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Argo floats are widely used to characterize vertical structures of ocean eddies, yet their capability to invert sea surface features of eddies, especially those overlooked by available altimeters, has not been explored. In this paper, we propose an "interior-to-surface" inversion algorithm to effectively expand the capacity of eddy detection by estimating altimeter-missed eddies' surface attributes from their Argo-derived potential density anomaly profiles, given that the interior property and surface signature of eddies are highly correlated. An altimeter-calibrated machine learning ensemble is employed for the inversion training based on the joint altimeter–Argo eddy data and shows promising performance with mean absolute errors of 5.4 km, 0.5 cm, and 14.3 cm2 s−2 for eddy radius, amplitude, and kinetic energy, respectively. Then, the trained ensemble model is applied to independently invert the properties of eddies captured by an Argo-alone detection scheme, which yields high spatiotemporal consistency with their altimeter-captured counterparts. In particular, a portion of Argo-alone eddies is ∼25% smaller than altimeter-derived ones, indicating Argo's unique capability of profiling weaker submesoscale eddies. Sea surface temperature and chlorophyll data are further applied to validate the reliability of eddies identified and characterized by the Argo-only algorithm. This new methodology effectively complements that of altimetry in eddy detection and can be expanded to estimate other physical/biochemical eddy variables from a variety of in situ observations. Significance Statement: Despite thousands of eddies being routinely identified on a daily basis, it has been recognized that a substantial portion of eddies may still be missed due to inadequate sampling of altimeter constellations. Taking advantage of eddy's correlation between surface and interior, a considerable number of eddies are discovered for the first time through an Argo-based eddy identification scheme. Here, we propose a new methodology to independently infer these recaptured eddies' surface properties from their vertical signals through an "interior-to-surface" inversion process. The inferred eddy properties are verified by the spatiotemporal consistency with those derived from altimetry. Since Argo is capable of profiling smaller and weaker eddies, the proposed methodology significantly complements and expands that of altimetry in eddy observation. [ABSTRACT FROM AUTHOR]

Polar and subpolar oceans play a particularly important role in the global climate and its temporal changes, yet these regions are less well sampled than the rest of the global ocean. To better understand the physical or biogeochemical properties and their variabilities in these regions, accurate data mapping is crucial. In this paper, we introduce a mapping methodology that includes a water column shrinking and stretching constraint (SSC) based on the principle of conservation of potential vorticity. To demonstrate the mapping scheme efficiency, we map the ocean temperature in the southern Sea of Okhotsk, where the bottom topography comprises a broad and shallow shelf, a sharp continental slope, and a deep oceanic basin. Such topographic features are typical of polar and subpolar marginal seas. Results reveal that the SSC integrated (SSCI) mapping strongly reduces the mapping error in the broad and shallow shelf compared with a recently introduced topographic constraint integrated (TCI) mapping procedure. We also tested our mapping scheme in the Southern Ocean, which has a comparatively slanted shelf, a wider and gentler slope, and a deep and broad oceanic basin. We found that the SSCI and TCI methods are practically equivalent there. The SSCI mapping is thus an effective method to map the ocean's properties in various topographic environments and should be adequate in all polar and subpolar regions. Importantly, we introduced a standardized procedure for determining the decorrelation length scales—a necessary step prior to implementing any mapping scheme—in any topographic conditions. [ABSTRACT FROM AUTHOR]

This paper describes the new International Comprehensive Ocean–Atmosphere Data Set (ICOADS) near-real-time (NRT) release (R3.0.2), with greatly enhanced completeness over the previous version (R3.0.1). R3.0.1 had been operationally produced monthly from January 2015 onward, with input data from the World Meteorological Organization (WMO) Global Telecommunication Systems (GTS) transmissions in the Traditional Alphanumeric Codes (TAC) format. Since the release of R3.0.1, however, many observing platforms have changed, or are in the process of transitioning, to the Binary Universal Form for Representation of Meteorological Data (BUFR) format. R3.0.2 combines input data from both BUFR and TAC formats. In this paper, we describe input data sources; the BUFR decoding process for observations from drifting buoys, moored buoys, and ships; and the data quality control of the TAC and BUFR data streams. We also describe how the TAC and BUFR streams were merged to upgrade R3.0.1 into R3.0.2 with duplicates removed. Finally, we compare the number of reports and spatial coverage of essential climate variables (ECVs) between R3.0.1 and R3.0.2. ICOADS NRT R3.0.2 shows both quantitative and qualitative gains from the inclusion of BUFR reports. The number of observations in R3.0.2 increased by nearly 1 million reports per month, and the coverage of buoy and ship sea surface temperatures (SSTs) on monthly 2° × 2° grids increased by 20%. The number of reported ECVs also increased in R3.0.2. For example, observations of SST and sea level pressure (SLP) increased by around 30% and 20%, respectively, as compared to R3.0.1, and salinity is a new addition to the ICOADS NRT product in R3.0.2. Significance Statement: The International Comprehensive Ocean–Atmosphere Data Set (ICOADS) is the largest collection of surface marine observations spanning from 1662 to the present. A new version, ICOADS near-real-time 3.0.2, includes data transmitted in the Binary Universal Form for Representation of Meteorological Data (BUFR) format, in combination with Traditional Alphanumeric Codes (TAC) data. Many of the organizations that report observations in near–real time have moved to BUFR, so this update brings ICOADS into alignment with collections and archives of these international data distributions. By including the BUFR reports, the number of observations in the upgraded version of ICOADS increased by nearly one million reports per month and spatial coverage of buoy and ship SSTs increased by 20% over the previous version. [ABSTRACT FROM AUTHOR]

This paper addresses analysis of the global monthly sea surface temperatures using a reconstructed dataset that goes back to 1884. We use fractional integration methods to examine features such as persistence, seasonality, and time trends in the data. The results show that seasonality is a relevant issue, finding evidence of seasonal unit roots. With the seasonal component removed, persistence is also very significant, and, when looking at the data month by month, evidence of significant linear trends is detected in all cases. According to these results, monthly sea surface temperatures increase by between 0.07° and 0.11°C every 100 years. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) fronts are important for fisheries and marine ecology, as well as upper-ocean dynamics, weather forecasting, and climate monitoring. In this paper, we propose a new approach to detect SST fronts from RADARSAT-2 ScanSAR images, based on the correlation of SAR-derived wind speeds using the gray level cooccurrence matrix (GLCM) approach. Due to the large differences between the correlation of wind speeds for SST fronts compared to other areas, SST fronts can be detected by the threshold method. To eliminate small-scale features (or noise), the 30 km scale is used as the length threshold for the detection of the SST fronts. The proposed method is effective when wind speeds are between 3 and 13 m s−1. The overall accuracy of our method is about 93.6%, which is sufficient for operational applications. [ABSTRACT FROM AUTHOR]

Since the mid-2000s, the Argo oceanographic observational network has provided near-real-time fourdimensional data for the global ocean for the first time in history. Internet (i.e., the ''web'') applications that handle the more than two million Argo profiles of ocean temperature, salinity, and pressure are an active area of development. This paper introduces a new and efficient interactive Argo data visualization and delivery web application named Argovis that is built on a classic three-tier design consisting of a front end, back end, and database. Together these components allow users to navigate 4D data on a world map of Argo floats, with the option to select a custom region, depth range, and time period. Argovis's back end sends data to users in a simple format, and the front end quickly renders web-quality figures. More advanced applications query Argovis from other programming environments, such as Python, R, andMATLAB. Our Argovis architecture allows expert data users to build their own functionality for specific applications, such as the creation of spatially gridded data for a given time and advanced time-frequency analysis for a space-time selection. Argovis is aimed to both scientists and the public, with tutorials and examples available on the website, describing how to use the Argovis data delivery system--for example, how to plot profiles in a region over time or to monitor profile metadata. [ABSTRACT FROM AUTHOR]

