Find research datasets worth reusing

opencc-by-4.0Dec 2020View details →

Mangrove diversity loss under sea-level rise triggered by bio-morphodynamic feedbacks and anthropogenic pressures

To whom concerned, This dataset is the supplementary dataset for the publication in Environmental Research Letters entitled '<a href="https://dx.doi.org/10.1088/1748-9326/abc122">Mangrove diversity loss under sea-level rise triggered by bio-morphodynamic feedbacks and anthropogenic pressures</a>' authored by Danghan Xie, et al. in 2020. The publication can be freely downloaded here: <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abc122">https://iopscience.iop.org/article/10.1088/1748-9326/abc122</a>. The dataset consists of both model results and corresponding codes that one can easily reproduce figures either in the manuscript or the supplementary document.  To use the code, one needs to pre-install the Matlab (R2017a) and changes the pre-set route (in the code) to the directory where the dataset is stored. The figure shapes may vary with the size of the user's monitor so output figures may be either squeezed or extended in unpredictable ways, but the window size of the figure can be adjusted to match the shape and the results will not be affected. The author is appreciated that any potential concerns or questions regarding our research from any party or person, so please contact me through the email: <a href="mailto:d.xie@uu.nl">d.xie@uu.nl</a> or <a href="mailto:xiedanghan@gmail.com">xiedanghan@gmail.com</a>. To know more about my research, you can also follow the <a href="https://www.researchgate.net/profile/Danghan_Xie">ResearchGate</a>. With Kind Regards, Danghan 11th of November, 2020

opencc-by-4.0Feb 2021View details →

Computed Light Fields Within a Sea Ice Pressure Ridge

Calculated light fields in and around a sea-ice pressure ridge. The dataset contains total scalar irradiance and downwelling planar irradiance calculated in horizontal slices at the given distance form the ice surface. Calculations were performed using Monte-Carlo ray-tracing using Zemax Optic-Studio. In addition horizontal slices through the ridge geometry, as well as total and partial ice thickness in each point of the ridge are given. The fields are provided in python and matlab readable formats. For details please refer to the respective publication "The three-dimensional light field within sea ice ridges" by C. Katlein et al.

opencc-by-4.0Jul 2021View details →

Model projection of the effect of climate change and fishing pressure on key species of the South East Asia Seas

The dataset contain Projection from the Size-Spectra Bioclimatic Envelop Model (SS-DBEM), this work was part of the GCRF Blue communities Programme (www.blue-communities.org). The model provides distribution and abundance and/or biomass of fish and other species of commercial interest under climate change and fishing pressure. The model outputs are yearly abundance/biomass on a 0.5-by-0.5 degree grid, covering the period from 2000 to 2098. Further description of the model and relevant references are listed in the following file: Guide-fish-model-output-use.docx The model was run under two climate scenario: RCP4.5 and RCP8.5, with different combinations of fishing pressure expressed as the Maximum Sustainable Yield (MSY) for the following values: 0 (no fishing, climate change alone will cause variation in fish biomass), 1 (sustainable fishing), 2, 3 (overfishing), and, 4 (overfishing with destructive practice). The intent is not to reproduce current fishing level but to provide a range of scenarios with which the future of fisheries can be explored. We projected fish species that were identified as key in the South East Asia seas region by our regional partners.The full list is provided in document: Fish-list-modelguide.xlsx There are 4 zip files that contain the model outputs of in either abundance (number of fish) or biomass grams of fish) for the two climate scenario. For example Biomass-RCP45.zip will contain model outputs in biomass for projections under RCP4.5 and all MSY. within the zip files are .csv files of the outputs for each species under the 5 MSY (0 to 4), the individual file names identify the species (identified by a 6digit code), the output provided (abundance or biomass), the RCP (8.5 or 4.5), and the MSY (0, 1, 2, 3, or 4). For example the file labelled 600107-Abundance-rcp85-msy4.csv contains the outputs for species 600107 (Skipjack tuna, Katsuwonnus pelamis), as abundance, under RCP8.5 with MSY4. Headers indicate what is in each column (latitude, longitude and year).   Note: some knowledge of Python, R, or a similar software is recommended to ensure easy of use.

opencc-by-4.0Mar 2023View details →

Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)

Based on in situ seawater pCO2 data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater pCO2 was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML). MeSAA-ML introduces semi-analytical parameters, including the temperature-dependent seawater pCO2 (pCO2,therm ) and upwelling index (UISST), to characterise the combined effect of atmospheric CO2 forcing, thermodynamic effects, and multiple mixing processes on seawater pCO2. Additionally, considering the biological effects and various sub-regional features, multiple ocean colour parameters were also used as inputs in XGBoost, the best-selected machine learning algorithm. Independent cruise-based data were used to validate the satellite-derived pCO2, which achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE=19.6 μatm) and mean absolute percentage deviation (APD=4.12%). Air-sea CO2 fluxes are calculated based on retrieved seawater pCO2.

