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133 results for “climate sensitivity”
Long-term trends and synchrony in dissolved organic matter characteristics in Wisconsin, USA lakes: quality, not quantity, is highly sensitive to climate
Dissolved organic matter (DOM) is a fundamental driver of many lake processes. In the past several decades, many lakes have exhibited a substantial increase in DOM quantity, measured as dissolved organic carbon (DOC) concentration. While increasing DOC is now widely recognized, fewer studies have sought to understand how characteristics of DOM (DOM quality) change over time. Quality can be measured in several ways, including the optical characteristics spectral slope (S275-295), spectral ratio (SR), absorbance at 254 nm (a254), and DOC-specific absorbance (SUVA; a254:DOC). However, long-term measurements of quality are not nearly as common as long-term measurements of DOC concentration. We used 24 years of DOC and absorbance data for seven lakes in the North Temperate Lakes Long Term Ecological Research site in northern Wisconsin, USA to examine temporal trends and synchrony in both DOC concentration and quality. We predicted lower SR and S275-295 and higher a254 and SUVA trends, consistent with increasing DOC and greater allochthony. DOC concentration exhibited both significant positive and negative trends among lakes. In contrast, DOC quality exhibited trends suggesting reduced allochthony or increased degradation, with significant long-term increases in SR in three lakes. Patterns and synchrony of DOM quality parameters suggest they are more responsive to climatic variations than DOC concentration. SUVA in particular tended to increase with greater moisture and decrease with drier conditions. These results demonstrate that DOC quantity and quality can exhibit different complex long-term trends and responses to climate components, with important implications for aquatic ecosystems.
Data files for figures in "Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability" by Bird et al.
<p>The data files for figures in <i>Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability</i> by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below:</p><p><strong>Figure 1</strong></p><p>The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:</p><ul><li>The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables. </li><li>The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables. </li><li>The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable.</li><li>The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable.</li><li>The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable.</li></ul><p><strong>Figure 2</strong></p><ul><li>The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure 3</strong></p><ul><li>The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_<lat>_<long>.dat where <lat> is the latitude and <long> is the longitude. Files for each region are zipped into .7z files named Figure3_<region>.7z where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The latitudes and longitudes for the upper panels can be inferred from the file names for each region.</li></ul><p><strong>Figure 4</strong></p><p>The data for each panel are provided in a netCDF file named Figure4_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 5</strong></p><p>The data are provided as text files named Figure5_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.</p><p><strong>Figure 6</strong></p><p>The data are provided as text files named Figure6_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years. </p><p><strong>Figure 7</strong></p><p>The data for each panel are provided in a netCDF file named Figure7_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 8</strong></p><p>The data are provided as text files named Figure8_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood.</p><p><strong>Figure S1 and Figure S3</strong></p><p>The data are provided as text files named Figure_S1_and_S3_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents.</p><p><strong>Figure S2</strong></p><ul><li>The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure S4</strong></p><ul><li>The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents. </li><li>The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents. </li></ul><p><strong>Figure S5</strong></p><p>There are no data associated with this figure. This figure was made using Microsoft Powerpoint.</p><p><strong>Figure S6</strong></p><p>The data are provided as text files named FigureS6_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.</p>
Associating Land Cover Changes with Climate Sensitive Infection in Fennoscandia, as part of the CLINF project: Example on Tick-Borne Diseases
