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A Comparison of the Variability and Changes in Global Ocean Heat Content from Multiple Objective Analysis Products During the Argo Period

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Abstract

Ocean heat content (OHC) is key to estimating the energy imbalance of the earth system. Over the past two decades, an increasing number of OHC studies were conducted using oceanic objective analysis (OA) products. Here we perform an intercomparison of OHC from eight OA products with a focus on their robust features and significant differences over the Argo period (2005-2019), when the most reliable global scale oceanic measurements are available. For the global ocean, robust warming in the upper 2000 m is confirmed. The 0-300 m layer shows the highest warming rate but is heavily modulated by interannual variability, particularly the El Niño-Southern Oscillation. The 300-700 m and 700-2000 m layers, on the other hand, show unabated warming. Regionally, the Southern Ocean and mid-latitude North Atlantic show a substantial OHC increase, and the subpolar North Atlantic displays an OHC decrease. A few apparent differences in OHC among the examined OA products were identified. In particular, temporal means of a few OA products that incorporated other ocean measurements besides Argo show a global-scale cooling difference, which is likely related to the baseline climatology fields used to generate those products. Large differences also appear in the interannual variability in the Southern Ocean and in the long-term trends in the subpolar North Atlantic. These differences remind us of the possibility of product-dependent conclusions on OHC variations. Caution is therefore warranted when using merely one OA product to conduct OHC studies, particularly in regions and on timescales that display significant differences.
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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
15
the past two decades, an increasing number of OHC studies were conducted using oceanic
16
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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NiñoSouthern 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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3
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 NiñoSouthern 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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   

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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
265
examined OA products. The integrated OHC in different layers show variations on different
266
timescales, ranging from interannual variations to decadal trends. Regarding the interannual
267
variations, ENSO is the dominant climate mode (Balmaseda et al. 2013; Trenberth et al. 2014).
268
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
270
events, likely related to the energy exchange at the sea surface (Trenberth et al. 2002; Mayer et
271
al. 2014). On the decadal scale, robust warming occurs in all three layers. Our calculation shows
272
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%.
274
We also calculated the 5-year rolling trends of OHC in each layer following Smith et al. (2015).
275
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
277
m and 700-2000 m layers, most of the 5-year trends are positive except a few years at the
278
beginning and end of the examined period. The most apparent differences among the examined
279
OA products appear in the 0-300 m layer, where much larger trends are estimated from SIO
280
between 2013 and 2016, indicating much faster warming in SIO over that period. Further
281
examination reveals that these differences (see Fig. 12) are mostly from the top 200 m and are
282
14
likely due to the uncertainties related to the unusual ENSO events in the most recent decade
283
(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
290
(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
295
with the vertical movement of the thermocline, likely contributes to the upper-ocean temperature
296
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
298
evolution of the spread of OHC anomaly (Fig. 11b,d), which is generally small except the
299
beginning years of the examined period (spread-to-mean ratio around 0.5) and decreases with
300
increasing depth. The large spread at the beginning of the Argo period is likely due to the smaller
301
number of Argo floats.
302
The differences in temporal variations of the OHC profiles between each OA product and the
303
ensemble mean are shown in Figure 12. The difference from the ensemble mean ranges about
304
half of the mean OHC in Figure 11, and varies distinctively across the examined products. For
305
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instance, EN4 displays stronger temporal variations than the ensemble mean for its differences
306
present the same pattern to the ensemble mean (cf. Fig. 11a & c and Fig. 12 e & f). On the
307
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
309
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
311
the spike in Figure 8b. Overall, while the ENSO signal in the upper ocean and the widespread
312
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
316
layer, the tropical region between 20°N and 20°S presents high interannual variability associated
317
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
320
the northernmost region, the variation of the zonally averaged OHC is dominated by long-term
321
warming with different strength. The spread of the zonally averaged OHC (Fig. 13d-f) shows
322
that the most substantial difference appears in the high latitude region in the Northern
323
Hemisphere. More specifically, as shown in the differences between each OA product and the
324
ensemble mean (Fig. 14), the cooling in the northern subpolar region is weaker in at least five of
325
the eight examined OAs (i.e., IAP, EN4, Ishii, MOAA and NCEI) than results from the ensemble
326
mean; each of these OA products incorporates other ocean measurements besides Argo data. In
327
16
addition, a number of differences on interannual timescales appear in different regions of the
328
various OA products, mostly poleward of 30°S and 30°N.
