Float Data Research Articles

Delayed-Mode Quality Control of Oxygen, Nitrate, and pH Data on SOCCOM Biogeochemical Profiling Floats

The Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) project has deployed 194 profiling floats equipped with biogeochemical (BGC) sensors, making it one of the largest contributors to global BGC-Argo. Post-deployment quality control (QC) of float-based oxygen, nitrate, and pH data is a crucial step in the processing and dissemination of such data, as in situ chemical sensors remain in early stages of development. In situ calibration of chemical sensors on profiling floats using atmospheric reanalysis and empirical algorithms can bring accuracy to within 3 μmol O2 kg–1, 0.5 μmol NO3– kg–1, and 0.007 pH units. Routine QC efforts utilizing these methods can be conducted manually through visual inspection of data to assess sensor drifts and offsets, but more automated processes are preferred to support the growing number of BGC floats and reduce subjectivity among delayed-mode operators. Here we present a methodology and accompanying software designed to easily visualize float data against select reference datasets and assess QC adjustments within a quantitative framework. The software is intended for global use and has been used successfully in the post-deployment calibration and QC of over 250 BGC floats, including all floats within the SOCCOM array. Results from validation of the proposed methodology are also presented which help to verify the quality of the data adjustments through time.

Global mapping of 10-day differences of temperature and salinity in the intermediate layer observed with Argo floats

The authors examine small-scale spatiotemporal variability of the layer nearly 2000-m depth, which is the “bottom” of the present Argo observation system, using all of available Argo float data. The 10-day change, ΔT10, is defined as the difference of temperature between two successive observations with an interval of nearly 10 days for each individual float at an isobaric surface. |ΔT10| is large along the western boundary currents at 1000 dbar, and becomes less remarkable with depth. At 1950 dbar, mean |ΔT10| is noticeable in the northeastern Atlantic Ocean (NEAO), the Argentine basin, and the northwestern Indian Ocean. In the Southern Ocean, large |ΔT10| is localized in some areas located over the ridges or leeward of the plateau. Basically, ΔT10 at isobaric surfaces is accounted for by the heave component, but the spiciness component is dominant or comparable to the other in the NEAO and the Argentine basin. ΔT10 decreases with depth monotonically most of the world, suggesting that wind energy input is attenuated with depth. In some areas in the Southern Ocean, however, the vertical profile of |ΔT10| implies enhanced bottom-induced turbulence. |ΔT10| peaks at 1300 dbar in the NEAO, corresponding to the spread of the Mediterranean Outflow Water. |ΔT10| is smaller in the Pacific Ocean compared with the other oceans, but is enhanced along the equator, the Kuroshio and its Extension, the Kuril, Aleutian, Hawaii, and Mariana Islands, and the Emperor Seamount Chain.

Super-resolution of subsurface temperature field from remote sensing observations based on machine learning

Subsurface ocean observations are sparse and insufficient, significantly constraining studies of ocean processes. Retrieving high-resolution subsurface dynamic parameters from remote sensing observations using specific inversion models is possible but challenging. This study proposed two kinds of machine learning algorithms, namely, Convolutional Neural Network (CNN) and Light Gradient Boosting Machine (LightGBM), to reconstruct the subsurface temperature (ST) of the ocean’s upper 1000 m with a high resolution of 0.25° based on the satellite-based sea surface parameters combined with Argo float and EN4 profile data. We managed to improve the spatial resolution of ST from 1° to 0.25°. We employed two machine learning algorithms to set up monotemporal models of the four seasons and time-series models and adopted the determination coefficient (R2) and Root Mean Squared Error (RMSE) to evaluate the models’ prediction accuracy. The results show that LightGBM outperformed CNN in the case of small training samples. By contrast, in the case of big training samples, CNN outperformed LightGBM. Meanwhile, the ST with a high resolution of 0.25° predicted by the time-series CNN model can better observe mesoscale phenomena. This study provides more useful and higher-resolution data support for further studies on the warming and variability of the ocean interior under global warming.