<p>The data was used as part of the IJERPH article below. The GeoJSON and shapefile ZIP archive are two versions of the same geometries to represent geographically the districts whole of Fennoscandia and the Russian districts of Leningrad, St Petersburg, Vologda, Arkhangelsk, Nenetsia, Murmansk, Karelia, and Komi, making up 69 districts used for the analysis.</p> <p>Leibovici DG, Bylund H, Björkman C, Tokarevich N, Thierfelder T, Evengård B, Quegan S (2021). Associating Land Cover Changes with Patterns of Incidences of Climate Sensitive Infections: An Example on Tick-Borne Diseases in the Nordic Area. <strong><em>International Journal of Environmental Research and Public Health, 18(20):10963. <a href="https://doi.org/10.3390/ijerph182010963">doi:10.3390/ijerph182010963</a></em></strong></p> <p>Special Issue: <a href="https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects">https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects</a></p> <p> </p>
Data for figures in the Publication "The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2"
<p>This repository contains the data to produce figures for the paper:</p> <p>"Lohmann, U. and Neubauer, D.: The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2, Atmos. Chem. Phys., 18, 8807–8828, https://doi.org/10.5194/acp-18-8807-2018, 2018."</p> <p>Note that the scripts are to be found in the accompanying package (https://doi.org/10.5281/zenodo.8183412)</p>
Sevilleta Plant Species Sensitivity of Dryland Plant Allometry to Climate
Patterns of plant biomass partitioning are fundamental to estimates of primary productivity and ecosystem process rates. Allometric relationships between aboveground plant biomass and non-destructive measures of plant size, such as cover, volume, or stem density are widely used in plant ecology. Such size-biomass allometry is often assumed to be invariant for a given plant species, plant functional group, or ecosystem type. Allometric adjustments may be an important component of the short- or long-term responses of plants to abiotic conditions. We used 18 years of size-biomass data describing 85 plant species to investigate the sensitivity of allometry to precipitation, temperature, or drought across two seasons and four ecosystems in central New Mexico, USA. Our results demonstrate that many plant species adjust patterns in the partitioning of aboveground biomass under different climates and highlight the importance of long-term data for understanding functional differences among plant species.
Supplementary data for "Effect of Uncertainty in Water Vapor Continuum Absorption on CO2 Forcing, Longwave Feedback, and Climate Sensitivity"
<h3>This dataset is supplementary to the article "Effect of Uncertainty in Water Vapor Continuum Absorption on CO2 Forcing, Longwave Feedback, and Climate Sensitivity".</h3> <h3>spectral_olr.nc</h3> <p>This file contains the spectral outgoing longwave radiation (OLR) calculated using the line-by-line radiative transfer model ARTS and the radiative-convective equilibrium model konrad. It contains spectral OLR for surface temperatures from 270K to 330K for different strengths of the water vapor continuum absorption.</p> <h3>opacity_emission_level.py</h3> <p>This file also contains the spectrally resolved optical depth and the emission level of outgoing longwave radiation for the considered absorption species (H2O lines, H2O continuum, H2O self continuum, H2O foreign continuum, CO2, N2, and O2).</p> <h3>continuum_reference_conditions.nc</h3> <p>This file contains the reference continuum absorption coefficients that were used to calculate the adjustment to the foreign continuum for the single-constraint experiment.</p> <h3>continuum_all_profiles.nc</h3> <p>This file contains the reference continuum absorption coefficients that were used to calculate the adjustment to the foreign continuum for the general-constraint experiment.</p> <h3>modified_continuum_input_files_single_constraint.zip and modified_continuum_input_files_general_constraint.zip</h3> <p>These files contain the modified continuum data files used for the implementation of the MT_CKD continuum model in the line-by-line model ARTS for the single-constraint and general-constraint experiments, respectively.</p> <h3>tau_column.nc and tau_profile.nc</h3> <p>These files contain separately for each absorption species the vertically integrated opacity spectra, and the opacity profiles at two selected wavenumbers.</p> <p> </p>
Data for the publication "The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity"
<p>This repository contains the data for the paper:</p> <p>"Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Stier, P. Partridge, D. G., Tegen, I., Bey, I., Stanelle, T., Kokkola, H., and Lohmann, U.: The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity, Geosci. Mod. Dev., https://doi.org/10.5194/gmd-2018-307, 2019."</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.2553891)</p> <p> </p>
The topographic signature of ecosystem climate sensitivities in the western U.S.