opencc-by-4.0Mar 2023View details →

Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the South China Sea (2003-2019)

The South China Sea (SCS) is one of the largest marginal seas worldwide. It includes a river-dominated, highly productive marginal sea on the north shelf and a wide, oligotrophic ocean-dominated basin with various dynamic sub-regions. Based on an in situ seawater partial pressure of CO2 (pCO2) datasets of 44 cruises/legs collected for the last two decades in the SCS, we proposed a seawater pCO2 retrieval algorithm by combining the semi-mechanistic and machine learning (ML) methods (MeSAA-ML). The parameter selection strategy was based on the mechanistic analysis of pCO2 variation, separating impacts of thermodynamics, biological activities, water mixing, and the atmospheric CO2 forcing. We set a few semi-analytical parameters: pCO2_therm, which was a proxy for the combined effect of thermodynamics and the atmospheric CO2 forcing on seawater pCO2; an upwelling index (UISST) and mixing layer depth (MLD) to characterize the multiple mixing processes; chlorophyll-a concentration (Chl-a) with remote sensing reflectance at 443 and 555 nm (Rrs(443) and Rrs(555)), which were the inputs to proxy the biological effect and other characteristics for distinguishing shelf, basin, and sub-regions. As the seawater pCO2 and atmospheric pCO2 ( pCO2air) have similar data values and characteristics in the vast SCS oligotrophic basin, it will cause instability of the model if one is input and the other is output; thus the difference between them (ΔpCO2sea-air) was set as the output, and the seawater pCO2 was obtained finally by summing pCO2air and ΔpCO2sea-air. We compared several ML models, and the XGBoost model was confirmed as the best model. Completely independent cruise-based and observed datasets from Southeastern Asia Time-series Study (SEATS) were used to validate the satellite products, with low root mean square error (RMSE = 11.69 μatm) and mean absolute percentage deviation (APD = 1.59%). The increasing trend of satellite-derived pCO2 (2.44 ± 0.24 μatm/yr) at the location of SEATS was found to be consistent with observed data. We presented that the SCS as a whole is a source of atmospheric CO2, releasing an average of 11.00 ± 2.45 Tg C/yr from a total area of 3.32 × 106 km2, and the northern shelf is a sink (1.69 ± 0.53 Tg C/yr). The area-integrated CO2 efflux over the entire SCS may decrease with a rate of 0.34 Tg C/yr during 2003–2019. This high-accuracy dataset with 1 km resolution provides a refined understanding of the air-sea CO2 exchange dynamics in the SCS during 2003–2019.

opencc-by-4.0Mar 2023View details →

Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)

opencc-by-4.0Sep 2023View details →

Updated gridded reconstruction of sea level pressure, temperature, and precipitation during winter in the North Atlantic region covering 1241-1970 CE

<ul> <li>This dataset is an updated version of the gridded climate reconstruction by Sjolte et al. 2018 (SEA18): Solar and volcanic forcing of North Atlantic climate inferred from a process-based reconstruction, Climate of the Past, 14, 1179–1194, https://doi.org/10.5194/cp-14-1179-2018. </li> </ul>   <ul> <li>Relevant results of this new version (SEA18v2) are available in our recent paper: Tao, Q. , Sjolte, J. , & Muscheler, R. (2023). Persistent model biases in the spatial variability of winter North Atlantic atmospheric circulation. Geophysical Research Letters, 50, e2023GL105231. https://doi.org/10.1029/2023GL105231</li> </ul>   <ul> <li>This dataset contains the gridded reconstruction of winter sea level pressure (slp), 2m temperature (t2m) and precipitation (precip) for the North Atlantic region over 1241-1970.</li> </ul>   <ul> <li>Methodology: The new reconstruction (SEA18v2), has been optimized for a better representation of the variability of the main modes of sea level pressure. The original reconstruction, SEA18, was an ensemble of 39 model analogues for each year and the reconstruction comprised of the mean of the analogues. For the new version, SEA18v2, a different approach to calculating the ensemble mean of the analogues has been applied. While the overall evaluation and ranking of model analogues are the same as for SEA18, we now apply a weighting function so that poor-fitting model analogues receive less weight and good-fitting analogues receive more weight. Furthermore, we evaluate the main modes of the reconstructed SLP and test the minimum number of ensemble members that can be used and still retain skill for the temporal and spatial variability of the first three modes. Retaining 16 ensemble members gives better performance for the spatial patterns for the first three EOFs of SLP compared to SEA18 and good skill for the temporal variability of the NAO.</li> </ul>

opencc-by-4.0Mar 2017View details →

The Impact of a Pressurized Regional Sea or Global Ocean on Stresses on Enceladus: Numerical models

