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72 results for “Energy Climate”
Dwelling conversion and energy retrofit modify building anthropogenic heat emission under past and future climates: a case study of London terraced houses
<p>This archive includes the data used (e.g. Time use survey (UK-TUS) data), model files (idf files for running EnergyPlus) and codes for analysis in the paper (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.enbuild.2024.114668" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.enbuild.2024.114668</a>).</p> <p>Files in this archive should include:</p> <ul> <li>Time use survey data analysis</li> </ul> <p>o Main dataset: TUS_activity.zip</p> <p>o Code: TUS_clustering_code.zip</p> <p>o Output: InternalHeatProfile.zip</p> <ul> <li>Building energy modeling </li> </ul> <p>o Main dataset (run in EnergPlus 9.4): IDFfiles.zip</p> <p>o Output: Eplus_output.zip</p> <ul> <li>PostProcess analysis</li> </ul> <p>o Code: QF_analysis_code.zip</p> <p>o Output: QF_output.zip</p> <p> </p> <p>Note: this version currently only includes the outputs of all processes, the main dataset and code will be updated later.</p>
Energy-water and seasonal variations in climate underlie the spatial distribution patterns of gymnosperms species richness in China
<p>Studying the pattern of species richness is crucial in understanding the diversity and distribution of organisms in the earth. Climate and human influences are the major driving factors that directly influence the large-scale distributions of plant species, including gymnosperms. Understanding how gymnosperms respond to climate, topography, and human-induced changes is useful in predicting the impacts of global change. Here, we attempt to evaluate how climatic and human-induced processes could affect the spatial richness patterns of gymnosperms in China. Initially, we divided a map of the country into grid cells of 50 × 50 km<sup>2 </sup>spatial resolution and plotted the geographical coordinate distribution occurrence of 236 native gymnosperm taxa. The gymnosperm taxa were separated into three response variables: (i) all species, (ii) endemic species, and (iii) non-endemic species, based on their distribution. The species richness patterns of these response variables to four predictor sets were also evaluated: (i) energy-water, (ii) climatic seasonality, (iii) habitat heterogeneity, and (iv) human influences. We performed generalized linear models (GLMs) and variation partitioning analyses to determine the effect of predictors on spatial richness patterns. The results showed that the distribution pattern of species richness was highest in the southwestern mountainous area and Taiwan in China. We found a significant relationship between the predictor variable set and species richness pattern. Further, our findings provide evidence that climatic seasonality is the most important factor in explaining distinct fractions of variations in the species richness patterns of all studied response variables. Moreover, it was found that energy-water was the best predictor set to determine the richness pattern of all species and endemic species, while habitat-heterogeneity has a better influence on non-endemic species. Therefore, we conclude that with the current climate fluctuations as a result of climate change and increasing human activities, gymnosperms might face a high risk of extinction.</p>
Output data: China's energy-water-land system co-evolution under carbon neutrality goal and climate impacts
<p>Outputs for Wang, J., Duan, Y., Wang, C., 2023. China’s energy-water-land system co-evolution under carbon neutrality goal and climate impacts. (In progress)</p> <p>Folder demeter contains the spatially downscaled land use/land cover datasets.<br> Folder tethys contains the spatially downscaled water withdrawal datasets.</p> <p>The sub-folder names correspond to the scenarios described in the paper.</p> <p>For landcover datasets, land type ratios in each grid are presented. Land types include water, forest, shrub, grass, urban, snow, sparse and crops.</p> <p>For water withdrawal datasets, “wd” = “water withdrawal spatially downscaled”, “twd” = “water withdrawal spatially and temporally downscaled”, “dom”=”domestic/municipal sector”, “elec”=”electricity sector”, ”irr”=”irrigation sector”, “liv”=”livestock sector”, “mfg”=”manufacturing/industry sector”, “min”=”mining/primary energy sector”, “nonag”=”non-agricultural sector”, “total”=”all sectors”.<br> </p>
Case study result data set for Energy Systems (submitted) article "Influence of hydrogen import prices on hydropower systems in climate-neutral Europe"
<p>The data set contains result data for the European system in a long term climate-neutral European energy system (scenario year 2050) as described in the publication "Influence of hydrogen import prices on hydropower systems in climate-neutral Europe". The results have been generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. </p> <p><strong>Abbreviations:</strong></p> <ul> <li>BEV - Battery Electric Vehicles</li> <li>CCGT - Combined Cycle Gas Turbine</li> <li>CHP - Combined heat and power</li> <li>con - consumption</li> <li>gen - generation</li> <li>HighCLEQ - High import prices / clustered-equivalent hydropower units</li> <li>HighEQ - High import prices / equivalent hydropower units</li> <li>LowCLEQ - Low import prices / clustered-equivalent hydropower units</li> <li>LowEQ - Low import prices / equivalent hydropower units</li> <li>MedCLEQ - Medium import prices / clustered-equivalent hydropower units</li> <li>MedEQ - Medium import prices / equivalent hydropower units</li> <li>OCGT - Open Cycle Gas Turbine</li> <li>PHEV - Plug-In Hybrid Vehicles</li> <li>PS - Pumped Storage</li> <li>w/ - with</li> <li>w/o - without</li> <li>yr - year</li> </ul>
