Find research datasets worth reusing

opencc-by-4.0Jul 2024View details →

Daily streamflow at 0.5 deg resolution generated using 0.22 deg runoff from the historical (1986-2005) and two future scenarios' (RCP 4.5 and 8.5, 2081-2100) simulations of CanRCM4 for its North American domain

These data are provided in support of the following manuscript which is currently (July 2024) under revision for the Hydrology and Earth System Science journal. The details about how these data were generated can be found in this manuscript (or its final accepted version, hoping our manuscript will be accepted).  <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-182/">https://egusphere.copernicus.org/preprints/2024/egusphere-2024-182/</a> The effect of climate change on the simulated streamflow of six Canadian rivers based on the CanRCM4 regional climate model Vivek K. Arora, Aranildo Lima, and Rajesh Shrestha  The netcdf files provide here contain two variables. 1) streamflow, variable name is fout_land, units are m3/s 2) flow velocity, variale name is velocity, units are m/s

opencc-by-4.0Dec 2015View details →

Fig. 1 in Past, present and future of host‾parasite co-extinctions

Fig. 1. Cumulative number of documented species extinctions according to IUCN (2014).

opencc-by-4.0Apr 2018View details →

Dataset to reproduce the paper "A new framework to evaluate urban design using urban microclimatic modelling in future climatic conditions"

This dataset has been generated with the paper " A new framework to evaluate urban design using urban microclimatic modelling in future climatic conditions" (https://doi.org/10.3390/su10041134). A Python notebook is also included to conduct the analysis. <ol> <li>Data analysis - Sustainability paper.ipynb : Python notebook</li> <li>Geneva_Eur11_TDY_2010_2039 : Climate file for the year 2039 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2039_cim : Climate file for the year 2039 obtained from RCA4-CIM</li> <li>Geneva_Eur11_TDY_2010_2039 : Climate file for the year 2069 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2069_cim : Climate file for the year 2069 obtained from RCA4-CIM</li> <li>Geneva_Eur11_TDY_2010_2099 : Climate file for the year 2099 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2099_cim : Climate file for the year 2099 obtained from RCA4-CIM</li> <li>heating_2039 : Heating demand from CitySim for the year 2039</li> <li>heating_2039_cim : Heating demand from CitySim-CIM for the year 2039</li> <li>heating_2069 : Heating demand from CitySim for the year 2069</li> <li>heating_2069_cim : Heating demand from CitySim-CIM for the year 2069</li> <li>heating_2099 : Heating demand from CitySim for the year 2099</li> <li>heating_2099_cim : Heating demand from CitySim-CIM for the year 2099</li> <li>heating_2099_minP : Heating demand from CitySim for the year 2099 with Minergie-P scenario</li> <li>heating_2099__minP_cim : Heating demand from CitySim-CIM for the year 2099 with Minergie-P scenario</li> <li>cooling_2039 : Cooling demand from CitySim for the year 2039</li> <li>cooling_2039_cim : Cooling demand from CitySim-CIM for the year 2039</li> <li>cooling_2069 : Cooling demand from CitySim for the year 2069</li> <li>cooling_2069_cim : Cooling demand from CitySim-CIM for the year 2069</li> <li>cooling_2099 : Cooling demand from CitySim for the year 2099</li> <li>cooling_2099_cim : Cooling demand from CitySim-CIM for the year 2099</li> <li>cooling_2099_minP : Cooling demand from CitySim for the year 2099 with Minergie-P scenario</li> <li>cooling_2099__minP_cim : Cooling demand from CitySim-CIM for the year 2099 with Minergie-P scenario</li> <li>temp_cim : simulated temperature from CIM using Meteonorm</li> <li>temp_meteonorm : temperature from Meteonorm</li> <li>u_cim : simulated wind speedfrom CIM using Meteonorm</li> <li>u_meteonorm : wind speed from Meteonorm</li> </ol>

opencc-by-4.0Sep 2018View details →

The futures of 193 Popular Financial Reporting of Awards Program 2017 and social economic elements

Dataset The futures of 193 Popular Financial Reporting of Awards Program 2017 and social economic elements

opencc-by-4.0Mar 2019View details →

Heatwave metrics data supporting the paper "Strong influence of aerosol reductions on future heatwaves "