329
5 Spatial-Temporal Modes
330
To reveal the possible differences in interpreting OHC variations from different OA products, a
331
common EOF analysis was applied to each OA product and their ensemble mean to identify and
332
compare their major spatial-temporal modes. Figure 15 shows the first two EOF modes in each
333
of the examined layers based on the ensemble of the selected OA products. For the upper 300 m,
334
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
Data
Source
Interpolation
Method
References
BOA
80S-80N
2004-
present
Argo only
Barnes Successive
Method
Li et al. 2017
IAP
90S-90N
1940-
present
Argo plus
others
Ensemble Optimum
Interpolation
Cheng and Zhu, 2016
EN4
90S-90N
1900-
present
Argo plus
others
Objective Analysis
Good et al. 2013
IPRC
63S-63N
2005-
present
Argo plus
Altimetry
Variational
Analysis Technique
http://apdrc.soest.hawa
ii.edu/projects/argo/
Ishii
90S-90N
1955-
present
Argo plus
others
Bin-Average
Ishii et al. 2017
MOAA
60S-70N
2001-
present
Argo plus
others
Objective Analysis
Hosoda et al. 2008
SIO
64S-80N
2004-
present
Argo only
Weighted Least-
Squares Fit
Roemmich and Gilson,
2009
NCEI
90S-90N
1955-
present
Argo plus
others
Bin-Average
Levitus et al. 2012
WOA18
90S-90N
2005-
2017
Argo plus
others
Bin-Average
Boyer et al. 2005
WOCE-
Argo
80S-90N
1985-
2016
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
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the amplitude of interannual variability in the depth-averaged OHC for the period 2005-2019.
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For panels a-f, the unit is 106 J m-2.
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Fig. 4 Difference of interannual variability of depth-averaged OHC between each product and
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the ensemble mean. (Unit: 106 J m-2)
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Fig. 5 Linear trend of the global and regional integrated OHC. The error-bar shows the
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uncertainty (±2σ). (Unit: 1021J yr-1)
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Fig. 6 OHC trends of the ensemble mean of depth-averaged OHC for the period 2005-2019. The
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statistically significant trends at 95% confidence level are highlighted by stippling. The
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corresponding spread is also given. (Unit: 105 J m-2 yr-1)
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Fig. 7 Difference of OHC trends between each product and the ensemble mean. For each
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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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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
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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)
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Fig. 9 Monthly OHC anomaly time series globally and vertically integrated over the selected
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layers. Grey shading is the ensemble spread. Warm (tan) and cool (blue) ENSO events are
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marked based on a threshold of ± 0.5°C for the Oceanic Niño Index (ONI). (Unit: 1022 J)
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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
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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)
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Fig. 11 Ensemble mean and ensemble spread of monthly evolution of the globally integrated
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OHC anomaly (Unit: 1020 J)
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Fig. 12 Difference of monthly evolution of OHC anomaly between each product and the
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ensemble mean. (Unit: 1020 J)
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Fig. 13 Ensemble mean and ensemble spread of the zonal mean OHC anomaly. (Unit: 106 J m-2)
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Fig. 14 Difference of zonal mean OHC anomaly between each product and the ensemble mean.
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(Unit: 106 J m-2)
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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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Fig.16 Normalized first EOF mode of depth-averaged OHC from the in-situ products for the
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examined layers. The percentage of total variance explained by each EOF mode is also provided.
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Fig.17 Normalized second EOF mode of depth-averaged OHC from the in-situ products for the
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examined layers. The percentage of total variance explained by each EOF mode is also provided.
