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138 results for “upwelling”
Figure 4 in Comparison of feeding apparatus and feeding intensity of the European sardine (Sardina pilchardus) off Morocco in two Atlantic upwelling areas with different seasonal activity
Figure 4. – Box plots of mean gill raker number of young and adult of Sardina pilchardus in the Atlantic area B off Morocco.
Figure 3 in Comparison of feeding apparatus and feeding intensity of the European sardine (Sardina pilchardus) off Morocco in two Atlantic upwelling areas with different seasonal activity
Figure 3. –Monthly variation of fullness index of Sardina pilchardus from the areas B and C during 2003.
Decoupling silicon metabolism from carbon and nitrogen assimilation poises diatoms to exploit episodic nutrient pulses in a coastal upwelling system
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Data for: Wintertime coastal upwelling in Lake Geneva: An efficient transport process for deep-water renewal in a large, deep lake
<p>Combining field measurements, 3D numerical modeling and Lagrangian particle tracking,<br> we investigated wind-driven, Ekman-type coastal upwelling during the weakly stratified winter period 2017/2018<br> in Lake Geneva, a large and deep lake in western Europe. The data include measurements from<br> moored Acoustic Doppler Current Profilers (ADCP) and vertical temperature profiles<br> along with the corresponding 3D modeling and particle tracking results.<br> The three-dimensional model used in this study is based on the MIT General<br> Circulation Model (MITgcm, http://mitgcm.org/, https://doi.org/10.1029/96JC02775).<br> The particle tracking code is based on ctracker (https://doi.org/10.5281/zenodo.1034118).</p>
Data from: Transgenerational effects in an ecological context: conditioning of adult sea urchins to upwelling conditions alters maternal provisioning and progeny phenotype
Transgenerational plasticity occurs when the conditions experienced by the parental generation influences the phenotype of their progeny. This may in turn affect progeny performance and physiological tolerance, providing a means by which organisms cope with rapid environmental change. We conditioned adult purple sea urchins, Strongylocentrotus purpuratus, to combined pCO2 and temperature conditions reflective of in situ conditions of their natural habitat, the benthos in kelp forests of nearshore California, and then assessed the performance of their progeny raised under different pCO2 levels. Adults were conditioned during gametogenesis to treatments that reflected static non-upwelling (~650 μatm pCO2, ~17°C) and upwelling (~1300 μatm pCO2, ~13°C) conditions. Following approximately 4 months of conditioning, the adults were spawned and embryos were raised under low pCO2 (~450 μatm pCO2) or high pCO2 (~1050 μatm pCO2) treatments to determine if differential maternal conditioning impacted the progeny response to a single abiotic stressor: pCO2. We examined the size, protein content, and lipid content of eggs from both sets of conditioned female urchins. Offspring were sampled at four stages of early development: hatched blastula, gastrula, prism, and echinopluteus. This resulted in four sets of offspring: (1) progeny from non-upwelling-conditioned mothers raised under low pCO2, (2) progeny from non-upwelling-conditioned mothers raised under high pCO2, (3) progeny from upwelling-conditioned mothers raised under low pCO2, and (4) progeny from upwelling-conditioned mothers raised under high pCO2. We then assessed the effects of maternal conditioning along with the effects of developmental pCO2 levels on body size of the progeny. Our results showed that differential maternal conditioning had no impact on average egg size, although non-upwelling females produced eggs that were more variable in size. Maternal conditioning did not affect protein content but did have a modest impact on egg lipid content. Developing embryos whose mothers were conditioned to simulated upwelling conditions (~1300 μatm pCO2, ~13°C) were greater in body size, although this effect was no longer evident at the echinopluteus larval stage. Although maternal conditioning affected offspring body size, the pCO2 levels under which the embryos were raised did not. Overall, this laboratory study provides insight into how transgenerational effects may function in nature. The impacts of parental environmental history on progeny phenotype during early development have important implications regarding recruitment success and population-level effects.