Decomposed bounded floats for fast compression and queries

Modern data-intensive applications often generate large amounts of low precision float data with a limited range of values. Despite the prevalence of such data, there is a lack of an effective solution to ingest, store, and analyze bounded, low-precision, numeric data. To address this gap, we propose Buff, a new compression technique that uses a decomposed columnar storage and encoding methods to provide effective compression, fast ingestion, and high-speed in-situ adaptive query operators with SIMD support.

Subtropical Mode Water in a recent persisting Kuroshio large-meander period: part I\u2014formation and advection over the entire distribution region

Since August 2017, the Kuroshio has taken a large-meander (LM) path, which has forced the Kuroshio extension (KE) to be in its stable state against its wind-forced decadal variability. How such current conditions have impacted the formation and advection of North Pacific subtropical mode water (STMW) over its distribution region was examined using Argo float data during 2005–2020. Out of the whole STMW defined as a low-potential vorticity layer of 16–19.5 ºC, a relatively cold variety of 16–18 ºC, which was formed south of the KE and advected westward and southward, occupied more than 80% of the total volume. The formation rate of the 16–18 ºC variety was low during 2006–2009 in an unstable-KE period and high during 2010–2015 in a stable-KE period, and then dropped drastically in 2016 despite the KE still being in the stable state. After a short unstable-KE period in 2016–2017, the LM-forced, stable-KE period began, but the formation rate of the 16–18 ºC variety has not restored, possibly due to stronger background stratification propagated from the central North Pacific. In addition, the 16–18 ºC variety has had to make a southern detour around the LM, and its westward advection from the formation region south of the KE to the region south of Japan has been significantly decreased, possibly because it is dissipated more strongly over a southern part of the Izu–Ogasawara Ridge. Due to such decline in the formation and advection, the volume of the 16–18 ºC variety and hence that of the whole STMW have gradually decreased since 2016.

Enhanced internal tidal mixing in the Philippine Sea mesoscale environment

Abstract. Turbulent mixing in the ocean interior is mainly attributed to internal wave breaking; however, the mixing properties and the modulation effects of mesoscale environmental factors are not well known. Here, the spatially inhomogeneous and seasonally variable diapycnal diffusivities in the upper Philippine Sea were estimated from Argo float data using a strain-based, fine-scale parameterization. Based on a coordinated analysis of multi-source data, we found that the driving processes for diapycnal diffusivities mainly included the near-inertial waves and internal tides. Mesoscale features were important in intensifying the mixing and modulating of its spatial pattern. An interesting finding was that, besides near-inertial waves, internal tides also contributed significant diapycnal mixing in the upper Philippine Sea. The seasonal cycles of diapycnal diffusivities and their contributors differed zonally. In the midlatitudes, wind mixing dominated and was strongest in winter and weakest in summer. In contrast, tidal mixing was more predominant in the lower latitudes and had no apparent seasonal variability. Furthermore, we provide evidence that the mesoscale environment in the Philippine Sea played a significant role in regulating the intensity and shaping the spatial inhomogeneity of the internal tidal mixing. The magnitudes of internal tidal mixing were greatly elevated in regions of energetic mesoscale processes. Anticyclonic mesoscale features were found to enhance diapycnal mixing more significantly than cyclonic ones.

Sea level fingerprints and regional sea level change

At global and basin scales, sea level change estimated from Gravity Recovery And Climate Experiment (GRACE) gravity data (measuring mass) and Argo float data (measuring thermal expansion) agree well with satellite radar altimetry (measuring sea surface height). At regional scales, especially near coastlines, it is more difficult to compare GRACE + Argo estimates with altimetry due to limited spatial resolution of GRACE data, and associated signal leakage from land into oceans. Here we estimate ocean mass changes from GRACE data corrected for land to ocean signal leakage. The ocean mass estimates are compared with altimetry data over six selected coastal regions after considering steric changes and sea floor surface load deformation. The results show that sea level fingerprint mass change estimated using leakage-corrected GRACE data is in the better agreement with altimetry rates than Mascon estimates for these oceanic locations.