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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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Fig. 19 (a-c) OHC Climatology from ensemble mean of the eight OA products (same as Fig. 1,
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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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... However, our analysis shows that it is necessary to divide it into three layers to reach the objective of this study. Similar vertical division can also be seen in Liang et al. (2021). ...
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In this study, we have compared the ocean heat content (OHC), estimated using two eddy-resolving hindcast simulations based on Ocean General Circulation Model for the Earth Simulator version 1 (OFES1) and version 2 (OFES2). Results from a global objective analysis of subsurface temperature (EN4) were taken as a reference. Both EN4 and OFES1 suggest that OHC has increased in most regions of the top 2000 m during 1960–2016, which is mainly associated with the deepening of neutral density surfaces and variations along the neutral density surfaces of regional importance. Upon comparing the results obtained from the two OFES hindcasts, we found substantial differences in the temporal and spatial distributions of the OHC, especially in the Atlantic Ocean. A basin-wide heat budget analysis showed that there was less surface heating for the major basins in OFES2. The horizontal heat advection was mostly similar; however, OFES2 had a significantly stronger meridional heat advection associated with the Indonesian Throughflow (ITF) above 300 m. Additionally, large discrepancies in the vertical heat advection were also evinced when the two OFES results were compared, especially at a depth of 300 m in the Indian Ocean. We inferred that there are large discrepancies in the vertical heat diffusion (those that cannot be directly evaluated in this study due to data unavailability), which, along with the different magnitudes of sea surface heat flux and vertical heat advection, were the major factors responsible for the examined differences in OHC. This work suggests that OFES1 provides a reasonable multi-decadal estimate of global and basin-integrated warming trends above 700 m, except for the top 300 m for the Pacific Ocean and between 300–700 m for the Indian Ocean. Although the estimates of the global OHC during 1960–2016 are consistent with observations between 700–2000 m, caution is warranted while examining the basin-wide multi-decadal OHC variations using OFES1. The seemingly suboptimal OHC estimate based on OFES2 suggests that any conclusions on long-term climate variations derived from OFES2 might suffer from large drifts, necessitating audits.
... The global Ocean Heat Content (OHC) is generally well-constrained among ocean reanalyses, but regional differ-385 ences may warrant further investigation (Palmer et al., 2017). The boundary currents are choke points of the OHC estimation accuracy, because the relatively sparse data or low resolutions of the objective analysis products are inadequate to resolve the mesoscale structures (Liang et al., 2021). We found that OSnet respects well the SST warming trend (Fig. 12a) and the mesoscale structures and it would be interesting to apply OSnet on other boundary currents and to compare the resulting OHC with previous reconstructions (e.g. ...
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Despite the ever-growing amount of ocean’s data, the interior of the ocean remains under sampled in regions of high variability such as the Gulf Stream. In this context, neural networks have been shown to be effective for interpolating properties and understanding ocean processes. We introduce OSnet (Ocean Stratification network), a new ocean reconstruction system aimed at providing a physically consistent analysis of the upper ocean stratification. The proposed scheme is a bootstrapped multilayer perceptron trained to predict simultaneously temperature and salinity (T-S) profiles down to 1000 m and the Mixed Layer Depth (MLD) from surface data covering 1993 to 2019. OSnet is trained to fit sea surface temperature and sea level anomalies onto all historical in-situ profiles in the Gulf Stream region. To achieve vertical coherence of the profiles, the MLD prediction is used to adjust a posteriori the vertical gradients of predicted T-S profiles, thus increasing the accuracy of the solution and removing vertical density inversions. The prediction is generalized on a 1/4◦ daily grid, producing four-dimensional fields of temperature and salinity, with their associated confidence interval issued from the bootstrap. OSnet profiles have root mean square error comparable with the observation-based Armor3D weekly product and the physics-based ocean reanalysis Glorys12. The maximum of uncertainty is located north of the Gulf Stream, between the shelf and the current, where the thermohaline variability is large. The OSnet reconstructed field is coherent even in the pre-ARGO years, demonstrating the good generalization properties of the network. It reproduces the warming trend of surface temperature, the seasonal cycle of surface salinity and mesoscale structures of temperature, salinity and MLD. While OSnet delivers an accurate interpolation of the ocean’s stratification, it is also a tool to study how the interior of the ocean’s behaviour reflects on surface data. We can compute the relative importance of each input for each T-S prediction and analyse how the network learns which surface feature influences most which property and at which depth. Our results are promising and demonstrate the power of machine learning methods to improve the prediction of ocean interior properties from observations of the ocean surface.