This folder contains the heatwave metrics calculated from the CESM-LENS project. The raw temperature data are archived in the climate data gateway at NCAR. Also included are the heatwave metrics calculated using the NCEP/NCAR reanalysis and the  Met Office Hadley Centre gridded daily temperatures. All data are in NetCDF4 format.

opencc-by-4.0Mar 2019View details →

HD 49798: Its History of Binary Interaction and Future Evolution

MESA inlists and run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017ApJ...847...78B">Brooks et al. (2017)</a>. MESA version 8118. Publication DOI: <a href="https://doi.org/10.3847/1538-4357/aa87b3">10.3847/1538-4357/aa87b3</a>

opencc-by-4.0Mar 2019View details →

Supplementary data for "An initial assessment of the value of Allam Cycle power plants with liquid oxygen storage in future GB electricity system"

The code for the Unit Commitment & Economic Dispatch model that was used in this work is available at: https://gist.github.com/vitali87/20688c161d7b5ad598b5d52b524f4585 Sample output data can be found in the "Example Outputs.zip" file. This corresponds to the case outlined in the article that simulates a system with 5 Allam Cycle plants without Liquid Oxygen Storage, for the winter test week. To run the UCED model: <ul> <li>Download "UC AIMMS Allam Cycle Model" code from the github and save as an AIMMS project file.</li> <li>Save the file in a folder that contains all the necessary input datasets, found in the "Universal Inputs for UCED Model.zip" file, and the example outputs, found in the "Example Outputs.zip" file, which are to be overwritten. Do not change the name of the input or output files.</li> <li>Open the project and execute the following procedures:  <ul> <li>"Main Initialisation" - to initialise the problem</li> <li>"Read from Excell" - to read data from the input files</li> <li>"Main Execution" - to begin running the problem</li> </ul> </li> <li>Once the run is complete, execute "Run External Procedure" to overwrite the output files with the new data.</li> </ul> To change the test week: <ul> <li>Open "Demand Profiles" in 'sets' and change the set definition. Enter "C1" for the winter week and "C21" for the summer week. Another week can alternatively be selected. For example, entering "C45" would allow the model to run with the weather and demand data from the 45th week in the year 2010. </li> <li>Save and close the set.</li> </ul> To change the number of plants in the system: <ul> <li>Open "PCCSGenerators" in 'sets' and change the set definition. To run with 5 Post Combustion Capture plants, end the list of generators after plant number 5 by commenting the remaining plants. This is done by using "!" after the 5th plant name in the string. Then save and close the set.</li> <li>Repeat the above step for the "ACGenerators" and "AirSeparationUnits" sets, to change the number of Allam Cycle plants in the system.</li> </ul> To add or remove oxygen storage capability from the Allam Cycle plants: <ul> <li>Open the "Main Initialisation" procedure.</li> <li>To run the model without oxygen storage: <ul> <li>make sure the following command is stated: "AC_ASU_coupled := 0;"</li> <li>save and close the procedure</li> </ul> </li> <li>To run the model with oxygen storage: <ul> <li>make sure the following is command is stated: "AC_ASU_coupled := 1;"</li> <li>make sure that the number, 'X', of "map_AC_to_ASU('Gas_CCS_AC_X') := 'ASU_X';" commands that are active matches the number of active Allam Cycle plants in the model</li> <li>save and close the procedure.</li> </ul> </li> </ul>

opencc-by-4.0Jul 2019View details →

Model simulated potential natural vegetation state in the western US under preindustrial, historic, and future (RCP8.5) atmospheric conditions using multiple parameterizations of the dynamic vegetation model TRIFFID.

