Estimation of Mixed Layer Depth in the Gulf of Aden: A New Approach.
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ABSTRACT:
SUBMITTER: P AC
PROVIDER: S-EPMC5082926 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere-ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal ( <90 day) MLD variability. Yet, many multivariate observations-based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid-latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction.
| S-EPMC9286844 | biostudies-literature
Project description:The surface mixed layer of the world ocean regulates global climate by controlling heat and carbon exchange between the atmosphere and the oceanic interior1-3. The mixed layer also shapes marine ecosystems by hosting most of the ocean's primary production4 and providing the conduit for oxygenation of deep oceanic layers. Despite these important climatic and life-supporting roles, possible changes in the mixed layer during an era of global climate change remain uncertain. Here we use oceanographic observations to show that from 1970 to 2018 the density contrast across the base of the mixed layer increased and that the mixed layer itself became deeper. Using a physically based definition of upper-ocean stability that follows different dynamical regimes across the global ocean, we find that the summertime density contrast increased by 8.9 ± 2.7 per cent per decade (10-6-10-5 per second squared per decade, depending on region), more than six times greater than previous estimates. Whereas prior work has suggested that a thinner mixed layer should accompany a more stratified upper ocean5-7, we find instead that the summertime mixed layer deepened by 2.9 ± 0.5 per cent per decade, or several metres per decade (typically 5-10 metres per decade, depending on region). A detailed mechanistic interpretation is challenging, but the concurrent stratification and deepening of the mixed layer are related to an increase in stability associated with surface warming and high-latitude surface freshening8,9, accompanied by a wind-driven intensification of upper-ocean turbulence10,11. Our findings are based on a complex dataset with incomplete coverage of a vast area. Although our results are robust within a wide range of sensitivity analyses, important uncertainties remain, such as those related to sparse coverage in the early years of the 1970-2018 period. Nonetheless, our work calls for reconsideration of the drivers of ongoing shifts in marine primary production, and reveals stark changes in the world's upper ocean over the past five decades.
| S-EPMC7610469 | biostudies-literature
Project description:Knowledge on large-scale biological processes in the southern Red Sea is relatively limited, primarily due to the scarce in situ, and satellite-derived chlorophyll-a (Chl-a) datasets. During summer, adverse atmospheric conditions in the southern Red Sea (haze and clouds) have long severely limited the retrieval of satellite ocean colour observations. Recently, a new merged ocean colour product developed by the European Space Agency (ESA)-the Ocean Color Climate Change Initiative (OC-CCI)-has substantially improved the southern Red Sea coverage of Chl-a, allowing the discovery of unexpected intense summer blooms. Here we provide the first detailed description of their spatiotemporal distribution and report the mechanisms regulating them. During summer, the monsoon-driven wind reversal modifies the circulation dynamics at the Bab-el-Mandeb strait, leading to a subsurface influx of colder, fresher, nutrient-rich water from the Indian Ocean. Using satellite observations, model simulation outputs, and in situ datasets, we track the pathway of this intrusion into the extensive shallow areas and coral reef complexes along the basin's shores. We also provide statistical evidence that the subsurface intrusion plays a key role in the development of the southern Red Sea phytoplankton blooms.
| S-EPMC5178994 | biostudies-literature
Project description:The worldwide deployment of Argo floats has enabled much more detailed studies of global and regional seas over the last decade. Here, the seasonal variability of the mixed layer depth (MLD) within the Coral Sea was examined with CTD profiles from Argo floats. Multiple threshold values for both temperature and density have been employed to determine the most suitable threshold values for the Coral Sea. A threshold value of 0.04 kg/m(3) for density and 0.2°C for temperature appear the most fitting for this region. Although MLD and isothermal layer depth (ILD) coincide quite well in most cases, the relatively common presence of temporary, non-seasonal barrier layers induces an ILD that is significantly deeper than the MLD. Consequently, an MLD estimation based on density is more appropriate. A distinct seasonality in the MLD is evident throughout the Coral Sea, but is generally more pronounced in higher southern latitudes (20-30°S). Salinity inversions are rare and mainly occur in the south-eastern Coral Sea, while barrier layers are more commonly associated with the north-eastern Coral Sea, a region characterised by high rainfall. The significance of regional currents is evident in the north-western Coral Sea, where temperature and ocean heat content is relatively low due to a northward moving boundary current. Shallow bathymetry, in turn, is linked to the absence of Argo data on the continental shelf and in the central Coral Sea.
