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This work assesses the value of expendable bathythermograph (XBT) and Argo profiling float observations to monitor the Atlantic Ocean boundary current systems (BCS), meridional overturning circulation (MOC), and meridional heat transport (MHT). Data from six XBT transects and available Argo floats in the Atlantic Ocean for the period from 2000 to 2018 are used to estimate the structure and variability of the BCS, MOC, and MHT, taking into account different temporal and spatial mapping strategies. The comparison of Argo data density along these six XBT transects shows that Argo observations outnumber XBT observations only above mapping scales of 30 days and 3° boxes. The comparison of Argo and XBT data for the Brazil Current and Gulf Stream shows that Argo cannot reproduce the structure and variability of these currents, as it lacks sufficient resolution to resolve the gradients across these narrow jets. For the MHT and MOC, Argo estimates are similar to those produced by XBTs at a coarse mapping resolution of 5° and 30 days. However, at such a coarse resolution the root-mean-square errors calculated for both XBT and Argo estimates relative to a high-resolution baseline are higher than 3 Sv (1 Sv ≡ 106 m3 s−1) and 0.25 PW for the MOC and MHT, respectively, accounting for about 25%–30% of their mean values due to the smoothing of eddy variability along the transects. A key result of this study is that using Argo and XBT data jointly, rather than separately, improves the estimates of MHT, MOC, and BCS.

State of the climate in 2019

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 ∼60% 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 spatiotemporal spread in upper OHCA estimates arising from two choices: 1) those arising from instrumental bias adjustments and 2) 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 found not only in the poorly observed Southern Ocean but also in 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.

The needs of society and the emerging blue economy require access and integration of data and information for the construction of dedicated products. A “transparent and accessible ocean” is one of the key objectives of the Ocean Decade 2021–30. In this context, marine infrastructures become significant components of a global knowledge environment, enabling environmental assessment and providing the necessary data for scientifically valid actions to protect and restore ocean health, to use marine resources in a sustainable way. The data is collected, analyzed, organized, and used by people and their good use/reuse can be obtained with social practices, technological and physical agreements aimed at facilitating collaborative knowledge, decision-making, inference. The vision is a digital ocean data ecosystem made up of multiple, interoperable, and scalable components. The huge amount of data and the resulting products can drive the development of new knowledge as well as new applications and services. Predictive capabilities that derive from the digital ecosystem enable the implementation of services for real-time decision-making, multihazard warning systems, and advance marine space planning. The chapter develops following the progressive complexity and information content of products deriving from oceanic data: data cycle and data collections, data products, oceanic reanalysis. The chapter discusses the new challenges of data products and the complexity of deriving them.

This analysis estimates uncertainty in the NOAA global surface temperature (GST) version 5 (NOAAGlobalTemp v5) product, which consists of sea surface temperature (SST) from the Extended Reconstructed SST version 5 (ERSSTv5) and land surface air temperature (LSAT) from the Global Historical Climatology Network monthly version 4 (GHCNm v4). Total uncertainty in SST and LSAT consists of parametric and reconstruction uncertainties. The parametric uncertainty represents the dependence of SST/LSAT reconstructions on selecting 28 (6) internal parameters of SST (LSAT), and is estimated by a 1000-member ensemble from 1854 to 2016. The reconstruction uncertainty represents the residual error of using a limited number of 140 (65) modes for SST (LSAT). Uncertainty is quantified at the global scale as well as the local grid scale. Uncertainties in SST and LSAT at the local grid scale are larger in the earlier period (1880s–1910s) and during the two world wars due to sparse observations, then decrease in the modern period (1950s–2010s) due to increased data coverage. Uncertainties in SST and LSAT at the global scale are much smaller than those at the local grid scale due to error cancellations by averaging. Uncertainties are smaller in SST than in LSAT due to smaller SST variabilities. Comparisons show that GST and its uncertainty in NOAAGlobalTemp v5 are comparable to those in other internationally recognized GST products. The differences between NOAAGlobalTemp v5 and other GST products are within their uncertainties at the 95% confidence level.

Mean monthly climatological mixed layer depth (MLD) combined with temperature, dissolved oxygen, and apparent oxygen utilization (AOU) are used to produce global estimates of the seasonal variabili...

Marine microplastics (< 5 mm) pollution is a growing problem affecting coastal communities, marine ecosystems, marine life and human health. Microplastics have been detected throughout the entire g...

In recent years, Unmanned Maritime Systems (UMS) have become widely used in hydrographic surveys and oceanographic studies, especially in coastal regions. NOAA's National Centers for Environmental Information (NCEI) has received an increased amount of data collected by UMS in the past 10 years. A variety of different types of data are typically collected simultaneously by UMS, which raises questions about the best practices for UMS data management. In this manuscript, we discuss enhancing the value of UMS data through integration into NCEI synthesized products. These products range from data-type specific databases like the Surface Underway Marine Database, and the World Ocean Database to analyses (value-added) products such as climatological mean fields and ocean time series of variables such as ocean heat and oxygen content. Data from Saildrones and Wave Glider missions demonstrate how data collected by Unmanned Surface Vehicles (USV) are integrated into the Surface Underway Marine Database. The integration of data from Unmanned Underwater Vehicles (UUV) systems - such as gliders - into the World Ocean Database is also described. Data management procedures and quality controls are discussed. The application of standardized methods adds long-term value to the original UMS, and improves the quality and reliability of the synthesized products. NCEI stewardship practices maximize NOAA's investment in environmental research, converting scientific insights into dynamic, usable information that inform strategy and decision making in government, academia, and the private sector.

