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17 results for “Storylines”
Auxiliary Euro-Calliope datasets: QTDIAN storyline-specific spatial data to represent a European energy system model at several spatial resolutions
<p>Custom output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with three additional land area scenarios.</p> <p>These scenarios are in line with three storylines from the <a href="https://zenodo.org/record/5834010">QTDIAN toolbox</a> and are based on updating the `possibility-for-electricity-autarky` workflow configuration to include the following parameters (also included in `config.yaml`):</p> <p> </p> <pre><code> scenarios: people-powered: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 0.2 # agro pv share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 0.1 share-rooftop-used: 1.0 government-directed: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0 market-driven: use-of-protected areas: true pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 1.0 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0</code></pre> <p> </p> <p>This dataset includes different spatial resolutions of land availability. For more information on the `ehighways` resolution, see <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a>.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p> </p>
Storyline Simulations Data for the paper Athanase et al.: Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean
<p>Data used for producing the Figures in the paper entitled "Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean", Athanase et al. (Communications Earth & Environment).</p> <p>The AWI-CM-1-1-MR free runs are available in the Earth System Grid Federation (ESGF) data nodes (https://esgf-data.dkrz.de/search/cmip6-dkrz/). The ERA5 reanalysis data used in the paper can be accessed from the European Centre for Medium-Range Weather Forecasts (ECMWF; https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5). Here, we provide data from the nudged storyline simulations carried out with the AWI-CM-1-1-MR coupled climate model.</p> <p>Parameters naming convention:</p> <p>- Sea Surface Temperature ("tos").</p> <p>- Radiative Fluxes ("radiations"), including net surface heat flux ("qnet"), net surface thermal radiation ("trads"), net surface solar radiation ("srads"), latent heat flux ("ahfl"), sensible heat flux ("ahfs").</p> <p>- Low Clouds Cover ("lcc").</p> <p>- Mixed Layer Depth ("mlotst").</p> <p>- Surface Air Temperature ("tas").</p> <p>- 10 m winds ("u10","v10").</p> <p>All data is provided as the 5-member ensemble mean from the nudged storyline simulations. Data is provided for the storyline simulations of the summer 2019 Northeast Pacific marine heatwave, in different background climate conditions: preindustrial ("PI"), present-day ("PD"), and +4°C warmer world ("4K"). </p> <p> </p>
Global hourly t2m storyline data in the 2015-2019 (2017-2019) period in the present (2 and 4 K warmer climates)
<p>Global hourly t2m storyline data (in the 2015-2019 (2017-2019) period in the present (2 and 4 K warmer climates) are provided in three .nc files (one file for each climate, with 2038-2040 and 2093-2095 in the model representing 2017-2019 for the 2 and 4 K warmer climates respectively). For the joint 2017-2019 period, ensemble member one used in Sánchez-Benítez et al. 2022 (see more details about the methodology used there) is provided. Meanwhile, the 2015-2016 present-climate data come from a nudged simulation which is identical to the present-climate nudged simulation described in Sánchez-Benítez et al. 2022, except that the simulation is branched off already in 1979 instead of 2017.</p>
Model output for a storyline analysis of hurricane Irma's precipitation under various levels of climate warming