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The ocean vertical circulation has been historically underappreciated compared to the lateral circulation, largely due to the poor availability of the ocean vertical-velocity information. With the advent of high-performance ocean models, especially those constrained by the most available observations, it is now possible and incentive to dig into the vertical branch of ocean circulation. In this study, we used a state-of-the-art and dynamically-consistent ocean state estimate to investigate the seasonal variations and trend of the global upper-ocean (in the top 200 m) vertical velocity, with emphasis on the widely recognized upwelling and downwelling systems. Significant seasonal variations were noted. All around the global ocean, the North Indian Ocean and the Equator exhibited the strongest seasonality. There existed an equatorial Rossby wave propagating the equatorial Pacific upwelling at a phase speed of approximately -0.60 m/s (westward). Over 1998–2017, there were not basin-scale patterns of statistically-significant trend in the upper-ocean vertical velocity. In addition, our results did not support the classical Bakun’s 1990 hypothesis on the upwelling intensification along the major eastern boundary upwelling systems in the context of global warming. This, however, may be due to the short period considered in this study. Four extended datasets were also examined. Patterns of seasonal variations were largely robust among these datasets. Results from these extended datasets further confirmed that there were not basin-scale patterns of statistically significant intensification or weakening of vertical circulations in the top 200 m of the global ocean during 1998–2017.
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The Earth system is accumulating energy due to human-induced activities. More than 90% of this energy has been stored in the ocean as heat since 1970, with ~64% of that in the upper 700 m. Differences in upper ocean heat content anomaly (OHCA) estimates, however, exist. Here, we use a dataset protocol for 1970–2008 – with six instrumental bias adjustments applied to expendable bathythermograph (XBT) data, and mapped by six research groups – to evaluate the spatio-temporal spread in upper OHCA estimates arising from two choices: firstly, those arising from instrumental bias adjustments; and secondly those arising from mathematical (i.e. mapping) techniques to interpolate and extrapolate data in space and time. We also examined the effect of a common ocean mask, which reveals that exclusion of shallow seas can reduce global OHCA estimates up to 13%. Spread due to mapping method is largest in the Indian Ocean and in the eddy-rich and frontal regions of all basins. Spread due to XBT bias adjustment is largest in the Pacific Ocean within 30°N–30°S. In both mapping and XBT cases, spread is higher for 1990–2004. Statistically different trends among mapping methods are not only found in the poorly-observed Southern Ocean but also on the well-observed Northwest Atlantic. Our results cannot determine the best mapping or bias adjustment schemes but they identify where important sensitivities exist, and thus where further understanding will help to refine OHCA estimates. These results highlight the need for further coordinated OHCA studies to evaluate the performance of existing mapping methods along with comprehensive assessment of uncertainty estimates.
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Salinity is one of the fundamental ocean state variables and has been used to infer important information about climate change and variability. Previous studies have found inconsistent salinity variations in various objective ocean analyses that are based on the Argo measurements. However, as far as we are aware, a comprehensive assessment of those inconsistencies, as well as robust spatial and temporal features of salinity variability among the Argo-based products, has not been conducted. Here we compare and evaluate ocean salinity variability from five objective ocean analyses that are solely or primarily based on Argo measurements for their overlapping period 2005 to 2015. We examine the salinity variability at the sea surface and within two depth intervals (0-700 m and 700-2000 m). Our results show that the climatological mean is generally consistent among all examined products, although regional discrepancies are evident in the subsurface ocean. The time evolution, vertical structure, and leading EOF modes of salinity variations show good agreement among most of the examined products, indicating that a number of robust features of the salinity variability can be obtained by examining gridded Argo products. However, significant discrepancies in these variations exist, particularly in the subsurface North Atlantic and Southern Oceans. Also, despite of the increasing number of Argo floats deployed in the ocean the discrepancies were not significantly reduced over time. Our analyses, particularly those of the discrepancies between products, can serve as a useful reference for utilizing and improving the existing objective ocean analyses that are based on Argo measurements.