The difficulty in effectively evaluating sea surface temperature (SST) analyses is finding independent observations, since most available observations have been used in the SST analyses. In this study, the ocean profile measurements [from reverse thermometer, CTD, mechanical bathythermograph (MBT), and XBT] above 5-m depth over 1950–2016 from the World Ocean Database (WOD) are used (data labeled pSSTW). The biases of MBT and XBT are corrected based on currently available algorithms. The bias-corrected pSSTW over 1950–2016 and satellite-based SST from the European Space Agency (ESA) Climate Change Initiative (CCI) over 1992–2010 are used to evaluate commonly available SST analyses. These SST analyses are the Extended Reconstructed SST (ERSST), versions 5, 4, and 3b, the Met Office Hadley Centre Sea Ice and SST dataset (HadISST), and the Japan Meteorological Administration (JMA) Centennial In Situ Observation-Based Estimates of SST version 2.9.2 (COBE-SST2). Our comparisons show that the SST from COBE-SST2 is the closest to pSSTW and CCI in most of the Pacific, Atlantic, and Southern Oceans, which may result from its unique bias correction to ship observations. The SST from ERSST version 5 is more consistent with pSSTW than its previous versions over 1950–2016, and is more consistent with CCI than its previous versions over 1992–2010. The better performance of ERSST version 5 over its previous versions is attributed to its improved bias correction applied to ship observations with a baseline of buoy observations, and is seen in most of the Pacific and Atlantic.

Maintaining North Atlantic (NA) intra-basin near-surface salinity (NSS) contrast between the high NSS (>37.0) in the subtropical NA (STNA) and low NSS (

The ocean is the primary heat sink of the global climate system. Since 1971, it has been responsible for storing more than 90% ofthe excess heat added to the Earth system by anthropogenic greenhouse-gas emissions. Adding this heat to the ocean contributes substantially to sea level rise and affects vital marine ecosystems. Considering the global ocean’s large role in ongoing climate variability and change, it is a good place to focus in order to understand what observed changes have occurred to date and, by using models, what future changes might arise under continued anthropogenic forcing of the climate system. While sparse measurement coverage leads to enhanced uncertainties with long-term historical estimates of change, modern measurements are beginning to provide the clearest picture yet of ongoing global ocean change. Observations show that the ocean is warming from the near-surface through to the abyss, a conclusion that is strengthened with each new analysis. In this assessment, we revisit observation- and model-based estimates of ocean warming from the industrial era to the present and show a consistent, full-depth pattern of change over the observed record that is likely to continue at an ever-increasing pace if effective actions to reduce greenhouse-gas emissions are not taken.

Biases have been identified in historical expendable bathythermograph (XBT) datasets, which are one of the major sources of uncertainty in the ocean subsurface database. More than 10 correction schemes were proposed; however, their performance has not been collectively evaluated and compared. This study quantifies how well 10 different available schemes can correct the historical XBT data by comparing the corrected XBT data with collocated reference data in both the World Ocean Database (WOD) 2013 and the EN4 dataset. Four different metrics are proposed to quantify their performances. The results indicate CH14 is the best among the currently available methods, and L09/G12/GR10 can be used with some caveats. To test the robustness of the schemes, we further train the CH14 and L09 by using 50% of the XBT–reference data and the schemes are tested by using the remaining data. The results indicate that the two schemes are robust. Moreover, the EN4 and WOD comparison datasets show a systematic difference of XBT error (~0.01°C on a global scale and 0–700 m on average). influences of quality control and data processing have been investigated. Additionally, the side-by-side XBT–CTD comparison experiment is used to examine the correction schemes and provides independent high-quality data for the assessment. The schemes that best correct the global datasets do not always perform as well at correcting the side-by-side dataset, and further examination of the discrepancy in performance is still required. Finally, CH14 and L09 result in very similar ocean heat content (OHC) change estimates in the upper 700 m since 1966, suggesting the potential of reducing XBT-induced error in OHC estimates.

The World Ocean Database (WOD) contains over 1.3 million oceanographic casts (where cast refers to an oceanographic profile or set of profiles collected concurrently at more than one depth between the ocean surface and ocean bottom) collected in the Arctic Ocean basin and its surrounding marginal seas. The data, collected from 1849 to the present, come from many submitters and countries, and were collected using a variety of instruments and platforms. These data, along with the derived products World Ocean Atlas (WOA) and the Arctic Regional Climatologies, are exceptionally useful – the data are presented in a standardized, easy to use format and include metadata and quality control information. Collecting data in the Arctic Ocean is challenging, and coverage in space and time ranges from excellent to nearly non-existent. WOD continues to compile a comprehensive collection of Arctic Ocean profile data, ideal for oceanographic, environmental and climatic analyses (https://doi.org/10.7289/V54Q7S16).

Time-varying biases in expendable bathythermograph (XBT) instruments have emerged as a key uncertainty in estimates of historical ocean heat content variability and change. One of the challenges in the development of XBT bias corrections is the lack of metadata in ocean profile databases. Approximately 50% of XBT profiles in the World Ocean database (WOD) have no information about manufacturer or probe type. Building on previous research efforts, this paper presents a deterministic algorithm for assigning missing XBT manufacturer and probe type for individual temperature profiles based on 1) the reporting country, 2) the maximum reported depth, and 3) the record date. The criteria used are based on bulk analysis of known XBT profiles in the WOD for the period 1966–2015. A basic skill assessment demonstrates a 77% success rate at correctly assigning manufacturer and probe type for profiles where this information is available. The skill rate is lowest during the early 1990s, which is also a period when metadata information is particularly poor. The results suggest that substantive improvements could be made through further data analysis and that future algorithms may benefit from including a larger number of predictor variables.

Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. This Earth Energy Imbalance (EEI) is a fundamental metric of climate change. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system – is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This study is a Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory, and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960–2018. The study obtains a consistent long-term Earth system heat gain over the past 58 years, with a total heat gain of 393 &pm; 40 ZJ, which is equivalent to a heating rate of 0.42 &pm; 0.04 W m−2. The majority of the heat gain (89 %) takes place in the global ocean (0–700 m: 53 %; 700–2000 m: 28 %; > 2000 m: 8 %), while it amounts to 6 % for the land heat gain, to 4 % available for the melting of grounded and floating ice, and to 1 % for atmospheric warming. These new estimates indicate a larger contribution of land and ice heat gain (10 % in total) compared to previous estimates (7 %). There is a regime shift of the Earth heat inventory over the past 2 decades, which appears to be predominantly driven by heat sequestration into the deeper layers of the global ocean, and a doubling of heat gain in the atmosphere. However, a major challenge is to reduce uncertainties in the Earth heat inventory, which can be best achieved through the maintenance of the current global climate observing system, its extension into areas of gaps in the sampling, as well as to establish an international framework for concerted multi-disciplinary research of the Earth heat inventory. Earth heat inventory is published at DKRZ (https://www.dkrz.de/) under the doi: https://doi.org/10.26050/WDCC/GCOS_EHI_EXP (von Schuckmann et al., 2020).