Data from: Lizard richness in mainland China is more strongly correlated with energy and climatic stability than with diversification rates
<p><strong><span>Aim</span></strong><span>: Contemporary environmental, historical, and evolutionary factors are increasingly used to decipher the drivers of spatial patterns of species richness. Evidence of such correlations for Chinese reptiles is scarce and poorly understood. We therefore explored the validity of the environmental capacity, historical climatic stability, and diversification rates hypotheses on Chinese lizard richness.</span></p> <p><span><strong>Location</strong>:</span><span> Mainland China</span></p> <p><span><strong>Taxon</strong>:</span><span> Squamata: Sauria</span></p> <p><strong><span>Methods</span></strong><span>: We mapped the distribution ranges of all 237 lizards in mainland China using a combination of different datasets. We used current environmental conditions (ambient energy, environmental productivity, and habitat heterogeneity), historical climate stability indices (long-term: since ~3.3 Ma and short-term: since the Last Glacial Maximum), and mean tip diversification rates to test whether current environmental conditions, historical climate change, and diversification rates drive contemporary richness patterns of lizards in China. We applied piecewise structural equation models (pSEM) to jointly evaluate our hypotheses, considering direct and indirect effects.</span></p> <p><strong><span>Results</span></strong><span>: Chinese lizards showed latitudinal diversity gradients. We found consistent support for contemporary climatic and environmental factors' relationships with richness. Richness was also positively correlated with short-term climatic stability, but less so with long-term stability. Diversification rates were only seldom found to be positively correlated with lizard richness.</span></p> <p><span><strong>Main conclusions</strong>:</span><span> Our results support the</span> <span>environmental capacity and historical climate hypotheses, which link high richness to highly productive warm and stable regions (and low richness to cold and unstable regions). We conclude that post-speciation dispersal and short-term climatic oscillations quickly swamp the long-term signal of diversification rates and climatic fluctuations, creating strong current climate-richness associations.</span></p>
Energy-water and seasonal variations in climate underlie the spatial distribution patterns of gymnosperms species richness in China
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Data from: Lizard richness in mainland China is more strongly correlated with energy and climatic stability than with diversification rates
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Aligning renewable energy expansion with climate-driven range shifts
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Supplementary Data: Yarlagadda-etal_2022_energy_and_climate_change
<p>GCAM input files needed to run the scenarios in the paper "Climate and air pollution implications of potential energy infrastructure and policy measures in India".</p>
Biochar Technologies at the Energy-Food nexus in sub-Saharan Africa: A unique window for Climate-Smart Agriculture
<p>Dataset to support the publication</p>
Precipitation-induced dissipation limits storm kinetic energy in a warming climate
<p>This zip file contains data and codes to reproduce the figures of a manuscript on precipitation-induced dissipation in X-SHiELD.</p> <p>Contact mbolot@princeton.edu for questions.</p>
Climate change and energy policy control the future of Myanmar's rivers
<p>This data repository holds input and output data for the paper Jin XY., Chowdhury, A.K., Dang, T.D., Deshmukh, R. and Galelli, S. “Climate change and energy policy control the future of Myanmar's rivers”. See Readme for more details. </p>
Data for "Elevated urban energy risks due to climate-driven biophysical feedbacks"
<p>This dataset contains the global multi-model urban climate and energy projections from Li et al. (2024), "Elevated urban energy risks due to climate-driven biophysical feedbacks", published in <em>Nature Climate Change</em>. It contains global monthly mean projections of urban 2-meter air temperature, and urban cooling and heating energy fluxes derived from 25 Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Details about how this dataset was generated are described in the article. This dataset may be useful for multiple communities interested in future energy risks, climate change impacts and vulnerability, and climate-sensitive adaptation and energy planning.</p> <p>For more details, please refer to the README.md file included in the dataset.</p>
Data for "The influence of internal variability on Earth's energy balance framework and implications for estimating climate sensitivity"
<p>This archive contains processed model output necessary for reproducing the figures in Dessler, Maurtisen, Stevens, ACP, 2018</p> <p>historicalEnsemble.nc contains data from the 100-member MPI historical ensemble</p> <p>forcing_aed_ensemble.nc contains the forcing for the historical runs</p> <p>model0001.nc is the control run of the MPI model<br> model0003.nc is an abrupt 4xCO2 run of the MPI model</p> <p>cmip5 contains data from individual CMIP5 models </p> <p>the folder "fig 5" contains zonal average fields necessary for that figure</p>
Future Projections and Life Cycle Assessment of End-of-life Tires to Energy Conversion in Hong Kong: Environmental, Climate and Energy Benefits for Regional Sustainability