Data from: Transcriptomics reveal transgenerational effects in purple sea urchin embryos: adult acclimation to upwelling conditions alters the response of their progeny to differential pCO2 levels
Understanding the mechanisms with which organisms can respond to a rapidly changing ocean is an important research priority in marine sciences, especially in light of recent predictions regarding the pace of ocean change in the coming decades. Transgenerational effects, in which the experience of the parental generation can shape the phenotype of their offspring, may serve as such a mechanism. In this study, adult purple sea urchins, Strongylocentrotus purpuratus, were conditioned to regionally and ecologically relevant pCO2 levels and temperatures representative of upwelling (low temperature, high pCO2) and non-upwelling (average temperature, low pCO2) conditions typical of coastal upwelling regions in the California Current System. Following 4.5 months of conditioning, adults were spawned and offspring were raised under either high or low pCO2 levels, to examine the role of maternal effects. Using RNA-seq and comparative transcriptomics, our results indicate that differential conditioning of the adults had an effect on the gene expression patterns of the progeny during the gastrula stage of early development. For example, maternal conditioning under upwelling conditions intensified the transcriptomic response of the progeny when they were raised under high versus low pCO2 conditions. Additionally, mothers that experienced upwelling conditions produced larger progeny. The overall findings of this study are complex, but do suggest that transgenerational plasticity in situ could act as an important mechanism by which populations might keep pace with rapid environmental change.
Data from: Evaluation of different bias correction methods for dynamical downscaled future projections of the California Current Upwelling System
<p class="Abstract">Biases in global Earth System Models (ESMs) are an important source of errors when used to obtain boundary conditions for regional models. Here we examine historical and future conditions in the California Current System (CCS) using three different methods to force the regional model: (1) interpolation of ESM output to the regional grid with no bias correction; (2) a "seasonally-varying" delta method that obtains a season-dependent mean climate change signal from the ESM for a 30-year future period; and (3) a "time-varying" delta method that includes the interannual variability of the ESM over the 1980–2100 period. To compare these methods, we use a high-resolution (0.1˚) physical-biogeochemical regional model to dynamically downscale an ESM projection under the RCP8.5 emission scenario. Using different downscaling methods, the sign of future changes agrees for most of the physical and ecosystem variables, but the spatial patterns and magnitudes of these changes differ, with the seasonal- and time-varying delta simulations showing more similar changes. Not correcting the ESM forcing leads to amplification of biases in some ecosystem variables as well as misrepresentation of the California Undercurrent and CCS source waters. In the non-bias corrected and time-varying delta simulations, most of the ecosystem variables inherit trends and decadal variability from the ESM, while in the seasonally-varying delta simulation, the future variability reflects the observed historical variability (1980–2010). Our results demonstrate that bias correcting the forcing prior to downscaling improves historical simulations and that the bias correction method may impact the spatial and temporal variability of future projections. </p>
Physical and biogeochemical properties of California Current upwelled source waters
<p>Coastal upwelling variability in the California Current region, one of the four main eastern boundary current upwelling systems, is controlled by processes acting over a wide range of spatial and temporal scales. While the ensuing ecosystem response depends strongly on upwelled water properties, determining their exact physical and biogeochemical characteristics is notoriously difficult as it requires tracking water masses backward in space and time from the moment they upwell near the coast to their subsurface origin. Adjoint model simulations have been used successfully to track water masses in coastal upwelling systems and the work presented here extends these applications to determining the co-variability of physical and biogeochemical properties of source waters at spatial scales that resolve the known alongshore variability of coastal upwelling dynamics in the region. Notably, the results identify that the