The energy radiated by the Earth toward space does not compensate the incoming radiation from the Sun leading to a small positive energy imbalance at the top of the atmosphere (0.4-1 Wm(-2)). This imbalance is coined Earth's Energy Imbalance (EEI). It is mostly caused by anthropogenic greenhouse gas emissions and is driving the current warming of the planet. Precise monitoring of EEI is critical to assess the current status of climate change and the future evolution of climate. But the monitoring of EEI is challenging as EEI is two orders of magnitude smaller than the radiation fluxes in and out of the Earth system. Over 93% of the excess energy that is gained by the Earth in response to the positive EEI accumulates into the ocean in the form of heat. This accumulation of heat can be tracked with the ocean observing system such that today, the monitoring of Ocean Heat Content (OHC) and its long-term change provide the most efficient approach to estimate EEI. In this community paper we review the current four state-of-the-art methods to estimate global OHC changes and evaluate their relevance to derive EEI estimates on different time scales. These four methods make use of: (1) direct observations of in situ temperature; (2) satellite-based measurements of the ocean surface net heat fluxes; (3) satellite-based estimates of the thermal expansion of the ocean and (4) ocean reanalyses that assimilate observations from both satellite and in situ instruments. For each method we review the potential and the uncertainty of the method to estimate global OHC changes. We also analyze gaps in the current capability of each method and identify ways of progress for the future to fulfill the requirements of EEI monitoring. Achieving the observation of EEI with sufficient accuracy will depend on merging the remote sensing techniques with in situ measurements of key variables as an integral part of the Ocean Observing System.

Graphical abstract, A new method of identifying anomalous oceanic temperature and salinity (T/S) data from Argo profiling floats is proposed. The proposed method uses World Ocean Database 2013 climatology to classify good against anomalous data by using convex hulls. An n-sided polygon (convex hull) with least area encompassing all the climatological points is constructed using Jarvis March algorithm. Subsequently Points In Polygon (PIP) principle implemented using ray casting algorithm is used to classify the T/S data as within or without acceptable bounds. It is observed that various types of anomalies associated with the oceanographic data viz., spikes, bias, sensor drifts etc can be identified using this method. Though demonstrated for Argo data it can be applied to any oceanographic data. • The patterns of variation of the parameter (temperature or salinity) corresponding to a particular depth, along the longitude or latitude can be used to build convex hulls. • This method can be effectively used for quality control by building Convex hulls for various observed depths corresponding to biogeochemical data which are sparsely observed. • This method has the advantage of treating the bulk of oceanographic in situ data in a single iteration which filters out anomalous data.

Ocean warming accounts for the majority of the earth’s recent energy imbalance. Historic ocean heat content (OHC) changes are important for understanding changing climate. Calculations of OHC anomalies (OHCA) from in situ measurements provide estimates of these changes. Uncertainties in OHCA estimates arise from calculating global fields from temporally and spatially irregular data (mapping method), instrument bias corrections, and the definitions of a baseline climatology from which anomalies are calculated. To investigate sensitivity of OHCA estimates for the upper 700 m to these different factors, the same quality-controlled dataset is used by seven groups and comparisons are made. Two time periods (1970–2008 and 1993–2008) are examined. Uncertainty due to the mapping method is 16.5 ZJ for 1970–2008 and 17.1 ZJ for 1993–2008 (1 ZJ = 1 × 1021 J). Uncertainty due to instrument bias correction varied from 8.0 to 17.9 ZJ for 1970–2008 and from 10.9 to 22.4 ZJ for 1993–2008, depending on mapping method. Uncertainty due to baseline mean varied from 3.5 to 14.5 ZJ for 1970–2008 and from 2.7 to 9.8 ZJ for 1993–2008, depending on mapping method and offsets. On average mapping method is the largest source of uncertainty. The linear trend varied from 1.3 to 5.0 ZJ yr−1 (0.08–0.31 W m−2) for 1970–2008 and from 1.5 to 9.4 ZJ yr−1 (0.09–0.58 W m−2) for 1993–2008, depending on method, instrument bias correction, and baseline mean. Despite these complications, a statistically robust upper-ocean warming was found in all cases for the full time period.

Expendable bathythermograph (XBT) data were the major component of the ocean temperature profile observations from the late 1960s through the early 2000s, and XBTs still continue to provide critical data to monitor surface and subsurface currents, meridional heat transport, and ocean heat content. Systematic errors have been identified in the XBT data, some of which originate from computing the depth in the profile using a theoretically and experimentally derived fall-rate equation (FRE). After in-depth studies of these biases and discussions held in several workshops dedicated to discussing XBT biases, the XBT science community met at the Fourth XBT Science Workshop and concluded that XBT biases consist of 1) errors in depth values due to the inadequacy of the probe motion description done by standard FRE and 2) independent pure temperature biases. The depth error and temperature bias are temperature dependent and may depend on the data acquisition and recording system. In addition, the depth bias also includes an offset term. Some biases affecting the XBT-derived temperature profiles vary with manufacturer/probe type and have been shown to be time dependent. Best practices for historical XBT data corrections, recommendations for future collection of metadata to accompany XBT data, impact of XBT biases on scientific applications, and challenges encountered are presented in this manuscript. Analysis of XBT data shows that, despite the existence of these biases, historical XBT data without bias corrections are still suitable for many scientific applications, and that bias-corrected data can be used for climate research.

