scholarly journals On the First Observed Wave-induced Stress over the Global Ocean

2021 ◽  
Author(s):  
Sheng Chen
Keyword(s):  
Surface Wave ◽  
Wind Stress ◽  
Large Scale ◽  
Late Summer ◽  
Global Scale ◽  
Global Ocean ◽  
Wave Spectra ◽  
Wind Speeds ◽  
Induced Stress ◽  
Wave Induced

<p>Despite many investigations/studies on the surface wave-induced stress, the global feature of the wave-induced stress has not been obtained previously as that requires a simultaneous observation of wave spectra and wind on a global scale. The China France Oceanography Satellite (CFOSAT) provided an opportunity for the first time to evaluate the global wave-induced stress and its contribution to the total wind stress. In this study, the global spatial distributions of wave-induced stress and its correlated index for August to November in 2019 are presented using the simultaneous ocean surface winds and wave spectra from the CFOSAT. The main results show that the wave-induced stress is fundamentally dependent on the wind and wave fields on a global scale and shows significant temporal and spatial variations. Further analyses indicate that there is an upward momentum flux under strong swells and low wind speeds (below approximately 5 m/s), and an anti-correlation between the dimensionless wave-induced stress and the proportion of swell energy to the total. Finally, the variations of the surface wave induced wind stress are clear asymmetric between northern and southern hemispheres in late summer but symmetric in late fall, which are closely associated with the seasonal changes in large-scale atmospheric circulation.</p>

scholarly journals Air–Sea Momentum Fluxes during Tropical Cyclone Olwyn

2019 ◽  
Vol 49 (6) ◽  
pp. 1369-1379 ◽  
Author(s):  
Joey J. Voermans ◽  
Henrique Rapizo ◽  
Hongyu Ma ◽  
Fangli Qiao ◽  
Alexander V. Babanin
Keyword(s):  
Tropical Cyclone ◽  
Wind Stress ◽  
Momentum Flux ◽  
Turbulent Stress ◽  
High Wind ◽  
Wind Speeds ◽  
Induced Stress ◽  
Induced Stresses ◽  
Turbulent Momentum ◽  
Wave Induced

AbstractObservations of wind stress during extreme winds are required to improve predictability of tropical cyclone track and intensity. A common method to approximate the wind stress is by measuring the turbulent momentum flux directly. However, during high wind speeds, wave heights are typically of the same order of magnitude as instrument heights, and thus, turbulent momentum flux observations alone are insufficient to estimate wind stresses in tropical cyclones, as wave-induced stresses contribute to the wind stress at the height of measurements. In this study, wind stress observations during the near passage of Tropical Cyclone Olwyn are presented through measurements of the mean wind speed and turbulent momentum flux at 8.8 and 14.8 m above the ocean surface. The high sampling frequency of the water surface displacement (up to 2.5 Hz) allowed for estimations of the wave-induced stresses by parameterizing the wave input source function. During high wind speeds, our results show that the discrepancy between the wind stress and the turbulent stress can be attributed to the wave-induced stress. It is observed that for > 1 m s−1, the wave-induced stress contributes to 63% and 47% of the wind stress at 8.8 and 14.8 m above the ocean surface, respectively. Thus, measurements of wind stresses based on turbulent stresses alone underestimate wind stresses during high wind speed conditions. We show that this discrepancy can be solved for through a simple predictive model of the wave-induced stress using only observations of the turbulent stress and significant wave height.

scholarly journals Diatoms Are Selective Segregators in Global Ocean Planktonic Communities

mSystems ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Flora Vincent ◽  
Chris Bowler
Keyword(s):  
Open Access ◽  
Large Scale ◽  
Abiotic Factors ◽  
Biotic Interactions ◽  
Distribution Patterns ◽  
Global Scale ◽  
Global Ocean ◽  
Extensive Literature ◽  
Microbial Association ◽  
Species Specific

ABSTRACT Diatoms are a major component of phytoplankton, believed to be responsible for around 20% of the annual primary production on Earth. As abundant and ubiquitous organisms, they are known to establish biotic interactions with many other members of plankton. Through analyses of cooccurrence networks derived from the Tara Oceans expedition that take into account both biotic and abiotic factors in shaping the spatial distributions of species, we show that only 13% of diatom pairwise associations are driven by environmental conditions; the vast majority are independent of abiotic factors. In contrast to most other plankton groups, on a global scale, diatoms display a much higher proportion of negative correlations with other organisms, particularly toward potential predators and parasites, suggesting that their biogeography is constrained by top-down pressure. Genus-level analyses indicate that abundant diatoms are not necessarily the most connected and that species-specific abundance distribution patterns lead to negative associations with other organisms. In order to move forward in the biological interpretation of cooccurrence networks, an open-access extensive literature survey of diatom biotic interactions was compiled, of which 18.5% were recovered in the computed network. This result reveals the extent of what likely remains to be discovered in the field of planktonic biotic interactions, even for one of the best-known organismal groups. IMPORTANCE Diatoms are key phytoplankton in the modern ocean that are involved in numerous biotic interactions, ranging from symbiosis to predation and viral infection, which have considerable effects on global biogeochemical cycles. However, despite recent large-scale studies of plankton, we are still lacking a comprehensive picture of the diversity of diatom biotic interactions in the marine microbial community. Through the ecological interpretation of both inferred microbial association networks and available knowledge on diatom interactions compiled in an open-access database, we propose an ecosystems approach for exploring diatom interactions in the ocean.