Comsol Multiphysics models solving the stress field in Enceladus' ice shell when a regional sea of global ocean is pressurized. Model parameters are included as part of the file name according to the template EnceladusTtDdLabel.mph with <ul> <li>t is the ice shell thickness</li> <li>d is the thickness of the south polar sea or indentation</li> <li>Label indicates model configuration <ul> <li>Fixed: The base of the ice shell (outside the south polar sea) is in contact with the core with a no-slip boundary condition</li> <li>Roller: The base of the ice shell (outside the south polar sea) is in contact with the core with a free-slip boundary condition</li> <li>Ocean: The base of the ice shell is floating with a constant pressure condition; there is a single indentation at the South pole</li> <li>North: The base of the ice shell is floating with a constant pressure condition; there are indentations at both poles, with the north pole indentation having half the thickness of the South pole indentation</li> </ul> </li> </ul> There are two solved datasets in each model. The first uses a default value of the ocean angle (40°). The second results from a parameter sweep in which the sea angle varies systematically in increments of 2°.

opencc-by-4.0Dec 2023View details →

Data Sets: Unsteady Land-Sea Breeze Circulations in the Presence of a Synoptic Pressure Forcing

{Mg (m/s): 0, 0.4, 1.2, 2 and α: 0°, and 180°} Consult the details in Allouche et al. (2023): https://doi.org/10.1002/qj.4552. The latter corresponds to the steady state simulations of these transient ones here. All of these simulations have a domain extent (L_x, L_y, L_z=z_i) of (80 km, 5 km, 1.6 km). The numerical mesh (Nx, Ny, Nz) is (384, 24, 64). For plotting, the vertical levels vary for each variable. A variable at 'c_s nodes' is plotted at dz/2, dz, 2*dz, 3*dz, and so forth up to z_i. A variable on 'w nodes' is plotted at 0, dz, 2*dz, 3*dz, and so forth up to z_i. This information is given in the variable table below. These simulations are given in a netcdf format (.nc). One is able to download and reshape these matrices.

Fig. 6 in Seasonal response of benthic foraminifera to anthropogenic pressure in two stations of the Gulf of Trieste (northern Adriatic Sea, Italy): the marine protected area of Miramare versus the Servola water sewage outfall

Fig. 6: Rank-frequency diagram for the Ser(a) and Res(b) stations using the cumulative abundance (as relative frequency) for y-axe and the decreasing rank order for x-axe of each species for each sample. Both axes are on logarithmic scale.

Fig. 4 in Seasonal response of benthic foraminifera to anthropogenic pressure in two stations of the Gulf of Trieste (northern Adriatic Sea, Italy): the marine protected area of Miramare versus the Servola water sewage outfall

Fig. 4: Foraminiferal data for Ser and Res stations using the complete living assemblage of the 0-2 cm level: a) Foraminiferal density (FD) as the number of specimens normalised to 50 cc of sediments and species richness; b) Dominance index; c) bias corrected Shannon (H' bc) and the exponential function Exp(H' ) indexes.

Fig. 3 in Seasonal response of benthic foraminifera to anthropogenic pressure in two stations of the Gulf of Trieste (northern Adriatic Sea, Italy): the marine protected area of Miramare versus the Servola water sewage outfall

Fig. 3: Potentially toxic elements (PTE) concentration in the Ser (a) and Res (b) stations calculated for the 0-1 cm level. PTEs are expressed in micrograms per gram (µg/g).

Fig. 1 in Seasonal response of benthic foraminifera to anthropogenic pressure in two stations of the Gulf of Trieste (northern Adriatic Sea, Italy): the marine protected area of Miramare versus the Servola water sewage outfall

Fig. 1: Study area and location of the Ser (Servola) and Res (Riserva Naturale Marina di Miramare) stations. The Servola pipeline is evidenced in the enlarged square.

Fig. 2 in Seasonal response of benthic foraminifera to anthropogenic pressure in two stations of the Gulf of Trieste (northern Adriatic Sea, Italy): the marine protected area of Miramare versus the Servola water sewage outfall

Fig. 2: Vertical profiles of temperature, salinity, pH, oxygen saturation and chlorophyll a at Ser and Res stations during the seasonal sampling.

opencc-by-4.0Nov 2020View details →

Figure 3 in Phytoplankton adaptation strategies under the influence of climatic changes and anthropogenic pressure on the Black Sea coastal ecosystems on the example Sevastopol Bay

Figure 3. Multiannual dynamics: a, d – concentrations of chlorophyll a (1), phytoplankton biomass (2) and water temperature (3), b, e – concentrations of nitrates (1), silicon (2), phosphates (3) and ammonium (4), c, f- diatoms contribution (1), dinoflagellates (2) and coccolithophorides (3) in the total phytoplankton biomass in Sevastopol Bay in summer and autumn.