Advances in ocean observing technologies and modeling provide the capacity to revolutionize the management of living marine resources. While traditional fisheries management approaches like single-species stock assessments are still common, a global effort is underway to adopt ecosystem-based fisheries management (EBFM) approaches. These approaches consider changes in the physical environment and interactions between ecosystem elements, including human uses, holistically. For example, integrated ecosystem assessments aim to synthesize a suite of observations (physical, biological, socioeconomic) and modeling platforms [ocean circulation models, ecological models, short-term forecasts, management strategy evaluations (MSEs)] to assess the current status and recent and future trends of ecosystem components. This information provides guidance for better management strategies. A common thread in EBFM approaches is the need for high-quality observations of ocean conditions, at scales that resolve critical physical-biological processes and are timely for management needs. Here we explore options for a future observing system that meets the needs of EBFM by (i) identifying observing needs for different user groups, (ii) reviewing relevant datasets and existing technologies, (iii) showcasing regional case studies, and (iv) recommending observational approaches required to implement EBFM. We recommend linking ocean observing within the context of Global Ocean Observing System (GOOS) and other regional ocean observing efforts with fisheries observations, new forecasting methods, and capacity development, in a comprehensive ocean observing framework.

The National Oceanographic Data Center (NODC) has produced high-resolution regional ocean climatologies that reveal much greater detail than previously available in selected regions. A pilot study based on the NODC Arctic Regional Climatology (ARC) was carried out to track multidecadal ocean climate variability of the oceans and seas north of 60°N (referred to as Northern Waters). The structure and data coverage for the Northern Waters provided by this climatology are discussed. Multidecadal variability of the Northern Waters is analyzed with one- and quarter-degree resolutions. Our analysis indicates that although the Northern Waters are warming as a whole, the overall climatic trend and spatial distribution of warming and cooling areas are rather intricate and patchy, with some areas even having intermittent cooling episodes. Complex relations of the upper ocean warming and two major climate indices—the Atlantic Multidecadal Oscillation (AMO) and North Atlantic Oscillation (NAO)—are discussed based on new ocean heat content calculations for the Greenland–Norwegian–Iceland Seas (GINS) and Arctic Ocean. Preliminary estimates suggest that multidecadal variability in the Northern Waters is more closely correlated with the AMO than the NAO, especially in the GINS.

The electrical conductivity of the ocean is a fundamental parameter in the electrodynamics of the Earth System. This parameter is involved in a number of applications ranging from the calibration of in situ ocean flow meters, through extensions of traditional induction studies, and into quite new opportunities involving the remote sensing of ocean flow and properties from space-borne magnetometers such as carried aboard the three satellites of the Swarm mission launched in 2013. Here, the first ocean conductivity data set calculated directly from observed temperature and salinity measurements is provided. These data describe the globally gridded, three-dimensional mean conductivity as well as seasonal variations, and the statistics of spatial and seasonal variations are shown. This “climatology” data set of ocean conductivity is offered as a standard reference similar to the ocean temperature and salinity climatologies that have long been available. Electronic supplementary material The online version of this article (10.1186/s40623-017-0739-7) contains supplementary material, which is available to authorized users.

Quantifying the effect of the seawater density changes on sea level variability is of crucial importance for climate change studies, as the sea level cumulative rise can be regarded as both an important climate change indicator and a possible danger for human activities in coastal areas. In this work, as part of the Ocean Reanalysis Intercomparison Project, the global and regional steric sea level changes are estimated and compared from an ensemble of 16 ocean reanalyses and 4 objective analyses. These estimates are initially compared with a satellite-derived (altimetry minus gravimetry) dataset for a short period (2003-2010). The ensemble mean exhibits a significant high correlation at both global and regional scale, and the ensemble of ocean reanalyses outperforms that of objective analyses, in particular in the Southern Ocean. The reanalysis ensemble mean thus represents a valuable tool for further analyses, although large uncertainties remain for the inter-annual trends. Within the extended intercomparison period that spans the altimetry era (1993-2010), we find that the ensemble of reanalyses and objective analyses are in good agreement, and both detect a trend of the global steric sea level of 1.0 and 1.1 +/- 0.05 mm/year, respectively. However, the spread among the products of the halosteric component trend exceeds the mean trend itself, questioning the reliability of its estimate. This is related to the scarcity of salinity observations before the Argo era. Furthermore, the impact of deep ocean layers is non-negligible on the steric sea level variability (22 and 12 % for the layers below 700 and 1500 m of depth, respectively), although the small deep ocean trends are not significant with respect to the products spread.

A new monthly sea surface salinity (SSS) product calculated from profile data within the World Ocean Database (WOD) is compared and contrasted with Aquarius SSS, both standard and Combined Active-Passive (CAP) products, from September 2011 through September 2013. Aquarius exhibits similar biases as shown in previous comparison SSS studies, with negative biases in the tropics transitioning to positive biases in the higher latitudes when compared to WOD SSS. These biases are generally much weaker in CAP than the standard version, indicating that the biases are strongly related to the differences in algorithms used to retrieve satellite SSS. Non-Argo data utilized in the study are shown to be of great use to validate Aquarius in regions with little to no Argo coverage and helps provide SSS measurements in regions where there are known errors in Aquarius retrievals. The annual cycle of WOD and Aquarius is found to be very similar, with Aquarius being generally more coherent and robust. All three products' annual cycles compared favorably to the World Ocean Atlas 2013. The interannual changes in all three products generally corresponded well to one another and to changes in evaporation and precipitation (E-P). Overall, Aquarius compares very well with in situ sea surface salinity fields under multiple comparison examinations; however, both products have their own strengths and weaknesses and a synthesis of the two should be used to study global scale SSS variability.

The blended monthly sea surface salinity (SSS) analysis, called the NOAA “Blended Analysis of Surface Salinity” (BASS), is constructed for the 4 year period from 2010 to 2013. Three data sets are employed as inputs to the blended analysis: in situ SSS measurements aggregated and quality controlled by NOAA/NODC, and passive microwave (PMW) retrievals from both the National Aeronautics and Space Administration's (NASA) Aquarius/SAC-D and the European Space Agency's (ESA) Soil Moisture-Ocean Salinity (SMOS) satellites. The blended analysis comprises two steps. First, the biases in the satellite retrievals are removed through probability distribution function (PDF) matching against temporally spatially colocated in situ measurements. The blended analysis is then achieved through optimal interpolation (OI), where the analysis for the previous time step is used as the first guess while the in situ measurements and bias-corrected satellite retrievals are employed as the observations to update the first guess. Cross validations illustrate improved quality of the blended analysis, with reduction in bias and random errors over most of the global oceans as compared to the individual inputs. Large uncertainty, however, remains in high-latitude oceans and coastal regions where the in situ networks are sparse and current-generation satellite retrievals have limitations. Our blended SSS analysis shows good agreements with the NODC in situ-based analysis over most of the tropical and subtropical oceans, but large differences are observed for high-latitude oceans and along coasts. In the tropical oceans, the BASS is shown to have coherent variability with precipitation and evaporation associated with the evolution of the El Nino-Southern Oscillation (ENSO).