Seasonal and Interannual Variability of the CO2 System in the Eastern Mediterranean Sea: A Case Study in the North Western Levantine Basin

The seasonal variability of the carbonate system in the eastern Mediterranean Sea (EMed) was investigated based on discrete total alkalinity (AT), total dissolved inorganic carbon (CT), and pH measurements collected during three cruises around Crete between June 2018 and March 2019. This study presents a detailed description of this new carbonate chemistry dataset in the eastern Mediterranean Sea. We show that the North Western Levantine Basin (NWLB) is unique in terms of range of AT variation vs. CT variation in the upper water column over an annual cycle. The reasons for this singularity of the NWLB can be explained by the interplay between strong evaporation and the concomitant consumption of CT by autotrophic processes. The high range of AT variations, combined to temperature changes, has a strong impact on the variability of the seawater pCO2 (pCO2SW). Based on Argo float data, an entire annual cycle for pCO2SW in the NWLB has been reconstructed in order to estimate the temporal sequence of the potential “source” and “sink” of atmospheric CO2. By combining this dataset with previous observations in the NWLB, this study shows a significant ocean acidification and a decrease in the oceanic surface pHT25 of −0.0024 ± 0.0004 pHT25 units.a–1. The changes in the carbonate system are driven by the increase of atmospheric CO2 but also by unexplained temporal changes in the surface AT content. If we consider that the EMed will, in the future, encounter longer, more intense and warmer summer seasons, this study proposes some perspectives on the carbonate system functioning of the “future” EMed.

The Dynamic of Convergence Zone Displacement in Western Pacific Ocean on 2015 Super El Niňo Event

Warm pool and the existence of equatorial processes in the Pacific Ocean have an important role on El-Nino Southern Oscillation (ENSO). The eastward advection of warm and less saline water from the western Pacific together with the westward advection of cold and more saline water from the central-eastern Pacific induces a convergence of water masses at the eastern edge of the warm pool. The aim of this study is to determine convergence zone displacement in Western Pacific Ocean based on oceanography parameters such as temperature, salinity and surface current from insitu Argo Float, satellite and model data. The convergence zone displacement was characterized by proxies variable of isotherm 28,5 °C and isohaline 34,6 psu. The convergence zone is zonally displaced in association with EI Niño-La Niña and wind-driven surface current variations. The displacement of the convergence zone moved as far east as 136 °W in the eastern Pacific during the 2015 Super El Niňo. Otherwise, convergence zone moved westward near 154 °E in the western Pacific, during La Niňa periods. During Super El Niňo, the stronger-than-normal speed of the North Equatorial Counter Current (NECC) was increased by 0,8 m/s and appears to have been a major factor in convergence zone reaching the far eastern Pacific. A strong significant positive correlation (R= 0.88, 0.82 and 0.84) between SST and skipjack tuna catch on Super El Niňo, La Niňa and Normal respectively suggested that changes in skipjack CPUE occurred in phase with movement of convergence zone.

Winter Air‐Sea CO2 Fluxes Constructed From Summer Observations of the Polar Southern Ocean Suggest Weak Outgassing

AbstractThe Southern Ocean plays an important role in the global oceanic uptake of CO2. Estimates of the air‐sea CO2 flux are made using the partial pressure of CO2 at the sea surface (), but winter observations of the region historically have been sparse, with almost no coverage in the Pacific or Indian ocean sectors south of the Polar front in the period 2004–2017. Here, we use summertime observations of relevant properties in this region to identify subsurface waters that were last in contact with the atmosphere in the preceding winter, and then reconstruct “pseudo observations” of the wintertime . These greatly improve wintertime coverage south of the Polar Front in all sectors, improving the robustness of flux estimates there. We add the pseudo observations to other available observations of and use a multiple linear regression to produce a gap‐filled time‐evolving estimate of from which we calculate the air‐sea flux. The inclusion of the pseudo observations increases outgassing at the beginning of the period, but the effect reduces with time. We estimate a 2004–2017 long‐term mean flux of −0.02 ± 0.02 Pg C yr−1 for the Southern Ocean south of the Polar Front, similar to comparable studies based on shipboard data. However, we diverge somewhat from an estimate which utilized autonomous float data for recent years: we find a small sink in 2017 of −0.08 ± 0.03 Pg C yr−1 where the float‐based estimate suggested a small source.