DATA DESCRIPTION Author contact information: Linnia R. Hawkins Oregon State University lhawkins@oregonstate.edu; linnia.hawkins@gmail.com Data supporting 2019 Journal of Advances in Modeling Earth Systems publication Simulations of the equilibrium vegetation distribution in the western US performed with the climate model HadAM3p-HadRM3p-MOSES2-TRIFFID step1: Identify Influential Parameters All files labeled step1. EXPERIMENT DESCRIPTION: Data used in step 1: identify influential parameters     sensitivity experiment adjusting one parameter at a time 38 individual parameters were adjusted to 7 values, equally spaced over a defined plausible range. For reference nine simulations with the default model parameterization are included, initiated with unique initial potential temperature perturbations.  The data contains the vegetation state variables at the end of four-year simulations (January 2004 to December 2007) during which two equilibrium time steps with the dynamic vegetation model TRIFFID (Cox et al., 2001). The results are averaged over three simulations initiated with unique atmospheric potential temperature perturbations. FILE DESCRIPTION: NETCDF: Each netcdf file contains the fractional coverage (field1391), leaf area index (field1392), and the canopy height (field1393) for 5 plant functional types (PFTs: broadleaf, needleleaf, c3 grass, c4 grass, shrub) simulated for November 29, 2007.      File labeling scheme:            step1_parameter_settingindex_3ICave.nc         parameter: the name of the only model parameter adjusted         setting index: the index of the parameter setting (1-7)             index of 1 references the lowest plausible parameter setting             index of 7 references the highest plausible parameter setting             index of 4 references the parameter setting half way between the lowest and highest plausible parameter settings.          3ICave: references that the results have been averaged over 3 initial conditions.     Variables:         field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub]          field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]         field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]   step2: ParameterSensitivity all files labeled step2 EXPERIMENT DESCRIPTION: Data used in step 2: parameter sensitivity  Perturbed Parameter Experiment (PPE) simultaneously adjusting 18 parameters. Latin hypercube sampling was employed to generate 250 unique parameterizations references with a SETID (ranging from 1-359) The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds.  Data is averaged over 5 initial atmospheric conditions. FILE DESCRIPTION: NETCDF: files contain either the fractional coverage (field1391) or the above ground biomass (field1512) for 5 plant functional types (PFTs) simulated for November 1907.     File labeling scheme:            step2_variable_parametersetindex_5ICave.nc         variable: the name of the variable contained in the file         setting index: the index of the parameter setting corresponding to the parameter set text files         5ICave: references that the results have been averaged over 5 initial atmospheric conditions.     Variables:         field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub]          field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub] TXT: files contain a list of the model parameterizations (for each PFT and variable) and the corresponding to the parameter set index.      Parameters are labeled in row 1     Parameter set indices are shown in column 1 RESTART: restart_region_TPPE_c374_1903-12-01.nc     The restart file contains the model state variables after spinup. This file was used to initiate all model simulations in step2. step3: ParameterSetSelection All files labeled step3 EXPERIMENT DESCRIPTION: Data used in step 3: parameter set selection PPE simultaneously adjusting 10 parameters.  Latin hypercube sampling was employed to generate 140 unique model parameterizations, referenced with a SETID (ranging from 3-276). The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds.  Data is averaged over 5 initial atmospheric conditions. FILE DESCRIPTION: NETCDF: files contain either the biomass (field1512) fractional coverage (field1391) canopy height (field1393) for 5 plant functional types (PFTs) simulated for November 1907 or the net primary productivity (NPP; item3262_monthly_mean) for December 1903 through November 1907.      File labeling scheme:            step3_variable_parametersetindex_5ICave.nc         variable: the name of the variable(s) contained in the file         setting index: the index of the parameter setting corresponding to the parameter set text files         5ICave: references that the results have been averaged over 5 initial atmospheric conditions.     Variables:         field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub]          field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]         field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]         item3262_monthly_mean – Net primary productivity – units: (kgC/m2/sec) – all PFTs TXT: files contain a list of the model parameterizations (for each PFT) and the corresponding to the parameter set index.      Parameters are labeled in row 1     Parameter set indices are shown in column 1 production_runs files labeled PI, historical, and future EXPERIMENT DESCRIPTION: Data simulated in the production runs. Model spinup was performed under preindustrial conditions with 10 unique model parameterizations (pset0-pset9). The resulting vegetation distribution for each parameterization after spinup are included and labeled PIrestarts. These restarts were used to initiate (or restart) the simulations under historic and future (RCP8.5) climate conditions. Files labeled historic contains the simulated vegetation state after 5 year simulations (2004-09-01 to 2009-08-30) with one TRIFFID equilibrium round occurring at the end. Files labeled future contain the simulated vegetation state after 5 year simulations (2054-09-01 to 2059-08-30) with one TRIFFID equilibrium round occurring at the end.  FILE DESCRIPTION: NETCDF: files contain the fractional coverage (field1391), leaf area index (field1392), canopy height (field1393), and biomass(field1512) for 5 plant functional types (in the order broadleaf, needleleaf, C3 grass, C4 grass, shrub).     File labeling scheme:         pset* where * refers to the model parameterization 0-9         files were simulated with the model parameterization *, initiated with a unique initial condition (perturbation to the potential temperature field).      Variables (PIrestart)         field1391 – fractional coverage of PFT – units: fraction – [broadleaf]          field1391_1 – fractional coverage of PFT – units: fraction – [needleleaf]         field1391_2 – fractional coverage of PFT – units: fraction – [c3grass]         field1391_3 – fractional coverage of PFT – units: fraction – [c4grass]         field1391_4 – fractional coverage of PFT – units: fraction – [shrub]         field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf]         field1392_1 – leaf area index of PFT – units: m2/m2 – [needleleaf]         field1392_2 – leaf area index of PFT – units: m2/m2 – [c3grass]         field1392_3 – leaf area index of PFT – units: m2/m2 – [c4grass]         field1392_4 – leaf area index of PFT – units: m2/m2 – [shrub]         field1393 – canopy height of PFT – units: meters – [broadleaf]         field1393_1 – canopy height of PFT – units: meters – [needleleaf]         field1393_2 – canopy height of PFT – units: meters – [c3grass]         field1393_3 – canopy height of PFT – units: meters – [c4grass]         field1393_4 – canopy height of PFT – units: meters – [shrub]              Variables (historic/future)         field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub]          field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]         field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]         field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]