Systematic biases in historical expendable bathythermograph (XBT) data are examined using two datasets: 4151 XBT–CTD side-by-side pairs from 1967 to 2011 and 218 653 global-scale XBT–CTD pairs (within one month and 1°) extracted from the World Ocean Database 2009 (WOD09) from 1966 to 2010. Using the side-by-side dataset, it was found that both the pure thermal bias and the XBT fall rate (from which the depth of observation is calculated) increase with water temperature. Correlations between the terminal velocity A and deceleration B terms of the fall-rate equation (FRE) and between A and the offset from the surface terms are obtained, with A as the dominant term in XBT fall-rate behavior. To quantify the time variation of the XBT fall-rate and pure temperature biases, global-scale XBT–CTD pairs are used. Based on the results from the two datasets, a new correction scheme for historical XBT data is proposed for nine independent probe-type groups. The scheme includes corrections for both temperature and depth records, which are all variable with calendar year, water temperature, and probe type. The results confirm those found in previous studies: a slowing in fall rate during the 1970s and 2000s and the large pure thermal biases during 1970–85. The performance of nine different correction schemes is compared. After the proposed corrections are applied to the XBT data in the WOD09 dataset, global ocean heat content from 1967 to 2010 is reestimated.

Because they make up 56% of ocean temperature profile data between 1967 and 2001, quantifying the biases in expendable bathythermograph (XBT) data is fundamental to understanding the evolution of the planetary energy and sea level budgets over recent decades. The nature and time history of these biases remain in dispute and dominate differences in analyses of the history of ocean warming. A database of over 4100 side-by-side deployments of XBTs and conductivity–temperature–depth (CTD) data has been assembled, and this unique resource is used to characterize and separate out the pure temperature bias from depth error in a way that was not previously possible. Two independent methods of bias extraction confirm that the results are robust to bias model and fitting method. It was found that there is a pure temperature bias in Sippican probes of ~0.05°C, independent of depth. The temperature bias has a time dependency, being larger (~0.1°C) in the earlier analog acquisition era and being likely due to changes in recorder type. Large depth errors are found in the 1970s–80s in shallower-measuring Sippican T4/T6 probe types, but the deeper-measuring Sippican T7/Deep Blue (DB) types have no error during this time. The Sippican T7/DB fall rate slows from ~1990 onward. It is found that year-to-year variations in fall rate have a bigger effect on corrections to the global XBT database than do any small effects of ocean temperature on fall rate. This study has large implications for the future development of better schemes to correct the global historical XBT archive.

A new assessment of how much heat Earth has accumulated since 1960 is made by examining ocean heat content changes., Earth’s energy imbalance (EEI) drives the ongoing global warming and can best be assessed across the historical record (that is, since 1960) from ocean heat content (OHC) changes. An accurate assessment of OHC is a challenge, mainly because of insufficient and irregular data coverage. We provide updated OHC estimates with the goal of minimizing associated sampling error. We performed a subsample test, in which subsets of data during the data-rich Argo era are colocated with locations of earlier ocean observations, to quantify this error. Our results provide a new OHC estimate with an unbiased mean sampling error and with variability on decadal and multidecadal time scales (signal) that can be reliably distinguished from sampling error (noise) with signal-to-noise ratios higher than 3. The inferred integrated EEI is greater than that reported in previous assessments and is consistent with a reconstruction of the radiative imbalance at the top of atmosphere starting in 1985. We found that changes in OHC are relatively small before about 1980; since then, OHC has increased fairly steadily and, since 1990, has increasingly involved deeper layers of the ocean. In addition, OHC changes in six major oceans are reliable on decadal time scales. All ocean basins examined have experienced significant warming since 1998, with the greatest warming in the southern oceans, the tropical/subtropical Pacific Ocean, and the tropical/subtropical Atlantic Ocean. This new look at OHC and EEI changes over time provides greater confidence than previously possible, and the data sets produced are a valuable resource for further study.

The monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are decreased by 0.1°–0.2°C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from remaining innovations are mostly important at small space and time scales, primarily having an impact where and when input observations are sparse. Cross validations and verifications with independent modern observations show that the updates incorporated in ERSSTv5 have improved the representation of spatial variability over the global oceans, the magnitude of El Niño and La Niña events, and the decadal nature of SST changes over 1930s–40s when observation instruments changed rapidly. Both long- (1900–2015) and short-term (2000–15) SST trends in ERSSTv5 remain significant as in ERSSTv4.

Global mean surface temperatures (GMST) exhibited a smaller rate of warming during 1998–2013, compared to the warming in the latter half of the 20th Century. Although, not a “true” hiatus in the strict definition of the word, this has been termed the “global warming hiatus” by IPCC (2013). There have been other periods that have also been defined as the “hiatus” depending on the analysis. There are a number of uncertainties and knowledge gaps regarding the “hiatus.” This report reviews these issues and also posits insights from a collective set of diverse information that helps us understand what we do and do not know. One salient insight is that the GMST phenomenon is a surface characteristic that does not represent a slowdown in warming of the climate system but rather is an energy redistribution within the oceans. Improved understanding of the ocean distribution and redistribution of heat will help better monitor Earth's energy budget and its consequences. A review of recent scientific publications on the “hiatus” shows the difficulty and complexities in pinpointing the oceanic sink of the “missing heat” from the atmosphere and the upper layer of the oceans, which defines the “hiatus.” Advances in “hiatus” research and outlooks (recommendations) are given in this report., Key Points From 1998 to 2013, the rate of global mean surface warming slowed (some have termed this a global warming hiatus); we argue that this represents a redistribution of energy within the Earth systemNatural, decadal variability plays a crucial role in the rate of global surface warmingImproved understanding of ocean distribution and redistribution of heat will help us better monitor Earth energy budget and will maintain and increase ocean monitoring

The possibility of anthropogenic ocean warming has led to a range of concerns, from impacts on fisheries and ocean acidification to rising sea level and changes in tropical cyclone frequency and intensity. This study substantially strengthens the attribution of the recently observed global ocean warming to human activity.