| S-EPMC3623957 | biostudies-literature
Project description:To gain insight into whale shark (Rhincodon typus) movement patterns in the Western Indian Ocean, we deployed eight pop-up satellite tags at an aggregation site in the Arta Bay region of the Gulf of Tadjoura, Djibouti in the winter months of 2012, 2016, and 2017. Tags revealed movements ranging from local-scale around the Djibouti aggregation site, regional movements along the coastline of Somaliland, movements north into the Red Sea, and a large-scale (>1,000 km) movement to the east coast of Somalia, outside of the Gulf of Aden. Vertical movement data revealed high occupation of the top ten meters of the water column, diel vertical movement patterns, and deep diving behavior. Long-distance movements recorded both here and in previous studies suggest that connectivity between the whale sharks tagged at the Djibouti aggregation and other documented aggregations in the region are likely within annual timeframes. In addition, wide-ranging movements through multiple nations, as well as the high use of surface waters recorded, likely exposes whale sharks in this region to several anthropogenic threats, including targeted and bycatch fisheries and ship-strikes. Area-based management approaches focusing on seasonal hotspots offer a way forward in the conservation of whale sharks in the Western Indian Ocean.
| S-EPMC8093710 | biostudies-literature
Project description:Seasonal shifts in phytoplankton accumulation and loss largely follow changes in mixed layer depth, but the impact of mixed layer depth on cell physiology remains unexplored. Here, we investigate the physiological state of phytoplankton populations associated with distinct bloom phases and mixing regimes in the North Atlantic. Stratification and deep mixing alter community physiology and viral production, effectively shaping accumulation rates. Communities in relatively deep, early-spring mixed layers are characterized by low levels of stress and high accumulation rates, while those in the recently shallowed mixed layers in late-spring have high levels of oxidative stress. Prolonged stratification into early autumn manifests in negative accumulation rates, along with pronounced signatures of compromised membranes, death-related protease activity, virus production, nutrient drawdown, and lipid markers indicative of nutrient stress. Positive accumulation renews during mixed layer deepening with transition into winter, concomitant with enhanced nutrient supply and lessened viral pressure.
| S-EPMC8599477 | biostudies-literature
Project description:Accurate estimates of population abundance are a critical component of species conservation efforts in order to monitor the potential recovery of populations. Capture-mark-recapture (CMR) is a widely used approach to estimate population abundance, yet social species moving in groups violate the assumption of CMR approaches that all individuals in the population are detected independently. We developed a closed CMR model that addresses an important characteristic of group-living species-that individual-detection probability typically is conditional on group detection. Henceforth termed the Two-Step model, this approach first estimates group-detection probability and then-conditional on group detection-estimates individual-detection probability for individuals within detected groups. Overall abundance is estimated assuming that undetected groups have the same average group size as detected groups. We compared the performance of this Two-Step CMR model to a conventional (One-Step) closed CMR model that ignored group structure. We assessed model sensitivity to variation in both group- and individual-detection probability. Both models returned overall unbiased estimates of abundance, but the One-Step model returned deceptively narrow Bayesian confidence intervals (BCI) that failed to encompass the correct population abundance an average 52% of the time. Contrary, under the Two-Step model, CI coverage was on average 96%. Both models had similar root mean squared errors (RMSE), except for scenarios with low group detection probability, where the Two-Step model had much lower RMSE. For illustration with a real data set, we applied the Two-Step and regular model to non-invasive genetic capture-recapture data of mountain gorillas (Gorilla beringei beringei). As with simulations, abundance estimates under both models were similar, but the Two-Step model estimate had a wider confidence interval. Results support using the Two-Step model for species living in constant groups, particularly when group detection probability is low, to reduce risk of bias and adequately portray uncertainty in abundance estimates. Important sources of variation in detection need to be incorporated into the Two-Step model when applying it to field data.
