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Consensuses and discrepancies of basin-scale ocean heat content changes (OHC) in different ocean analyses – part I

Reading Time: 9 minutes

Authors and authors and affiliations Gongjie Wang, Lijing Cheng (), John Abraham, Chongyin Li

Please cite this article as “Wang, G., Cheng, L., Abraham, J. et al. Clim Dyn (2017). doi:10.1007/s00382-017-3751-5” 

Abstract
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.

Riassunto
In studi recenti sono stati riscontrati cambiamenti inconsistenti del contenuto di calore dell’oceano (OHC). Questi valori, sia globali sia relativi ai bacini oceanici, sono stati raccolti in diverse analisi di temperatura delle profondità oceaniche, e sembrano mostrare un rallentamento dell’innalzamento delle temperature superficiali mettendo in dubbio l’affidabilità delle analisi delle temperature nel volume oceanico fino ad oggi effettuate. Nello studio vengono descritte le modifiche dell’OHC in tre analisi oceaniche (Ishii, EN4 e IAP) per indagare l’incertezza in quattro bacini oceanici principali. In primo luogo, tutti i dati mostrano un aumento di OHC dal 1970 in ciascun bacino oceanico che rivelano un riscaldamento notevole (anche se i tassi di riscaldamento non sono identici). I modelli geografici, le modalità chiave e la struttura verticale delle modifiche OHC sono coerenti tra i tre set di dati, il che implica che le principali variazioni OHC possano essere rappresentate in modo coerente. Tuttavia, si riscontrano ampie discrepanze nella percentuale di riscaldamento dell’acqua dei bacini legate all’oceano globale, con le maggiori differenze nell’Oceano Pacifico e meridionale. Nel frattempo, troviamo una grande discrepanza di immagazzinamento di calore oceanico in diversi livelli, in particolare entro 300-700 m nel Pacifico e negli Oceani del Sud. Inoltre, l’analisi della superficie vicina di Ishii e IAP è coerente con i prodotti della temperatura del mare (SST), ma la EN4 si trova a sottovalutare la tendenza a lungo termine. Rispetto allo stoccaggio di calore oceanico derivato dall’equazione di bilancio atmosferica, tutti i dati mostrano cicli stagionali consistenti di OHC nei primi 1500 metri, soprattutto nel periodo 2008-2012. Nel complesso, le nostre analisi approfondiscono la comprensione delle variazioni osservate di OHC e raccomandano una quantificazione accurata dei dati nelle analisi oceaniche. Per motivi di lunghezza del testo, lo studio originale (in lingua inglese) è stato suddiviso in 3 parti che usciranno con cadenza settimanale. Il testo originale è comunque scaricabile da questo link in PDF.

Keywords: Ocean heat content Climate change Hiatus EN4 Ishii IAP TOA SST – Download fulltext PDF

nasa

this image is NOT part of the study – credit to NASA – reference to NASA’s improved global climate computer model, which simulates and projects how the Earth’s climate may change, indicates that the oceans have been absorbing heat since 1951 and will continue to absorb more heat from the atmosphere over the next 50 years.

Introduction
An understanding of global and regional ocean heat content (OHC) change is essential to understand both past and future climate change. It has been shown that more than 90% of the earth’s energy imbalance (EEI) in the climate system is sequestered in the ocean (increasing the OHC), the rest goes into warming the land and atmosphere and melting ice (Trenberth et al. 2016; von Schuckmann et al. 2016). Therefore, OHC is the most robust indicator of climate change. During the past 30 years, many independent groups worked to estimate historical OHC changes, however large uncertainty has been found among the published global OHC time series (Rhein et al. 2013). The uncertainty is sourced from instrument biases (specially for eXpendable BathyThermograph bias), mapping methods, and definitions of a baseline climatology (Abraham et al. 2013; Cheng et al. 2015b; Boyer et al. 2016). Most of these previous studies focused on global OHC changes (Lyman et al. 20062010; Levitus et al. 20092012; Balmaseda et al. 2013; Lyman and Johnson 2014; Cheng et al. 2015b; Roemmich et al. 2015), while the uncertainty of basin-scale OHC remains unknown.

Regional OHC changes are crucial to understand the energy flows between the ocean basins. For example, during the current surge of research on the so-called “hiatus”, some of the essential questions were, “Where is the heat redistributed in the ocean?” and “Which ocean basin is the key driver of the recent slowdown of global surface temperature increase?” (Trenberth and Fasullo 2013; Clement and DiNezio 2014; Trenberth 2015; von Schuckmann et al. 2016).