Ocean heat content (HC) is one of the key indicators of climate variability and also provides ocean memory critical for seasonal and decadal predictions. The availability of multiple operational ocean analyses (ORAs) now routinely produced around the world is an opportunity for estimation of uncertainties in HC analysis and development of ensemble-based operational HC climate indices. In this context, the spread across the ORAs is used to quantify uncertainties in HC analysis and the ensemble mean of ORAs to identify, and to monitor, climate signals. Toward this goal, this study analyzed 10 ORAs, two objective analyses based on in situ data only, and eight model analyses based on ocean data assimilation systems. The mean, annual cycle, interannual variability, and long-term trend of HC in the upper 300 m (HC300) from 1980 to 2009 are compared. The spread across HC300 analyses generally decreased with time and reached a minimum in the early 2000s when the Argo data became available. There was a good correspondence between the increase of data counts and reduction of the spread. The agreement of HC300 anomalies among different ORAs, measured by the signal-to-noise ratio (S/N), is generally high in the tropical Pacific, tropical Indian Ocean, North Pacific, and North Atlantic but low in the tropical Atlantic and extratropical southern oceans where observations are very sparse. A set of climate indices was derived as HC300 anomalies averaged over the areas where the covariability between SST and HC300 represents the major climate modes such as ENSO, Indian Ocean dipole, Atlantic Niño, Pacific decadal oscillation, and Atlantic multidecadal oscillation.

Long time series of XBT data in the Bay of Bengal and the Arabian Sea are valuable datasets for exploring and understanding climate variability. However, such studies of interannual and longer-scale variability of temperature require an understanding, and, if possible, a correction of errors introduced by biases in the XBT and expendable conductivity–temperature–depth (XCTD) data. Two cruises in each basin were undertaken in 2008/09 on which series of tests of XBTs and XCTDs dropped simultaneously with CTD casts were performed. The XBT and XCTD depths were corrected by comparison with CTD data using a modification of an existing algorithm. Significant probe-to-probe fall-rate equation (FRE) velocity and deceleration coefficient variability was found within a cruise, as well as cruise-to-cruise variability. A small (∼0.01°C) temperature bias was also identified for XBTs on each cruise. No new FRE can be proposed for either the Bay of Bengal or the Arabian Sea for XBTs. For the more consistent XCTD, basin-specific FREs are possible for the Bay of Bengal, but not for the Arabian Sea. The XCTD FRE velocity coefficients are significantly higher than the XCTD manufacturers’ FRE coefficient or those from previous tests, possibly resulting from the influence of temperature on XCTD FRE. Mean temperature anomalies versus a long-term mean climatology for XBT data using the present default FRE have a bias (which is positive for three cruises and negative for one cruise) compared to the mean temperature anomalies for CTD data. Some improvement is found when applying newly calculated cruise-specific FREs. This temperature error must be accounted for in any study of temperature change in the regions.

Historical bathythermograph datasets are known to be biased, and there have been several efforts to model this bias. Three different correction models of temperature bias in the historical bathythermograph dataset are compared here: the steady model of Hanawa et al. and the time-dependent models of Levitus et al. and Wijffels et al. The impact of these different models is examined in the context of global analysis experiments using the Simple Ocean Data Assimilation system. The results show that the two time-dependent bias models significantly reduce warm bias in global heat content, notably in the 10 years starting in the early 1970s and again in the early 1990s. Overall, the Levitus et al. model has its greatest impact near the surface and the Wijffels et al. model has its greatest impact at subtropical thermocline depths. Examination of the vertical structure of temperature error shows that at thermocline depths the Wijffels et al. model overcompensates, leading to a slight cool bias, while at shallow levels the same model causes a slight warm bias in the central and eastern subtropics and at thermocline depths on the equator in the Pacific Ocean as a result of reduced vertical entrainment. The results also show that the bias-correction models may alter the representation of interannual variability. During the 1997/98 El Niño and the subsequent La Niña the Levitus et al. model, which has its main impact at shallow depths, reduces the 50-m temperature anomalies in the eastern equatorial Pacific by 10%–20% and strengthens the zonal currents by up to 50%. The Wijffels et al. correction, which has its main impact at deeper levels, has much less effect on the oceanic expression of ENSO.

This paper provides the first analysis of the seasonal evolution of the sonic layer depth (SLD) relative to the mixed layer depth (MLD) for the Aegean, Marmara, Black, and Azov Seas. SLD identifies the acoustic ducting capabilities of the upper ocean and is of interest to investigations of upper ocean acoustics. A monthly SLD climatology on a regular 0.25° × 0.25° grid is constructed from interpolation of available quality-controlled ocean temperature and salinity profiles using the kriging methodology. A four step pre-processing procedure is designed to reduce noise and the effects of sampling irregularities. Monthly SLD fields are then compared relative to the much more widely studied MLD as computed using four different methods from the recent scientific literature. The goals of this analysis are to characterize the SLD relative to the MLD and provide a means for computing SLD from limited hydrographic information and/or MLD estimates. Very deep SLD values are found during winter, in the Aegean and Black Seas, when the near surface temperature values become lower than the temperature below the permanent pycnocline. When this occurs, the SLD drops to the bottom while the MLD remains much shallower at the seasonal pycnocline. For the months of May through October the SLD tends to be less than 25 m for the entire region. It is demonstrated that MLD obtained from the four methodologies have high correlations with SLD over the annual cycle, indicating a robust relationship. As a result, SLD can be estimated using least squares regression coefficients when salinity is unavailable or when observation profiles do not extend to deeper levels.

