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16 results for “ocean pCO2”
OSSE dataset for assessing the sensitivity of pCO2 reconstructions to sampling scales across a Southern Ocean sub-domain
<p>The data stored in this repository are part of the manuscript entitled "The sensitivity of pCO<sub>2</sub> reconstructions to sampling scales across a Southern Ocean sub-domain: a semi-idealized ocean sampling simulation approach" submitted in consideration for publication for in European Geosciences Union: Biogeosciences.</p> <p> </p> <p>The netcdf file includes oceanographic (physical + biogeochemical) data, namely the partial pressure of carbon dioxide (pCO<sub>2</sub>) data at the surface ocean from the high-resolution (±10km) forced NEMO-PISCES coupled ocean model (BIOPERIANT12) and from the semi-idealized observing system simulation experiments (OSSEs) we performed.</p>
SOCAT+USV sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed
<p>Here we provide sampling masks used in the study "Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling" (Heimdal et al., 2023, https://doi.org/10.5194/bg-2023-160). In this paper, we reconstruct surface ocean pCO2 using the Large Ensemble Testbed (Gloege et al., 2021, https://doi.org/10.1029/2020GB006788) and the pCO2-Residual method (Bennington et al., 2022, https://doi.org/10.1029/2021MS002960). We provide 11 different sampling masks that correspond to the experiments presented in Heimdal et al. (2023), which include different sampling patterns of USV Saildrones in the Southern Ocean (SOCAT+USV sampling).</p>
Meta-analysis suggests variable, but pCO2-specific, effects of ocean acidification on crustacean biomaterials
Crustaceans comprise an ecologically and morphologically diverse taxonomic group. They are typically considered resilient to many environmental perturbations found in marine and coastal environments, due to effective physiological regulation of ions and hemolymph pH, and a robust exoskeleton. Ocean acidification can affect the ability of marine calcifying organisms to build and maintain mineralized tissue and poses a threat for all marine calcifying taxa. Currently, there is no consensus on how ocean acidification will alter the ecologically-relevant exoskeletal properties of crustaceans. Here, we present a systematic review and meta-analysis on the effects of ocean acidification on the crustacean exoskeleton, assessing both exoskeletal ion content (calcium and magnesium) and functional properties (biomechanical resistance and cuticle thickness). Our results suggest that the effect of ocean acidification on crustacean exoskeletal properties varies based upon seawater <i>p</i>CO<sub>2</sub> and species identity, with significant levels of heterogeneity for all analyses. Calcium and magnesium content were significantly lower in animals held at <i>p</i>CO<sub>2</sub> levels of 1500-1999 μatm as compared to those under ambient <i>p</i>CO<sub>2</sub>. At lower <i>p</i>CO<sub>2</sub> levels, however, statistically significant relationships between changes in calcium and magnesium content within the same experiment were observed: a negative relationship between calcium and magnesium content at <i>p</i>CO<sub>2</sub> of 500-999 μatm and a positive relationship at 1000-1499 μatm. Exoskeleton biomechanics, such as resistance to deformation (microhardness) and shell strength, also significantly decreased under <i>p</i>CO<sub>2</sub> regimes of 500-999 μatm and 1500-1999 μatm, indicating functional exoskeletal change coincident with decreases in calcification. Overall, these results suggest that the crustacean exoskeleton can be susceptible to ocean acidification at the biomechanical level, potentially predicated on changes in ion content, when exposed to high influxes of CO<sub>2</sub>. Future studies will need to accommodate the high variability of crustacean responses to ocean acidification, as well as ecologically-relevant ranges of <i>p</i>CO<sub>2</sub> conditions, when designing experiments with conservation-level endpoints. --
TSSCXG-17: Global Gridded Dataset of Surface Ocean pCO2 and Air-Sea CO2 Flux (1993-2020)
<p>This dataset presents a global gridded reconstruction of the partial pressure of CO2 (pCO2) in the surface ocean and the corresponding air-sea CO2 flux, covering the period from 1993 to 2020. Developed to enhance understanding of climate change and the global carbon cycle, this dataset addresses gaps in oceanic carbon flux data through innovative machine learning techniques. The reconstruction process integrates in situ observations, satellite data, and reanalysis products, employing a three-step algorithm involving dimensionality reduction, clustering, and regression.</p>
Anthropogenic attribution of the increasing seasonal amplitude in surface ocean pCO2: data to prepare figures