The accumulated remote sensing data of altimeters and scatterometers have provided new opportunities for ocean state forecasting and have improved our knowledge of ocean–atmosphere exchanges. Studies on multivariate, multistep, spatiotemporal sequence forecasts of sea level anomalies (SLA) for different modalities, however, remain problematic. In this paper, we present a novel hybrid and multivariate deep neural network, named HMnet3, which can be used for SLA forecasting in the South China Sea (SCS). First, a spatiotemporal sequence forecasting network is trained by an improved convolutional long short-term memory (ConvLSTM) network using a channelwise attention mechanism and multivariate data from 1993 to 2015. Then a time series forecasting network is trained by an improved long short-term memory (LSTM) network, which is realized by ensemble empirical mode decomposition (EEMD). Finally, the two networks are combined by a successive correction method to produce SLA forecasts for lead times of up to 15 days, with a special focus on the open sea and coastal regions of the SCS. During the testing period of 2016–18, the performance of HMnet3 with sea surface temperature anomaly (SSTA), wind speed anomaly (SPDA), and SLA data is much better than those of state-of-the-art dynamic and statistical (ConvLSTM, persistence, and climatology) forecast models. Stricter testbeds for trial simulation experiments with real-time datasets are investigated, where the eddy classification metrics of HMnet3 are favorable for all properties, especially for those of small-scale eddies. [ABSTRACT FROM AUTHOR]

The multifractal fusion method has proved to be an effective algorithm to mitigate the noise of the sea surface salinity (SSS) of Soil Moisture Ocean Salinity (SMOS) mission. However, the traditional nonparametric weight function used in this method is unable to fully capture the dynamic evolution of the oceanic environment. Considering the multiscale, nonuniform, anisotropic, and flow-dependent nature of the ocean, a prototype with the so-called flexible circle (FLC) weight function or flexible ellipse (FLE) weight function with a set of predefined parameters is proposed in this paper. The improved weight functions could draw dynamic information from the sea surface temperature, Rossby radius of deformation, and surface geostrophic flow to improve the quality of the remotely sensed SSS. The validation against the in situ data indicates that the improved weight functions perform better than the traditional one with a reduced root-mean-square (RMS) and standard deviation (STD) of the differences with respect to EN 4.2.0 profiles (from 0.50 and 0.46 to 0.42 and 0.38 for FLC and 0.39 and 0.36 for FLE in the global ocean). In particular, the FLE scheme could highlight the variation of the strong currents without affecting the computational efficiency. Furthermore, this paper discusses the influences of the error distribution on the fusion results and underlines the importance of error-based adaptions for further improvements. [ABSTRACT FROM AUTHOR]

Expendable bathythermographs (XBT) to profile upper-ocean temperatures from vessels in motion have been in use for some 50 years now. Developed originally for navy use, they were soon adapted by oceanographers to map out upper-ocean thermal structure and its space-time variability from both research vessels and merchant marine vessels in regular traffic. These activities continue today. This paper describes a new technology--the Autonomous Expendable Instrument System (AXIS)--that has been developed to provide the capability to deploy XBT probes on a predefined schedule, or adaptively in response to specific events without the presence of an observer on board. AXIS is a completely self-contained system that can hold up to 12 expendable probes [XBTs, XCTDs, expendable sound velocimeter (XSV)] in any combination. A single-board Linux computer keeps track of what probes are available, takes commands from ashore via Iridium satellite on what deployment schedule to follow, and records and forwards the probe data immediately with a time stamp and the GPS position. This paper provides a brief overview of its operation, capabilities, and some examples of how it is improving coverage along two lines in the Atlantic. [ABSTRACT FROM AUTHOR]

Both observed and averaged oceanographic data often contain regions with density inversions. This paper presents two methods of stabilizing a water column. The first method is intended for use with observed data; it minimally adjusts Absolute Salinity while leaving the values of in situ temperature unchanged. The second method adjusts the values of both Absolute Salinity and Conservative Temperature, and these adjustments are made in such a way as to cause the least possible damage to the water-mass structure of the vertical cast. [ABSTRACT FROM AUTHOR]

Historical in situ ocean temperature profile measurements are important for a wide range of ocean and climate research activities. A large proportion of the profile observations have been recorded using expendable bathythermographs (XBTs), and required bias corrections for use in climate change studies. It is generally accepted that the bias, and therefore bias correction, depends on the type of XBT used. However, poor historical metadata collection practices mean the XBT probe type information is often missing, for 59% of profiles between 1967 and 2000, limiting the development of reliable bias corrections. We develop a process of estimating missing instrument type metadata (the combination of both model and manufacturer) systematically, constructing a machine learning pipeline based on thorough data exploration to inform these choices. The predicted instrument type, where missing, will facilitate improved XBT bias corrections. The new approach improves the accuracy of the XBT type classification compared to previous approaches from a recall value of 0.75–0.94. We also develop an approach to account for the uncertainty associated with metadata assignments using ensembles of decision trees, which could feed into an ensemble approach to creating ocean temperature datasets. We describe the challenges arising from the nature of the dataset in applying standard machine learning techniques to the problem. We have implemented this in a portable, reproducible way using standard data science tools, with a view to these techniques being applied to other similar problems in climate science. [ABSTRACT FROM AUTHOR]

To ensure confidence, measurements carried out by imaging radiometers mounted on satellites require robust validation using "fiducial quality" measurements of the same in situ parameter. For surface temperature measurements this is optimally carried out by radiometers measuring radiation emitted in the infrared region of the spectrum, collocated to that of a satellite overpass. For ocean surface temperatures the radiometers are usually on board ships to sample large areas but for land and ice they are typically deployed at defined geographical sites. It is of course critical that the validation measurements and associated instrumentation are internationally consistent and traceable to international standards. The Committee on Earth Observation Satellites (CEOS) facilitates this process and over the last two decades has organized a series of comparisons, initially to develop and share best practice, but now to assess metrological uncertainties and degree of consistency of all the participants. The fourth CEOS comparison of validation instrumentation: blackbodies and infrared radiometers, was held at the National Physical Laboratory (NPL) during June and July 2016, sponsored by the European Space Agency (ESA). The 2016 campaign was completed over a period of three weeks and included not only laboratory-based measurements but also representative measurements carried out in field conditions, over land and water. This paper is one of a series and reports the results obtained when radiometers participating in this comparison were used to measure the radiance temperature of the NPL ammonia heat-pipe blackbody during the 2016 comparison activities (i.e., an assessment of radiometer performance compared to international standards). This comparison showed that the differences between the participating radiometer readings and the corresponding temperature of the reference blackbody were within the uncertainty of the measurements, but there were a few exceptions, particularly for a reference blackbody temperature of −30°C. Reasons that give rise to the discrepancies observed at the low blackbody temperatures were identified. [ABSTRACT FROM AUTHOR]