opencc-by-4.0Nov 2019View details →

Picophytoplankton lineages display clear niche partitioning but overall positive response to future ocean warming

This repository contains data used in the Flombaum et al. (2019) Nature Geoscience paper entitled "Picophytoplankton lineages display clear niche partitioning but overall positive response to future ocean warming" for sensitivity tests. npp_91x180.mat is a 2D net primary production data from satellite ( SeaWiFS). <a href="https://zenodo.org/api/files/ea47715e-18fe-42d4-8d36-848824829b53/omega2_ad1e-05_ai1000.mat">omega2_ad1e-05_ai1000.mat</a> contains advection and diffusion transport operator. <a href="https://zenodo.org/api/files/ea47715e-18fe-42d4-8d36-848824829b53/po4obs_91x180x24.mat">po4obs_91x180x24.mat</a> is 3D dissolved inorganic phosphorus concentrations obtained from WOA2013 and interpolated into the inverse model gird (91x180x24); <a href="https://zenodo.org/api/files/ea47715e-18fe-42d4-8d36-848824829b53/xhat_91x180_control.mat">xhat_91x180_control.mat</a> is optimal phosphorus model parameters; It has four parameters: b - Marine curve exponent; kappa_d - dissolved organic phosphorus remineralization rate constant; alpha and beta: parameters used to scale satellite NPP to model organic phosphorus production.

opencc-by-4.0Oct 2016View details →

Figure 1 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method

Figure 1. Modeled range and presence records for Arborophila crudigularis.

Data set from the design of experiment assessment rubric, from university students / future teachers

Using an assessment rubric comprising three levels of success, we evaluated six aspects of designing an experiment, which we call dimensions. This way, specific challenges were identified in two of the dimensions: forming a hypothesis and manipulating the experimental variables, where students often failed to succeed and get lower scores. The data presented here were collected from four (4) diagnostic worksheets, which were made available to students at the beginning of the semester, where a theoretical introduction to the course is given, with reference to learning theories and their use in the teaching of Physics. Therefore, data were collected prior to discussions about the inquiry-based approach, experimental investigations, and the scientific content, such as the concepts and phenomena mentioned in the worksheets.  For the present study, we slightly modified and used a rubric that has been proposed to assess the DoE by primary and secondary school students (Lefkos et al., 2011). The modified rubric, has already been tested in a pilot study with fruitful results (Lefkos, 2024). Lefkos, I. (2024). An Assessment Rubric for Future Teachers’ Ability to Design Experiments. In C. Fazio & P. Logman (Eds.), Challenges in Physics Education (pp. 105–117). Springer. https://doi.org/10.1007/978-3-031-48667-8_7 Lefkos, I., Psillos, D., & Hatzikraniotis, E. (2011). Designing experiments on thermal interactions by secondary-school students in a simulated laboratory environment. Research in Science & Technological Education, 29(2), 189–204. https://doi.org/10.1080/02635143.2010.533266