<p>The file contains the data to plot the graphics displayed in Joos et al., Anthropogenic attribution of the increasing seasonal amplitude in surface ocean pCO2, Geophys. Res. Letters, in press, June 2023.</p>
Meta-analysis suggests variable, but pCO2-specific, effects of ocean acidification on crustacean biomaterials
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An improved long-term high-resolution surface pCO2 data product for the Indian Ocean using machine learning
<p>This dataset contains two improved surface pCO2 products, along with surface pCO2 from the INCOIS-BIO-ROMS model (pCO2_model) and other input variables. It is a long-term, high-resolution dataset developed for the Indian Ocean region (30°E - 120°E, 30°S - 30°N), covering the period from 1980 to 2019. The dataset features a monthly temporal resolution and a spatial resolution of 1/12 degree.</p> <p> The file includes INCOIS-BIO-ROMS model outputs (sea surface temperature (SST), sea surface salinity (SSS), mixed layer depth (MLD), nitrate (NO3), dissolved inorganic carbon (DIC), and chlorophyll-a (CHL)). These variables are used as inputs for machine learning models to improve the pCO2_model. The machine learning model predicts the surface pCO2 deviants (pCO2_obs - pCO2_model). The file also provides spatiotemporally varying uncertainties associated with the predicted pCO2 deviants.</p> <p><strong>**Users are advised to download Version v2 of the data product, as Version v1 has been deprecated and is no longer recommended for use.</strong></p>
Modeling surface pCO2 variability in two contrasting basins of North Indian Ocean using advanced machine learning algorithms
<p>The dataset contains surface ocean <em>p</em>CO2, uncertainty and air-sea CO2 flux for the North Indian Ocean region. The data is available from 1993 to 2020 on a monthly time scale. Each of these data has a spatial resolution of 1/12º. Air-sea CO2 flux is calculated using a bulk parameterization, which is a function of wind speed. A positive CO2 flux value signifies CO2 outgassing, while a negative value indicates atmospheric CO2 uptake. </p> <p><strong>**It is recommended to use the latest version V3. Previous versions are depricated.</strong></p>
Eocene to Oligocene vegetation and climate in the Tasmanian Gateway region controlled by changes in ocean currents and pCO2
<p>Datasets accompanying Eocene to Oligocene vegetation and climate in the Tasmanian Gateway region controlled by changes in ocean currents and pCO2 by Amoo et al.</p> <p>Supplementary table S1: Raw palynomorph assemblage data, total counts, ODP Site 1172 </p> <p>Supplementary table S2: Sporomorph-based climate estimates including MAT, WMMT, CMMT and MAP </p> <p>Supplementary table S3: Sporomorph diversity indices</p>
Data for "Global ocean pCO2 variation regimes: spatial patterns and the emergence of a hybrid regime"
<p>Model data for article "Global ocean pCO2 variation regimes: spatial patterns and the emergence of a hybrid regime".</p>
Float+SOCAT sampling masks for ML reconstruction of surface ocean pCO2 using the Large Ensemble Testbed
<p>Here we provide sampling masks used in the study "The importance of adding unbiased Argo observations to the ocean carbon observing system" (Heimdal & McKinley, 2024, Scientific Reports). In this paper, we reconstruct surface ocean pCO2 using the Large Ensemble Testbed (Gloege et al., 2021, https://doi.org/10.1029/2020GB006788) and the pCO2-Residual method (Bennington et al., 2022, https://doi.org/10.1029/2021MS002960). We provide 2 different sampling masks used in the experiments presented in Heimdal & McKinley (2024). These masks represent two different float sampling schemes (+SOCAT) including 500 floats, corresponding to historical Argo float observations (https://fleetmonitoring.euro-argo.eu/dashboardpatterns) and potential optimized float sampling (following Chamberlain et al., 2023, <a href="https://doi.org/10.1175/JTECH-D-22-0093.1" target="_blank" rel="noopener">https://doi.org/10.1175/JTECH-D-22-0093.1</a>). </p>
A Reconstructed Coastal Acidification Database (ReCAD) pCO2 data product for the North American Atlantic Coastal Ocean Margins
<h1><strong>Reconstructed Coastal Acidification Database (ReCAD)</strong></h1> <p>Insufficient spatiotemporal coverage of the partial pressure of CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>) observations has hindered precise carbon cycle studies in coastal oceans and justifies the development of spatially and temporally continuous <em>p</em>CO<sub>2</sub> data products. Earlier <em>p</em>CO<sub>2</sub> products have difficulties in capturing the heterogeneity of regional variations and decadal trends of <em>p</em>CO<sub>2</sub> in the North American Atlantic Coastal Ocean Margin (NAACOM). This study developed a regional reconstructed <em>p</em>CO<sub>2</sub> product for the NAACOM (Reconstructed Coastal Acidification Database-<em>p</em>CO<sub>2</sub>, or ReCAD-NAACOM-<em>p</em>CO<sub>2</sub>) using