modulation of coastal upwelling efficiency by onshore/offshore geostrophic meanders is the dominant mechanism explaining alongshore variability in source depth and properties of upwelled waters. The simulations also reveal that source water properties vary seasonally in response to different balances between coastal upwelling intensity and biogeochemical processes. During spring, interannual variability of physical and biogeochemical properties is directly tied to the intensity of upwelling-favorable alongshore winds, whereas, during summer, biogeochemical properties respond more strongly to biological activity and subsequent organic matter remineralization at depth. Overall, the present work provides important insight into the mechanisms responsible for the alongshore mosaic and seasonal variation upwelled source water properties in the central California Current region.</p>
Data from: environmental DNA reveals temporal variation in mesophotic reefs of the Humboldt upwelling ecosystems of central Chile: towards a baseline for biodiversity monitoring of unexplored marine habitats
<p>Temperate mesophotic reef ecosystems (TMREs) are among the least known marine habitats. Information on their diversity and ecology is geographically and temporally scarce, especially in highly productive large upwelling ecosystems. Lack of information remains an obstacle to understanding the importance of TMREs as habitats, biodiversity reservoirs and their connections with better-studied shallow reefs. Here, we use environmental DNA (eDNA) from water samples to characterize the community composition of TMREs on the central Chilean coast generating the first baseline for monitoring the biodiversity of these habitats. We analyzed samples from two depths (30 and 60m) over four seasons (spring, summer, autumn, and winter) and at two locations approximately 16 km apart. We used a panel of three metabarcodes, two that target all eukaryotes (18S rRNA and mitochondrial COI) and one specifically targeting fishes (16S rRNA). All panels combined encompassed eDNA assigned to 42 phyla, 90 classes, 237 orders, and 402 families. The highest family richness was found for the phyla Arthropoda, Bacillariophyta and Chordata. Overall, family richness was similar between depths but decreased during summer, a pattern consistent at both locations. Our results indicate that the structure (composition) of the mesophotic communities varied predominantly with seasons. We analyzed further the better-resolved fish assemblage and compared eDNA with other visual methods at the same locations and depths. We recovered eDNA from nineteen genera of fish, six of these have also been observed on towed underwater videos, while thirteen were unique to eDNA. We discuss the potential drivers of seasonal differences in community composition and richness. Our results suggest that eDNA can provide valuable insights for monitoring TMRE communities but highlight the necessity of completing reference DNA databases available for this region.</p>
Data for "Diel Variability and Decoupled pH-Oxygen Dynamics Drive Metabolic Plasticity in Kelp Crabs from an Upwelling Seascape".
<p>Data for “<span>Diel Variability and Decoupled pH-Oxygen Dynamics Drive Metabolic Plasticity in Kelp Crabs from an Upwelling Seascape</span>”.</p> <p>By Vargas, C.A. et al.</p> <p> </p> <p>Two files:</p> <p>Readme.txt</p> <p>Diel cycle Taliepus upwelling dataset.xlsx</p> <p> </p> <p>-Sheet “Upwelling index” Time-series of “u” and “v” component of the wind, and calculated upwelling index (UI) for two coastal sites in Arauco Gulf, an upwelling shadow (US) area, and an active upwelling (AU) area. Dataset used in Figure 1A.</p> <p>-Sheet “Time series”. Time-series data of seawater temperature (T), dissolved oxygen (DO), and pH for an upwelling shadow (US) area, and an active upwelling (AU) area. T in ºC, DO in mg L-1, and pH units. Dataset used in Figure 1D, E, Figure 2A, B, C, and Figure 3A, B, C, D.</p> <p>-Sheet “O2 consumption and C production”. Results data for oxygen consumption (umol O2 g-1 h-1), carbon production (umol C g-1 h-1), and respiratory quotient (RQ) during incubations. Datast used in Figure 5A, B, C, D, E, F.</p> <p>-Sheet “Glucose and lactate content”. Results data for hemolymph pH, glucose and lactate content (mg mg-tissue-1) in Taliepus dentatus muscle during incubations. Datast used in Figure 6A, B, C, D, E, F.</p> <p>All date and time (Date_Time) data format (mm/dd/yyyy hh:mm:ss)</p>
Data underlying the figures "Diabatic Upwelling in the Tropical Pacific: Seasonal and subseasonal variability"
<p>Data for the figures in the paper Diabatic Upwelling in the Tropical Pacific: Seasonal and subseasonal variability </p>