<p>It has been suggested that hillslope topography can promote the persistence of hydrologic refugia, sites where ecosystem net primary productivity (NPP) is relatively insensitive to climate variation. However, the mechanisms that promote the persistence of these locations and their spatial distributions are poorly resolved. We quantified the response of ecosystem NPP to variability in the annual climatic water balance for 30 years across the western U.S. The slope of this pixel-specific linear regression represents ecosystem-climate sensitivity and provides a means to identify ecosystems that are buffered from droughts. Environmental conditions produced by hillslope convergence reduced ecosystem sensitivity to climate fluctuations across the entirety of the western U.S. We observed the greatest topographic effect in semi-arid climates, while vulnerability to drought was maximized in flat, arid landscapes. In aggregate, spatial patterns of ecosystem sensitivity can be implemented for regional planning to maximize conservation in landscapes that are more resistant to perturbations.</p>
Supplementary material for "Increased sensitivity of marine invertebrates to metal toxicity in the past two decades linked to Climate Change and Ocean Acidification: revelations from a natural population of sea urchins in the Mediterranean Sea." by "Davide Sartori, Guido Scatena, Cristina Vrinceanu, Andrea Gaion".
<p>Satellite observations of environmental factors and effect concentration 50 for copper to sea urchin, from 2003 to 2022.</p>
Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts. Soil and airborne mineral fraction datasets.
<p>These datasets correspond to soil and airbone mass mineral fractions as described and generated for "Modeling dust mineralogical composition: sensitivity to soil mineralogy" by Gonçalves Ageitos, M., Obiso, V., Miller, R.L., Jorba, O., Klose, M., Dawson, M., Balkanski, Y., Perlwitz, J., Basart, S., Di Tomaso, E., Escribano, J., Macchia, F., Montané, G., Mahowald, M.M., Green, R.O., Thompson, D.R. and Pérez García-Pando, C., ACP, 2023. </p> <p>There are 4 netCDF files that include the soil mass mineralogical fractions (0-1) in the clay (0-2 <span class="math-tex">\(\mu\)</span>m in diameter) and silt (2-63 <span class="math-tex">\(\mu\)</span>m in diameter) size classes as derived from the works of Claquin et al., (1999), and updated by Nickovic et al. (2012): <strong>C1999-SMA</strong>, and Journet et al. (2014): <strong>J2014-SMA</strong>. The data is mapped in a regular global grid with a horizontal resolution of 0.083º. Additional information on the FAO soil units, and soil texture data from HWSDv1.2 is provided in the J2014-SMA files. </p> <p>File details: </p> <ul> <li>C1999-SMA_CLAY_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>C1999-SMA_SILT_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_CLAY_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_SILT_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> </ul> <p>There are 2 additional files that report the multiannual (2006-2010 period) monthly mean of the <strong>aerosol mass mineral fractions</strong> as obtained from the <strong>MONARCH model</strong> simulations described in Gonçalves Ageitos et al. (2023). The mass fractions are provided in each of the 8 size bins used in the model (ranging from 0.2 to 20 <span class="math-tex">\(\mu\)</span>m in diameter), and normalized so as to sum 1 (i.e., the sum of all minerals in all bins equals 1). Note that in order to reduce the size of these files, the variables have been compressed to short format and include an offset and scale factor as attributes. </p> <p>File details: </p> <ul> <li>20062010_monarch_minfrac_C1999.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH C1999 experiment. </li> <li>20062010_monarch_minfrac_J2014.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH J2014 experiment. </li> </ul> <p> </p> <p><em>Legend for the minerals:</em></p> <p>quar: quartz, feld: feldspars, calc: calcite, gyps: gypsum, illi: illite, mont: montmorillonite/smectite, kaol: kaolinite, verm:vermiculite, chlo: chlorite, mica: mica, hema: hematite, goet: goethite, irox:iron oxides (hematite and goethite). </p> <p>References:</p> <p>Claquin, T., Schulz, M., and Balkanski, Y. J.: Modeling the mineralogy of atmospheric dust sources, Journal of Geophysical Research<br> Atmospheres, https://doi.org/10.1029/1999JD900416, 1999.