Observational estimates of turbulent mixing in the southeast Indian Ocean

AbstractThis study investigates the spatio-temporal variability of turbulent mixing in the eastern South Indian Ocean using a collection of data from EM-APEX profiling floats, shipboard CTD and microstructure profilers. The floats collected 1566 profiles of temperature, salinity and horizontal velocity data down to 1200 m over a period of about four months. A fine-scale parameterization is applied to the float and CTD data to estimate turbulent mixing. Elevated mixing is observed in the upper ocean, over bottom topography and in mesoscale eddies. Mixing is enhanced in the anticyclonic eddies due to trapped near-inertial waves within the eddy. We found that cyclonic eddies contribute to turbulent mixing in the depth range of 500 – 1000 m, which is associated with downward propagating internal waves. The mean diapycnal diffusivity over 250 – 500 m depth is O(10−6) m2 s−1 and it increases to O(10−5) m2 s−1 in 500 – 1000 m in cyclonic eddies. The turbulent mixing in this region has implications for watermass transformation and large-scale circulation. Higher diffusivity (O(10−5) m2 s−1) is observed in the Antarctic Intermediate Water (AAIW) layer in cyclonic eddies whereas weak diffusivity is observed in the Subantarctic Mode Water (SAMW) layer (O(10−6) m2 s−1). Counter-intuitively, then, the SAMW watermass properties are strongly affected in cyclonic eddies whereas the AAIW layer is less affected. Comparatively high diffusivity at the location of the South Indian Countercurrent (SICC) jets suggests there are wave-mean flow interactions in addition to the wave-eddy interactions that warrant further investigation.

Rapid photoacclimation during episodic deep mixing augments the biological carbon pump

Episodic deep mixing events are one component of the biological carbon pump that physically transports organic carbon into the mesopelagic. Episodic deep mixing also disrupts summertime thermal stratification thereby changing the light field and nutrient concentrations available for phytoplankton growth. Phytoplankton survival and growth below the mixed layer following restratification depends on how rapidly cells can employ a variety of photoacclimation processes in response to the environmental changes. To compare the relative timescales of summertime episodic deep mixing events with the timescales of phytoplankton photoacclimation processes, we first analyzed autonomous float data to survey the frequency and magnitude of deep mixing events in the western North Atlantic Ocean. Next, we simulated a sustained deep mixing event in the laboratory and measured rates of acclimation processes ranging from light harvesting to growth in a model diatom and green alga. In both algae increases in chlorophyll (Chl) were coupled to growth, but growth of the green alga lagged the diatom by about a day. In float profiles, significant increases in Chl and phytoplankton carbon (C phyto) were detected below the mixed layer following episodic deep mixing events. These events pose a previously unrecognized source of new production below the mixed layer that can significantly boost the amount of carbon available for export to the deep ocean.

Mediterranean Water Properties at the Eastern Limit of the North Atlantic Subtropical Gyre since 1981

A high-quality hydrographic CTD and Argo float data was used to study the property changes along the westward branch of the Mediterranean Outflow Water (MOW) in the northeast Atlantic between 1981 and 2018. In this period, the temperature and salinity are marked by periods of cooling/freshening and warming/salinification. Since 1981, the MOW properties at the core decreased by −0.015 ± 0.07 °C year−1 and −0.003 ± 0.002 year−1. The different phases of the North Atlantic Oscillation (NAO) influence the main propagation pathways of the MOW into the North Atlantic basin, thus affecting the trends determined within different NAO-phases. The temperature and salinity show a strong correlation with NAO, with NAO leading the properties by 8 and 7 years, respectively, indicating a delayed response of the ocean to different forcing conditions. A decrease in oxygen concentration (−0.426 ± 0.276 μmol kg−1 year−1) was calculated for the same period; however, no connection with the NAO was found.

Effects of ocean eddies on the tropical storm Roanu intensity in the Bay of Bengal.