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As the strongest interannual perturbation to the climate system, El Niño–Southern Oscillation (ENSO) dominates the year-to-year variability of the ocean energy budget. Here we combine ocean observations, reanalyses, and surface flux data with Earth system model simulations to obtain estimates of the different terms affecting the redistribution of energy in the Earth system during ENSO events, including exchanges between ocean and atmosphere and among different ocean basins, and lateral and vertical rearrangements. This comprehensive inventory allows better understanding of the regional and global evolution of ocean heat related to ENSO and provides observational metrics to benchmark performance of climate models. Results confirm that there is a strong negative ocean heat content tendency (OHCT) in the tropical Pacific Ocean during El Niño, mainly through enhanced air–sea heat fluxes Q into the atmosphere driven by high sea surface temperatures. In addition to this diabatic component, there is an adiabatic redistribution of heat both laterally and vertically (0–100 and 100–300 m) in the tropical Pacific and Indian oceans that dominates the local OHCT. Heat is also transported and discharged from 20°S–5°N into off-equatorial regions within 5°–20°N during and after El Niño. OHCT and Q changes outside the tropical Pacific Ocean indicate the ENSO-driven atmospheric teleconnections and changes of ocean heat transport (i.e., Indonesian Throughflow). The tropical Atlantic and Indian Oceans warm during El Niño, partly offsetting the tropical Pacific cooling for the tropical oceans as a whole. While there are distinct regional OHCT changes, many compensate each other, resulting in a weak but robust net global ocean cooling during and after El Niño.
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Circulation patterns and thermohaline fields of the Northwest Atlantic are highly variable in space and time and are strongly impacted by various mesoscale phenomena such as quasi-stationary frontal zones with sharp gradients, meandering jet-like currents, vortexes, and filaments. These all contribute to building and maintaining complex large-scale regional structures of ocean tracers, such as temperature and salinity, which persist over time periods of decades and longer. To reflect the existence of these long-term mesoscale phenomena and diagnose their changes, a new high-resolution in situ climatology of the Northwest Atlantic was developed. At its core, this eddy-resolving climatology, with 1/10° horizontal resolution, reveals a cumulative effect of mesoscale dynamics within the Northwest Atlantic. Additionally, strong agreement exists between this in situ climatology and climatologies derived from high-resolution satellite data, thus providing a validation of the presence of stochastic periodicity of ocean tracer patterns on decadal timescales. Furthermore, large and very localized multidecadal subsurface heat gains southeast of the Gulf Stream was diagnosed using this new high-resolution regional climatology. It was demonstrated that the climatic shifts in the wind stress over the Northwest Atlantic may play a leading role in this heat accumulation due to subtropical water heaving through Ekman pumping. It is argued that uncovering many important details of long-term ocean climate variability from in situ ocean data can only be ascertained through the use of eddy-resolving climatologies.
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The paper describes the new gridded World Ocean Circulation Experiment-Argo Global Hydrographic Climatology (WAGHC). The climatology has a 1∕4° spatial resolution resolving the annual cycle of temperature and salinity on a monthly basis. Two versions of the climatology were produced and differ with respect to whether the spatial interpolation was performed on isobaric or isopycnal surfaces, respectively. The WAGHC climatology is based on the quality controlled temperature and salinity profiles obtained before January 2016, and the average climatological year is in the range from 2008 to 2012. To avoid biases due to the significant step-like decrease of the data below 2km, the profile extrapolation procedure is implemented. We compare the WAGHC climatology to the 1∕4° resolution isobarically averaged WOA13 climatology, produced by the NOAA Ocean Climate Laboratory (Locarnini et al., 2013) and diagnose a generally good agreement between these two gridded products. The differences between the two climatologies are basically attributed to the interpolation method and the considerably extended data basis. Specifically, the WAGHC climatology improved the representation of the thermohaline structure, in both the data poor polar regions and several data abundant regions like the Baltic Sea, the Caspian sea, the Gulf of California, the Caribbean Sea, and the Weddell Sea. Further, the dependence of the ocean heat content anomaly (OHCA) time series on the baseline climatology was tested. Since the 1950s, both of the baseline climatologies produce almost identical OHCA time series. The gridded dataset can be found at https://doi.org/10.1594/WDCC/WAGHC_V1.0 (Gouretski, 2018).