Identification and exclusion of clouds from satellite-based infrared fields is critical to achieve accurate retrievals of sea surface temperature (SST). Historically, identification of clouds has been driven primarily by a few uniformity tests involving a small number of pixels, brightness temperature range tests, and comparisons to low-resolution gap-free reference fields. Collectively these tests are adequate at identifying large, upper-level, very cold cumulus clouds, and uniformity tests identify moderately sized patchy cumulus clouds. But the efficacy of cloud identification often decreases at cloud edges, for small or thin cirrus clouds, and for the lower, more uniform stratus clouds, for which cloud-top temperature can be comparable to that of the sea surface, particularly at high latitudes. The heavy reliance on stringent uniformity thresholds often also has the unintended consequence of eliminating strong SST frontal regions fromthe pool of best-quality retrievals. This paper presents results for an ensemble cloud classifier based on a machine-learning approach, boosted alternating decision trees (ADtrees), applied to NASA MODIS and VIIRS SST imagery. The ADtree algorithm relies on the use of a majority vote from a collection of both ''weak'' and ''strong'' classifiers. This approach offers the potential to identifymore cloud types and improve the retention of SST gradients in best-quality SST retrievals and also provides a per pixel confidence estimate in the classification. [ABSTRACT FROM AUTHOR]

The T5 expendable bathythermographs reach the greatest depth within the current XBT family. Since the early 1970s, in several areas they have been providing a significant part of available temperature profiles below 1000 m and therefore represent an important resource for ocean climate study. In this paper we present new results from laboratory tests of Sippican T5 and T5/20 probes and analyses of more than 350 XBT–CTD matched pairs from our own field trials and the World Ocean Database (WOD), and we propose an improved fall rate equation (coefficients: A = 6.720 ± 0.025 m s−1, B = 0.001 60 ± 0.000 15 m s−2, Offset = 1.00 ± 0.65 m). Possible influences of probe physical characteristics and initial launch conditions on the probe motion have also been investigated with launching height and probe weight being identified as important factors. Analyses also confirm that fall speed and pure temperature error increase with water temperature, as previously reported for other XBT types. The uncertainties in depth and temperature measurements are then calculated. Finally, a new correction for a global T5 dataset is proposed, with an update of the currently available schemes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper provides a spectral optimal gridding (SOG) method to make a dynamically consistent reconstruction of water temperature for the global ocean at different depth levels. The dynamical consistency is achieved by using the basis of empirical orthogonal functions (EOFs) derived from NASA Jet Propulsion Laboratory (JPL) non-Boussinesq ocean general circulation model (OGCM) output at ¼° resolution from 1958 to 2013. A convenient singular value decomposition (SVD) method is used to calculate the EOFs, in order to enable efficient computing for a fine spatial grid globally. These EOFs are used as explainable variables and are regressed against the sparsely distributed in situ ocean temperature data at 33 standard depth levels. The observed data are aggregated onto a 1° latitude-longitude grid at each level from the surface to the 5500-m layer for the period 1950-2014. Three representative temperature reconstruction examples are presented and validated: two 10-m-layer (i.e., the second layer from the surface) reconstructions for January 2008 and January 1998, which are compared with independent sea surface temperature (SST) observations; and one 100-m-layer reconstruction for January 1998, which shows a strong cold anomaly El Niño signal in the western tropical Pacific up to −5°C from 150°E to 140°W. The SOG reconstruction can accurately locate the El Niño signal region in different ocean layers. The SOG reconstruction method is shown reliable and yields satisfactory accuracy even with sparse data. Validation and error analysis indicate that no systematic biases exist in the observed and reconstructed data. [ABSTRACT FROM AUTHOR]

A quality control (QC) procedure is developed to estimate monthly mean climatologies from the large Argo dataset (2005-12) over the North Pacific western boundary current region. In addition to the individual QC procedure, which checks for instrumental, transmission, and gross errors, the paper describes and shows the impact of climatological checks (collective QC) on the quality of both processed profiles and resultant climatological distributions. Objective analysis (OA) is applied progressively to produce the gridded climatological fields. The method uses horizontal regional climatological averages defined in five regime-oriented subregions in the Kuroshio area and the Japan Sea. Performing the QC procedure on specific coherent subregions produces improved profiling data and climatological fields because more details about the local hydrodynamics are taken into consideration. Nonrepresentative data and random noises are more effectively rejected by this method, which has value both in defining a climatological mean and identifying outlier data. Assessing with both profiling and coordinated datasets, the agreement is reasonably good (particularly for those areas with abundant observations), but the results (although already smoothed) can capture more detailed or mesoscale features for further regional studies. The method described has the potential to meet future challenges in processing accumulating Argo observations in the coming decades. [ABSTRACT FROM AUTHOR]

The 2010-12 Acoustic Technology for Observing the Interior of the Arctic Ocean (ACOBAR) experiment provided acoustic tomography data along three 167-301-km-long sections in Fram Strait between Greenland and Spitsbergen. Ocean sound speed data were assimilated into a regional numerical ocean model using the Massachusetts Institute of Technology General Circulation Model--Estimating the Circulation and Climate of the Ocean four-dimensional variational (MITgcm-ECCO 4DVAR) assimilation system. The resulting state estimate matched the assimilated sound speed time series; the root-mean-squared (RMS) error of the sound speed estimate (~0.4 m s-1) is smaller than the uncertainty of the measurement (~0.8 m s-1). Data assimilation improved modeled range- and depth-averaged ocean temperatures at the 78°50'N oceanographic mooring section in Fram Strait. The RMS error of the state estimate (0.21°C) is comparable to the uncertainty of the interpolated mooring section (0.23°C). Lack of depth information in the assimilated ocean sound speed measurements caused an increased temperature bias in the upper ocean (0-500 m). The correlations with the mooring section were not improved as short-term variations in the mooring measurements and the ocean state estimate do not always coincide in time. This is likely due to the small-scale eddying and nonlinearity of the ocean circulation in Fram Strait. Furthermore, the horizontal resolution of the state estimate (4.5 km) is eddy permitting, rather than eddy resolving. Thus, the state estimate cannot represent the full ocean dynamics of the region. This study is the first to demonstrate the usefulness of large-scale acoustic measurements for improving ocean state estimates at high latitudes. [ABSTRACT FROM AUTHOR]

A retrospective tropical Indian Ocean dipole mode (IOD) hindcast for 1958-2014 was conducted using 20 models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), with a model-based analog forecast (MAF) method. In the MAF approach, forecast ensembles are extracted from preexisting model simulations by finding the states that initially best match an observed anomaly and tracking their subsequent evolution, with no additional model integrations. By optimizing the key factors in the MAF method, we suggest that the optimal domain for the analog criteria should be concentrated in the tropical Indian Ocean region for IOD predictions. Including external forcing trends improves the skills of the east and west poles of the IOD, but not the IOD prediction itself. The MAF IOD prediction showed comparable skills to the assimilation-initialized hindcast, with skillful predictions corresponding to a 4- and 3-month lead, respectively. The IOD forecast skill had significant decadal variations during the 55-yr period, with low skill after the early 2000s and before 1985 and high skill during 1985-2000. This work offers a computational efficient and practical approach for seasonal prediction of the tropical Indian Ocean sea surface temperature. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) forecast products from the NCEP Climate Forecast System (CFSv2) that are widely used in climate research and prediction have nonstationary bias. In this study, we develop single- (ANN1) and threehidden- layer (ANN3) neural networks and examine their ability to correct the SST bias in the NCEP CFSv2 extended seasonal forecast starting from July in the extratropical Northern Hemisphere. Our results show that the ensemble-based ANN1 and ANN3 can reduce the uncertainty associated with parameters assigned initially and dependence on random sampling. Overall, ANN1 reduces RMSE of the CFSv2 forecast SST substantially by 0.35°C (0.34°C) for the testing (training) data and ANN3 further reduces RMSE relatively by 0.49°C (0.47°C). Both the ensemble-based ANN1 and ANN3 can significantly reduce the spatially and temporally varying bias of the CFSv2 forecast SST in the Pacific and Atlantic Oceans, and ANN3 shows better agreement with the observation than that of ANN1 in some subregions. [ABSTRACT FROM AUTHOR]

