
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The time series of the zonally averaged OHC anomaly is shown in Figure 13. For the 0-300 m
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layer, the tropical region between 20°N and 20°S presents high interannual variability associated
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with ENSO, appearing as strong meridional redistribution of OHC. Mid-latitude Southern
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Hemisphere and Northern Hemisphere show clear warming, and the subpolar region of 60°N -
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65°N displays substantial cooling. In the deeper layers, except for a significant cooling trend in
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the northernmost region, the variation of the zonally averaged OHC is dominated by long-term
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warming with different strength. The spread of the zonally averaged OHC (Fig. 13d-f) shows
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that the most substantial difference appears in the high latitude region in the Northern
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Hemisphere. More specifically, as shown in the differences between each OA product and the
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ensemble mean (Fig. 14), the cooling in the northern subpolar region is weaker in at least five of
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the eight examined OAs (i.e., IAP, EN4, Ishii, MOAA and NCEI) than results from the ensemble
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mean; each of these OA products incorporates other ocean measurements besides Argo data. In
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addition, a number of differences on interannual timescales appear in different regions of the
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various OA products, mostly poleward of 30°S and 30°N.
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5 Spatial-Temporal Modes
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To reveal the possible differences in interpreting OHC variations from different OA products, a
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common EOF analysis was applied to each OA product and their ensemble mean to identify and
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compare their major spatial-temporal modes. Figure 15 shows the first two EOF modes in each
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of the examined layers based on the ensemble of the selected OA products. For the upper 300 m,
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the leading mode shows an ENSO-related pattern in the Pacific Ocean and a dipole pattern in the
335
North Atlantic (Fig. 15a). The second mode shows some banded structures in the Pacific and a
336
dipole in the North Atlantic but with different polarities from the first mode (Fig. 15d). For the
337
300-700 m layer, its first EOF mode displays similar but nosier patterns to the first mode of the
338
0-300 m layer, suggesting this layer is affected by similar dynamics as the top layer. For the 700-
339
2000 m layer, the first mode displays uniform change in most regions of the global ocean except
340
the North Atlantic and the Southern Ocean. The second mode also shows various basin-scale
341
changes, but their spatial patterns are nosier than the first mode.
342
The normalized EOF patterns from each of the examined OAs are presented in Figures 16&17.
343
In general, the EOF patterns are more consistent in the top layer than in the deep layers, and the
344
first mode is more consistent than the second and other higher modes. For instance, the first EOF
345
mode (Fig. 16) in the 0-300 m layer from all the products shows almost identical large-scale
346
spatial patterns despite the different percentage of explained variance. Although their agreement
347
is not as striking as in the 0-300 m, reasonable consistency in large-scale structures is also
348
achieved for the first EOF mode of the 300-700 m and 700-2000 m layers. The structures of the
349
second mode show more differences than the first mode does (Fig. 17). In particular, the zonal
350
17
banded structure in the Pacific Ocean and the dipole pattern in the North Atlantic in the 0-300
351
layer are much weaker in EN4 than in other OAs. In addition, many relatively small-scale
352
patterns appear in BOA but not that clear in the other OA products.
353
The PCs of the leading EOF modes (Fig. 18) are usually used to detect major modes of climate
354
variability (e.g., Chen and Tung, 2014). For instance, in the 0-300 m layer, PC1 is correlated
355
with the Oceanic Niño Index at relatively high rates around 0.80. In deeper layers, PCs show
356
long-term change in the first mode, and decadal oscillation in the second mode. Although the
357
EOF patterns in general agree well among the examined OA products, particularly in the 0-300
358
m layer, noticeable differences are found in the PCs. More specifically, although the PC1 values
359
agree well in the 0-300 layer, the PC2 values have considerably larger spread, with the results
360
from EN4 the most noticeable. SIO has a positive signal in PC2 between 2016 and 2017, while
361
the others are either negative or close to zero.
362
In the 300-700 m layer, all PC1 values indicate a trend. However, the trend from BOA is
363
significantly larger (more negative at beginning of record and more positive at the end). PC1
364
from SIO generally agrees with the values from other OA products at the beginning of the record
365
but is substantially higher at the end, although not as much as that from the BOA product. The
366
PC2 from BOA is also a clear in the 300-700m layer. All products have similar PCs for the
367
deepest layer considered. The differences revealed here could potentially lead to different
368
interpretation on the strength of the observed climate variability depending on the product used,
369
especially for the 300-700 m layer.