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Inconsistent global/basin ocean heat content (OHC) changes were found in different ocean subsurface temperature analyses, especially in recent studies related to the slowdown in global surface temperature rise. This finding challenges the reliability of the ocean subsurface temperature analyses and motivates a more comprehensive inter-comparison between the analyses. Here we compare the OHC changes in three ocean analyses (Ishii, EN4 and IAP) to investigate the uncertainty in OHC in four major ocean basins from decadal to multi-decadal scales. First, all products show an increase of OHC since 1970 in each ocean basin revealing a robust warming, although the warming rates are not identical. The geographical patterns, the key modes and the vertical structure of OHC changes are consistent among the three datasets, implying that the main OHC variabilities can be robustly represented. However, large discrepancies are found in the percentage of basinal ocean heating related to the global ocean, with the largest differences in the Pacific and Southern Ocean. Meanwhile, we find a large discrepancy of ocean heat storage in different layers, especially within 300–700 m in the Pacific and Southern Oceans. Furthermore, the near surface analysis of Ishii and IAP are consistent with sea surface temperature (SST) products, but EN4 is found to underestimate the long-term trend. Compared with ocean heat storage derived from the atmospheric budget equation, all products show consistent seasonal cycles of OHC in the upper 1500 m especially during 2008 to 2012. Overall, our analyses further the understanding of the observed OHC variations, and we recommend a careful quantification of errors in the ocean analyses.
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A dynamically and data-consistent ocean state estimate during 1993-2010 is analyzed for bidecadal changes in the mechanisms of heat exchange between the upper and lower oceans. Many patterns of change are consistent with prior studies. However, at various levels above 1800 m the global integral of the change in ocean vertical heat flux involves the summation of positive and negative regional contributions and is not statistically significant. The non-significance of change in the global ocean vertical heat transport from an ocean state estimate, " data " from which are of global coverage and are regularly " sampled " spatially and temporally, raises the question whether an adequate observational data base exists to assess changes in the upper ocean heat content over the past few decades. Also, whereas the advective term largely determines the spatial pattern of the change in ocean vertical heat flux, its global integral is not significantly different from zero. In contrast, the diffusive term, although regionally weak except in high-latitude oceans, produces a statistically significant extra downward heat flux during the 00s. This suggests that besides ocean advection, ocean mixing processes, including isopycnal, diapycnal as well as convective mixing, are important for the decadal variation of the heat exchange between upper and deep oceans as well. Furthermore, our analyses indicate that focusing on any particular region in explaining changes of the global ocean heat content could be misleading and not necessarily correspond to changes in the global mean.
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At the beginning of 2015, as one year earlier in 2014, the scientific community anticipated that El Niño conditions could develop in the tropical Pacific by year-end. Such projections were related to the occurrence of westerly wind bursts during winter–spring of each year that generated strong downwelling Kelvin waves indicative of an emerging El Niño. However, the event’s progression quickly stalled in 2014, but actively continued in 2015, leading to an extreme warm event (comparable to 1997 or 1982). Here, we compare climate evolution during these two years using satellite observations and numerical simulations. We show that during 2014, El Niño development was interrupted mid-year by an exceptionally strong easterly wind burst, whereas during the second year it continued through the summer. Further, we show that the failed 2014 event created favorable conditions for El Niño development during the next year, as it kept ocean heat content recharged and the western Pacific warm pool extended eastward. Subsequently, the winter–spring westerly wind bursts in 2015 were followed by a series of state-dependent westerly bursts as part of a strong positive Bjerknes feedback. Analogue simulations with a coupled GCM wherein we superimpose the observed sequences of westerly and easterly wind bursts support these conclusions, stressing the role of the failed 2014 event in preconditioning the ocean–atmosphere system for the development of an extreme El Niño. In our simulations the probability of an extreme event following early-year westerly wind bursts increases from 14% to nearly 60% due to this preconditioning. Thus, the interplay between westerly and easterly wind bursts shapes El Niño development and diversity.