A tropical storm (TS) Roanu occurred in northern Sri Lanka in 2016, which transported northwards along the west coast of the Bay of Bengal (BoB). During the development of the TS, ocean eddies on its track had an important effect on the intensity of Roanu. The dynamic mechanism was investigated with multisource reanalysis and Argo float data in this study. The results show that ocean eddies were the main reason why Roanu first enhanced, weakened, and then enhanced again. Warm eddy W1 supports the initial development of the TS, cold eddy C1 weakens Roanu, and warm eddy W2 continues to support Roanu. On May 19, 2016, the maximum average latent heat flux over W1 was 260.85 w/m2, while that of C1 was only 200.71 w/m2. After the passage of Roanu, the tropical cyclone heat potential (TCHP) of eddies significantly decreased. The TCHP of W1, W2, C1 and C2 decreased by 20.95 kJ/cm2, 11.07 kJ/cm2, 29.82 kJ/cm2, 9.31 kJ/cm2, respectively. The mixed layer of warm eddies deepened much more than that of cold eddies, supporting Roanu development. In addition, changes in potential vorticity (PV) values caused by the disturbance of eddies may also reflect changes in the TS intensity. This study offers new insights on the influence of ocean eddies in regulating the development of tropical cyclone (TC) in the BoB.

Online Residual Quantization Via Streaming Data Correlation Preserving

Recently, the online retrieval task has been receiving widespread attention, which is closely related to many real-world applications. However, existing online retrieval methods based on hashing suffer from two main problems: a) the models tend to be biased towards the current streaming data due to unavailable history streaming data; b) when new streaming data comes in and the hashing functions have been updated, all history binary codes should be recomputed, which takes much computation burden. To address the above two issues, we propose a novel Online Residual Quantization (ORQ) method that can achieve efficient streaming data quantization via the small-scale residual quantization codebooks. For the first problem, we design a residual quantization module by learning multiple residual codebooks to quantize the float streaming data, which effectively reduces the quantization error and enables the binary codes to be easily reconstructed back to original float data. Then, with the reconstructed history data, a balanced affinity matrix is developed to model the semantic relationship, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> similarity and difference, between the history and current data distributions, which can prevent the model from being biased towards the current data distribution. For the second problem, when inputting current streaming data, only the residual codebooks should be updated, instead of the whole history binary codes in hashing-based methods, which significantly reduces the computation burden. Comprehensive experiments on six benchmarks demonstrate that ORQ yields significant improvements ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> 1.2% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 4.9% in average mAP) compared to the state-of-the-art methods.

Observational study of super typhoon Meranti (2016) using satellite, surface drifter, Argo float and reanalysis data

The present work describes the basic features of super typhoon Meranti (2016) by multiple data sources. We mainly focus on the upper ocean response to Meranti using multiplatform satellites, in situ surface drifter and Argo floats, and compare the results with the widely used idealized wind vortex model and reanalysis datasets. The pre-existing meso-scale eddy provided a favor underlying surface boundary condition and also modulated the upper ocean response to Meranti. Results show that the maximum sea surface cooling was 2.0°C after Meranti. The satellite surface wind failed to capture the core structure of Meranti as the idealized wind vortex model deduced. According to the observation of sea surface drifters, the near-inertial currents were significantly enhanced during the passage of Meranti. The temperature and salinity profiles from Argo floats revealed both the mixed-layer extension and subsurface upwelling induced by Meranti. The comparison results show that the sea surface temperature and surface wind in the reanalysis datasets differs from those in remote sensing system. Sea surface cooling is similar in both satellite and in situ observation, and sea surface salinity response has a lower correlation with the precipitation rate.

Non-Profiled Side-Channel Attack Based on Deep Learning Using Picture Trace

Over the years, deep learning algorithms have advanced a lot and any innovation in the algorithms are demonstrated and benchmarked for image classification. Several other field including side-channel analysis (SCA) have recently adopted deep learning with great success. In SCA, the deep learning algorithms are typically working with 1-dimensional (1-D) data. In this work, we propose a unique method to improve deep learning based side-channel analysis by converting the measurements from raw-trace of 1-dimension data based on float or byte data into picture-formatted trace that has information based on the data position. We demonstrate why “Picturization” is more suitable for deep learning and compare how input and hidden layers interact for each raw (1-D) and picture form. As one potential application, we use a Binarized Neural Network (BNN) learning method that relies on a BNN's natural properties to improve variables. In addition, we propose a novel criterion for attack success or failure based on statistical confidence level rather than determination of a correct key using a ranking system.

