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Abstract: This dataset contains PISM simulation results of the Antarctic Ice Sheet based on code release v1.0-paleo-ensemble (https://doi.org/10.5281/zenodo.3574033). PISM is the open-source Parallel Ice Sheet Model developed mainly at UAF, USA and PIK, Germany. See documentation in http://www.pism-docs.org. With the help of the added jupyter notebook (Python 2.7.3), all figures can be reproduced as published in the article: - Albrecht et al., 2020, doi:10.5194/tc-14-633-2020. --- Data: Find PISM results as netCDF data. See 'README.md' for a list of all performed experiment. All forcing input data for the experiments and plots can be downloaded and remapped via https://github.com/pism/pism-ais. Some of the original input data files are freely available, for others please contact the author or the corresponding data publisher. The jupyter notebook (https://jupyter.org) paleo_paper2_final.ipynb (based on python) in 'plot_scripts' accesses the uploaded PISM results in 'model_data' or 'supplement' and saves the plots as vector and pixel graphics to 'final_figures'. Edit header for changing work paths. Jupyter notebook can be run in the browser and shared, see https://nbviewer.jupyter.org/url/www.pik-potsdam.de/~albrecht/notebooks/paleo_paper/paleo_paper2_final.ipynb. --- Methods: The scoring scheme with respect to modern and paleo data based on Python 2.7.3 can be downloaded from (https://doi.org/10.5281/zenodo.3585118). The ensemble analysis calculates misfits to the paleo constraint database AntICEdat (Briggs & Tarasov, 2013) and to RAISED Consortium (2014) as well as to modern ice geometry from Bedmap2 (Fretwell et al., 2013), ice speed (Rignot et al., 2011) an GPS (Whitehouse et al., 2011). The analysis is based on Pollard et al., (2016) and Briggs et al., (2014). --- Contact : Albrecht, Torsten (albrecht@pik-potdam.de) ; Potsdam-Institute for Climate Impact Research (PIK), Potsdam, Germany Category: geoscientificInformation Source: Supplement to: Albrecht, Torsten; Winkelmann, Ricarda; Levermann, Anders (2020): Glacial-cycle simulations of the Antarctic Ice Sheet with the Parallel Ice Sheet Model (PISM) - Part 2: Parameter ensemble analysis. The Cryosphere, 14(2), 633-656, https://doi.org/10.5194/tc-14-633-2020 Supplemental Information: Not Availble Coverage: EVENT LABEL: * LATITUDE: -90.000000 * LONGITUDE: 0.000000
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  • File Set
Abstract: Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred as Linear Kinematic Features (LKFs). This data-set includes LKFs that were detected and tracked in sea ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing and an active 5-class ice thickness distribution. The model data is sampled for the entire observing period of the RADARSAT Geophysical Processor System (RGPS). The data-set spans the winter month (November to May) from 1997 to 2008 and covers the entire Arctic Ocean. A detailed description of the model configuration and the data-set is provided in: Hutter, N. and Losch, M.: Feature-based comparison of sea-ice deformation in lead-resolving sea-ice simulations, The Cryosphere, https://doi.org/10.5194/tc-2019-88, accepted for publication, 2019. A detailed description of the algorithms deriving the data set is provided in: Hutter, N., Zampieri, L., and Losch, M.: Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm, The Cryosphere, 13, 627-645, https://doi.org/10.5194/tc-13-627-2019, 2019. Category: geoscientificInformation Source: Not Available Supplemental Information: Data Description: The data set covers all RGPS winter data, i.e. November to May for the years 1996/97 to 2007/08. The LKFs of each winter season are saved in one TAB-delimited text-file (ASCII). In total the data-set contains in 12 files. In the csv-file each row corresponds to one pixel of an LKF in this year. In individual pixels are sorted by date and LKF. Each LKF gets a identifier number (LKF No.) that is unique in this winter. For track features the LKF No.(s) of parent LKF(s) from the previous RGPS time record are provided. The columns of the csv-files are structured in the following way: Start Year, Start Month, Start Day, End Year, End Month, End Day, Date(RGPS format), LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate Specific comments: Start Year, Start Month, Start Day -> Start date of the RGPS time record in which LKFs are detected End Year, End Month, End Day -> End date of the RGPS time record in which LKFs are detected Date in original RGPS format -> RGPS format of date (first for digits are the year, the last three digits are the number of days). This format is used as filename by RGPS. LKF No. -> each LKF in one winter has its unique identifier number that can be used to track the feature Parent LKF No. -> LKF No. of the LKF from the previous time records, for that this LKF is a temporal continuation determined by the tracking algorithm. This entry can contain multiple numbers if the current LKF was formed from multiple LKFs. '0' is used as a fill value, if there is no parent LKF. ind_x,ind_y -> Indexes of the LKF pixel in original RGPS data that can be used to index original RGPS fields lon, lat -> position of LKF pixel divergence and shear rate -> The divergence and shear rate of RGPS deformation data at LKF pixel. The divergence rate can be used to distinguish leads and pressure ridges in the data-set, please see Hutter et al. (2019). Coverage: Not Available