370
6 Summary and Discussion
371
In this study, we compared the global OHC variations from eight widely used OA products with
372
a focus on their robust features as well as differences over the Argo period, when Argo
373
18
measurements serve as their only or major data source. Our intercomparison confirmed that
374
widespread warming occurred in the upper 2000 m of the global ocean and the largest warming
375
rate appeared in the top 150 m (Figs. 8&9). Robust spatial patterns of the OHC changes in all
376
examined layers were also obtained. In particular, as many prior studies have shown (e.g.,
377
Roemmich et al. 2015; von Schuckmann et al. 2020), the Southern Ocean and mid-latitude North
378
Atlantic have experienced substantial warming, and the subpolar North Atlantic shows an
379
apparent cooling (Figs. 6&8).
380
In comparison to a similar study on ocean salinity (Liu et al. 2020), the overall differences in
381
OHC across the examined OA products are not as marked as in the salinity products. With that
382
said, a few substantial differences were still identified on various timescales and in different
383
regions (e.g., major ocean currents). By examining their spatial patterns, we found that a few OA
384
products that incorporated measurements other than Argo and had longer time span (e.g., EN4)
385
display global-scale differences of negative sign from the others in the temporal mean,
386
particularly in the 0-300 m layer (Fig. 2). As previous studies (e.g., Boyer et al. 2016; Wang et
387
al. 2017) have suggested, these differences are likely related to the choice of the climatological
388
field that were used to generate the OA products. On the interannual timescale, substantial
389
differences appear in regions with strong interannual variability, such as near major ocean
390
currents (Fig. 4). In particular, BOA disagrees significantly from the others in the Southern
391
Ocean and North Atlantic in the 0-300 m layer. On the long-term trend, while the global and
392
regional OHC trends are consistent among the OAs, there are also some noticeable exceptions
393
like the patch in the tropical Atlantic in the 700-2000 m layer from IAP (Fig. 6), the different
394
magnitude of warming between 300 and 2000 m, and the divergent cooling magnitude in the
395
subpolar northern latitudes (Fig. 8).
396
19
By examining the spatial and temporal structure of OHC variations, we were able to provide
397
more information on when and where these differences happened. Vertically, EN4 shows higher-
398
than-average trends at all depth, and NCEI shows a suspicious trend at 800 m depth (Fig. 8). For
399
the other OAs, small differences are also observed on interannual to decadal timescales. Zonally,
400
most examined OAs (i.e., IAP, EN4, Ishii, MOAA and NCEI) show a weaker cooling in the
401
northern subpolar regions than the ensemble (Fig. 14), indicating the OAs solely based on Argo
402
floats, which are not so abundant in the subpolar latitudes, could overestimate the warming in
403
that region. Through a common EOF analysis, we found that the leading modes of the OAs are
404
largely similar on the spatial patterns and PCs. While the 0-300 m layer is clearly under impact
405
of ENSO, the deeper layers show long-term trend in mode 1 and decadal oscillation in mode 2.
406
Nevertheless, noticeable differences were also identified, and these differences are more likely
407
coming from OAs with longer time span, which could make it harder for them to adjust the
408
warming acceleration correctly.
409
It is worth noting that the patterns of ensemble spreads of the time mean, interannual variability
410
and long-term trend are all closed related to the most dynamic regions in the ocean, where
411
mesoscale eddies are abundant (Figs. 1, 3&6). The most substantial differences appearing in
412
those regions suggest that the selected OA products are limited by the relatively sparse data or
413
their low resolutions, which are inadequate to resolve the mesoscale structures (e.g., Seidov et al.
414
2019). As a preliminary test of the possible impacts of higher grid resolution, we compared the
415
temporal mean from the one-degree ensemble mean with two currently available ¼ degree OA
416
products, WOA and WOCE-Argo Climatology, both of which were subsampled at the same one-
417
degree grids as the other OAs (Fig. 19). Both high resolution products show good agreement
418
with the ensemble mean, but noticeable differences still appear along the major ocean current
419
20
systems, particularly in the 300-700 m layer. This test indicates that although increased OA
420
resolutions could help, ultimately denser in-situ observations are needed to reduce the
421
differences in those dynamic regions.