Using a new divergence method to quantify methane emissions with TROPOMI on Sentinel-5p

2021 ◽  
Author(s):  
Mengyao Liu ◽  
Ronald Van der A ◽  
Michiel Van Weele ◽  
Henk Eskes ◽  
Xiao Lu ◽  
...  
Keyword(s):  
Large Scale ◽  
A Priori ◽  
The United States ◽  
Global Scale ◽  
Methane Emissions ◽  
Atmospheric Methane ◽  
Permian Basin ◽  
Wind Speeds ◽  
Gas Fields ◽  
Oil Gas

<p>The high-resolution Tropospheric Monitoring Instrument (TROPOMI) satellite observations of atmospheric methane offer a powerful tool to identify emission hot spots and quantify regional emissions. The divergence of horizontal fluxes of NO<sub>2</sub> has already been proven to be an efficient way to resolve and quantify high sources on a global scale. Since the lifetime of CH<sub>4</sub> is in the order of 10 years, the sinks can be ignored at the synoptic time scale which makes the divergence method even more applicable to CH<sub>4 </sub>than to short-lived NO<sub>2</sub>. <br>Because plumes of newly emitted CH<sub>4 </sub>disperse within the Planetary Boundary Layer (PBL), we first convert the satellite observed total column average (XCH<sub>4</sub>) to a regional enhancement of methane in the PBL (∆XCH<sub>4_PBL</sub>) by using the CAMS global methane background reanalysis fields above the PBL. These model fields represent the transport- and chemically-modulated large-scale distribution of methane. Secondly, the divergence of ∆XCH<sub>4_PBL</sub> is derived by the use of the wind speeds halfway within the PBL. Based on the divergence, methane emissions are estimated on a 0.25°× 0.25° grid. We tested our new method for Texas in the United States and quantified methane emissions from the well-known oil-gas fields in the Permian Basin, as well as from – less well quantitatively established – oil-gas fields located in southern coastal areas. <br>Compared to traditional inverse methods, our method is not restricted by an a priori emission inventory and so far unidentified local sources (i.e. emissions from livestock in feed yards) may be found. Due to its computational efficiency, the method might be applied in the near future globally on the current spatial resolution.</p>

scholarly journals On the First Observed Wave‐Induced Stress Over the Global Ocean

2020 ◽  
Vol 125 (12) ◽  
Author(s):  
Sheng Chen ◽  
Anna Rutgersson ◽  
Xunqiang Yin ◽  
Ying Xu ◽  
Fangli Qiao
Keyword(s):  
Global Ocean ◽  
Induced Stress ◽  
Wave Induced

scholarly journals Technological Trends and Significance of the Essential Ocean Variables by the Indian Moored Observatories: Relevance to UN Decade of Ocean Sciences

2021 ◽  
Vol 55 (3) ◽  
pp. 34-49
Author(s):  
Ramasamy Venkatesan ◽  
Manickavasagam Arul Muthiah ◽  
Narayanaswamy Vedachalam ◽  
Gopal Vengatesan ◽  
Krishnamoorthy Ramesh ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Large Scale ◽  
The United States ◽  
Global Scale ◽  
Global Ocean ◽  
Comprehensive Overview ◽  
Essential Information ◽  
Ocean Science ◽  
Observation Network ◽  
Ocean Observing

Abstract The ocean plays a key role in regulating the climate as well as supporting diverse ecosystems. Technology is the key for the sustained and precise in-situ spatio-temporal measurements of the physical, biological, biogeochemical, and near-atmospheric meteorological parameters essential for carrying out effective assessments of the status, variability, and change in the ocean ecosystems and for creating policies at the right time. The United Nations Decade of Ocean Science for Sustainable Development 2021‐2030 provides a timeframe to build a comprehensive, sustainable, and data-based informed decision-making global ocean observing system. This demands global-scale investigations, trans-disciplinary science, and mechanisms to integrate and distribute data that otherwise would appear to be disparate. The essential ocean variables (EOVs) conceptualized by the Global Ocean Observing System (GOOS) of UNESCO's Intergovernmental Oceanographic Commission guide observation of the ocean. In order to achieve the goal of UN Decade envisaged and to have an Earth System approach under the World Meteorological Organization reforms, it is imperative to address globally and nationally relevant indicators and assessments, which require increased sharing of data and analytical methods, sustained long-term and large-scale observations, and resources dedicated to these tasks. Technology for observing the ocean is important, which is not addressed in detail in the recent past. In this paper we provide a comprehensive overview of Sensor versus Essential Ocean Variable from our experience in sustained 25 years of moored ocean observation network and collaborating with institutions and experts in the United States and GOOS. An attempt has been made to furnish an overview for any group or nation to start or sustain an observation network using EOVs with guiding principles of Findable, Accessible, Interoperable, Reusable data that is targeted to deliver essential information needed for sustainable development and protecting ocean health.