opencc-by-4.0Jun 2024View details →

The past and future changes of river sediment in the U.S. Mid-Atlantic

<a href="../api/records/12597593/draft/files/E3SM-tanzeli-lnd-elm-erosion-v3.zip/content" target="_blank" rel="noopener noreferrer">E3SM-tanzeli-lnd-elm-erosion-v3.zip</a>: Model code <a href="../api/records/12597593/draft/files/domain_lnd_Mid-Atlantic_MPAS_c220107.nc/content" target="_blank" rel="noopener noreferrer">domain_lnd_Mid-Atlantic_MPAS_c220107.nc</a>: Mid-Atlantic mesh grid <a href="../api/records/12597593/draft/files/ancillary.pk/content" target="_blank" rel="noopener noreferrer">ancillary.pk</a>: ancillary variables including "area" (grid cell area: m^2), "areaTotal" (upstream drainage area: m^2), "DSIG" (downstream index), "GINDEX" (grid cell index), "outletG" (river basin index), and "rlen" (river channel length: m). <a href="../api/records/12597593/draft/files/Baseline.pk/content" target="_blank" rel="noopener noreferrer">Baseline.pk</a>: Baseline simulation: "Q": discharge (m^3/s), "Qs": sediment discharge (kg/s) <a href="../api/records/12597593/draft/files/CLIM_noLU_noDAM.pk/content" target="_blank" rel="noopener noreferrer">CLIM_noLU_noDAM.pk</a>: CLIM_noLU_noDAM simulation <a href="../api/records/12597593/draft/files/noCLIM_LU_noDAM.pk/content" target="_blank" rel="noopener noreferrer">noCLIM_LU_noDAM.pk</a>: noCLIM_LU_noDAM simulation <a href="../api/records/12597593/draft/files/noCLIM_noLU_DAM.pk/content" target="_blank" rel="noopener noreferrer">noCLIM_noLU_DAM.pk</a>: noCLIM_noLU_DAM simulation <a href="../api/records/12597593/draft/files/SSP585_UKESM1-0-LL.pk/content" target="_blank" rel="noopener noreferrer">SSP585_UKESM1-0-LL.pk</a>: SSP585_UKESM1-0-LL simulation <a href="../api/records/12597593/draft/files/SSP585_MPI-ESM1-2-HR.pk/content" target="_blank" rel="noopener noreferrer">SSP585_MPI-ESM1-2-HR.pk</a>: SSP585_MPI-ESM1-2-HR simulation <a href="../api/records/12597593/draft/files/SSP585_GFDL-ESM4.pk/content" target="_blank" rel="noopener noreferrer">SSP585_GFDL-ESM4.pk</a>: SSP585_GFDL-ESM4 simulation <a href="../api/records/12597593/draft/files/SSP585_IPSL-CM6A-LR.pk/content" target="_blank" rel="noopener noreferrer">SSP585_IPSL-CM6A-LR.pk</a>: SSP585_IPSL-CM6A-LR simulation <a href="../api/records/12597593/draft/files/draw_ssc_channel_vari4pub.py/content" target="_blank" rel="noopener noreferrer">draw_ssc_channel_vari4pub.py</a>: Python script to plot longitudinal SSC variations <a href="../api/records/12597593/draft/files/cmp_qs_icom_future4pub.py/content" target="_blank" rel="noopener noreferrer">cmp_qs_icom_future4pub.py</a>: Python script to plot future sediment discharge change

Unveiling the Future Water Pulse of Central Asia: A Comprehensive 21st Century Hydrological Forecast from Stochastic Water Balance Modeling

This dataset and the scripts accompany the manuscript "Unveiling the Future Water Pulse of Central Asia: A Comprehensive 21st Century Hydrological Forecast from Stochastic Water Balance Modeling". The manuscript is published in the Journal Climatic Change.

