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33 results for “climate uncertainty”
Supplementary data to research paper "Linking local climate scenarios to global warming levels: Applicability, prospects and uncertainties"
<p>This file contains supplementary data for the research paper "<span>Linking local climate scenarios to global warming levels: Applicability, prospects and uncertainties"", submitted to IOP Publishing Environmental Research: Climate. The DOI and link to the paper will be added once it is published.</span></p> <p><span>Each file lists the annual mean temperature anomalies relative to the period 1991-2020 for a model of the Austrian climate scenarios OEKS15 (Leuprecht, 2018). The full dataset is available here: https://data.hub.geosphere.at/dataset/oks15_bias_corrected<br></span></p> <p> </p> <div> <div>Leuprecht, A. (2018). <em>ÖKS15 Bias Corrected EURO-CORDEX Model Precipitaion, Radiation, Temperature</em> [dataset]. <a href="https://doi.org/10.60669/B37Q-JD39">https://doi.org/10.60669/B37Q-JD39</a></div> </div>
FIG. 2 in Clearing up the Crystal Ball: Understanding Uncertainty in Future Climate Suitability Projections for Amphibians
FIG. 2.—Mean predicted change in suitable climate for amphibian species by citation and model parameters from the meta-analysis. Points represent means and bars represent 95% confidence intervals. Mean predicted change in suitable climate was calculated for each amphibian order within each study for each of the model settings, including Representative Concentration Pathway, Year, and Dispersal Limitation. Triangles indicate estimates from the case study, and circles indicate all other studies. A color version of this figure is available online.
FIG. 1 in Clearing up the Crystal Ball: Understanding Uncertainty in Future Climate Suitability Projections for Amphibians
FIG. 1.—Map of countries for which suitable climate has been projected under future climate scenarios for at least one amphibian species. Colors represent the number of studies in which a given country was included. A color version of this figure is available online.
FIG. 3 in Clearing up the Crystal Ball: Understanding Uncertainty in Future Climate Suitability Projections for Amphibians
FIG. 3.—Percent change in climate suitability predicted for each species in the case study. For each species, predicted percent change in climate suitability is shown for the year 2050 under 13 general circulation models and two representative concentration pathways (2.6, 8.5) climate scenarios for both the default Maxent species distribution models (SDMs; regularization multiplier ¼ 1; open red boxes) and the best-fit SDM (Akaike information criterion; ΔAICc ¼ 0; shaded blue boxes) for three levels of dispersal: no dispersal, limited, or unlimited. Boxplots show median, interquartile range, and minimum and maximum values. A color version of this figure is available online.
Data from: Incorporating uncertainty into forest management planning: timber harvest, wildfire and climate change in the boreal forest
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Characterizing uncertainty in climate impact projections: a case study with seven marine species on the North American continental shelf
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Data from: Predicting ecological responses in a changing ocean: the effects of future climate uncertainty
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Planning for Resilience: Incorporating scenario and model uncertainty and trade-offs when prioritizing management of climate refugia
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Distributions of Uncertainty by Climate Impact
<p>These summary tables describe the distributions of results produced by allowing only one of statistical, model, and weather uncertainty to vary, to determine the contributions of each.</p> <p>These files are extracted from the general ACP Monte Carlo results using scripts from https://github.com/ClimateImpactLab/acp-impacts/tree/master/extract as defined below:</p> <p>The contents of variation-split are produced by uncertain.py. These results are organized into files with the filename template <VARIATION>-<IMPACT>--<RCP>-<YEAR>.csv. VARIATION may be "impact", for the variation driven by statistical uncertainty; "model", for the uncertainty driven by GCM choice; "weather", for the variation driven by weather realization; or "total", allowing all of these to vary simultaneously. IMPACT may be any of the estimated impacts, RCP can be RCP 2.6, RCP 4.5, RCP 6.0, or RCP 8.5, and YEAR is 2020, for an average from 2020 - 2039; 2040 for an average of 2040 - 2059; or 2080 for an average of 2080 - 2099.</p> <p>The contents of baseline-testing are produced by uncertain-test.py. These results are organized into files with the filename template <IMPACT>-<VARIATION>-<GCM>-<RCP>-<YEAR>.csv. The filename parts are as described above, with the addition of the name of the GCM that is held fixed when the GCM is not allowed to change.</p> <p>variation-split.tsv is produced by calcvar.py and is a summary of the results in variation-split/.</p> <p>baseline-testing.tsv is produced by calcvar-test.py and is a summary of the results in baseline-testing/.</p> <p><em>This data is provided for non-commercial research and educational purposes.</em></p>
Uncertainties Inherent from Large-Scale Climate Projections in the Statistical Downscaling Projection of North Atlantic Tropical Cyclone Activity
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Data used in "Anderson et al. 2024. Unexpected sources of uncertainty in projecting habitat shifts for Arctic shorebirds under climate change"
<p>Data used in "Anderson et al. 2023. Unexpected sources of uncertainty in projecting habitat shifts for Arctic shorebirds under climate change". Includes shorebird observations from Environment and Climate Change Canada PRISM dataset, and environmental covariates. See Anderson et al. 2023 methods for more details. Data was funded and collected by Environment and Climate Change Canada.</p>
Supporting Data for Hahn et al. J. Climate: Contribution of AMOC Decline to Uncertainty in Global Warming via Ocean Heat Uptake and Climate Feedbacks
<p>This dataset includes CESM2 model output for the dehose4x experiment in Hahn et al.: “Contribution of AMOC Decline to Uncertainty in Global Warming via Ocean Heat Uptake and Climate Feedbacks” submitted to Journal of Climate. The piControl and abrupt-4xCO2 experiments for CESM2 and other CMIP6 models can be found in the Earth System Grid Federation (ESGF) repository at <a href="https://esgf-node.llnl.gov/projects/esgf-llnl/" target="_blank" rel="noopener">https://esgf-node.llnl.gov/projects/esgf-llnl/</a>.</p>
Supporting datasets used in the paper entitled "Substantial uncertainties in Arctic aerosol simulations by microphysical processes within the global climate-aerosol model CAM-ATRAS"
<p>This archive contains datasets used in the paper entitled "Substantial uncertainties in Arctic aerosol simulations by microphysical processes within the global climate-aerosol model CAM-ATRAS".</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.