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Dataset results
13 results for “climate impact projection”
Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing
<p>The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing".<br><br>OGGM_area_projections.nc contains data for Figure 3.<br>OGGM_volume_projections contains data for Figure 4.</p> <p>OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5.</p> <p>OGGM_solid_ice_discharge_regions.nc contains data for Figure 6.</p> <p>OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7.</p> <p>OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.</p>
Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"
<p>This dataset is associated with the following publication:</p> <p>Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2.</p> <p>The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error.</p> <p>The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Cloudiness delays projected impact of climate change on coral reefs
<p>The increasing frequency of mass coral bleaching and associated coral mortality threaten the future of warmwater coral reefs. Although thermal stress is widely recognized as the main driver of coral bleaching, exposure to light also plays a central role. Future projections of the impacts of climate change on coral reefs have to date focused on temperature change and not considered the role of clouds in attenuating the bleaching response of corals. In this study, we develop temperature- and light-based bleaching prediction algorithms using historical sea surface temperature, cloud cover fraction and downwelling shortwave radiation data together with a global-scale observational bleaching dataset observations. The model is applied to CMIP6 output from the GFDL-ESM4 Earth System Model under four different future scenarios to estimate the effect of incorporating cloudiness on future bleaching frequency, with and without thermal adaptation or acclimation by corals. The results show that in the low emission scenario SSP1-2.6 incorporating clouds delays the bleaching frequency conditions by multiple decades in some regions, yet the majority (>70%) of coral reef cells still experience dangerously frequent bleaching conditions by the end of the century. In the moderate scenario SSP2-4.5, however, thermal stress would overwhelm the mitigating effect of clouds by mid-century. Thermal adaptation or acclimation by corals could further shift the bleaching projections by up to 40 years, yet coral reefs would still experience dangerously frequent bleaching conditions by the end of century in SPP2-4.5. The findings show that multivariate models incorporating factors like light may improve the near-term outlook for coral reefs and help identify future climate refugia, but the long-term future of coral reefs remains questionable in moderate to higher emissions scenario.</p>
Data output from Projecting future climate change impacts on the distribution of pelagic squid in the Southern Ocean
<p>Data output from Projecting future climate change impacts on the distribution of pelagic squid in the Southern Ocean:<br>Rasters, R models and scripts</p>
Projected impacts of climate and land use changes on the habitat of Atlantic Forest plants in Brazil
<p>Aim:<b> </b>To provide novel evidence on the average impact of climate and land use changes on habitat suitability for tropical plants and to test previous conclusions on the relative importance of these two drivers in shaping future availability of habitat for tropical plant species.</p> <p>Location<b>: </b>Brazil's Atlantic Forest domain.</p> <p>Time period: Plant occurrences recorded between 1960 and 2014. Baseline climate from 1960-2000 and land use from 2015. Projected scenarios of climate for 2041-2060 and land use for 2050.</p> <p>Major taxa studied: Angiosperms.</p> <p>Results: Our results suggest that climate change alone will, surprisingly, have only a modest negative impact on the mean habitat suitability, decreasing it by 2% (median = -5% to -7%, variation associated with scenarios). Land use change alone had a more consistent negative impact on habitat suitability, causing mean and median reductions of 4% to 6%. When the effects of climate and land use are combined, the mean habitat suitability was reduced by 4% (median = -9% to -11%).</p> <p>Main conclusions: The combined impacts of climate and land use changes were substantial, although smaller than expected. Habitat suitability decreased for most species, but it increased substantially for some species, suggesting that the distribution of impacts across species is markedly right skewed. The impacts were typically detrimental to small-ranged species and neutral or beneficial to widespread species. Land use change rather than climate change will likely cause more losses to the habitat of Atlantic Forest plant species within the next several decades.</p>
Projected impacts of climate and land use changes on the habitat of Atlantic Forest plants in Brazil
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Characterizing uncertainty in climate impact projections: a case study with seven marine species on the North American continental shelf
<p>Projections of climate change impacts on living resources are being conducted frequently, and the goal is often to inform policy. Species projections will be more useful if uncertainty is effectively quantified. However, few studies have comprehensively characterized the projection uncertainty arising from greenhouse gas scenarios, Earth system models, and both structural and parameter uncertainty in species distribution modeling. Here we conducted 8964 unique 21st century projections for shifts in suitable habitat for seven economically important marine species including American lobster, Pacific halibut, Pacific ocean perch, and summer flounder. For all species, both the Earth system model used to simulate future temperatures and the niche modeling approach used to represent species distributions were important sources of uncertainty, while variation associated with parameter values in niche models was minor. Greenhouse gas emissions scenario contributed to uncertainty for projections at the century scale. The characteristics of projection uncertainty differed among species and also varied spatially, which underscores the need for improved multi-model approaches with a suite of Earth system models and niche models forming the basis for uncertainty around projected impacts. Ensemble projections show the potential for major shifts in future distributions. Therefore, rigorous future projections are important for informing climate adaptation efforts.</p>
Data for the project: Climatic impacts on the availability and transfer of chemical elements through Arctic terrestrial food-chains: a pilot study
<p>Uploaded data contain trace element concentrations measured in various biological samples collected at Zackenberg Research station (northeast Greenland) in August 2021</p> <p>Fifty (50) sample plots were visited to collect invertebrates (using pitfalls) and sample soil, vegetation, muskox feces and wool.</p> <p>The sheet PlotInfo provides information on the sample plots, which were placed using a stratified random placement procedure across 5 vegetation types and the elevational gradient</p> <p>The sheet Soil contains trace element concentrations (ppm) as well as N and C (%) measured in the soil samples across all 50 sample plots</p> <p>The sheet Vegetation contains trace element concentrations (ppm) as well as N and C (%) measured in the vegetation samples across all 50 sample plots</p> <p>The sheet Insects contains trace element concentrations (ppm) measured in the various insect groups captured in pitfall traps in 9 sample plots</p> <p>The sheet Muskox feces contains trace element concentrations (ppm) measured in the feces samples collected at all 50 sample plots</p> <p>The sheet Muskox Wool contains trace element concentrations (ppm) measured in 25 wool samples collected throughout the sampling area (not at specific sample plots).</p> <p> </p> <p>Trace element concentrations were measured at the ICP-MS platform at the Observatoirie Midi-Pyrenees, Toulouse, France</p> <p>N and C analyses were done at Department of Biology, University of Copenhagen, Denmark</p> <p> </p> <p>For further information on the project, samples, methods used contact flbe@ecos.au.dk</p>
Characterizing uncertainty in climate impact projections: a case study with seven marine species on the North American continental shelf
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Historical and projected impact of global climate change on the extrinsic incubation of <em>Dirofilaria immitis</em>
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The impact of prescribed ozone in climate projections
<p>Code and data to produce figures for journal article "The impact of prescribed ozone in climate projections", submitted to Journal of Advances in Modeling Earth Systems. Please reference this publication if using any of the code/data provided here.</p>
The impact of prescribed ozone in climate projections
<p>Data and code to produce figures in the JAMES publication "The impact of prescribed ozone in climate projections run with HadGEM3-GC3.1</p>
Projecting future climate change impacts on the distribution of pelagic squid in the Southern Ocean
<p>Dataset of PROJECTING FUTURE CLIMATE CHANGE IMPACTS ON THE DISTRIBUTION OF PELAGIC SQUID IN THE SOUTHERN OCEAN</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.