Our study shows that the intercomparison among sea surface temperature (SST) products is influenced by the choice of SST reference, and the interpolation of SST products. The influence of reference SST depends on whether the reference SSTs are averaged to a grid or in pointwise in situ locations, including buoy or Argo observations, and filtered by first-guess or climatology quality control (QC) algorithms. The influence of the interpolation depends on whether SST products are in their original grids or preprocessed into common coarse grids. The impacts of these factors are demonstrated in our assessments of eight widely used SST products (DOISST, MUR25, MGDSST, GAMSSA, OSTIA, GPB, CCI, CMC) relative to buoy observations: (i) when the reference SSTs are averaged onto 0.25° × 0.25° grid boxes, the magnitude of biases is lower in DOISST and MGDSST (<0.03°C), and magnitude of root-mean-square differences (RMSDs) is lower in DOISST (0.38°C) and OSTIA (0.43°C); (ii) when the same reference SSTs are evaluated at pointwise in situ locations, the standard deviations (SDs) are smaller in DOISST (0.38°C) and OSTIA (0.39°C) on 0.25° × 0.25° grids; but the SDs become smaller in OSTIA (0.34°C) and CMC (0.37°C) on products' original grids, showing the advantage of those high-resolution analyses for resolving finer-scale SSTs; (iii) when a loose QC algorithm is applied to the reference buoy observations, SDs increase; and vice versa; however, the relative performance of products remains the same; and (iv) when the drifting-buoy or Argo observations are used as the reference, the magnitude of RMSDs and SDs become smaller, potentially due to changes in observing intervals. These results suggest that high-resolution SST analyses may take advantage in intercomparisons. Significance Statement: Intercomparisons of gridded SST products be affected by how the products are compared with in situ observations: whether the products are in coarse (0.25°) or original (0.05°–0.10°) grids, whether the in situ SSTs are in their reported locations or gridded and how they are quality controlled, and whether the biases of satellite SSTs are corrected by localized matchups or large-scale patterns. By taking all these factors into account, our analyses indicate that the NOAA DOISST is among the best SST products for the long period (1981–present) and relatively coarse (0.25°) resolution that it was designed for. [ABSTRACT FROM AUTHOR]

Stripe noise is a common issue in sea surface temperatures (SSTs) retrieved from thermal infrared data obtained by satellite-based multidetector radiometers. We developed a bispectral filter (BSF) to reduce the stripe noise. The BSF is a Gaussian filter and an optimal estimation method for the differences between the data obtained at the split window. A kernel function based on the physical processes of radiative transfer has made it possible to reduce stripe and random noise in retrieved SSTs without degrading the spatial resolution or generating bias. The Second-Generation Global Imager (SGLI) is an optical sensor on board the Global Change Observation Mission–Climate (GCOM-C) satellite. We applied the BSF to SGLI data and validated the retrieved SSTs. The validation results demonstrate the effectiveness of BSF, which reduced stripe noise in the retrieved SGLI SSTs without blurring SST fronts. It also improved the accuracy of the SSTs by about 0.04 K (about 13%) in the robust standard deviation. Significance Statement: This method reduces stripe noise and improves the accuracy of SST data with minimal compromise of spatial resolution. The method assumes the relationship between the brightness temperature and the brightness temperature difference in the split window based on the physical background of atmospheric radiative transfer. The physical background of the data provides an easy solution to a complex problem. Although destriping generally requires a complex algorithm, our approach is based on a simple Gaussian filter and is easy to implement. [ABSTRACT FROM AUTHOR]

Marine heatwaves (MHWs) exert devastating impacts on ecosystems. Understanding their responses to anthropogenic forcing has attracted rapidly growing scientific interest. Given the disparate adaptation capacity and mobility among marine species, it is crucial to disentangle changes of MHWs related to the rising mean temperature from those to the changing temperature variability. It has been suggested that the latter's effects could be isolated by replacing a fixed baseline with a moving one for calculating the climatological mean and percentile metrics in MHW analysis. In this study, the effects of a moving baseline on MHW statistics (annual MHW days and cumulative intensity) changes in a warming climate are systematically evaluated based on simulations from simple stochastic climate models and a set of coupled general circulation models in the Community Earth System Model Large Ensemble project. On the one hand, a moving baseline does not necessarily remove the influences of rising mean temperature on MHW changes and will artificially cause negative trends in MHW statistics when the ocean exhibits an accelerated warming rate as in the RCP8.5 scenario. On the other hand, it always underestimates the effects of changing temperature variability on MHW changes. We propose a new model that adopts a "partial" moving baseline combined with a local linear detrending to isolate MHW changes caused by changing temperature variability. Significance Statement: A new model is proposed to isolate marine heatwave changes caused by changing temperature variability from those by rising mean temperature under greenhouse warming. [ABSTRACT FROM AUTHOR]

Mesoscale eddies are a mechanism for ocean energy transfer, and identifying them on a global scale provides a means of exploring ocean mass and energy exchange between ocean basins. There are many widely used model-driven methods for detecting mesoscale eddies; however, these methods are not fully robust or generalizable. This study applies a data-driven method and proposes a mesoscale detection network based on the extraction of eddy-related spatiotemporal information from multisource remote sensing data. Focusing on the northwest Pacific, the study first analyzes mesoscale eddy characteristics using a combination of gridded data for the absolute dynamic topography (ADT), sea surface temperature (SST), and absolute geostrophic velocity (UVG). Then, a deep learning network with a dual-attention mechanism and a convolutional long short-term memory module is proposed, which can deeply exploit spatiotemporal feature relevance while encoding and decoding information in the gridded data. Based on the analysis of mesoscale eddy characteristics, ADT and UVG gridded data are selected to be the inputs for the detection network. The experiments show that the accuracy of the proposed network reaches 93.38%, and the weighted mean dice coefficient reaches 0.8918, which is a better score than those achieved by some of the detection networks proposed in previous studies, including U-Net, SymmetricNet, and ResU-Net. Moreover, compared with the model-driven approach used to generate the ground-truth dataset, the network method proposed here demonstrates better performance in detecting mesoscale eddies at smaller scales, partially addressing the problem of ghost eddies. [ABSTRACT FROM AUTHOR]

The Air-Launched Autonomous Micro Observer (ALAMO) is a versatile profiling float that can be launched from an aircraft to make temperature and salinity observations of the upper ocean for over a year with high temporal sampling. Similar in dimensions and weight to an airborne expendable bathythermograph (AXBT), but with the same capability as Argo profiling floats, ALAMOs can be deployed from an A-sized (sonobuoy) launch tube, the stern ramp of a cargo plane, or the door of a small aircraft. Unlike an AXBT, however, the ALAMO float directly measures pressure, can incorporate additional sensors, and is capable of performing hundreds of ocean profiles compared to the single temperature profile provided by an AXBT. Upon deployment, the float parachutes to the ocean, releases the air-deployment package, and immediately begins profiling. Ocean profile data along with position and engineering information are transmitted via the Iridium satellite network, automatically processed, and then distributed by the Global Telecommunications System for use by the operational forecasting community. The ALAMO profiling mission can be modified using the two-way Iridium communications to change the profiling frequency and depth. Example observations are included to demonstrate the ALAMO's utility. [ABSTRACT FROM AUTHOR]