</p> <p>FAO-UNESCO: Soil Map of the World- Volume I Legend, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Paris, http://www.fao.org/3/as360e/as360e.pdf, 1974.</p> <p>FAO-UNESCO: Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization. Digital Soil Map of the World and Derived Soil Properties, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Rome, 1995.</p> <p>FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.2), Food and Agriculture Organization, FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012.</p> <p>Journet, E., Balkanski, Y., and Harrison, S. P.: A new data set of soil mineralogy for dust-cycle modeling, Atmospheric Chemistry and<br> Physics, 14, 3801–3816, https://doi.org/10.5194/acp-14-3801-2014, 2014.</p> <p>Nickovic, S., Vukovic, A., Vujadinovic, M., Djurdjevic, V., and Pejanovic, G.: Technical Note: High-resolution mineralogical database of dust-productive soils for atmospheric dust modeling, Atmospheric Chemistry and Physics, 12, 845–855, https://doi.org/10.5194/acp-12-845-2012, 2012.</p> <p> </p>
Assessment of the Vulnerability of Permafrost Carbon to Climate Change: A Sensitivity Analysis among Models
This activity is a comparison of how large-scale models represent permafrost carbon dynamics into the future (2010-2299). Model responses were evaluated at several temporal scales. To the extent possible, we standardized driver data and simulation procedures among the models. However, the protocol has been set up so that each model can build upon the procedures used to produce the outputs for historical analysis (1960- 2009) that was published in McGuire et al. 2016 (Global Biogeochemical Cycles 30:1015-1037, doi:10.1002/2016GB005405). Note that this comparison is an offline model comparison in which we assessed the sensitivity of the responses of the models to somewhat standardized forcing data. The activity compared among the models: Carbon dynamics: Predictions of average annual C fluxes (GPP, NPP, RH, CH4 fluxes, disturbance-related emissions, dissolved organic carbon export, lateral land used fluxes, etc.) and major pools for the northern permafrost region for the 2010-2299 period. Soil thermal dynamics: Predictions of annual soil thermal and hydrological dynamics at prescribed depths and the maximum annual active layer depth (in permafrost locations) for the 2010-2299 time period. The spatial simulation data for this project are are available through the National Snow and Ice Data Center (doi: 10.5067/ZRL5WJKN01XM).
Data for the submitted paper by Yasunari et al., "Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments"
<p>The dataset contains some of the outputs from the global climate model experiments by MIROC5 on changing Siberian wildfire severities, the other data used in the paper (see READ_ME files on the data sources), the analyzed data, and the scripts for analyses, which were used in the following submitted paper. Note that this dataset also includes unused data for the paper:</p> <p><br>Yasunari, T. J., D. Narita, T. Takemura, S. Wakabayashi, and A. Takeshima, Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments, submitted.</p> <p>Please read the READ_ME files for detailed information in each directory (especially see the "about_figures_and_tables/" directory first). Because of their large sizes, the data were separated into three zipped files.</p>
Sensitivity of the global agricultural sector to changes in climate policy - EU countries compared to the rest of the world
<p>The files contain data from the FAOSTAT database used in the article: DOI:10.2478/oszn-2023-0012</p> <p>File content:<br>Agricultural emissions data for the period 1961-2020<br>Population data for 1950-2020<br>Production value from agriculture for the period 1961-2020<br>Agricultural area for the period 1961-2020</p> <p>The layout of the tables and the description of the columns is the same as the FAOSTAT database methodology</p>
Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity
<p><strong>Citation:</strong> Zhu, J., & Poulsen, C. J. (2021). Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity. <em>Clim. Past</em>, <em>17</em>(1), 253–267. <a href="https://doi.org/10.5194/cp-17-253-2021">https://doi.org/10.5194/cp-17-253-2021</a></p> <p>Casename:</p> <ul> <li>FCM_PI: b.e12.B1850C5.f19_g16.iPI.01</li> <li>FCM_LGM: b.e12.B1850C5.f19_g16.i21ka.03</li> <li>SOM_PI: e.e12.E1850C5.f19_g16.PI.02</li> <li>SOM_GHG: e.e12.E1850C5.f19_g16.PI.21kaGHG.02</li> <li>SOM_ICE: e.e12.E1850C5.f19_g16.PI.21kaICE.02</li> <li>SOM_2CO2: e.e12.E1850C5.f19_g16.PIx2.02</li> <li>ATM_PI: f.e12.F1850C5.f19_g16.iPI.01</li> <li>ATM_GHG: f.e12.F1850C5.f19_g16.iPI.21kaGHG_ERF</li> <li>ATM_ICE: f.e12.F1850C5.f19_g16.iPI.21kaICE_ERF</li> <li>ATM_2CO2: f.e12.F1850C5.f19_g16.iPI.01.x2</li> </ul> <p><strong>Boundary condition files and the restart files are also provided as .zip files (bc.zip & rest.zip).</strong></p> <p><strong>Check out the Github repository for the setup of the LGM simulation</strong> (i.e., the entire CESM case folder): <a href="https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne">https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne</a></p> <p><strong>[NEW IN V3] More monthly data for PMIP4 (cmorized) are provided (files starting with `PMIP4.NCAR.CESM1.2-FV2`).</strong></p>
Supplementary Material: Climate-sensitive disease outbreaks in the aftermath of extreme climatic events: a scoping review
<p><strong>Supplemental experimental procedures</strong></p> <p><em>General Information</em></p> <p>Here we provide the data extraction of the studies retrieved for the scoping review "Climate-sensitive disease outbreaks in the aftermath of extreme climatic events" following PRISMA-ScR guidelines. Data were extracted for the following variables: title, first author, year of publication, country/region studied, extreme climate event, extreme climate event name (tropical cyclones are often named e.g. Typhoon Haiyan), Index used to measure climate anomaly, extreme climate event definition, text description of extreme climate event, disease, outbreak definition, time period of the study, data source, baseline/reference period, study design, statistics, outcome, outcome quantification, outbreak risk, qualitative description of extreme climate event and outbreak risk, time lag. Outcome was defined as either disease cases or incidence. </p>
Climate warming shifts riverine macroinvertebrate communities to be more sensitive to chemical pollutants
<p>Freshwaters are highly threatened ecosystems that are vulnerable to chemical pollution and climate change. Freshwater taxa vary in their sensitivity to chemicals and changes in species composition can potentially affect the sensitivity of assemblages to chemical exposure. Here we explore the potential consequences of future climate change on the composition and sensitivity of freshwater macroinvertebrate assemblages to chemical stressors using the UK as a case study.</p> <p>Macroinvertebrate assemblages under end of century (2080-2100) and baseline (1980-2000) climate conditions were predicted for 608 UK sites for four climate scenarios corresponding to mean temperature changes of 1.28°C to 3.78°C. Freshwater macroinvertebrate toxicity data were collated for 19 chemicals and the hierarchical Species Sensitivity Distribution (hSSD) model was used to predict the sensitivity of untested taxa using relatedness within a Bayesian approach. All four future climate scenarios resulted in shifts in assemblage composition, with increases in the prevalence of molluscan, crustacean and annelid species, and towards increasing insect taxa of Odonata, Chironomidae, and Baetidae species. In contrast decreases in were projected for Plecoptera, Ephemeroptera (except for Baetidae) and Coleoptera species.</p> <p>Shifts in taxonomic composition were associated with changes in the percentage of species at risk from chemical exposure. For the 3.78°C climate scenario, 76% of all assemblages became more sensitive to chemicals and for 18 of the 19 chemicals, the percentage of species at risk increased. Climate warming-induced increases in sensitivity were greatest for assemblages exposed to metals and were dependent on baseline assemblage composition, which varied spatially.</p> <p>Climate warming is predicted to result in changes in the use, environmental exposure and toxicity of chemicals. Here we show that, even in the absence of these climate-chemical interactions, shifts in species composition due to climate warming will increase chemical risk and that the impact of chemical pollution on freshwater macroinvertebrate biodiversity may double or quadruple by the end of the 21st century.</p>