Percampuran Turbulen Di Tenggara Samudera Hindia Saat Siklon Tropis Marcus Menggunakan Data ARGO Float

Tropical cyclones are a phenomenon that occurs because of the interaction between oceans and atmospheric circulation. The southeastern Indian Ocean is one of the areas that has a high activity of tropical cyclones. Tropical cyclones that cross waters can result in mixing of water masses. The mixing process produces water mass entrainment between lower and upper layers which decreases temperature in the upper layer. The purpose of this study was to estimate the strength of turbulent mixing caused by tropical cyclones using Argo Float data. The result of this study shows the turbulent mixing caused by tropical cyclones was very strong in the surface layer. Turbulent mixing is not found before the cyclone and increases when the cyclone occurs. The increase in turbulent mixing was recorded at a depth of 50 m which had an energy dissipation value ranging from 6.86x10-8 - 1.93x10-4 W/Kg and a turbulent mixing value with a range of 1.0x10-3 - 2.51x10-1 m2 s-1. This study concluded that the turbulent mixing in the surface layer is caused by tropical cyclones which is the main factor triggering the increase in the dissipation of kinetic and turbulent energy at the sites.

Hydrographic effects of an intense “medicane” over the central-eastern Mediterranean Sea in 2018

The formation of cyclones over the sea surface is driven by air-sea interaction with feedbacks on wave generation, thermohaline structures, and biochemical properties of upper-ocean layers. In the Mediterranean basin, strong cyclonic systems having tropical-like characteristics are called “medicanes”. The consequences of such events are usually assessed over the land, however, hydrographic effects are particularly difficult to be quantified mostly due to the rarity of oceanographic in-situ monitoring systems. In this study, the hydrographic effects of a high-impact medicane, crossing the central-eastern Mediterranean in late September 2018 are investigated mainly based on Argo float measurements. We traced its hydrographic fingerprint and examined its effects on the upper-layer physical properties by analyzing temperature and salinity profiles from floats that overlapped with its track. Float data is supported by satellite sea surface temperature reanalysis data and meteorological records. The synergistic effects of intense evaporation and vertical mixing mechanisms triggered by the medicane, resulted in abrupt surface cooling, especially in the Ionian and Aegean Seas. A relatively homogenous decrease of temperature in the upper 50-m-depth layer followed, along with the deepening of the mixed-layer depth, corresponding to an estimated per profile average deficit of (-2.72 ± 1.23) x 108 J m−2 in the ocean heat content. Simultaneously, significant upper-layer freshening occurred because of vertical mixing and heavy rainfall. However, a salinity increase in the subsurface waters was observed after the medicane event, which is associated with both horizontal advection and vertical mixing which followed the weakening of the Atlantic Water signal and the dominance of the Levantine Surface Water in this zone. Our findings highlight strong, short-scale hydrographic alterations made available due to the expansion of the marginal seas operational oceanographic network.

Estimating Agulhas Leakage by Means of Satellite Altimetry and Argo Data

The study of mesoscale eddies provides an understanding of entire systems of interconnected oceanic characteristics. Mesoscale eddies have their own dynamics, which are dominated by nonlinear effects. Unlike waves, they can transfer heat, mass, kinetic energy, and biochemical characteristics from the region of their formation over vast distances, influencing climate fluctuations. “Agulhas leakage” refers to the waters exported from the Indian Ocean to the Atlantic Ocean by the Agulhas system of currents. These waters consist mainly of upper and intermediate waters of Indo-Oceanic origin. Mesoscale eddies formed by the Agulhas Current are the dominant structures that transport the waters of the Indian Ocean to the Atlantic. This work studies the Agulhas leakage via a comprehensive analysis of altimetry maps and Argo float data. Argo floats captured by the flow allow the vertical structure of Agulhas leakage eddies to be studied. It is found that the mesoscale eddies of the Agulhas leakage travel thousands of kilometers across the Atlantic while retaining their structure. It is shown that the mean Agulhas leakage transport by one mesoscale eddy is 8.5 Sv. The transport of heat and salt by an individual Agulhas leakage eddy is 2.25 × 109 W and 5.36 × 105 kg s−1, respectively. The heat and salt anomalies inside the eddy are 2.03 × 1015 J and 4.83 × 1011 kg, respectively.