The 2010–11 North Pacific Acoustic Laboratory (NPAL) Philippine Sea experiment measured travel times between six acoustic transceiver moorings in a 660-km diameter ocean acoustic tomography array in the northern Philippine Sea (NPS). The travel-time series compare favorably with travel times computed for a yearlong series of state estimates produced for this region using the Massachusetts Institute of Technology General Circulation Model–Estimating the Circulation and Climate of the Ocean four-dimensional variational (MITgcm-ECCO 4DVAR) assimilation system constrained by satellite sea surface height and sea surface temperature observations and by Argo temperature and salinity profiles. Fluctuations in the computed travel times largely match the fluctuations in the measurements caused by the intense mesoscale eddy field in the NPS, providing a powerful test of the observations and state estimates. The computed travel times tend to be shorter than the measured travel times, however, reflecting a warm bias in the state estimates. After processing the travel times to remove tidal signals and extract the low-frequency variability, the differences between the measured and computed travel times were used in addition to SSH, SST, and Argo temperature and salinity observations to further constrain the model and generate improved state estimates. The assimilation of the travel times reduced the misfit between the measured and computed travel times, while not increasing the misfits with the other assimilated observations. The state estimates that used the travel times are more consistent with temperature measurements from an independent oceanographic mooring than the state estimates that did not incorporate the travel times. [ABSTRACT FROM AUTHOR]

Upper-ocean turbulence is central to the exchanges of heat, momentum, and gases across the air–sea interface and therefore plays a large role in weather and climate. Current understanding of upper-ocean mixing is lacking, often leading models to misrepresent mixed layer depths and sea surface temperature. In part, progress has been limited by the difficulty of measuring turbulence from fixed moorings that can simultaneously measure surface fluxes and upper-ocean stratification over long time periods. Here we introduce a direct wavenumber method for measuring turbulent kinetic energy (TKE) dissipation rates ϵ from long-enduring moorings using pulse-coherent ADCPs. We discuss optimal programming of the ADCPs, a robust mechanical design for use on a mooring to maximize data return, and data processing techniques including phase-ambiguity unwrapping, spectral analysis, and a correction for instrument response. The method was used in the Salinity Processes Upper-Ocean Regional Study (SPURS) to collect two year-long datasets. We find that the mooring-derived TKE dissipation rates compare favorably to estimates made nearby from a microstructure shear probe mounted to a glider during its two separate 2-week missions for O(10−8) ≤ ϵ ≤ O(10−5) m2 s−3. Periods of disagreement between turbulence estimates from the two platforms coincide with differences in vertical temperature profiles, which may indicate that barrier layers can substantially modulate upper-ocean turbulence over horizontal scales of 1–10 km. We also find that dissipation estimates from two different moorings at 12.5 and at 7 m are in agreement with the surface buoyancy flux during periods of strong nighttime convection, consistent with classic boundary layer theory. Significance Statement: This study outlines a method to estimate ocean turbulence from long-enduring platforms. It is difficult to make this measurement using commonly accepted turbulence estimation methods because of ocean waves, platform motions, battery and data limitations, biofouling, and the fragility of some common turbulence instruments. We applied the method at three sites and compared the results from the new method with those from short-duration datasets that use a currently accepted method. We outline the range and limitations of the new method, based both on the instrument's principles of operation and on the comparison with an established method. Our intention is that the new method may be applied by others in future long-enduring deployments, which will increase the number of available turbulence datasets in the upper ocean. [ABSTRACT FROM AUTHOR]

In situ sea surface temperature (SST) measurements play a critical role in the calibration/validation (Cal/Val) of satellite SST retrievals and ocean data assimilation. However, their quality is not always optimal, and proper quality control (QC) is required before they can be used with confidence. The in situ SST Quality Monitor (iQuam) system was established at the National Oceanic and Atmospheric Administration (NOAA) in 2009, initially to support the Cal/Val of NOAA satellite SST products. It collects in situ SST data from multiple sources, performs uniform QC, monitors the QCed data online, and distributes them to users. In this study, the iQuam QC is compared with other QC methods available in some of the in situ data ingested in iQuam. Overall, the iQuam QC performs well on daily to monthly time scales over most global oceans and under a wide variety of environmental conditions. However, it may be less accurate in the daytime, when a pronounced diurnal cycle is present, and in dynamic regions, because of the strong reliance on the "reference SST check," which employs daily low-resolution level-4 analyses with no diurnal cycle resolved. The iQuam "performance history check," applied to all in situ platforms, is an effective alternative to the customary "black/gray" lists, available only for some platforms (e.g., drifters and Argo floats). In the future, iQuam QC will be upgraded [e.g., using improved reference field(s), with enhanced temporal and spatial resolutions]. More comparisons with external QC methods will be performed to learn and employ the best QC practices. [ABSTRACT FROM AUTHOR]

The deep ocean is severely undersampled. Whereas shipboard measurements provide irregular spatial and temporal records, moored records establish deep ocean high-resolution time series, but only at limited locations. Here, highlights and challenges of measuring abyssal temperature and salinity on the Kuroshio Extension Observatory (KEO) mooring (32.3°N, 144.6°E) from 2013 to 2019 are described. Using alternating SeaBird 37-SMP instruments on annual deployments, an apparent fresh drift of 0.03–0.06 psu was observed, with each newly deployed sensor returning to historical norms near 34.685 psu. Recurrent salinity discontinuities were pronounced between the termination of each deployment and the initiation of the next, yet consistent pre- and postdeployment calibrations suggested the freshening was "real." Because abyssal salinities do not vary by 0.03–0.06 psu between deployment locations, the contradictory salinities during mooring overlap pointed toward a sensor issue that self-corrects prior to postcalibration. A persistent nepheloid layer, unique to KEO and characterized by murky, sediment-filled water, is likely responsible for sediment accretion in the conductivity cell. As sediment (or biofouling) increasingly clogs the instrument, salinity drifts toward a fresh bias. During ascent, the cell is flushed, clearing the clogged instrument. In contrast to salinity, deep ocean temperatures appear to increase from 2013 to 2017 by 0.0059°C, whereas a comparison with historical deep temperature measurements does not support a secular temperature increase in the region. It is suggested that decadal or interannual variability associated with the Kuroshio Extension may have an imprint on deep temperatures. Recommendations are discussed for future abyssal temperature and salinity measurements. [ABSTRACT FROM AUTHOR]

One-dimensional synthetic aperture microwave radiometer (1D-SAMR) can provide remote sensing images at a higher spatial resolution than those from traditional real aperture microwave radiometers. As 1D-SAMR operates at multiple incidence angles, we proposed a multiple linear regression method to retrieve sea surface temperature at an incidence angle between 0° and 65°. Assuming that a 1D-SAMR operates at various frequencies (i.e., 6.9, 10.65, 18.7, 23.8 and 36.5 GHz), a radiation transmission forward model was developed to simulate the brightness temperature measured by the 1D-SAMR. The sensitivity of the five frequencies to sea surface temperature was examined, and we evaluated the reliability of the regression method proposed in this study. Furthermore, 11 schemes with various frequency combinations were applied to retrieve sea surface temperature. The results showed that the five-frequency combination scheme performed better than the other schemes. This study also found that the accuracy of retrieved sea surface temperature is dependent on incidence angles. Finally, we suggested that the incidence angle range of the 1D-SAMR is necessary to be 30°–60° based on the relationship between the accuracy of retrieved sea surface temperature and the incidence angles. [ABSTRACT FROM AUTHOR]

