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24 results for “pre-industrial”
Model output from the Bern3D model of pre-industrial and Last Glacial Maximum
<p>The here presented datasets contain the model results of the Bern3D model to be published in Pöppelmeier et al. (2021) <em>Climate of the Past Discussions</em> (https://doi.org/10.5194/cp-2020-135).</p> <p>The datasets of the five performed simulations are presented: PI_CTRL, LGM_CTRL, LGM_BS, LGM_BS+wind, LGM_BS+wind+tidal. For detailed information on the model set ups please see section 2 and Table 1 of Pöppelmeier et al. (2021).</p>
Chironomid taxa relative abundance information and lake identifiers for: Changes in midge assemblages reflect climate and trophic gradients across north temperate and boreal lakes since the pre-industrial period
<p>File 1: Relative abundances for chironomid taxa used in the manuscript: Changes in midge assemblages reflect climate and trophic gradients across north temperate and boreal lakes since the pre-industrial period. Lake_ID corresponds to the lake IDs attributed to each lake sampled as part of the LakePulse Network</p> <p>File 2: Lake_ID, lake name, latitude, longitude, sampling date, province, and ecozone for the 69 lakes examined in the manuscript: Changes in midge assemblages reflect climate and trophic gradients across north temperate and boreal lakes since the pre-industrial period. </p>
Data from: Drivers of global pre-industrial patterns of species turnover in planktonic foraminifera
<p>Anthropogenic climate change is altering global biogeographical patterns. However, it remains difficult to quantify how bioregions are changing because pre-industrial records of species distributions are rare. Marine microfossils, such as planktonic foraminifera, are preserved in seafloor sediments and allow the quantification of bioregions in the past. Using a recently compiled data set of pre-industrial species composition of planktonic foraminifera in 3802 worldwide seafloor sediments, we employed multivariate and statistical model-based approaches to study spatial turnover in order to 1) quantify planktonic foraminifera bioregions and 2) understand the environmental drivers of species turnover. Four latitudinally banded bioregions emerge from the global assemblage data. The polar and temperate bioregions are bi-hemispheric, supporting the idea that planktonic foraminifera species are not limited by dispersal. The equatorial bioregion shows complex longitudinal patterns and overlaps in sea surface temperature (SST) range with the tropical bioregion. Compositional-turnover models (Bayesian bootstrap generalised dissimilarity models) identify SST as the strongest driver of species turnover. The turnover rate is constant across most of the SST gradient, showing no SST threshold values with rapid shifts in species composition, but decelerates above 25°C, suggesting SST is less predictive of species composition in warmer waters. Other environmental predictors affect species turnover non-linearly, and their importance differs across regions. In the Pacific ocean, net primary productivity below 500 mgC m<sup>−2</sup> day<sup>−1</sup> drives fast compositional change. Water depth values below 3000 m (which affect calcareous microfossil preservation) increasingly drive changes in species composition among death assemblages in the Pacific and Indian oceans. Together, our results suggest that the dynamics of planktonic foraminifera bioregions are expected to be highly responsive to climate change; however, at lower latitudes, environmental drivers other than SST may affect these dynamics.</p>
Varied oxygen simulations with WACCM6 (Proterozoic to pre-industrial atmosphere)