Climatological fields of mixed layer depth (MLD) are presented over the Aegean, Marmara, Black and Azov Seas. Monthly fields of MLD are formed by historical individual temperature and salinity profiles from combination of various data sets with additional quality control procedures applied. Various definitions that are based solely on temperature (T) or those that include the impact of salinity (S) are applied to investigate the robustness in the pattern and values of the MLDs. Interpolation of the MLD fields to a 0.25 ◦ × 0.25 ◦ regular grid over the region is accomplished using a combination of median filter and ordinary kriging. Strong seasonal variability is noted in all regions. Given a density-based MLD criterion that includes both T and S, deep mixed layers (> 200 m) are noted in the Aegean Sea, especially eastern part of the region during winter while MLDs are generally much shallower (< 60 m) in the Black Sea. A criterion based on curvature method, which determines MLD according to first maximum of curvature of T may fail in representing deep MLDs in the Aegean Sea when the water column is well mixed. MLD fields obtained from all definitions are found to be strongly correlated to each other over the seasonal cycle, confirming the strong seasonal cycle. While the curvature method gives shallow MLDs only during winter, it has relatively large skill in comparison to the density-based MLD criterion. In general, MLD fields suffer from lack of input T and S profiles in the Marmara and Azov Seas, thus they may not be representative. Monthly MLD fields presented in this paper are available for various applications, such as mixed layer studies, ocean biology, ocean modeling and acoustic propagation.

Objectively analysed climatological mean fields of temperature and salinity have been calculated on a 0.25° grid for the World Ocean for the annual, seasonal, and monthly compositing periods using data from the World Ocean Database 2001. The annual and seasonal fields are calculated at standard levels from the surface to 5500 m. The monthly fields are calculated at standard levels from the surface to 1500 m. In comparison with similarly computed climatologies calculated on a 1° grid, ocean circulation features, such as the Gulf of Mexico Loop Current, are more clearly represented. The new 0.25° climatologies preserve most of the spatial resolution of earlier 0.25° temperature and salinity climatologies, while reducing noise by additional smoothing in horizontal space (geographically at each depth), vertically (along depth at each grid), as well as in time (Fourier filtering). Copyright  2005 Royal Meteorological Society.

Uncertainty in ocean analysis methods and deficiencies in the observing system are major obstacles for the reliable reconstruction of the past ocean climate. The variety of existing ocean reanalyses is exploited in a multi-reanalysis ensemble to improve the ocean state estimation and to gauge uncertainty levels. The ensemble-based analysis of signal-to-noise ratio allows the identification of ocean characteristics for which the estimation is robust (such as tropical mixed-layer-depth, upper ocean heat content), and where large uncertainty exists (deep ocean, Southern Ocean, sea ice thickness, salinity), providing guidance for future enhancement of the observing and data assimilation systems.

In addition to the sea level (SL) change, or anomaly (SLA), due to ocean thermal expansion, total steric SLA (SSLA, all change to the existing volume of ocean water) is also affected by ocean salinity variation. Less attention, however, has been paid to this halosteric effect, due to the global dominance of thermosteric SLA (TSLA) and the scarcity of salinity measurements. Here, we analyze halosteric SLA (HSLA) since 2005, when Argo data reached near-global ocean coverage, based on several observational products. We find that, on global average, the halosteric component contributes negatively by ~5.8% to SSLA during the 2005–2015 period, and reveals a modest correlation (~0.38) with ENSO on the inter-annual scale. Vertically, the global ocean was saltier in the upper 200-m and fresher within 200 to 600-m since 2005, while the change below 600-m was not significantly different from zero. The upper 200-m changes dominate the HSLA, suggesting the importance of surface fresh water flux forcing; meanwhile, the ocean dynamic might also play a role. The inconsistent pattern of salinity trend between upper 200-m and 200 to 600-m implies the importance of ocean dynamics. Our analysis suggests that local salinity changes cannot be neglected, and can even play a more important role in SSLA than the thermosteric component in some regions, such as the Tropical/North Pacific Ocean, the Southern Ocean, and the North Atlantic Ocean. This study highlights the need to better reconstruct historical salinity datasets, to better monitor the past SSLA changes. Also, it is important to understand the mechanisms (ocean dynamics vs. surface flux) related to regional ocean salinity changes.

The temporal variability of Pacific Ocean upper ocean heat content is examined for the 1948–1998 period using gridded, objectively analyzed temperature anomaly fields. Results indicate a “regime shift” in upper ocean temperature structure occurred in conjunction with the atmospheric shift in sea level pressure around 1975 leading to warming (cooling) in the eastern (western) equatorial Pacific. The shift is a basin-wide phenomenon affecting the thermal structure from 60°S to 70°N. EOF analysis of the Pacific Ocean heat content (0–125 m) shows a shift from a relatively cool to a warm state in the equatorial Pacific during the mid-1970s. Further analysis of the gridded temperature anomaly fields shows equatorial warming to be as much as 1.5°C and a cooling in the North Pacific of 1°C, down to 250 meters, after the mid-1970s. Overall, the analysis indicates the “regime shift” continues through 1998 with no signs of returning to a cooler phase.

We quantify the interannual-to-decadal variability of the heat content (mean temperature) of the world ocean from the surface through 3000-meter depth for the period 1948 to 1998. The heat content of the world ocean increased by ∼2 × 10 23 joules between the mid-1950s and mid-1990s, representing a volume mean warming of 0.06°C. This corresponds to a warming rate of 0.3 watt per meter squared (per unit area of Earth's surface). Substantial changes in heat content occurred in the 300- to 1000-meter layers of each ocean and in depths greater than 1000 meters of the North Atlantic. The global volume mean temperature increase for the 0- to 300-meter layer was 0.31°C, corresponding to an increase in heat content for this layer of ∼10 23 joules between the mid-1950s and mid-1990s. The Atlantic and Pacific Oceans have undergone a net warming since the 1950s and the Indian Ocean has warmed since the mid-1960s, although the warming is not monotonic.