<p>Understanding how extreme weather, such as tropical cyclones, will change with future climate warming is an interesting computational challenge. Here, the hindcast approach is used to create different storylines of a particular tropical cyclone, Hurricane Irma (2017). Using the Community Atmosphere Model, we explore how Irma's precipitation would change under various levels of climate warming. Analysis is focused on a 48-hour period where the simulated hurricane tracks reasonably represent Irma's observed track. Under future scenarios of 2 K, 3 K, and 4 K global average surface temperature increase above pre-industrial levels, the mean 3-hourly rainfall rates in the simulated storms increase by 3-7%/K compared to present. This change increases in magnitude for the 95th and 99th percentile 3-hourly rates, which intensify by 10-13%/K and 17-21%/K, respectively. Over Florida, the simulated mean rainfall accumulations increase by 16-26%/K, with local maxima increasing by 18-43%/K. All percent changes increase monotonically with warming level.</p>
Storyline data used in the paper "The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging"
<p>We provide the storyline data (in NetCDF format) used in the paper: “The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging” published in Journal of Climate. The data is structured in four .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climate. The data from the five ensemble members (E1 to E5) have been included separately in 3-months files.</p> <p>Atmospheric variables (Files are named as: {variable}_E{ensemble member}_{starting month}{year}.nc:</p> <ul> <li> <p>Latent heat flux (ahfl)</p> </li> <li> <p>Sensible heat flux (ahfs)</p> </li> <li> <p>Monthly Global Mean 2m Temperature (GMTT2mMonthly)</p> </li> <li> <p>Maximum 2m Temperature (t2max)</p> </li> <li> <p>Mean 2m Temperature (t2mean)</p> </li> <li> <p>Minimum 2m Temperature (t2max)</p> </li> <li> <p>Soil Wetness (ws)</p> </li> </ul> <p> Only for present climate:</p> <ul> <li> <p>850 hPa Temperature (T850)</p> </li> <li> <p>Total Cloud Cover (TCC)</p> </li> <li> <p>500 hPa Geopotential Height (Z500)</p> </li> </ul> <p>Five layers soil moisture (Only for present climate, Files are named as: From20172019in2017Climatessp370{ensemble member}_{year}{starting month}.01_jsbid.nc) </p> <p>Oceanic variables (from FESOM, Files are named as: {variable}_E{ensemble member}_{year}{starting month}01.nc:</p> <ul> <li> <p>Sea Ice Concentration (SIC)</p> </li> <li> <p>Sea Surface Temperature (SST)</p> </li> </ul> <p><strong>Please, note that FESOM uses an unstructured mesh.</strong></p>
Output data for: Flammable Futures – Storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia
<p>This repository contains the output data associated with the publication "Flammable Futures – Storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia". It contains the FLAM modeled burned area and the GLOBIOM output, as well as the a downscaling grid.</p> <p>Descriptions of the results can be found in the publication (DOI will follow).</p>
Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"
<p>Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"<br><br><span><a href="../api/records/12664900/draft/files/hmax_idai_ifs_rebuild_bc_hist_rain_surge_noadapt.tiff/content" target="_blank" rel="noopener noreferrer">hmax_idai_ifs_rebuild*</a> -> Flood maps<br><a href="../api/records/12664900/draft/files/spatial_idai_ifs_rebuild_bc_3c-hightide_rain_surge_retreat.gpkg/content" target="_blank" rel="noopener noreferrer">spatial_idai_ifs_rebuild*</a> -> Impacts<br></span></p>
Crowdsourcing StoryLines with CrowdTruth
<p>Crowdsourced ground truth dataset for 1,204 sentences and 7,778 event pairs covering 22 news topics. The corpus was created by using the CrowdTruth methodology, as described in the following paper:</p> <ul> <li>Tommaso Caselli and Oana Inel: Crowdsourcing StoryLines: Harnessing the Crowd for Causal Relation Annotation. Events and Stories in the News Workshops, COLING 2018</li> </ul> <p>If you find this data useful in your research, please consider citing:</p> <pre>@inproceedings{caselli2018crowdsourcing, title={Crowdsourcing StoryLines: Harnessing the Crowd for Causal Relation Annotation}, author={Caselli, Tommaso and Inel, Oana}, booktitle={Proceedings of the Workshop Events and Stories in the News 2018}, pages={44--54}, year={2018} } </pre> <p>Crowdsourcing results and evaluation against expert data are available in folder: <code>|--data/results/</code></p> <p>Expert ground truth data is available in folder: <code>|--data/ground_truth/</code></p> <p>Aggregated raw crowdsourcig data is available in folder: <code>|--data/aggregated_input/</code></p> <p>Raw crowdsourcig data is available in folder: <code>|--data/input/</code></p> <p> </p> <p><strong>Running the notebooks</strong></p> <p>To run and regenerate the results, you need to install the stable version of the <em><strong>crowdtruth==2.0</strong></em> package from PyPI using:</p> <p>pip install crowdtruth==2.0</p> <pre> </pre>