<p>The history of molecular oxygen (O<sub>2</sub>) in Earth's atmosphere is still debated; however, geological evidence supports at least two major episodes where O<sub>2</sub> increased by an order of magnitude or more: the Great Oxidation Event (GOE) and the Neoproterozoic Oxidation Event. O<sub>2 </sub>concentrations have likely fluctuated (between 10<sup>−3</sup> and 1.5 times the present atmospheric level) since the GOE ∼ 2.4 Gyr ago, resulting in a time-varying ozone (O<sub>3</sub>) layer. Using a three-dimensional (3D) chemistry climate model, we simulate changes in O<sub>3</sub> in Earth's atmosphere since the GOE and consider the implications for surface habitability, and glaciation during the Mesoproterozoic. We find lower O<sub>3</sub> columns (reduced by up to 4.68 times for a given O<sub>2</sub> level) compared to previous work; hence, higher fluxes of biologically harmful UV radiation would have reached the surface. Reduced O<sub>3</sub> leads to enhanced tropospheric production of the hydroxyl radical (OH) which then substantially reduces the lifetime of methane (CH<sub>4</sub>). We show that a CH<sub>4</sub> supported greenhouse effect during the Mesoproterozoic is highly unlikely. The reduced O<sub>3</sub> columns we simulate have important implications for astrobiological and terrestrial habitability, demonstrating the relevance of 3D chemistry-climate simulations when assessing paleoclimates and the habitability of faraway worlds.</p>
Q-MARE database on pre-industrial climate and human impacts on marine ecosystems
<p>A systematic literature review was carried out using two bibliographic databases the Web of Science (WoS; www.webofknowledge.com; Clarivate) and Scopus (www.scopus.com; Elsevier). In the former searches were completed by searching the “core collection” using the “topic” field (which searches the paper titles, abstracts, author keywords and keywords plus; the latter determined by a Clarivate algorithm using synonymy), and in Scopus the abstract, title and keyword fields were searched. Searches were completed between July and November 2023.</p> <p> </p>
Simulations for pre-industrial climate using EC-Earth3-LR model — selected data for a study on AMOC
<p>A long-term control simulation of pre-industrial period (1850 CE) climates were performed by the EC-Earth3-LR climate model with a horizontal resolution of ~1.125°. The dataset contains selected output data from the simulations.</p> <p>In total, a 2000-year long control simulation was made, which has pre-industrial orbital boundary conditions, initialized by a pre-run steady restart file (the output of approximately 500-year pre-industrial control simulation). This dataset is used to investigate internal climate variability without external forcing changes under pre-industrial climate conditions.</p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Cao et al. (2022).</p> <p><strong>Model configuration</strong><br> Time periods: Pre-Industrial (2000-year time slice)<br> ESM configuration: EC-Earth3-LR<br> Horizontal resolution: ~1.125° (~125 km)</p> <p><strong>Available data</strong><br> Annual mean data for standard oceanographic and meteorological variables.</p>
Dataset of pre-industrial climate from publication "Modeled storm surge changes in a warmer world: the Last Interglacial" by P. Scussolini et al.
<p>Results from the simulation of pre-industrial climate with climate model CESM1.2. Variables are: sea-level pressure (PSL); meridional wind (V), and zonal wind (U). Time step is 6-hourly.</p> <p>Detailed description of the methods are in the original publication:</p> <p>Scussolini, P., Dullaart, J., Muis, S., Rovere, A., Bakker, P., Coumou, D., Renssen, H., Ward, P. J., and Aerts, J. C. J. H.: Modelled storm surge changes in a warmer world: the Last Interglacial, EGUsphere, 2022, 1-20, 10.5194/egusphere-2022-101, 2022.</p>
Data and code for Global change drives modern plankton communities away from pre-industrial state