Using objectively analyzed seasonal fields of dissolved oxygen content, percent oxygen saturation, and apparent oxygen utilization (AOU), we describe the large-scale seasonal variability of oxygen for the Atlantic and Pacific Oceans in the upper 400 m. The winter minus summer basin zonal averages of AOU reveal a two-layer feature in both the Atlantic and the Pacific, for both hemispheres. Biological activity and seasonal stratification in the summer give the upper 50–75 m of the water column in each basin a lower AOU in summer than winter. Greater mixing of upper ocean waters in winter gives the 75–400 m layer lower AOU values in that season. The basin integral seasonal volumes of oxygen for both the North Atlantic and the North Pacific mirror what is occurring in the atmosphere, indicating that there is a seasonal flux of oxygen across the air–sea interface. Winter total O2 volume in the ocean is above the annual mean; the summer volume is below. Larger seasonal differences in the total O2 content are observed in the North Atlantic Ocean than the North Pacific Ocean. A seasonal net outgassing (SNO) of 8.3×1014 moles O2 is calculated from basin means, which is 25% higher than previous results.

In 2013, the vast majority of the monitored climate variables reported here maintained trends established in recent decades. ENSO was in a neutral state during the entire year, remaining mostly on the cool side of neutral with modest impacts on regional weather patterns around the world. This follows several years dominated by the effects of either La Niña or El Niño events. According to several independent analyses, 2013 was again among the 10 warmest years on record at the global scale, both at the Earths surface and through the troposphere. Some regions in the Southern Hemisphere had record or near-record high temperatures for the year. Australia observed its hottest year on record, while Argentina and New Zealand reported their second and third hottest years, respectively. In Antarctica, Amundsen-Scott South Pole Station reported its highest annual temperature since records began in 1957. At the opposite pole, the Arctic observed its seventh warmest year since records began in the early 20th century. At 20-m depth, record high temperatures were measured at some permafrost stations on the North Slope of Alaska and in the Brooks Range. In the Northern Hemisphere extratropics, anomalous meridional atmospheric circulation occurred throughout much of the year, leading to marked regional extremes of both temperature and precipitation. Cold temperature anomalies during winter across Eurasia were followed by warm spring temperature anomalies, which were linked to a new record low Eurasian snow cover extent in May. Minimum sea ice extent in the Arctic was the sixth lowest since satellite observations began in 1979. Including 2013, all seven lowest extents on record have occurred in the past seven years. Antarctica, on the other hand, had above-average sea ice extent throughout 2013, with 116 days of new daily high extent records, including a new daily maximum sea ice area of 19.57 million km2 reached on 1 October. ENSO-neutral conditions in the eastern central Pacific Ocean and a negative Pacific decadal oscillation pattern in the North Pacific had the largest impacts on the global sea surface temperature in 2013. The North Pacific reached a historic high temperature in 2013 and on balance the globally-averaged sea surface temperature was among the 10 highest on record. Overall, the salt content in nearsurface ocean waters increased while in intermediate waters it decreased. Global mean sea level continued to rise during 2013, on pace with a trend of 3.2 mm yr-1 over the past two decades. A portion of this trend (0.5 mm yr-1) has been attributed to natural variability associated with the Pacific decadal oscillation as well as to ongoing contributions from the melting of glaciers and ice sheets and ocean warming. Global tropical cyclone frequency during 2013 was slightly above average with a total of 94 storms, although the North Atlantic Basin had its quietest hurricane season since 1994. In the Western North Pacific Basin, Super Typhoon Haiyan, the deadliest tropical cyclone of 2013, had 1-minute sustained winds estimated to be 170 kt (87.5 m s-1) on 7 November, the highest wind speed ever assigned to a tropical cyclone. High storm surge was also associated with Haiyan as it made landfall over the central Philippines, an area where sea level is currently at historic highs, increasing by 200 mm since 1970. In the atmosphere, carbon dioxide, methane, and nitrous oxide all continued to increase in 2013. As in previous years, each of these major greenhouse gases once again reached historic high concentrations. In the Arctic, carbon dioxide and methane increased at the same rate as the global increase. These increases are likely due to export from lower latitudes rather than a consequence of increases in Arctic sources, such as thawing permafrost. At Mauna Loa, Hawaii, for the first time since measurements began in 1958, the daily average mixing ratio of carbon dioxide exceeded 400 ppm on 9 May. The state of these variables, along with dozens of others, and the 2013 climate conditions of regions around the world are discussed in further detail in this 24th edition of the State of the Climate series. © 2014, American Meteorological Society. All rights reserved.

The evolution of ocean temperature measurement systems is presented with a focus on the development and accuracy of two critical devices in use today (expendable bathythermographs and conductivity-temperature-depth instruments used on Argo floats). A detailed discussion of the accuracy of these devices and a projection of the future of ocean temperature measurements are provided. The accuracy of ocean temperature measurements is discussed in detail in the context of ocean heat content, Earth's energy imbalance, and thermosteric sea level rise. Up-to-date estimates are provided for these three important quantities. The total energy imbalance at the top of atmosphere is best assessed by taking an inventory of changes in energy storage. The main storage is in the ocean, the latest values of which are presented. Furthermore, despite differences in measurement methods and analysis techniques, multiple studies show that there has been a multidecadal increase in the heat content of both the upper and deep ocean regions, which reflects the impact of anthropogenic warming. With respect to sea level rise, mutually reinforcing information from tide gauges and radar altimetry shows that presently, sea level is rising at approximately 3 mm yr-1 with contributions from both thermal expansion and mass accumulation from ice melt. The latest data for thermal expansion sea level rise are included here and analyzed. Key Points Oceanographic techniques and analysis have improved over many decadesThese improvements allow more accurate Earth-energy balance estimatesUnderstanding of ocean heat content and sea-level rise has also increased ©2013. American Geophysical Union. All Rights Reserved.

The World Ocean Database (WOD) is the most comprehensive global ocean profile-plankton database available internationally without restriction. All data are in one well-documented format and are available both on DVDs for a minimal charge and on-line without charge. The latest DVD version of the WOD is the World Ocean Database 2009 (WOD09). All data in the WOD are associated with as much metadata as possible, and every ocean data value has a quality control flag associated with it. The WOD is a product of the U.S. National Oceanographic Data Center and its co-located World Data Center for Oceanography. However, the WOD exists because of the international oceanographic data exchange that has occurred under the auspices of the Intergovernmental Oceanographic Commission (IOC) and the International Council of Science (ICSU) World Data Center (WDC) system. World Data Centers are part of the ICSU World Data System.