Independent studies have given different observational OHC changes and then proposed different mechanisms to explain the ocean heat redistribution (Meehl et al. 2011; Kosaka and Xie 2013; England et al. 2014; Balmaseda et al. 2013; Chen and Tung 2014; Drijfhout et al. 2014; Nieves et al. 2015; Lee et al. 2015; Liu et al. 2016). For instance, Chen and Tung (2014) found that ocean warming below 300 m depth in the Atlantic and Southern Ocean dominated the ocean heat uptake during 1998–2012 period based on both ocean reanalysis data—Ocean Reanalysis System 4 (ORAS4) and ocean objective analysis—Ishii data (Ishii et al. 2003). They hence argued that the multidecadal variability of Atlantic Meridional Overturning Current (AMOC) contributed to the movement of heat to deeper layers. Lee et al. (2015) presented an abrupt increase of OHC in the Indian Ocean due to the enhancement of Indonesian Through Flow (ITF), which accounts for ~70% of global upper 0–700 m ocean heat increase since 2003. Nieves et al. (2015) indicated that the cooling in the top 100-m layer of the Pacific Ocean was mainly compensated by the warming within the 100-to-300-m layer of the Indian and Pacific Oceans since 2003. Cheng et al. (2015a) gave a distinctive pattern of global OHC change in the interior ocean: cooling in the upper 100-m depth and 300-to-700-m layers, warming in the 100-to-300-m and 700-to-1500-m layers and contributed the opposite warming trend in upper 300-m to the changes in frequency of ENSO warm and cool events.

Apparent discrepancies occur among the literature discussed above. Therefore, it is an urgent scientific issue to revisit the global and basin scale OHC changes revealed by different data products in order to examine what level of consensus can be achieved when using ocean analyses. What’s more, detecting the uncertainty between different datasets will provide a basis for the further improvements of ocean subsurface temperature analyses. In this study, we investigate the basin-scale OHC changes on decadal scales by using three different ocean analyses, providing both the consensus and the discrepancies among the three datasets. It is vital to understand why the discrepancies occur for the different ocean analyses. We use two independent datasets—sea surface temperature (SST), and net radiative flux at the top of atmosphere (TOA) from satellite observations to validate the ocean analyses.

This manuscript is constructed as follows: an introduction of the datasets and methods is presented in Sect. 2. An inter-comparison of the OHC and the related subsurface thermal structure changes from the three products is made in Sect. 3. In this study, we will compare the global and basin-scale OHC changes by using three different objectively analyzed ocean datasets (Ishii et al. 2003; Good et al. 2013; Cheng and Zhu 2016). The first two datasets are widely used in climate and oceanography studies, and the last one is a new ocean analysis that was a result of a careful evaluation of the impact of insufficient sampling on the temperature reconstruction (Cheng and Zhu 2016; Cheng et al. 2017). Two periods (1998–2012 and 1983–1998) are used to examine the decadal OHC variation. A summary of this study and an outlook for the future improvement of the ocean analysis are provided in Sect. 4.

Datasets and methodology

2.1 Gridded temperature datasets
Three independent gridded temperature analyses are used in this study, which are briefly introduced below.
Ishii and Kimoto (2009) (hereafter Ishii data) uses a 3-dimensional variational method to fill the data gaps. Biases in expendable bathythermograph (XBT) and mechanical bathythermograph (MBT) data were corrected by their proposed method (hereafter IK09). The result is a monthly mean gridded map for the period of 1945–2012 with 1° by 1° horizontal revolution and 24 vertical levels from 0 to 1500-m. The EN4 analysis uses an optimal interpolation method for the reconstruction at each ocean layer, with 1° by 1° horizontal revolution and 42 vertical levels from ~5.0-m down to about 5500-m layer (Good et al. 2013). The XBT bias is respectively corrected by using the Levitus et al. (2009) method (L09 for the EN4-L09 analysis) and Gouretski and Reseghetti (2010) method (GR10, in the EN-GR10 analysis).