<p>Data and R code for "Global change drives modern plankton communities away from pre-industrial state" by Lukas Jonkers, Helmut Hillebrandt and Michal Kucera (https://doi.org/10.1038/s41586-019-1230-3).</p> <p>Compare planktonic foraminifera species assemblages from sediments and sediment traps.</p> <p>Scripts written by Lukas Jonkers</p> <p>DATA SOURCES<br> * HadISST: Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. Journal of Geophysical Research: Atmospheres 108, doi:10.1029/2002JD002670 (2003).<br> * ERSST v5: Huang, B. et al. NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 5. Monthly mean. NOAA National Centers for Environmental Information. doi:10.7289/V5T72FNM. Access date: 14 Sep 2018. (2017).<br> * sediment assemblages: Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. Scientific Data 4, 170109, doi:10.1038/sdata.2017.109 (2017).<br> * sediment traps: citations provided in data files</p> <p>DATA<br> 1. Planktonic foraminifera shell flux time series<br> 1.1. all data: dat_sel.RDS<br> 1.2. time series with >125 and >150 micron data: dat_small.RDS<br> 1.3. shell flux data in csv format: shell_flux_data.csv</p> <p>2. ForCenS core top sediment assemblages<br> 2.1. all data (excluding duplicates and samples with incomplete taxonomy): forcens_trimmed_compare.RDS<br> 2.2. all data, split by region: species_domains_compare.RDS<br> 2.3. indices of samples: domain_indeces_compare.RDS</p> <p>3. SST<br> 3.1. average SST for each sample in ForCenS for 1870-1899 period based on HadISST: forcens_HadSST_1870-1899.RDS<br> 3.2. average SST for each sample in ForCenS for 1854-1883 period based on ERSST v5: forcens_ERSST_1854-1883.RDS<br> 3.3. average SST for each sediment trap site for 1870-1899 period based on HadISST: traps_HadSST_1870-1899.RDS<br> 3.4. average SST for each sediment trap site for 1854-1883 period based on ERSST v5: traps_ERSST_1854-1883.RDS<br> 3.5. average SST for each sediment trap site for deployment period based on HadISST: traps_HadSST_period.RDS<br> 3.6. average SST for each sediment trap site for deployment period based on ERSST v5: traps_ERSST_period.RDS<br> 3.7. linear SST trend between 1870 and 2015 based on HadISST: hadisst_trend_1870-2015.RDS<br> 3.8. average SST for period of sediment trap observations: hadisst_mean_1978-2013.RDS</p> <p>CODE<br> 1. get_ForCenS.R: selection of ForCenS data. Used to generate data 2.1-2.3<br> 2. make_polygons.R: make circles with 100 km radius around trap and core tope sites used in 4 and 5<br> 3. make_polygon_function.R: used by 2<br> 4. extract_HadSST.R: extraction of data 3.1, 3.3, 3.5, 3.7, 3.8<br> 5. extract_ERRSTv5.R: extraction of data 3.2, 3.4, 3.6<br> 6. make_annual_assemblages.R: process shell flux time series (data 1.1 and 1.2)<br> 7. make_annual_fluxes_function.R: used by 6<br> 8. compare_trap_sed_publish.R: code to compare sediment trap and core top assemblages<br> 9. compare_figs.R: code to create figures<br> 10. maps_robinson.R: code to create maps</p>
Varied oxygen simulations with WACCM6 (Proterozoic to pre-industrial atmosphere)
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Data from: Drivers of global pre-industrial patterns of species turnover in planktonic foraminifera
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Causes and importance of new particle formation in the present-day and pre-industrial atmospheres: supporting data
<p>Data presented in the manuscript "Causes and importance of new particle formation in the present-day and pre-industrial atmospheres" currently in review.</p> <p>Particle number concentrations (files with initial word "CCN" or "N3") have units of particles per cubic centimetre, calculated at ambient temperature and pressure. Files with initial word "solar" have units of percent. Ion production rates have units ion pairs per cubic centimetre per second.</p> <p>The simulation data presented here was generated with the GLOMAP aerosol model, https://www.see.leeds.ac.uk/research/icas/research-themes/atmospheric-chemistry-and-aerosols/groups/aerosols-and-climate/the-glomap-model/ running on a T42 grid.</p> <p>The manuscript associated with this data was written using results from the CLOUD experiment at CERN, and the author list is a subset of the CLOUD collaboration.</p> <p> </p> <p> </p>
Data to support: Anthropogenic influence on tropospheric reactive bromine since the pre-industrial: Implications for ice-core bromine trends