Data for "Africa's Climate Response to Marine Cloud Brightening Strategies is Highly Sensitive to Deployment Region"
<p>Community Earth System Model 2 simulation data with marine cloud brightening perturbations used to compute climate impact metrics in "Africa's Climate Response to Marine Cloud Brightening Strategies is Highly Sensitive to Deployment Region" by Romaric C. Odoulami, Haruki Hirasawa, Kouakou Kouadio, Trisha D. Patel, Kwesi A. Quagraine, Izidine Pinto, Temitope S. Egbebiyi, Babatunde J. Abiodun, Christopher Lennard, and Mark G. New. Simulation descriptions can be found in Hirasawa et al., 2023 <em>Geophysical Research Letters</em> doi.org/10.1029/2023GL104314.</p>
CESM1.2 simulation data for "Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks"
<p>CESM1.2 simulation data for Early Eocene</p> <p><strong>Citations:</strong></p> <p>Zhu, J., Poulsen, C. J., & Tierney, J. E. (2019). Simulation of Eocene extreme warmth and high climate sensitivity through cloud feedbacks. <em>Science Advances</em>, 5(9), eaax1874. <a href="https://doi.org/10.1126/sciadv.aax1874">https://doi.org/10.1126/sciadv.aax1874</a></p> <p>Zhu, J., Poulsen, C. J., Otto-Bliesner, B. L., Liu, Z., Brady, E. C., & Noone, D. C. (2020). Simulation of early Eocene water isotopes using an Earth system model and its implication for past climate reconstruction. Earth and Planetary Science Letters, 537, 116164. <a href="https://doi.org/10.1016/j.epsl.2020.116164" rel="nofollow">https://doi.org/10.1016/j.epsl.2020.116164</a></p> <p> </p> <ul> <li>Data set includes climatology (12 months) sea-surface temperature (TEMP), surface temperature (TS) and surface temperature at reference height (TREFHT) from four Eocene simulations with 1×, 3×, 6× and 9× preindustrial level of CO2 (284.7 ppmv), and a preindustrial simulation.</li> <li>Climatology was calculated from averaging data over the last 100 years of each simulation.</li> <li>TS and TREFHT are on the atmosphere grid of 1.9 × 2.5° (latitude × longitude).</li> <li>TEMP is on the POP ocean grid (~1°; see here: http://www.cesm.ucar.edu/models/cesm1.2/pop2/).</li> <li>NEW on July 09, 2024: restart files for the Eocene simulations.</li> </ul> <p>A case folder is available on GitHub: <a href="https://github.com/jiang-zhu/icesm1.2_eocene_cheyenne">https://github.com/jiang-zhu/icesm1.2_eocene_cheyenne</a></p> <p> </p>
Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model
<div> <div> <div> <p>Thermodynamic and microphysical data (matlab files) for NorESM2 simulations presented in the article "Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model" </p> </div> </div> </div>
The topographic signature of ecosystem climate sensitivities in the western U.S.
<p>It has been suggested that hillslope topography can promote the persistence of hydrologic refugia, sites where ecosystem productivity is relatively insensitive to climate variation. Currently, the mechanisms that promote the persistence of these locations and their spatial distributions are poorly resolved across gradients in climate. We quantified the response of ecosystem net primary productivity to variability in the annual climatic water balance for 30 years across the western U.S. The standardized slope of this pixel-specific linear regression represents ecosystem-climate sensitivity and provides a means to identify ecosystems that are buffered from droughts. Environmental conditions produced by hillslope convergence reduced ecosystem sensitivity to climate across the majority of the region. We observed the greatest topographic effect in semi-arid climates, while vulnerability to drought was maximized in flat, arid landscapes. In aggregate, spatial patterns of ecosystem sensitivity can be implemented for regional planning to maximize conservation in landscapes more resistant to perturbations.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.