Credible tropical cyclone (TC) intensity prediction by coupled models requires accurate forecasts of enthalpy flux from ocean to atmosphere, which in turn requires accurate forecasts of sea surface temperature cooling beneath storms. Initial ocean fieldsmust accurately represent ocean mesoscale features and the associated thermal and density structure. Observing system simulation experiments (OSSEs) are performed to quantitatively assess the impact of assimilating profiles collected from multiple underwater gliders deployed over the western North Atlantic Ocean TC region, emphasizing advantages gained by profiling from moving versus stationary platforms. Assimilating ocean profiles collected repeatedly at fixed locations produces large root-mean-square error reduction only within ;50 km of each profiler for two primary reasons. First, corrections performed during individual update cycles tend to introduce unphysical eddy structure resulting from smoothing properties of the background error covariance matrix and the tapering of innovations by a localization radius function. Second, advection produces rapid nonlinear error growth at larger distances from profiler locations. The ability of each individual moving glider to cross gradients and map mesoscale structure in its vicinity substantially reduces this nonlinear error growth. Glider arrays can be deployed with horizontal separation distances that are 50%-100% larger than those of fixed-location profilers to achieve similar mesoscale error reduction. By contrast, substantial larger-scale bias reduction in upper-ocean heat content can be achieved by deploying profiler arrays with separation distances up to several hundred kilometers, with moving gliders providing only modest additional improvement. Expected sensitivity of results to study region and data assimilation method is discussed. [ABSTRACT FROM AUTHOR]

Arctic sea surface temperatures (SSTs) are estimated mostly from satellite sea ice concentration (SIC) estimates. In regions with sea ice the SST is the temperature of open water or of the water under the ice. A number of different proxy SST estimates based on SIC have been developed. In recent years more Arctic quality-control buoy SSTs have become available, allowing better validation of different estimates and the development of improved proxy estimates. Here proxy SSTs from different approaches are evaluated and an improved proxy SST method is shown. The improved proxy SSTs were tested in an SST analysis, and showed reduced bias and random errors compared to the Arctic buoy SSTs. Almost all reduction in errors is in the warm melt season. In the cold season the SIC is typically high and all estimates tend to have low errors. The improved method will be incorporated into an operational SST analysis. [ABSTRACT FROM AUTHOR]

The rapid development of miniaturized satellite instrument technology has created a new opportunity to deploy constellations of passive microwave (PMW) radiometers to permit retrievals of the same Earth scene with very high temporal resolution to monitor cloud evolution and processes. For such a concept to be feasible, it must be shown that it is possible to distinguish actual changes in the atmospheric state from the variability induced by making observations at different Earth incidence angles (EIAs). To this end, we present a flexible and physical optimal estimation-based algorithm designed to retrieve profiles of atmospheric water vapor, cloud liquid water path, and cloud ice water path from cross-track PMW sounders. The algorithm is able to explicitly account for the dependence of forward model errors on EIA and atmospheric regime. When the algorithm is applied to data from the Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D) CubeSat mission, its retrieved products are generally in agreement with those obtained from the similar but larger Microwave Humidity Sounder instrument. More importantly, when forward model brightness temperature offsets and assumed error covariances are allowed to change with EIA and sea surface temperature, view-angle-related biases are greatly reduced. This finding is confirmed in two ways: through a comparison with reanalysis data and by making use of brief periods in early 2019 during which the TEMPEST-D spacecraft was rotated such that its scan pattern was along track, allowing dozens of separate observations of any given atmospheric feature along the satellite's ground track. [ABSTRACT FROM AUTHOR]

The nighttime ocean cool skin signal ΔT [defined as skin sea surface temperature (SSTskin) minus depth SST (SSTdepth)] is investigated using 103 days of matchups between shipborne Infrared SST Autonomous Radiometer (ISAR) SSTskin and water intake SSTdepth at ~7.1–9.9-m depths, in oceans around Australia. Before data analysis, strict quality control of ISAR SSTskin data is conducted and possible diurnal warming contamination is carefully minimized. The statistical distribution of ΔT, and its dependencies on wind speed, heat flux, etc., are consistent with previous findings. The overall average ΔT value is −0.23 K. It is observed that the magnitude of the cool skin signal increases after midnight and a coolest skin offset (with an average value of −0.36 K) is found at around dawn. The dependency of ΔT on SST conditions is observed. Direct warm skin events are discovered when the net heat flux direction is from the atmosphere to the ocean, which is more likely to occur at high latitudes when the air is very humid and warmer than the SST. In addition, several cool skin models are validated: one widely used physical model performs best and can capture most skin-effect trends and details; the empirical models only reflect the basic features of the observed ΔT values. If the user cannot apply the physical model (due to, e.g., the algorithm complexity or missing inputs), then the empirical parameterization in the form proposed in a 2002 study can be used. However, we recommend using a new set of parameters, calculated in this study, based on much more representative dataset, and with more rigorous quality control. [ABSTRACT FROM AUTHOR]

Previous studies are mainly limited to temperature and salinity (T/S) profiling data assimilation, while data assimilation based on Argo float trajectory information has received less research focus. In this study, a new method was proposed to assimilate Argo trajectory data: the middepth (indicates the parking depth of Argo floats in this study, ~1200 m) velocities are estimated from Argo trajectories and subsequently assimilated into the Regional Ocean Model System (ROMS) using four-dimensional variational data assimilation (4DVAR) method. This method can avoid a complicated float trajectory model in direct position assimilation. The 2-month assimilation experiments in South China Sea (SCS) showed that this proposed method can effectively assimilate Argo trajectory information into the model and improve middepth velocity field by adjusting the unbalanced component in the velocity increments. The assimilation of the Argo trajectory-derived middepth velocity with other observations (satellite observations and T/S profiling data) together yielded the best performance, and the velocity fields at the float parking depth are more consistent with the Argo float trajectories. In addition, this method will not decrease the assimilation performance of other observations [i.e., sea level anomaly (SLA), sea surface temperature (SST), and T/S profiles], which is indicative of compatibility with other observations in the 4DVAR assimilation system. [ABSTRACT FROM AUTHOR]

Deployment of Deep Argo regional pilot arrays is underway as a step toward a global array of 1250 surface-to-bottom profiling floats embedded in the upper-ocean (2000 m) Argo Program. Of the 80 active Deep Argo floats as of July 2019, 55 are Deep Sounding Oceanographic Lagrangian Observer (SOLO) 6000-m instruments, and the rest are composed of three additional models profiling to either 4000 or 6000 m. Early success of the Deep SOLO is owed partly to its evolution from the Core Argo SOLO-II. Here, Deep SOLO design choices are described, including the spherical glass pressure housing, the hydraulics system, and the passive bottom detection system. Operation of Deep SOLO is flexible, with the mission parameters being adjustable from shore via Iridium communications. Long lifetime is a key element in sustaining a global array, and Deep SOLO combines a long battery life of over 200 cycles to 6000 m with robust operation and a low failure rate. The scientific value of Deep SOLO is illustrated, including examples of its ability (i) to observe large-scale spatial and temporal variability in deep ocean temperature and salinity, (ii) to sample newly formed water masses year-round and within a few meters of the sea floor, and (iii) to explore the poorly known abyssal velocity field and deep circulation of the World Ocean. Deep SOLO's full-depth range and its potential for global coverage are critical attributes for complementing the Core Argo Program and achieving these objectives. [ABSTRACT FROM AUTHOR]

Satellite altimetry is one of the main sources of information used to constrain global ocean analysis and forecasting systems. In addition to in situ vertical temperature and salinity profiles and sea surface temperature (SST) data, sea level anomalies (SLA) from multiple altimeters are assimilated through the knowledge of a surface reference, the mean dynamic topography (MDT). The quality of analyses and forecasts mainly depends on the availability of SLA observations and on the accuracy of the MDT. A series of observing system evaluations (OSEs) were conducted to assess the relative importance of the number of assimilated altimeters and the accuracy of the MDT in a Mercator Ocean global 1/4° ocean data assimilation system. Dedicated tools were used to quantify impacts on analyzed and forecast sea surface height and temperature/salinity in deeper layers. The study shows that a constellation of four altimeters associated with a precise MDT is required to adequately describe and predict upper-ocean circulation in a global 1/4° ocean data assimilation system. Compared to a one-altimeter configuration, a four-altimeter configuration reduces the mean forecast error by about 30%, but the reduction can reach more than 80% in western boundary current (WBC) regions. The use of the most recent MDT updates improves the accuracy of analyses and forecasts to the same extent as assimilating a fourth altimeter. [ABSTRACT FROM AUTHOR]