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Project description:We propose a novel two-step procedure to combine epidemiological data obtained from diverse sources with the aim to quantify risk factors affecting the probability that an individual develops certain disease such as cancer. In the first step we derive all possible unbiased estimating functions based on a group of cases and a group of controls each time. In the second step, we combine these estimating functions efficiently in order to make full use of the information contained in data. Our approach is computationally simple and flexible. We illustrate its efficacy through simulation and apply it to investigate pancreatic cancer risks based on data obtained from the Connecticut Tumor Registry, a population-based case-control study, and the Behavioral Risk Factor Surveillance System which is a state-based system of health surveys.
| S-EPMC3964681 | biostudies-literature
Project description:Many alternative approaches for 3D object detection using a singular camera have been studied instead of leveraging high-precision 3D LiDAR sensors incurring a prohibitive cost. Recently, we proposed a novel approach for 3D object detection by employing a ground plane model that utilizes geometric constraints named GAC3D to improve the results of the deep-based detector. GAC3D adopts an adaptive depth convolution to replace the traditional 2D convolution to deal with the divergent context of the image's feature, leading to a significant improvement in both training convergence and testing accuracy on the KITTI 3D object detection benchmark. This article presents an alternative architecture named eGAC3D that adopts a revised depth adaptive convolution with variant guidance to improve detection accuracy. Additionally, eGAC3D utilizes the pixel adaptive convolution to leverage the depth map to guide our model for detection heads instead of using an external depth estimator like other methods leading to a significant reduction of time inference. The experimental results on the KITTI benchmark show that our eGAC3D outperforms not only our previous GAC3D but also many existing monocular methods in terms of accuracy and inference time. Moreover, we deployed and optimized the proposed eGAC3D framework on an embedded platform with a low-cost GPU. To the best of the authors' knowledge, we are the first to develop a monocular 3D detection framework on embedded devices. The experimental results on Jetson Xavier NX demonstrate that our proposed method can achieve nearly real-time performance with appropriate accuracy even with the modest hardware resource.
| S-EPMC9680869 | biostudies-literature
Project description:IntroductionThe existence of partial volume effects in brain MR images makes it challenging to understand physio-pathological alterations underlying signal changes due to pathology across groups of healthy subjects and patients. In this study, we implement a new approach to disentangle gray and white matter alterations in the thalamus and the basal ganglia. The proposed method was applied to a cohort of early multiple sclerosis (MS) patients and healthy subjects to evaluate tissue-specific alterations related to diffuse inflammatory or neurodegenerative processes.MethodForty-three relapsing-remitting MS patients and nineteen healthy controls underwent 3T MRI including: (i) fluid-attenuated inversion recovery, double inversion recovery, magnetization-prepared gradient echo for lesion count, and (ii) T1 relaxometry. We applied a partial volume estimation algorithm to T1 relaxometry maps to gray and white matter local concentrations as well as T1 values characteristic of gray and white matter in the thalamus and the basal ganglia. Statistical tests were performed to compare groups in terms of both global T1 values, tissue characteristic T1 values, and tissue concentrations.ResultsSignificant increases in global T1 values were observed in the thalamus (p = 0.038) and the putamen (p = 0.026) in RRMS patients compared to HC. In the Thalamus, the T1 increase was associated with a significant increase in gray matter characteristic T1 (p = 0.0016) with no significant effect in white matter.ConclusionThe presented methodology provides additional information to standard MR signal averaging approaches that holds promise to identify the presence and nature of diffuse pathology in neuro-inflammatory and neurodegenerative diseases.
| S-EPMC4741419 | biostudies-literature