Data Types:
  • Dataset
  • File Set
Abstract: Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred as Linear Kinematic Features (LKFs). This data-set includes LKFs that were detected and tracked in sea ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing. The model data is sampled for the entire observing period of the RADARSAT Geophysical Processor System (RGPS). The data-set spans the winter month (November to May) from 1997 to 2008 and covers the entire Arctic Ocean. A detailed description of the model configuration and the data-set is provided in: Hutter, N. and Losch, M.: Feature-based comparison of sea-ice deformation in lead-resolving sea-ice simulations, The Cryosphere, https://doi.org/10.5194/tc-2019-88, accepted for publication, 2019. A detailed description of the algorithms deriving the data set is provided in: Hutter, N., Zampieri, L., and Losch, M.: Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm, The Cryosphere, 13, 627-645, https://doi.org/10.5194/tc-13-627-2019, 2019. Category: geoscientificInformation Source: Not Available Supplemental Information: Data Description: The data set covers all RGPS winter data, i.e. November to May for the years 1996/97 to 2007/08. The LKFs of each winter season are saved in one TAB-delimited text-file (ASCII). In total the data-set contains in 12 files. In the csv-file each row corresponds to one pixel of an LKF in this year. In individual pixels are sorted by date and LKF. Each LKF gets a identifier number (LKF No.) that is unique in this winter. For track features the LKF No.(s) of parent LKF(s) from the previous RGPS time record are provided. The columns of the csv-files are structured in the following way: Start Year, Start Month, Start Day, End Year, End Month, End Day, Date(RGPS format), LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate Specific comments: Start Year, Start Month, Start Day -> Start date of the RGPS time record in which LKFs are detected End Year, End Month, End Day -> End date of the RGPS time record in which LKFs are detected Date in original RGPS format -> RGPS format of date (first for digits are the year, the last three digits are the number of days). This format is used as filename by RGPS. LKF No. -> each LKF in one winter has its unique identifier number that can be used to track the feature Parent LKF No. -> LKF No. of the LKF from the previous time records, for that this LKF is a temporal continuation determined by the tracking algorithm. This entry can contain multiple numbers if the current LKF was formed from multiple LKFs. '0' is used as a fill value, if there is no parent LKF. ind_x,ind_y -> Indexes of the LKF pixel in original RGPS data that can be used to index original RGPS fields lon, lat -> position of LKF pixel divergence and shear rate -> The divergence and shear rate of RGPS deformation data at LKF pixel. The divergence rate can be used to distinguish leads and pressure ridges in the data-set, please see Hutter et al. (2019). Coverage: Not Available
Data Types:
  • Dataset
  • File Set
Abstract: This vcf file is generated from genomic data of three-spined sticklebacks from Kiel (KIE), Germany and Nynäshamn (NYN), Sweden. The individuals from Kiel are S3, S4, S19, S20, S33, S34, S47, S48, S49, S50, S63, S64, S77, S78, S89, S90. The individuals from NYN are S5, S6, S21, S22, S35, S36, S37, S38, S51, S52, S65, S66, S79, S80, S85, S86. It contains high quality biallelic positions (GQ=20) and is filtered for minor allele frequencies of maf = 0.05, allows 40 % of missing data (13 individuals) and a read depth of >=5. Further this vcf file is subset to the inducible differentially methylated sites (DMS) (198sites) in decreasing PSU (_6PSU) with 5 kilobases (kb) before and after DMS. With this file further calculations were performed to estimate the genetic differentiation (i.e. Fst) between the different populations (see manuscript indicated below). Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: Not Available
Data Types:
  • File Set
Abstract: Coastal ecosystems are periodically exposed to short- and long-term hypoxia. Coastal organisms are thus exposed to these hypoxic conditions, though, many intertidal species are tolerant to this situation. The hypoxia tolerant species can endure hypoxia through metabolic rate depression. However, the effect of hypoxia and the following reoxygenation phase on the homeostasis of the intermediate metabolites are yet to be understood. In this study, we focused on the effects of 1 day and 6 days of hypoxia and 1 hour of reoxygenation after each hypoxic conditions on the homeostasis of the intermediate metabolites in the gill and helatopancreas tissue of two intertidal species, Mytilus edulis and Crassostrea gigas. According to our findings, the effect of hypoxia and reoxygenation on the intermediate metabolites in hypoxia tolerant C. gigas were (s)lower compared to the more sensitive M. edulis. The observed changes in multiple metabolic pathways were consistent with the higher resistance to oxidative injury during hypoxia-reoxygenation. Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: Not Available
Data Types:
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Abstract: To improve the methylation estimates in our study, we corrected for SNPs, which could have led to a wrong methylation call. The excluded positions were derived with custom written perl scripts from C-to-T and G-to-A-SNPs with genotype quality of 20 and a minimum allele frequency of 0.005 from the 96 wild caught three-spined sticklebacks with a combination of custom written Perl and R-scripts using packages from methylkit and GenomicRanges. This BED-file contains the C-to-T SNPs, in which the first field is the name of the chromosome; the second describes the start position and the third the end position of the feature in standard chromosomal coordinates. Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: Not Available
Data Types:
  • File Set
Abstract: To improve the methylation estimates in our study, we corrected for SNPs, which could have led to a wrong methylation call. The excluded positions were derived with custom written perl scripts from C-to-T and G-to-A-SNPs with genotype quality of 20 and a minimum allele frequency of 0.005 from the 96 wild caught three-spined sticklebacks with a combination of custom written Perl and R-scripts using packages from methylkit and GenomicRanges. This BED-file contains the G-to-A-SNPs, in which the first field is the name of the chromosome; the second describes the start position and the third the end position of the feature in standard chromosomal coordinates. Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: Not Available
Data Types:
  • File Set
Abstract: This vcf file is generated from genomic data of three-spined sticklebacks from Kiel (KIE) and Sylt (SYL), Germany. The individuals from Kiel are S3, S4, S19, S20, S33, S34, S47, S48, S49, S50, S63, S64, S77, S78, S89, S90. The individuals from SYL are S9, S10, S17, S18, S31, S32, S45, S46, S59, S60, S61, S62, S73, S74, S87, S88. It contains high quality biallelic positions (GQ=20) and is filtered for minor allele frequencies of maf = 0.05, allows 40 % of missing data (13 individuals) and a read depth of >=5. Further this vcf file is subset to the inducible differentially methylated sites (DMS) (148sites) in increasing PSU (_33PSU) with 5 kilobases (kb) before and after DMS. With this file further calculations were performed to estimate the genetic differentiation (i.e. Fst) between the different populations (see manuscript indicated below). Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: Not Available
Data Types:
  • File Set
Abstract: TEMPERSEA is a gridded temperature product for the Red Sea covering in the period 1958-2017 at monthly resolution. The product covers the Red Sea and the Gulf of Aden with a spatial resolution of 0.25°x 0.25° and 23 vertical levels. This product is based on a large number of in-situ observations collected in the region. After a specific quality control, a mapping algorithm has been applied to homogenize the data. Also, an estimate of the accuracy of the product has been generated to accurately define the uncertainties of the product. Category: geoscientificInformation Source: Supplement to: Agulles, Miguel; Jordà, Gabriel; Jones, Burt; Agustí, Susana; Duarte, Carlos M (2020): Temporal evolution of temperatures in the Red Sea and the Gulf of Aden based on in situ observations (1958-2017). Ocean Science, 16(1), 149-166, https://doi.org/10.5194/os-16-149-2020 Supplemental Information: Not Availble Coverage: EVENT LABEL: * LATITUDE: 12.300000 * LONGITUDE: 43.600000 * METHOD/DEVICE: Multiple investigations
Data Types:
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  • File Set
Abstract: These data are in association with a study conducted in the Santa Barbara Basin, California, USA that were used to develop a 20th century record of ocean acidification in the California Current Ecosystem. These data are based on the planktonic foraminifera species G. bulloides' shell characteristics, specifically area-normalized shell weight (ANSW) and include both core sample population average values and the individual data used to calculate sample averages. Additional information on collection of ANSW data can be obtained in Osborne et al., (2016) publication in Paleoceanography (doi:10.1002/2016PA002933). Carbonate system calculations for the downcore record determined based on the proxy CO32- values using the CO2SYS program are also included in this archive. Stable isotope geochemistry data measured by IRMS for individuals used in the ANSW and two additional study species (N. dutertrei) and N. incompta) are also included. Core radioisotope geochemistry data used to develop an age core for the multi-core record are included in this archive. Category: geoscientificInformation Source: Supplement to: Osborne, Emily B; Thunell, Robert C; Gruber, Nicolas; Feely, Richard A; Benitez-Nelson, Claudia R (2020): Decadal variability in twentieth-century ocean acidification in the California Current Ecosystem. Nature Geoscience, 13(1), 43-49, https://doi.org/10.1038/s41561-019-0499-z Supplemental Information: Not Availble Coverage: EVENT LABEL: (Santa Barbara Basin Multi-Core 2012) * LATITUDE: 34.223000 * LONGITUDE: -119.983000 * DATE/TIME: 2012-08-23T11:11:00 * ELEVATION: -590.0 m * Penetration: 0.49 m * Recovery: 0.49 m * LOCATION: Santa Barbara Basin * METHOD/DEVICE: MultiCorer
Data Types:
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  • File Set
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