Collocated CloudSat rain rates and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) 89-GHz brightness temperature Tb retrievals allow for the development of an algorithm to estimate light, warm rain statistics as a function of AMSR-E 89-GHz Tb for shallow marine clouds. Four statistics are calculated from CloudSat rainfall rate estimates within each 4 km × 6 km Tb pixel sampled by both sensors: the probability of rainfall, the mean rain rate, the mean rate when raining, and the maximum rain rate. Observations with overlying cold clouds are removed from the analysis. To account for confounding variables that modify Tb, curves are fit to the mean relationships between Tb and these four statistics within bins of constant column-integrated water vapor from AMSR-E, and sea surface temperature and wind speed from reanalysis grids. The coefficients that define these curves are then applied to all available AMSR-E Tb retrievals to estimate rain rate throughout the eastern subtropical oceans. A preliminary analysis shows strong agreement between AMSR-E rain rates and the CloudSat training dataset. Comparison with an existing microwave precipitation product shows that the new statistical product has an improved sensitivity to light rain. A climatology for the year 2007 shows that precipitation rates tend to be heavier where the sea surface is warmer and that rain is most frequent where stratocumulus transitions to trade cumulus in the subtropics. [ABSTRACT FROM AUTHOR]

An objective mapping exercise simulating observations of temperature in the North Atlantic Ocean was used to assess the resolution capabilities of ocean acoustic tomography in combination with Argo floats. A set of basis functions for a basinwide area was obtained from a singular value decomposition of a covariance derived from an ocean state estimate. As demonstrated by the formal uncertainty estimates from the objective maps, Argo and tomography are complementary measurements. In several examples, each separately obtained uncertainty for determining large-scale monthly average temperature of about 50% of prior (resolved 75% of variance), while when both data were employed, uncertainties were reduced to about 25% of prior (resolved 94% of variance). Possible tomography configurations range from arrays that span specific regions to line arrays that supplement existing observations to arrays that span the Atlantic basin. A basinwide array consisting of two acoustic sources and seven receivers can be used to significantly reduce the uncertainties of estimated broad-scale temperature. An optimal observing system study would comprise simulated measurements in combination with data assimilation techniques and numerical ocean modeling. This objective map study, however, showed that the addition of tomography to the existing observing system could substantially reduce the uncertainties for estimated large-scale temperature. To the extent that tomography offers a 50% reduction in uncertainty at a fraction of the cost of the Argo program, it is a cost-effective contribution to the ocean observing system. [ABSTRACT FROM AUTHOR]

A nonlinear empirical method, called the generalized regression neural network with the fruit fly optimization algorithm (FOAGRNN), is proposed to estimate subsurface salinity profiles from sea surface parameters in the Pacific Ocean. The purpose is to evaluate the ability of the FOAGRNN methodology and satellite salinity data to reconstruct salinity profiles. Compared with linear methodology, the estimated salinity profiles from the FOAGRNN method are in better agreement with the measured profiles at the halocline. Sensitivity studies of the FOAGRNN estimation model shows that, when applied to various types of sea surface parameters, latitude is the most significant variable in estimating salinity profiles in the tropical Pacific Ocean (correlation coefficient R greater than 0.9). In comparison, sea surface temperature (SST) and height (SSH) have minimal effects on the model. Based on FOAGRNN modeling, Pacific Ocean three-dimensional salinity fields are estimated for the year 2014 from remote sensing sea surface salinity (SSS) data. The performance of the satellite-based salinity field results and possible sources of error associated with the estimation methodology are briefly discussed. These results suggest a potential new approach for salinity profile estimation derived from sea surface data. In addition, the potential utilization of satellite SSS data is discussed. [ABSTRACT FROM AUTHOR]

In this study we examined the applicability of the threshold, curvature, maximum angle, and relative variance methods for identifying the oceanic bottom mixed layer (BML) thickness . Using full-depth temperature profiles along 17 WOCE sections covering the Atlantic, Indian, and Pacific Oceans, we found that the BML thicknesses determined based on the threshold, curvature, and maximum angle methods had wider 95% confidence intervals and much lower quality indexes compared with those based on the visual inspection (). The relative variance method appeared to perform better than the other methods because the 95% confidence interval and (0.60) values were closer to those determined based on the visual inspection, although differences were still present. We then proposed an integrated method by optimizing the possible values obtained from the four methods. The BML thicknesses determined using the integrated method were closest to those based on the visual inspection according to the higher (0.64) and more stations (71%) with . Compared with the results in previous studies, the integrated method determined the consistent BML thicknesses in most regions (e.g., the northern Atlantic), and it also effectively identified the BML thicknesses in some regions where the BML was considered to be not readily detectable (e.g., the Madeira Abyssal Plain). [ABSTRACT FROM AUTHOR]

The sea surface temperature (SST) record provides a unique view of the surface ocean at high spatiotemporal resolution and holds useful information on the kinematics underlying the SST variability. To access this information, we develop a new local matrix inversion method that allows us to quantify the evolution of a given SST perturbation with a response function and to estimate velocity, diffusivity, and decay fields associated with it. The matrix inversion makes use of the stochastic climate model paradigm—we assume that SST variations are governed by a linear transport operator and a forcing that has a relatively short autocorrelation time scale compared to that of SST. We show that under these assumptions, the transport operator can be inverted from the covariance matrices of the underlying SST data. The accuracy of the results depends on the length of the time series, and in general the inverted properties depend on the spatial and time resolution of the SST data. Future studies could use the methodology to explore the interannual variability of SST anomalies; to estimate the scale dependency of ocean mixing; and to estimate anomaly propagation, both at the surface and in the interior. The methodology can be easily used with any gridded observations or model output with adequate time and spatial resolution, and it is not restricted to SST. The inversion code is written in Python and distributed as a MicroInverse package through GitHub and the Python Package Index. [ABSTRACT FROM AUTHOR]

Argo float trajectories are simulated in the southwest Pacific basin (25°-45°S, 170°E-165°W) using velocity fields from a 1/12° Southern Ocean model and a Lagrangian particle tracking model programmed to represent the vertical motions of profiling Argo floats. The system is applied to simulate both core Argo floats (typically parked at 1000-m depth and profiling to 2000-m depth) and Deep Argo floats (parked 500 m above the seafloor). The goal is to estimate probability density functions (PDFs) predicting future float positions. Differences are expected in the trajectory statistics, largely because of limitations in the temporal and spatial resolution of the model fields and uncertainties associated with a random walk component included in the particle advection scheme to represent this unresolved variability. Nonetheless, the core Argo float displacements over ~ 100-day time intervals are mostly consistent with the derived PDFs, particularly in regions with stable midlayer flows. For the Deep Argo floats, which are released into the open ocean and parked near the bottom, the simulations predict an average total displacement of less than 50 km within 100 days, in good agreement with the Deep Argo floats deployed as part of a pilot study. The study explores both the representativeness and the predictability of float displacements, with an aim to contribute to planning for the float observing system. [ABSTRACT FROM AUTHOR]