422
The existence of various differences in OHC among the examined OA products during the most
423
data abundant Argo period suggests the possibility of product-dependent conclusions,
424
particularly for studies in regions and on timescales that display substantial differences. Caution
425
is therefore warranted before making any strong conclusions if only one OA data has been
426
examined. Also, similar to our previous finding on the ocean salinity, which is based on some of
427
the OA products used here (Liu et al. 2020), while the number of Argo floats has increased
428
stably since 2005 (Johnson et al. 2015; Wijffels et al. 2016; Riser et al. 2016; Boyer et al. 2018),
429
regional differences (e.g., Figs. 3 and 6) still exist in some of the most dynamic regions (e.g., the
430
Southern Ocean and the western boundary currents). This work highlights the necessity of denser
431
in-situ observations in those dynamic regions as well as OA products of higher-resolution, which
432
are and will be essential for addressing various ocean and climate questions.
433
434
Acknowledgments
435
The authors thank three anonymous reviewers for their helpful comments and suggestions. The
436
work was supported in part by the National Science Foundation through Grant OCE-2021274
437
and the National Aeronautics and Space Administration through Grant 80NSSC20K0752 and
438
80NSSC20K0728. All the data used in this study are publicly available. BOA
439
(ftp://data.argo.org.cn/pub/ARGO/BOA_Argo/); IAP (http://159.226.119.60/cheng/); EN4
440
(https://www.metoffice.gov.uk/hadobs/en4/index.html); IPRC
441
21
(http://apdrc.soest.hawaii.edu/projects/Argo/); Ishii (https://climate.mri-
442
jma.go.jp/pub/ocean/ts/v7.3/); MOAA (http://www.godac.jamstec.go.jp/argogpv/); SIO
443
(http://sio-argo.ucsd.edu/RG_Climatology.html); NCEI
444
(https://www.ncei.noaa.gov/access/global-ocean-heat-content/); WOA18
445
(https://www.ncei.noaa.gov/products/world-ocean-atlas); WOCE-Argo
446
(https://doi.org/10.1594/WDCC/WAGHC_V1.0).
447
448
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28
Table 1 Overview of the OA products used in this study. Data with ¼ degree resolution are in italics; the
586
others are with 1 degree resolution.
587
Product
Spatial
Coverage
Temporal
Coverage
Vertical
Resolution
Data
Source
Interpolation
Method
References
BOA
80S-80N
2004-
present
58 levels to
1975 m
Argo only
Barnes Successive
Method
Li et al. 2017
IAP
90S-90N
1940-
present
41 levels to
2000 m
Argo plus
others
Ensemble Optimum
Interpolation
Cheng and Zhu, 2016
EN4
90S-90N
1900-
present
42 levels to
5350 m
Argo plus
others
Objective Analysis
Good et al. 2013
IPRC
63S-63N
2005-
present
27 levels to
2000 m
Argo plus
Altimetry
Variational
Analysis Technique
http://apdrc.soest.hawa
ii.edu/projects/argo/
Ishii
90S-90N
1955-
present
28 levels to
3000 m
Argo plus
others
Bin-Average
Ishii et al. 2017
MOAA
60S-70N
2001-
present
25 levels to
2000 m
Argo plus
others
Objective Analysis
Hosoda et al. 2008
SIO
64S-80N
2004-
present
58 levels to
2000 m
Argo only
Weighted Least-
Squares Fit
Roemmich and Gilson,
2009
NCEI
90S-90N
1955-
present
26 levels to
2000 m
Argo plus
others
Bin-Average
Levitus et al. 2012
WOA18
90S-90N
2005-
2017
102 levels
to 5500 m
Argo plus
others
Bin-Average
Boyer et al. 2005
WOCE-
Argo
80S-90N
1985-
2016
65 levels to
6650 m
Argo plus
others
Optimal
Interpolation
Gouretski, 2018
588
29
589
Fig. 1 Ensemble mean (ESM), ensemble spread (SPD), and the spread/mean ratio (SPD/ESM) of
590
the time mean of depth-averaged OHC for the period 2005-2019. (Unit: 109 J m-2 for the
591
ensemble mean, 106 J m-2 for ensemble spread)
592
30
593
Fig. 2 Difference of time means of depth-averaged OHC between each product and the ensemble
594
mean. (Unit: 106 J m-2)
595
31
596
Fig. 3 Ensemble mean (ESM), ensemble spread (SPD), and the spread/mean ratio (SPD/ESM) of
597
the amplitude of interannual variability in the depth-averaged OHC for the period 2005-2019.