<p>Tropospheric reactive bromine (Br<sub>y</sub>) influences the oxidation capacity of the atmosphere by acting as a sink for ozone and nitrogen oxides. Aerosol acidity plays a crucial role in Br<sub>y</sub> abundances through acid-catalyzed debromination from sea-salt-aerosol, the largest global source. Bromine concentrations in a Russian Arctic ice-core, Akademii Nauk, show a 3.5-fold increase from pre-industrial (PI) to the 1970s (peak acidity, PA), and decreased by half to 1999 (present day, PD). Ice-core acidity mirrors this trend, showing robust correlation with bromine, especially after 1940 (<em>r</em>=0.9). Model simulations considering anthropogenic emission changes alone show that atmospheric acidity is the main driver of Br<sub>y</sub> changes, consistent with the observed relationship between acidity and bromine. The influence of atmospheric acidity and Br<sub>y</sub> should be considered in interpretation of ice-core bromine trends.</p>
Code and data to "The shifting of buffer crop repertoires in pre-industrial north-eastern Europe "
<p>This code and data can be used to replicate the plots and figures of the paper and to trace the correlation and tests of climate variability and crop development in the study area.</p> <p> </p>
Diatom relative abundances for LakePulse lakes (major taxa and full dataset) modern and pre-industrial samples
<p>Raw diatom relative abundance data and major species relative abundances used in: Griffiths K., Duda M. P., Antoniades D., Smol J. P., Gregory-Eaves I. 2024. Diatom species responses along gradients of dissolved inorganic carbon, total phosphorus, and lake depth from lakes across Canada. Journal of Phycology</p>
Dataset for "Climate and ice sheet evolutions from the last glacial maximum to the pre-industrial period with an ice sheet -- climate coupled model"
<p>This archive contains the source data of the figures presented in the manuscript "Climate and ice sheet evolutions from the last glacial maximum to the pre-industrial period with an ice sheet -- climate coupled model".</p> <p>Contact: aurelien.quiquet@lsce.ipsl.fr</p>
Pre-industrial, present and future atmospheric soluble iron deposition and the role of aerosol acidity and oxalate under CMIP6 emissions: DATASET
<p>This dataset provides output fields that were used in the research article titled "Pre-industrial, present and future atmospheric soluble iron deposition and the role of aerosol acidity and oxalate under CMIP6 emissions." The data includes the following fields presented as yearly means:</p> <ul> <li>Soluble Iron emissions (SFe_emi) in Tg/yr</li> <li>Total Iron emissions (TFe_emi) in Tg/yr</li> <li>Soluble Iron deposition (SFe_dep) (both wet and dry) in Tg/yr</li> <li>Total Iron deposition (TFe_dep) (both wet and dry) in Tg/yr</li> </ul> <p>The data covers the following periods:</p> <ul> <li>Pre-industrial period (PI), with the year 1850</li> <li>Present-day period (PD), with the climatology from 1985 to 2014</li> <li>Three future periods based on the SSP CMIP6 scenarios (SSP126, SSP245, SSP370), with the climatology from 2070 to 2099</li> </ul> <p>An additional file containing information on the cell areas of the output grid is also included (areacell_BergasMassoetal2023.nc).</p>
Non-target screening of a Siberian ice core reveals changes in the pre-industrial to industrial organic aerosol composition
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Data to support: Anthropogenic influence on tropospheric reactive bromine since the pre-industrial: Implications for ice-core bromine trends
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AWI-CM3 version 3.0 pre-industrial control data
<p>Pre-industrial control simulation output from AWI-CM3 version 3.0</p> <p>Years: 1850-2014</p> <p>Frequency: Annual means</p> <p>Variables:</p> <ul> <li>T2M: near surface (2m) air temperature</li> <li>PRECIP: precipitation</li> <li>a_ice: sea ice concentraion</li> <li>m_ice: sea ice mass per unit area</li> <li>salt: salinity</li> <li>w: vertical velocity</li> <li>MLD2: Mixed layer depth according to Large et al. (https://doi.org/10.1175/1520-0485(1997)027%3C2418:STSFAB%3E2.0.CO;2)</li> </ul>
Data and code of the article entitled " Sons shorten mother's lifespan in pre-industrial families with high level of infant mortality"
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