The prediction of sea surface temperature (SST) on the basis of artificial neural networks (ANNs) can be viewed as complementary to numerical SST predictions, and it has fairly sustained in the recent past. However, one of its limitations is that such ANNs are site specific and do not provide simultaneous spatial information similar to the numerical schemes. In this work we have addressed this issue by presenting basinscale SST predictions based on the operation of a very large number of individual ANNs simultaneously. The study area belongs to the basin of the tropical Indian Ocean (TIO) having coordinates of 30°N-30°S, 30°- 120°E. The network training and testing are done on the basis of HadISST data of the past 140 yr. Monthly SST anomalies are predicted at 3813 nodes in the basin and over nine time steps into the future with more than 20 million ANN models. The network testing indicated that the prediction skill of ANNs is attractive up to certain lead times depending on the subbasin. The ANNmodels performed well over both the western Indian Ocean (WIO) and eastern Indian Ocean (EIO) regions up to 5 and 4 months lead time, respectively, as judged by the error statistics of the correlation coefficient and the normalized root-mean-square error. The prediction skill of the ANN models for the TIO region is found to be better than the physics-based coupled atmosphere--ocean models. It is also observed that the ANNs are capable of providing an advanced warning of the Indian Ocean dipole as well as abnormal basin warming. [ABSTRACT FROM AUTHOR]

Surface ocean wind datasets are required to be of high spatial and temporal resolution and high precision to accurately force or be assimilated into coupled atmosphere-ocean numerical models and to understand ocean-atmospheric processes. In situ observed sea surface winds from the Southern Ocean are scarce and, consequently, the validity of simulation models is often questionable. Multiple wind data products were compared to the first known high-resolution in situ measurements of wind speed from Wave Glider (WG) deployments in the Southern Ocean with the intent to determine which blended satellite or reanalysis product best represents the magnitude and variability of the observed wind field. Results show that the ECMWF reanalysis product is the most accurate in representing the temporal variability of winds, exhibiting consistently higher correlation coefficients with in situ data across all wind speed categories. However, the NCEP-DOE AMIP-II Reanalysis product matches in situ trends of deviation from the mean and performs best in depicting the mean wind state, especially during high wind states. The ECMWF product also leads to smaller differences in wind speeds from the in situ data, while CFSv2 showed slightly higher biases and a greater RMSE. The SeaWinds (SW) product consistently performed poorly at representing the mean or wind stress variability compared to those observed by the WG. Overall, the study shows autonomous surface vehicles provide valuable observations by which to validate, understand, and potentially assist in correcting satellite/reanalysis products, particularly in remote regions, where few in situ estimates exist. [ABSTRACT FROM AUTHOR]

An analysis is conducted for the first time to statistically quantify the relationship between the evaporation duct and oceanic evaporation. Through sensitivity analysis, under unstable conditions (air-sea temperature difference less than zero), evaporation duct and evaporation are found to maintain a similar trend with variations in air-sea variables, indicating a possible inherent connection. Furthermore, scatterplots of relevant historical data reveal that the evaporation duct generally increases in a power-law manner with evaporation. Therefore, logarithmic transformation is performed on the data, and then linear regression is adopted to derive the analytical expression of the linear trend. Additionally, based on this analytical expression, a three-parameter empirical model is proposed to estimate the temporal clustering, and the estimated result shows good agreement with the real distribution. The spatial variations of the parameters modeled over different focus areas reflect the influence of geophysical parameters. [ABSTRACT FROM AUTHOR]

Using sea surface temperature from satellite images to retrieve sea surface currents is not a new idea, but so far its operational near-real-time implementation has not been possible. Validation studies are too region specific or uncertain, sometimes because of the satellite images themselves. Moreover, the sensitivity of the most common retrieval method, the maximum cross correlation, to the parameters that have to be set is unknown. Using model outputs instead of satellite images, biases induced by this method are assessed here, for four different seas of western Europe, and the best of nine settings and eight temporal resolutions are determined. The regions with strong currents return the most accurate results when tracking a 20-km pattern between two images separated by 6-9 h. The regions with weak currents favor a smaller pattern and a shorter time interval, although their main problem is not inaccurate results but missing results: where the velocity is too low to be picked by the retrieval. The results are not impaired by the restrictions imposed by ocean surface current dynamics and available satellite technology, indicating that automated sea surface current retrieval from sea surface temperature images is feasible, for pollution confinement, search and rescue, and even for more energy-efficient and comfortable ship navigation. [ABSTRACT FROM AUTHOR]

Ocean mixing has historically been estimated using Osborn's model by measuring the rate of dissipation of turbulent kinetic energy ϵ and the background density stratification N while assuming a value of the flux Richardson number . A constant is typically assumed, despite mounting field, laboratory, and modeling evidence that varies. This challenge can be overcome by estimating the turbulent diffusivity of heat using the Osborn-Cox model. This model, however, requires measuring the rate of dissipation of thermal variance χ, which has historically been challenging, particularly in energetic flows because the high wavenumbers of the temperature gradient spectra are unresolved with current technology. To overcome this difficulty, a method is described that determines χ by spectral fitting to the inertial-convective (IC) subrange of the temperature gradient spectra. While this concept has been exploited for moored time series, particularly near the bottom boundary, it has yet to be adapted to vertical microstructure profilers such as gliders, and autonomous and ship-based vertical profilers from which there are the most measurements. By using the IC subrange, χ, and hence , can be estimated even in very energetic events-precisely the conditions requiring more field observations. During less energetic periods, the temperature gradient spectra can also be integrated to obtain χ. By combining these two techniques, microstrucure profiles at a field site known for its very energetic internal waves are analyzed. This study demonstrates that the spectral fitting approach resolves intense mixing events with . By equating the Osborn and Osborn-Cox models, indirect estimates for can also be obtained. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) data from the Copernicus Marine Environment Monitoring Service are assimilated into a pan-Arctic ice-ocean coupled model using the ensemble-based local singular evolutive interpolated Kalman (LSEIK) filter. This study found that the SST deviation between model hindcasts and independent SST observations is reduced by the assimilation. Compared with model results without data assimilation, the deviation between the model hindcasts and independent SST observations has decreased by up to 0.2°C at the end of summer. The strongest SST improvements are located in the Greenland Sea, the Beaufort Sea, and the Canadian Arctic Archipelago. The SST assimilation also changes the sea ice concentration (SIC). Improvements of the ice concentrations are found in the Canadian Arctic Archipelago, the Beaufort Sea, and the central Arctic basin, while negative effects occur in the west area of the eastern Siberian Sea and the Laptev Sea. Also, sea ice thickness (SIT) benefits from ensemble SST assimilation. A comparison with upward-looking sonar observations reveals that hindcasts of SIT are improved in the Beaufort Sea by assimilating reliable SST observations into light ice areas. This study illustrates the advantages of assimilating SST observations into an ice-ocean coupled model system and suggests that SST assimilation can improve SIT hindcasts regionally during the melting season. [ABSTRACT FROM AUTHOR]

The response to temperature of sample hyperspectral radiometers commonly used to support the validation of satellite ocean color data was characterized in the 400-800-nm spectral range. Measurements performed in the 10°-40°C interval at 5°C increments showed mean temperature coefficients varying from −0.04 × 10−2 (°C)−1 at 400 nm to +0.33 × 10−2 (°C)−1 at 800 nm, which are largely explained by the temperature coefficient of the photodetector array constituting the core of the sensor. Overall, the results indicate the possibility of applying temperature corrections with an uncertainty of approximately 0.03 × 10−2 (°C)−1 for the class of hyperspectral radiometers investigated in the study. [ABSTRACT FROM AUTHOR]