Dwelling conversion and energy retrofit modify building anthropogenic heat emission under past and future climates: a case study of London terraced houses

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>). Files in this archive should include: <ul> <li>Time use survey data analysis</li> </ul> o   Main dataset: TUS_activity.zip o   Code: TUS_clustering_code.zip o   Output: InternalHeatProfile.zip <ul> <li>Building energy modeling </li> </ul> o   Main dataset (run in EnergPlus 9.4): IDFfiles.zip o   Output: Eplus_output.zip <ul> <li>PostProcess analysis</li> </ul> o   Code: QF_analysis_code.zip o   Output: QF_output.zip   Note: this version currently only includes the outputs of all processes, the main dataset and code will be updated later.

The INMCM-4.8 Earth system model data used in the paper by Guryanov V.V. et al. entitled ''The present-day and future lightning frequency as simulated by four CMIP6 models'

The INMCM-4.8 Earth system model data used in the paper by Guryanov V.V. et al. entitled ''The present-day and future lightning frequency as simulated  by four CMIP6 models'

APECOSM configuration files used in the Earth's Future "Past and Future of Marine Ecosystems" special issue

This dataset contains the configuration files to run the piControl-spinup and piControl experiment in the ToE paper. In the piControl experiment, light is provided by the rsdo variable, which is is converted into PAR by using a constant conversion factor (0.43). However, this variable is not available for the piControl-spinup experiment, in which PAR has been reconstructed from chlorophyll and solar radiation using the <a href="https://github.com/apecosm/par-calculation" target="_blank" rel="noopener">par-calculation</a> tool (version <code>58ab94c</code>) All the other simulations use the same parameters as in the piControl , except for the forcing and restart paths. The APECOSM version used is <code>c0a910b8</code>.