598
For panels a-f, the unit is 106 J m-2.
599
32
600
Fig. 4 Difference of interannual variability of depth-averaged OHC between each product and
601
the ensemble mean. (Unit: 106 J m-2)
602
603
33
604
Fig. 5 Linear trend of the global and regional integrated OHC. The error-bar shows the
605
uncertainty (±2σ). (Unit: 1021J yr-1)
606
34
607
Fig. 6 OHC trends of the ensemble mean of depth-averaged OHC for the period 2005-2019. The
608
statistically significant trends at 95% confidence level are highlighted by stippling. The
609
corresponding spread is also given. (Unit: 105 J m-2 yr-1)
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35
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Fig. 7 Difference of OHC trends between each product and the ensemble mean. For each
612
product, the statistically significant trends at 95% confidence level are highlighted by stippling.
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(Unit: 105 J m-2 yr-1)
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36
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Fig. 8 (a & b) OHC trends for the global ocean at different depths. (c-f) OHC trends for zonal
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mean anomalies. Grey dashed lines mark the uncertainty of ESM at 95% confidence level. Grey
617
shading is the spread of the trends. (Unit: 1019 J yr-1 for trends at depths, 105 J m-2 yr-1 for zonal
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trends)
619
37
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Fig. 9 Monthly OHC anomaly time series globally and vertically integrated over the selected
621
layers. Grey shading is the ensemble spread. Warm (tan) and cool (blue) ENSO events are
622
marked based on a threshold of ± 0.5°C for the Oceanic Niño Index (ONI). (Unit: 1022 J)
623
38
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Fig. 10 The 5-year rolling trends of globally integrated OHC anomalies over the selected layers
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(plotted at the mid-point of each 5-year period). Grey dashed lines mark the uncertainty of ESM
626
at 95% confidence level. Grey shading is the spread of the trends. Warm (tan) and cold (blue)
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ENSO events are marked based on a threshold of ± 0.5°C for the ONI. (Unit: 1021 J yr-1)
628
39
629
Fig. 11 Ensemble mean and ensemble spread of monthly evolution of the globally integrated
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OHC anomaly (Unit: 1020 J)
631
40
632
Fig. 12 Difference of monthly evolution of OHC anomaly between each product and the
633
ensemble mean. (Unit: 1020 J)
634
41
635
Fig. 13 Ensemble mean and ensemble spread of the zonal mean OHC anomaly. (Unit: 106 J m-2)
636
42
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Fig. 14 Difference of zonal mean OHC anomaly between each product and the ensemble mean.
638
(Unit: 106 J m-2)
639
43
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Fig. 15 Normalized first two EOF modes of depth-averaged OHC from the ensemble mean for
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the examined layers. The percentage of total variance explained by each EOF mode is provided.
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(Unit: m-2).
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44
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Fig.16 Normalized first EOF mode of depth-averaged OHC from the in-situ products for the
645
examined layers. The percentage of total variance explained by each EOF mode is also provided.
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45
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Fig.17 Normalized second EOF mode of depth-averaged OHC from the in-situ products for the
648
examined layers. The percentage of total variance explained by each EOF mode is also provided.
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46
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Fig. 18 Principal Components (PC) corresponding to the first three EOF modes for the examined
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layers from the in-situ products and the ensemble mean. (Unit: 106 J).
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47
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Fig. 19 (a-c) OHC Climatology from ensemble mean of the eight OA products (same as Fig. 1,
654
a-c), and (d-i) difference in OHC climatology between high resolution products (WOA18 and
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WOCE-Argo, both of which were interpolated and subsampled at 1 degree) and the ensemble
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mean. (Unit: 109 J m-2 for the ensemble mean climatology, 106 J m-2 for the difference).
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