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A new 11 year (2004-2014) monthly 1° gridded Argo temperature and salinity data set with 49 vertical levels from the surface to 1950 m depth (named BOA-Argo) is generated for use in ocean research and modeling studies. The data set is produced based on refined Barnes successive corrections by adopting flexible response functions based on a series of error analyses to minimize errors induced by nonuniform spatial distribution of Argo observations. These response functions allow BOA-Argo to capture a greater portion of mesoscale and large-scale signals while compressing small-sale and high-frequency noise relative to the most recent version of the World Ocean Atlas (WOA). BOA-Argo data set is evaluated against other gridded data sets, such as WOA13, Roemmich-Argo, Jamestec-Argo, EN4-Argo, and IPRC-Argo in terms of climatology, independent observations, mixed-layer depth, and so on. Generally, BOA-Argo compares well with other Argo gridded data sets. The RMSEs and correlation coefficients of compared variables from BOA-Argo agree most with those from the Roemmich-Argo. In particular, more mesoscale features are retained in BOA-Argo than others as compared to satellite sea surface heights. These results indicate that the BOA-Argo data set is a useful and promising adding to the current Argo data sets. The proposed refined Barnes method is computationally simple and efficient, so that the BOA-Argo data set can be easily updated to keep pace with tremendous daily increases in the volume of Argo temperature and salinity data.
Article
The simplest global mapping method and dense data coverage for the global oceans by the latest observation network ensure an estimate of global ocean heat content (OHC) within a satisfactory uncertainty for the last 60 years. The observational database conditionally presented a level high enough for practical use for the global OHC estimation when applying bias corrections of expendable bathythermograph, assuming that the other severe observational biases are not included in the database. Uncertainties in annual global mean temperatures averaged vertically from the surface to 1,500 m are within 0.01 K for the period from 1955 onward, when only sampling errors are taken into account. Those in annual mean global OHC of an improved objective analysis for 0-1,500 m depth is 16ZJ on average throughout the period. Compared to previous studies, the new objective analysis provides a higher estimation of the global 0-1,500 m OHC trend for a longer period from 1955 to 2015, which is an increase of 350 ± 57ZJ with a 95% confidence interval.
Article
The early 21st century’s warming trend of the full-depth global ocean is calculated by combining the analysis of Argo (top 2000m) and repeat hydrography into a blended full-depth observing system. The surface-to-bottom temperature change over the last decade of sustained observation is equivalent to a heat uptake of 0.72 ± 0.09 W m?2 applied over the surface of the earth, 90% of it being found above 2000m depth. We decompose the temperature trend point-wise into changes in isopycnal depth (heave) and temperature changes along an isopycnal (spiciness) to describe the mechanisms controlling the variability. The heave component dominates the global heat content increase, with the largest trends found in the southern hemisphere’s extratropics (0 - 2000m) highlighting a volumetric increase of subtropical mode waters. Significant heave-related warming is also found in the deep North Atlantic and Southern Ocean (2000m - 4000m), reflecting a potential decrease in deep water mass renewal rates. The spiciness component shows its strongest contribution at intermediate levels (700m - 2000m), with striking localised warming signals in regions of intense vertical mixing (North Atlantic and Southern oceans). Finally, the agreement between the independent Argo and repeat hydrography temperature changes at 2000m provides an overall good confidence in the blended heat content evaluation on global and ocean scales, but also highlights basin scale discrepancies between the two independent estimates. Those mismatches are largest in those basins with the largest heave signature (Southern Ocean) and reflect both the temporal and spatial sparseness of the hydrography sampling.