Stability-Dependent Exchange Coefficients for Air–Sea Fluxes*

2005 ◽  
Vol 22 (7) ◽  
pp. 1080-1094 ◽  
Author(s):  
A. Birol Kara ◽  
Harley E. Hurlburt ◽  
Alan J. Wallcraft
Keyword(s):  
Heat Flux ◽  
Water Vapor ◽  
Wind Stress ◽  
General Circulation ◽  
Global Ocean ◽  
Polynomial Functions ◽  
Substantial Impact ◽  
Wind Speeds ◽  
Wide Range ◽  
Low Wind Speeds

Abstract This study introduces exchange coefficients for wind stress (CD), latent heat flux (CL), and sensible heat flux (CS) over the global ocean. They are obtained from the state-of-the-art Coupled Ocean–Atmosphere Response Experiment (COARE) bulk algorithm (version 3.0). Using the exchange coefficients from this bulk scheme, CD, CL, and CS are then expressed as simple polynomial functions of air–sea temperature difference (Ta − Ts)—where air temperature (Ta) is at 10 m, wind speed (Va) is at 10 m, and relative humidity (RH) is at the air–sea interface—to parameterize stability. The advantage of using polynomial-based exchange coefficients is that they do not require any iterations for stability. In addition, they agree with results from the COARE algorithm but at ≈5 times lower computation cost, an advantage that is particularly needed for ocean general circulation models (OGCMs) and climate models running at high horizontal resolution and short time steps. The effects of any water vapor flux in calculating the exchange coefficients are taken into account in the polynomial functions, a feature that is especially important at low wind speeds (e.g., Va < 5 m s−1) because air–sea mixing ratio difference can have a major effect on the stability, particularly in tropical regions. Analyses of exchange coefficients demonstrate the fact that water vapor can have substantial impact on air–sea exchange coefficients at low wind speeds. An example application of the exchange coefficients from the polynomial approach is the recalculation of climatological mean wind stress magnitude from 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data in the North Pacific Ocean over 1979–2002. Using ECMWF 10-m winds and the authors’ methodology provides accurate surface stresses while largely eliminating the orographically induced Gibb’s waves found in the original ERA-40 surface wind stresses. These can have a large amplitude near mountainous regions and can extend far into the ocean interior. This study introduces exchange coefficients of air–sea fluxes, which are applicable to the wide range of conditions occurring over the global ocean, including the air–sea stability differences across the Gulf Stream and Kuroshio, regions which have been the subject of many climate model studies. This versatility results because CD, CL, and CS are determined for Va values of 1 to 40 m s−1, (Ta − Ts), intervals of −8° to 7°C, and RH values of 0% to 100%. Exchange coefficients presented here are called the Naval Research Laboratory (NRL) Air–Sea Exchange Coefficients (NASEC) and they are suitable for a wide range of air–sea interaction studies and model applications.

scholarly journals Characterization of ASCAT measurements based on buoy and QuikSCAT wind vector observations

Ocean Science ◽  
2008 ◽  
Vol 4 (4) ◽  
pp. 265-274 ◽  
Author(s):  
A. Bentamy ◽  
D. Croize-Fillon ◽  
C. Perigaud
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Global Scale ◽  
Global Ocean ◽  
Wind Vector ◽  
Wind Speeds ◽  
Wind Retrievals ◽  
Buoy Data

Abstract. The new scatterometer Advanced SCATterometer (ASCAT) onboard MetOp-A satellite provides surface wind speed and direction over global ocean with a spatial resolution of 25 km square over two swaths of 550 km widths. The accuracy of ASCAT wind retrievals is determined through various comparisons with moored buoys. The comparisons indicate that the remotely sensed wind speeds and directions agree well with buoy data. The root-mean-squared differences of the wind speed and direction are less than 1.72 m/s and 18°, respectively. At global scale, ASCAT winds are compared with surface winds derived from QuikSCAT scatterometer. The results confirm the buoy analyses, especially for wind speed ranging between 3 m/s and 20 m/s. For higher wind conditions, ASCAT is biased low. The ASCAT underestimation with respect to QuikSCAT winds is wind speed dependent. The comparisons based on the collocated scatterometer data collected after 17 of October 2007 indicate that there are significant improvements compared to previous periods.