a two-step approach combining random forest regression and linear regression. The product provides monthly <em>p</em>CO<sub>2</sub> data at 0.25° spatial resolution from 1993 to 2021, enabling investigation of regional spatial differences, seasonal cycles, and decadal changes in <em>p</em>CO<sub>2</sub>. The observation-based reconstruction was trained using Surface Ocean CO<sub>2</sub> Atlas (SOCAT) observations as observational values, with various satellite-derived and reanalysis environmental variables known to control sea surface <em>p</em>CO<sub>2</sub> as model inputs. The product shows high accuracy during the model training, validation, and independent test phases, demonstrating robustness and capability to accurately reconstruct <em>p</em>CO<sub>2</sub> in regions or periods lacking direct observational data. Compared with all the observation samples from SOCAT, the <em>p</em>CO<sub>2</sub> product yields a determination coefficient of 0.92, a root-mean-square error of 12.70 µatm, and an accumulative uncertainty of 23.25 µatm. The ReCAD-NAACOM-<em>p</em>CO<sub>2</sub> product demonstrates its capability to resolve seasonal cycles, regional-scale variations, and decadal trends of <em>p</em>CO<sub>2</sub> along the NAACOM. This new product provides reliable <em>p</em>CO<sub>2</sub> data for more precise studies of coastal carbon dynamics in the NAACOM region. The dataset is publicly accessible at <a href="https://doi.org/10.5281/zenodo.11500974">https://doi.org/10.5281/zenodo.11500974</a> (Wu et al., 2024a) and will be updated regularly.</p> <div> <div> </div> </div> <p><strong>Version 1.1: </strong>Update the training set output as the direct model outputs instead of being the 10-fold cross-validation output in v1.0.</p> <p><strong>Data description paper</strong><em><strong>: </strong></em>Wu, Z., Lu, W., Roobaert, A., Song, L., Yan, X.-H., and Cai, W.-J.: A machine-learning reconstruction of sea surface <em>p</em>CO<sub>2</sub> in the North American Atlantic Coastal Ocean Margin from 1993 to 2021, Earth Syst. Sci. Data, 17, 43–63, <a href="https://doi.org/10.5194/essd-17-43-2025">https://doi.org/10.5194/essd-17-43-2025</a>, 2025.</p>
Model data for: Drawdown of atmospheric pCO2 via dynamic particle export stoichiometry in the ocean twilight zone
<p>Understanding the global carbon cycle is key to understanding the climate system. One of the large unknowns is the processes happening in the twilight zone of the ocean. Here, we focus on how elemental stoichiometry of particulate organic matter in the twilight zone affects the strength of the biological pump and atmospheric CO2. We show through modeling that atmospheric CO2 is very sensitive to the change in C:P ratio in the twilight zone. Numerous model studies study the link between the carbon cycle and flexible elemental stoichiometry of organic matter in the surface ocean. However, our model study is unique. It investigates the effects of stoichiometric changes both at the surface and in the subsurface ocean that also involve stoichiometric interaction between phytoplankton and zooplankton.<br> <br> We use a 3D numerical model to illustrate how C:P variability in the twilight zone can significantly modulate the strength of carbon sequestration and atmospheric CO2. We used the biogeochemical model MOPS (Kriest and Oschlies, Geosci. Model Dev., 8, 2929–2957, 2015) coupled to ECCO Transport Matrices.</p> <p>This repository contains model input and output files for each sensitivity run outlined in the paper. The run ID corresponds to different sensitivity run. See README for more details. </p>
Model data for: Drawdown of atmospheric pCO2 via dynamic particle export stoichiometry in the ocean twilight zone
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Ocean Surface pCO2 and Air-Sea CO2 Flux in the Northern Gulf of America, 2006-2010
This dataset provides 1 km gridded monthly estimates of surface ocean partial pressure of CO2 (pCO2) and air-sea flux of CO2 (CO2 flux) for the northern Gulf of America for the period 2006 through 2010. Estimates of pCO2 were derived from MODIS/Aqua satellite imagery in combination with ship-based observations. Estimates of CO2 flux were derived from estimates of seawater pCO2, wind fields, and atmospheric pCO2.
Ocean Surface pCO2 and Air-Sea CO2 Flux in the Northern Gulf of Mexico, 2006-2010
This dataset provides 1 km gridded monthly estimates of surface ocean partial pressure of CO2 (pCO2) and air-sea flux of CO2 (CO2 flux) for the northern Gulf of Mexico for the period 2006 through 2010. Estimates of pCO2 were derived from MODIS/Aqua satellite imagery in combination with ship-based observations. Estimates of CO2 flux were derived from estimates of seawater pCO2, wind fields, and atmospheric pCO2.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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