The IAP analysis is based on the Cheng and Zhu (2016) study from the Institute of Atmospheric Physics (so labeled as IAP). It is then further improved in Cheng et al. (2017). The XBT profiles are corrected by using CH14 scheme proposed in Cheng et al. (2014). The mapping method is an ensemble optimum interpolation (En-OI) with CMIP5 model simulations providing error covariance and a first-guess. IAP analysis releases the data from 1940 to 2015, however more reliable reconstruction is possible since the late 1950s (Cheng et al. 2017). The horizontal resolution is 1° by 1° and there are 41 vertical levels from 1- to 2000-m depths. The major data source of all the three analyses is WOD (World Ocean Datasets) (Boyer et al. 2013), so they essentially use the same raw data. The differences among the three analyses reveal the uncertainty in the quality-control processes, mapping methods and XBT/MBT correction schemes. IK09 scheme assumes that XBT biases arise from depth error, and then they provide corrections for the XBT depths which are variable with time and probe type. L09 scheme corrects XBT temperatures by examining the temperature difference between XBT and CTD data, and their correction is time variable. GR10 scheme corrects both pure thermal bias and depth error, similar to CH14. And CH14 scheme explicitly accounts for many influencing factors of XBT bias, for instance: probe type, time and ocean temperature. Now the XBT community recommends the CH14 scheme be used because it currently provides the most appropriate bias correction strategy (Cheng et al. 2016). The mapping methods are the major error source according to a new comprehensive analysis (Boyer et al. 2016), and the XBT bias provides a secondary source of uncertainty.

2.2 Sea surface temperature datasets
The NOAA Extended Reconstruction Sea Surface Temperature (ERSST) provides global, monthly SST data with 1° by 1° horizontal resolution starting from 1854. Compared to the previous version, Version 4 uses the more extensive ICOADS Release 2.5 data and improved quality control, bias adjustment, and infilling procedures. Therefore, ERSST-v4 is used in this study. NOAA’s Optimum Interpolation Sea Surface Temperature (OISST, also known as Reynolds’ SST) is a series of global analysis products, including the weekly OISST on a 1° grid to the more recent daily on a 1/4° grid. This analysis merges both satellite and in situ platforms (i.e., ships and buoys) by using an optimum interpolation method. Here we use the OISST data with 1° by 1° resolution which derived by a linear interpolation of the weekly optimum interpolation (OI) version 2 fields to daily fields then averaging the daily values over a month.

2.3 Heat flux datasets
TOA radiation flux (RT) data are provided by CERES satellite (https://ceres.larc.nasa.gov). The monthly radiative flux dataset-EBAF, is used in this study, with a horizontal resolution of 1° ranging from 2000 March to 2016 August. The monthly mean vertical integrated divergence of total energy and tendency of the total energy’s vertical integral, which are associated with the atmospheric energy budget, are provided by the ERA-interim datasets with a horizontal resolution of 0.75° and can be downloaded from http://www.cgd.ucar.edu/cas/catalog/reanalysis/ecmwf/erai/index.html.
So the surface net heat flux (Fs) is diagnosed from the atmospheric budget equation (Fasullo and Trenberth 2008). Only the seasonal cycle of Fs the over global ocean is examined and compared with OHC since it is much more stable compared with inter-annual changes. Surface net heat flux is converted to ocean heat storage (OE) following the description in McKinnon and Huybers (2016).

2.4 Method
OHC is calculated by integrating the temperature anomalies within a certain layer as:

OHC=h1h2ρρCpTdzOHC=∫h1h2⁡ρρCpT′dz

where  ρρ ρρCpCpTT′ are the density of sea water, thermal capacity and temperature anomaly respectively.

Parameter h1h1 and h2h2 definite the lower and upper limits of the layer depth. In this study, the maximum value for h1h1 is set as 1500-m, which is the maximum depth of Ishii analysis. The values of ρρρρ and CpCp are calculated from monthly temperature and salinity fields in Ishii and EN4 data, and we use the climatological salinity from WOA13 in the IAP analysis. The linear trend in OHC is calculated by a least square regression and the error is two times standard error. A 12-month climatology is constructed by averaging data from 2008 to 2012. Then it is subtracted from the temperature field to remove the seasonal cycle (Cheng and Zhu 2015). A 12-month running mean is further applied to filter high-frequency signals from the monthly OHC time series since a recent study indicates that the monthly variation of OHC changes in ocean analyses are mostly nonphysical (Trenberth et al. 2016). In this study, the global ocean is divided into the Southern, Pacific, Atlantic, and the Indian Oceans as in Lee et al. (2015), except that the Southern Ocean is connected with other basins at 35°S.

 to be continued in part II

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If You Save the Ocean
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