scholarly journals Estimating Ocean Surface Currents With Machine Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Anirban Sinha ◽  
Ryan Abernathey
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Wind Stress ◽  
Large Scale ◽  
Surface Flow ◽  
Global Ocean ◽  
Surface Currents ◽  
Learning Models ◽  
Spatial Gradients ◽  
Temporal Sampling

Global surface currents are usually inferred from directly observed quantities like sea-surface height, wind stress by applying diagnostic balance relations (like geostrophy and Ekman flow), which provide a good approximation of the dynamics of slow, large-scale currents at large scales and low Rossby numbers. However, newer generation satellite altimeters (like the upcoming SWOT mission) will capture more of the high wavenumber variability associated with the unbalanced components, but the low temporal sampling can potentially lead to aliasing. Applying these balances directly may lead to an incorrect un-physical estimate of the surface flow. In this study we explore Machine Learning (ML) algorithms as an alternate route to infer surface currents from satellite observable quantities. We train our ML models with SSH, SST, and wind stress from available primitive equation ocean GCM simulation outputs as the inputs and make predictions of surface currents (u,v), which are then compared against the true GCM output. As a baseline example, we demonstrate that a linear regression model is ineffective at predicting velocities accurately beyond localized regions. In comparison, a relatively simple neural network (NN) can predict surface currents accurately over most of the global ocean, with lower mean squared errors than geostrophy + Ekman. Using a local stencil of neighboring grid points as additional input features, we can train the deep learning models to effectively “learn” spatial gradients and the physics of surface currents. By passing the stenciled variables through convolutional filters we can help the model learn spatial gradients much faster. Various training strategies are explored using systematic feature hold out and multiple combinations of point and stenciled input data fed through convolutional filters (2D/3D), to understand the effect of each input feature on the NN's ability to accurately represent surface flow. A model sensitivity analysis reveals that besides SSH, geographic information in some form is an essential ingredient required for making accurate predictions of surface currents with deep learning models.

scholarly journals Eukaryotic virus composition can predict the efficiency of carbon export in the global ocean

2019 ◽  
Author(s):  
Hiroto Kaneko ◽  
Romain Blanc-Mathieu ◽  
Hisashi Endo ◽  
Samuel Chaffron ◽  
Tom O. Delmont ◽  
...  
Keyword(s):  
Large Scale ◽  
Regional Scale ◽  
Deep Ocean ◽  
Global Scale ◽  
Ecosystem Dynamics ◽  
Global Ocean ◽  
Marker Genes ◽  
Carbon Export ◽  
Viral Marker ◽  
Carbon Pump

SummaryThe biological carbon pump, in which carbon fixed by photosynthesis is exported to the deep ocean through sinking, is a major process in Earth’s carbon cycle. The proportion of primary production that is exported is termed the carbon export efficiency (CEE). Based on in-lab or regional scale observations, viruses were previously suggested to affect the CEE (i.e., viral “shunt” and “shuttle”). In this study, we tested associations between viral community composition and CEE measured at a global scale. A regression model based on relative abundance of viral marker genes explained 67% of the variation in CEE. Viruses with high importance in the model were predicted to infect ecologically important hosts. These results are consistent with the view that the viral shunt and shuttle functions at a large scale and further imply that viruses likely act in this process in a way dependent on their hosts and ecosystem dynamics.

scholarly journals Characterization of ASCAT measurements based on buoy and QuikSCAT wind vector observations

2008 ◽  
Vol 5 (1) ◽  
pp. 77-101 ◽  
Author(s):  
A. Bentamy
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Global Scale ◽  
Global Ocean ◽  
Wind Vector ◽  
Wind Speeds ◽  
Wind Retrievals ◽  
Buoy Data

Abstract. The new scatterometer Advanced SCATterometer (ASCAT) onboard MetOp-A satellite provides surface wind speed and direction over global ocean with a spatial resolution of 25 km square over two swaths of 550 km widths. The accuracy of ASCAT wind retrievals is determined through various comparisons with moored buoys. The comparisons indicate that the remotely sensed wind speeds and directions agree well with buoy data. The root-mean-squared differences of the wind speed and direction are less than 1.72 m/s and 18°, respectively. At global scale, ASCAT winds are compared with surface winds derived from QuikSCAT scatterometer. The results confirm the buoy analyses, especially for wind speed ranging between 3 m/s and 20 m/s. For higher wind conditions, ASCAT is biased low. The ASCAT underestimation with respect to QuikSCAT winds is wind speed dependent. The comparisons based on the collocated scatterometer data collected after 17 October 2007 indicate that there are significant improvements compared to previous periods.

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