Large ensemble climate modelling time series for the Rhine catchment, including drought2018 storylines
<p>Dataset associated with <strong>Van der Wiel, Lenderink, De Vries (2021): Physical storylines of future European drought events like 2018 based on ensemble climate modelling, <em>Weather and Climate Extremes, </em>DOI <a href="http://doi.org/10.1016/j.wace.2021.100350">10.1016/j.wace.2021.100350</a>.</strong></p> <p>Large ensemble climate modelling time series for the Rhine catchment. The dataset contains three ensembles (present-day, pre-industrial + 2C-warming, pre-industrial + 3C-warming) of 2000 years each, various variables related to drought are included. All data is derived from the EC-Earth global climate model (v2, Hazeleger et al. 2012, DOI <a href="https://doi.org/10.1007/s00382-011-1228-5">10.1007/s00382-011-1228-5</a>). Descriptions of large ensemble experimental setup can be found in Van der Wiel et al. (2019, DOI <a href="http://doi.org/10.1029/2019GL081967">10.1029/2019GL081967</a>). Files: *_d_ECEarth_??_Rhine.tar.gz</p> <p>Additionally, three sets of storylines of droughts similar to the western European drought of 2018 are included. These are the simulated events selected from the large ensembles, using metrics 1, 2 and 3 of Van der Wiel et al. (2021, DOI <a href="http://doi.org/10.1016/j.wace.2021.100350">10.1016/j.wace.2021.100350</a>). Files: drought18_m[123]_Rhine.tar.gz</p>
Data and code for "Representing storylines with causal networks to support decision making: framework and example"
<p>Data and code for the paper "Representing storylines with causal networks to support decision making: framework and example", along with the Shiny webapp code accompanying the paper. The paper has been submitted to the journal of Climate Risk Management and is currently under review.</p>
Data from "Unraveling Cross-Border Climate Risks through Climate Storylines:An example from Europe's Cocoa Industry"
<p>The following files were used in the analysis in the article "Unraveling Cross-Border Climate Risks through Climate Storylines: An example from Europe's Cocoa Industry." The traditional assessment of climate change's impact on agriculture, confined by geographical borders, is insufficient due to global trade, allowing climate risks to affect multiple nations. This study uses cacao as an example to highlight cross-border climate risks, showing their implications for the EU's chocolate industry. Concentrating on cacao supply from Ivory Coast and Ghana, the study models various warming scenarios, revealing reduced cacao yields in both countries. Under RCP2.6, Ivory Coast could lose around 7% of cacao production by 2050, rising to 23% at RCP8.5. Ghana might experience losses of 39% by 2050, with a potential extreme loss of 86%. A no-deforestation import policy further affects production, reducing about 24% in the Ivory Coast and 16% in Ghana. These losses possibly cause EU supplies to decrease by 8% by 2050 in the long term and up to 20% in an extreme year. Predicted cacao price hikes of 30% by 2050 will cost the EU 2.5 billion euros annually by 2050. Extreme drought might elevate prices by 115% (RCP8.5), resulting in an additional annual expense of 10 billion euros for the EU. Findings indicate importance of addressing cross-border dimension in climate risk studies. These risks intensify with higher warming degrees, compounded by concentrated crop production, making countries dependent on these commodities highly susceptible to simultaneous, multi-location crop failures. Presently, EU trade agreements lack provisions for such scenarios. Given the increased likelihood of such shocks under a warming climate, more significant investment into disaster management strategies and policies is advisable.</p>
Model output for a storyline analysis of hurricane Irma's precipitation under various levels of climate warming
Open the record for dataset details and reuse information.