Replication Data for: ``Impact of Parameterized Isopycnal Diffusivity on Shelf-Ocean Exchanges under Upwelling-Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network''
<p>This dataset contains the modified MAMEBUS source code, configuration files for the MITgcm and MAMEBUS simulations, model diagnostics used in the paper, and scripts to train the Artificial Neural Networks.</p>
Monthly and Interannual variations in upwelling and chlorophyll off the Vietnam Coast in Summer based on deep learning
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Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy
<p>Model Output supporting the paper "Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy"</p> <p>The dataset includes four WRF runs, with upwelling (labeled 'operational') and with upwelling removed (labeled 'experimental'). Two of the runs have parameterized wind turbines, labeled "Fitch". </p> <p>This work was supported by NJ Board of Public Utilities. </p> <p> </p>
Roles of Upwelling on the Dynamics and Freshwater Transport of a River Plume over the Inner Shelf_original
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Mantle upwelling induced by slab rollover subduction could explain widespread intraplate volcanism in Tibet
<p>Data used to generate figures published in Strak et al. (2024) in the journal Communications Earth & Environment (LINK).</p>
Assembly, annotation, and read counts for a simulated upwelling experiment
<p>Data sets for:</p> <p><strong>Lampe RH</strong>, Cohen NR, Ellis KA, Bruland KW, Maldonado MT, Peterson TD, Till CP, Brzezinski MA, Bargu S, Thamatrakoln K, Kuzminov FI, Twining BS, and Marchetti A. (2018) <a href="http://dx.doi.org/10.1111/1462-2920.14361">Divergent gene expression among phytoplankton taxa in response to upwelling</a>. <em>Environmental Microbiology</em> 20, 3069-3082.</p> <p>File descriptions:</p> <ul> <li>contigs.fa - final assembly</li> <li>counts_KO.tab - Raw counts for each contig with taxonomic annotation and KEGG Ortholog (KO) annotation.</li> <li>counts_MO.tab - Raw counts for each contig with taxonomic annotation and KEGG Modules (MO) annotation.</li> <li>counts_kegghit.tab - Raw counts for each contig with taxonomic annotation and a KEGG gene.</li> </ul> <p>Counts column names correspond to the following treatments:</p> <pre>------------------------ Treatment | Sample IDs ------------------------ T0 | Q45 T72-C | Q46 T72-Fe | Q50 T72-DFB | Q53 T120-C | Q55, Q56, Q57 T120-Fe | Q58, Q59, Q60 T120-DFB | Q61, Q62, Q63 T120-FeDFB | Q64, Q65, Q66 </pre> <p>All counts files are tab delimited.</p>
Dataset associated with paper "Baroclinic control of Southern Ocean eddy upwelling near topography" (GRL2021)
<p>This dataset contains results from idealized 2-layer simulations investigating eddy-driven isopycnal cross-jet transport near topography due to an ACC-type jet impinging on an isolated piece of topography.</p> <p>It includes variables such as Eddy Energy, EKE, eddy-driven cross-jet transport, and energy conversions terms due to Reynolds and eddy form stresses, for several topography cases. It also includes the ipython notebooks to generate the figures included in the corresponding paper entitled "Baroclinic control of Southern Ocean eddy upwelling near topography" submitted to GRL.</p>
Seasonal upwelling forecasts in the California Current System
<div> <div> <div> <div> <div> <div> <div> <p>Daily mean upwelling forecasts used for analysis in Amaya et al. <em>in review</em>, "Seasonal upwelling forecasts in the California Current System". Forecasts are based on daily mean wind stress data from four initialized hindcasts: CanESM5, CanCM4i, CESM1, and GFDL-SPEAR. Forecasts are for the period 1991-2021.</p> </div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div>
Datasets and scripts to reproduce figures from the paper entitled "Impact of Chlorophyll Shading on the Peruvian Upwelling System" by Echevin et al.
<p>This dataset can be used to reproduce the 4 figures published in the paper entitled "Impact of Chlorophyll Shading on the Peruvian Upwelling System" by Echevin et al. The scripts must be run using ferret software as follows:</p> <p>> go script_figureX.jnl</p> <p>The netcdf files are described below:</p> <p>- model NSH and SH climatology (3D fields :x,y,time):</p> <p>Peru10km_SHADING_monthlyclim_Y16-Y25.nc</p> <p>Peru10km_NOSHADE_monthlyclim_Y16-Y25.nc</p> <p>- NSH and SH model along-shore averaged climatologies (3D fields: x,z,time):</p> <p>section_ave.mean.NOSH10km.8S13S.nc</p> <p>section_ave.mean.SH10km.8S13S.nc</p> <p>- model grid: Peru10km_grd.nc</p> <p>- 2-degrees-wide coastal mask for the grid: mask_Peru10km_w2deg.nc</p>
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