<p>The dataset presents the findings of the study "Future Projections and Lifecycle Assessment of End-of-life Tires to Energy Conversion in Hong Kong: Environmental, Climate and Energy Benefits for Regional Sustainability". The data results are contained in the files "Results_data.xlsx" and "LCIs and LCA results.zip," while the "Figures data.xlsx" file includes the data needed for plotting. </p>
Results and plotting scripts for the manuscript 'SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios'
<p><br>This archives the results for the manuscript 'SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios'</p> <p>For the model version used to create these results, please see: https://doi.org/10.5281/zenodo.13981520</p> <p>Files to reproduce the figures, in R language:<br>SuCCESs validation.R - Reads GDX files and produces plots for energy and emissions.<br>SuCCESs validation MC.R - The same, but with the Monte Carlo GDXs.</p> <p>External data sources:</p> <p>***<br>GHG emissions are from IGCC and PRIMAP</p> <p>IGCC: https://climatechangetracker.org/igcc (CC-BY license)</p> <p>PRIMAP:<br>Gütschow, Johannes; Jeffery, M. Louise; Gieseke, Robert; Gebel, Ronja; Stevens, David; Krapp, Mario; Rocha, Marcia (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, https://doi.org/10.5194/essd-8-571-2016<br>Gütschow, Johannes ; Busch, Daniel ; Pflüger, Mika (2024): The PRIMAP-hist national historical emissions time series (1750-2023) v2.6. Zenodo. https://doi.org/10.5281/zenodo.13752654<br>https://primap.org/primap-hist/ (CC-BY-4.0 license)</p> <p>***<br>Historical energy production and use data are from IEA Energy Statistics Data Browser (CC BY 4.0 licence).<br>https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=CO2%20emissions&indicator=CO2BySource</p> <p>***<br>IAM results are from the SSP database: https://tntcat.iiasa.ac.at/SspDb </p> <p>Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian C. O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlík, Florian Humpenöder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan C. Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, Massimo Tavoni.<br>The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Global Environmental Change, Volume 42, Pages 153-168, 2017,<br>DOI:110.1016/j.gloenvcha.2016.05.009</p> <p>Rogelj, J., Popp, A., Calvin, K.V., Luderer, G., Emmerling, J., Gernaat, D., Fujimori, S., Strefler, J., Hasegawa, T., Marangoni, G., Krey, V., Kriegler, E., Riahi, K., van Vuuren, D.P., Doelman, J., Drouet, L., Edmonds, J., Fricko, O., Harmsen, M., Havlik, P., Humpenöder, F., Stehfest, E., Tavoni, M., Scenarios towards limiting global mean temperature increase below 1.5 °C. Nature Climate Change 8, 2018, 325-332.<br>DOI:10.1038/s41558-018-0091-3</p>
Replication package for: Energy Efficiency and Directed Technical Change: Implications for Climate Change Mitigation
<p>Contains raw data and replication code for the following paper:</p> <p>Casey, Gregory (forthcoming). "Energy Efficiency and Directed Technical Change: Implications for Climate Change Mitigation." Review of Economic Studies.</p>
Data for "Energy Surplus and Atmosphere – Land-Surface "Tug of War" Induced by Climate Change Control Future Evapotranspiration"
<p>USGS gauges used in manuscript "<strong>Energy Surplus and An Atmosphere-Land-Surface “Tug of War” Control Future Evapotranspiration"</strong>. USGS_CTL15_Gage.mat contains the USGS gauge ID, and one can use retrieve_daily_streamflow.m to download the corresponding streamflow time series. </p>
Effects of low-carbon energy adoption on airborne particulate matter concentrations with feedbacks to future climate over California
<p>California plans to reduce emissions of long-lived greenhouse gases (GHGs) through adoption of new energy systems that will also lower concentrations of short-lived absorbing soot contained in airborne particulate matter (PM). Here we examine the direct and indirect effects of reduced PM concentrations under a low-carbon energy (GHG-Step) scenario on radiative forcing in California. Simulations were carried out using the source-oriented WRF/Chem (SOWC) model over California with 12 km spatial resolution for the year 2054. The avoided aerosol emissions due to technology advances in the GHG-step scenario reduce ground level PM concentrations by ~8.85% over land compared to the Business as Usual (BAU) scenario, but changes to meteorological parameters are more modest. Top of atmospheric forcing predicted by the SOWC model increased by 0.15 W m<sup>-2</sup>, surface temperature warmed by 0.001 K, and planetary boundary layer height (PBLH) increased by 2.20 cm in the GHG-Step scenario compared to the BAU scenario. PM climate feedbacks are small because the significant changes in ground level PM concentrations associated with the GHG-Step scenario are limited to the first few hundred meters of the atmosphere, with little change for the majority of the vertical column above that level. As an order-of-magnitude comparison, the long-term effects of global reductions in GHG emissions (RCP8.5 – RCP4.5) lowered average surface temperature over the California study domain by approximately 0.76 K. The effects of long-lived climate pollutants such as CO<sub>2</sub> are much stronger than the effects of short-lived climate pollutants such as PM soot over California in the year 2054. </p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Kortrijk Kennedy Park, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Kortrijk Kenny Park (50° 48' 2"N 3°16'13" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</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
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.