Storylines of UK drought based on the 2010–2012 event
<p>Model input (precipitation and PET) and output (simulated river flows) using the GR4J hydrological model for 100 UK catchments for the different storylines investigated in the paper submitted to Hydrology and Earth System Sciences (HESS). The storylines investigated are storylines of seasonal contribution, precondition severity, "three dry winters" and climate change.</p>
CMIP6 data for the analysis in the article "Storylines of Maritime Continent dry period precipitation changes under global warming"
<p>The files include the data required to generate the figures based on CMIP6 model simulation outputs</p>
Storyline data used in the paper "Storylines reveal contrasting thermodynamic effects of climate change on 2020/21 East Asian cold extremes"
<p>We provide the storyline data (in NetCDF format) used in the paper:”<strong>Storylines reveal contrasting thermodynamic effects of climate change on 2020/21 East Asian cold extremes”. </strong>The data is structured five .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climates. The data includes the five ensemble members (E1 to E5) and ensemble mean variables at winter season (DJF) in 2020/2021.</p> <p>Files of simulation ensemble member data are named as:</p> <p><span> </span>“AWICM1_ssp370/hist_f{begin year}_n2017_T20e24_{variable name}_E{ensemble member}_DJF-{years}_dailymean.nc”</p> <p>Files of simulation ensemble-mean data are names as:</p> <p>“AWICM1_ssp370/hist_f{begin year}_n2017_T20e24_{variable name}_DJF-{years}_ensmean.nc”</p> <p>Files of free-run (CMIP6) data are names as:</p> <p>“freerun_{variable name}_DJF-{year}_ensmean_31days-runmean_11years-ydaymean.nc”</p> <p>Variables includes:<span> </span></p> <ul> <li>Mean 2m Temperature (t2m)</li> </ul> <ul> <li>Downward net surface solar radiation (srads)</li> </ul> <ul> <li>Total cloud cover (aclcov)</li> <li>Downward solar radiation at clear sky (rsdscs)</li> <li>sea ice concentration (friac)</li> </ul> <p>Only for present climate:</p> <ul> <li>Zonal/meridional wind at 850hPa (u850,v850)</li> <li>500 hPa Geopotential Height (z500)</li> </ul> <p><span> </span></p>
Storylines of the 2022 UK drought using seasonal hindcasts at Anglian catchments and boreholes
<p>In this study, we investigate the drivers of winter rainfall for the region of eastern England supplied by Anglian Water using a large sample of plausible winters in hindcasts from the ECMWF seasonal forecasting system SEAS5. The SEAS5 hindcast dataset (1982-2021) is used to provide a large sample of plausible winters (Dec, Jan, Feb - DJF). A series of meteorological indices (such as NAO, EA, Nino3.4, polar vortex strength and the SST tripole index) describing atmospheric circulation patterns are calculated from both observed winters (ERA5 reanalysis - 1960-2015) and for each winter in the hindcast dataset. K-means clustering of all the calculated indices are used to create clusters with similar characteristics.</p> <p>Storylines were created to represent plausible pathways of the 2022 drought assuming winter 2022/23 resembled each of the four winter clusters. Storylines was simulated by running GR6J and Aquimod using the top parameter set for the baseline period up until November 2022 after which hindcast rainfall and PET data for each winter (DJF) in the four winter clusters were appended in place of winter 2022/23. This dataset contains simulated river flows and groundwater level for each storyline and for each of the selected river catchments or borehole in the Anglian Water region. Each file is named with the NRFA station id / groundwater borehole name and the cluster number (i.e. C1, C2, C3 or C4).</p> <p>In the LTA100 experiment, spring (MAM), summer (JJA) and autumn (SON) 2023 were assumed to have 100% long term average (LTA) rainfall by selecting the closest years matching 100% LTA rainfall in the observations. In the LTA60 experiment, it is assumed that summer (JJA) 2023 follow 60% LTA seasonal rainfall.</p>
Datasets for storyline case study for Hurricane Ian rainfall
<p>Dataset for results in paper.</p>
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