opencc-zeroSep 2024View details →

GODEEEP future energy drought data

This dataset has 3 components, (1) historical and future energy drought data for balancing authorities in the Western US (2) physically consistent wind, solar and load data aggregated to the balacing authority level in the Western US (3) plant level wind and solar generation data for both historical and future scenarios. For more information please refer to Bracken et al. 2024, Climate change impacts on compound renewable energy droughts under evolving infrastructure in the Western United States, in prep, or refer to the Github repository https://github.com/GODEEEP/future-energy-droughts <h2>Usage</h2> <ol> <li>Clone the repo: https://github.com/GODEEEP/future-energy-droughts</li> <li>Unzip <code>ba-aggregated.zip</code> into <code>data/</code>, this is required to run <code>future-energy-droughts.R</code></li> <li>Unzip <code>future-wind-solar.zip</code> into your preferred directory and change the path in the <code>process-data.R</code> script</li> </ol> <h2>Energy Drought data</h2> The file <code>future-energy-droughts.zip</code> contains energy drought data for historical and future scenarios. The files have the naming convention <code><drought type>_droughts_ba_<data period>_<infrastructure year>_<infrastructure scenario>_<time scale>.csv</code>where <code><data period></code> is either <code>historical</code> or <code>future</code>, <code><infrastructure year></code> is any 5 year increment between 2020 and 2050, <code><infrastructure scenario></code> is either <code>bau</code> (business as usual) or <code>nz</code> (net zero), and <code><time scale></code> is <code>daily</code>. Several kinds of energy droughts are available: <ul> <li>solar - Solar only droughts defined using a 10th percentile threshold</li> <li>wind - Wind only droughts defined using a 10th percentile threshold</li> <li>ws - Wind and solar droughts defined using a 10th percentile threshold</li> </ul> Each drought file has the following columns: <ul> <li>ba - Abbreviated name for the BA</li> <li>run_id - unique id for each drought event</li> <li>datetime_utc - Date stamp for the start of the drought, in UTC</li> <li>timezone - The predominant time zone for the BA</li> <li>run_length - The length of a drought in time steps</li> <li>run_length_days - The length of the drought in days</li> <li>severity_ws - Drought severity for wind and solar droughts, computed using the compound drought magnitude metric</li> <li>severity_mwh - Drought severity expressed as MWh</li> <li>zero_prob - For solar, this value indicates if the timestep has zero probability of solar production, i.e. night time</li> <li>year - The year of the timestep</li> <li>month - The month of the timestep</li> <li>hour - The hour of the timestep</li> <li>wind_cf - Wind capacity factor for the drought</li> <li>solar_cf - Solar capacity factor for the drought</li> <li>srepi_solar - Standardized renewable energy production index for solar</li> <li>srepi_wind - Standardized renewable energy production index for wind</li> </ul> <h2>BA level generation data</h2> The file <code>ba-aggregated.zip</code> contains ba level generation data that has been aggregated from plant level data. The files have the naming convention <code>ba_<data period>_<infrastructure year>_<infrastructure scenario>_<time scale>.csv</code> where <code><data period></code> is either <code>historical</code> or <code>future</code>, <code><infrastructure year></code> is any 5 year increment between 2020 and 2050, <code><infrastructure scenario></code> is either <code>bau</code> (business as usual) or <code>nz</code> (net zero), and <code><time scale></code> is either <code>hourly</code> or <code>daily</code>. <ul> <li>ba - Abbreviated name for the BA</li> <li>year - The current year as an integer</li> <li>period - A unique integer for the current time step</li> <li>solar_gen_mwh - Aggregated solar generation in units of MWh</li> <li>solar_capacity_mwh - Aggregated solar plant capacity expresed as MWh</li> <li>wind_gen_mwh - Aggregated wind generation in units of MWh</li> <li>wind_capacity_mwh - Aggregated wind plant capacity expresed as MWh</li> <li>load_mwh - BA load in MWh, not used in this study</li> <li>load_max_mwh - The maximum BA load over the entire historical period, not used in this study</li> <li>datetime_utc - Time stamp for the current time step, in UTC, All time stamps are beginning of period.</li> <li>timezone - The predominant time zone for the BA</li> <li>wind_cf - Wind capacity factor, wind_gen_mwh/wind_capacity_mwh</li> <li>solar_cf - Solar capacity factor, wind_gen_mwh/wind_capacity_mwh</li> <li>load_cf - Load "capacity factor", expresed as a fraction of the maximum BA load, load_mwh/load_max_mwh, not used in this study</li> </ul> <h2>Plant level wind and solar generation data</h2> The file <code>future-wind-solar.zip</code> contains hourly plant level generation data used to derive the energy drought data. Each folder contains one csv file per simulated year (eg. <code>solar_gen_cf_2020.csv</code>). The files <code>eia_solar_configs.csv</code> and <code>eia_wind_configs.csv</code> contain metadata for each EIA plant. <ul> <li>baseline-future - 2020 infrastructure under future weather years, 2020-2059</li> <li>baseline-historical - 2020 infrastructure under historical weather years</li> <li>baseline-historical-bc - 2020 infrastructure under historical weather years, bias corrected</li> <li>baseline2020 - 2020 infrastructure under future weather years, 2020-2099</li> <li>cerf-config - cerf sitings</li> <li>cerf-future-2040 - cerf sitings run through future climate 2020-2050</li> <li>cerf-future-2045 - cerf sitings run through future climate 2020-2050</li> <li>cerf-future-2050 - cerf sitings run through future climate 2020-2050</li> <li>cerf-historical-2040 - cerf sitings run through historical climate 2020-2050</li> <li>cerf-historical-2045 - cerf sitings run through historical climate 2020-2050</li> <li>cerf-historical-2050 - cerf sitings run through historical climate 2020-2050</li> </ul> <h2>Funding statement</h2> This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.

Data output from Projecting future climate change impacts on the distribution of pelagic squid in the Southern Ocean

Data output from Projecting future climate change impacts on the distribution of pelagic squid in the Southern Ocean: Rasters, R models and scripts

Future decline of Antarctic Circumpolar Current model data and figures

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Data used in "Revealing dominant patterns of aerosols regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations" (Li et al., Atmos. Chem. Phys. 2024)

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