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7 results for “probabilistic projections”
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
Interaction data for probabilistic ending tournament for the Axelrod Python project.
<p>This is the data from the tournament described here: http://axelrod-tournament.readthedocs.org/en/latest/prob_end/ordinary_strategies.html</p> <p>A description of the format is available here: http://axelrod.readthedocs.org/en/latest/tutorials/further_topics/reading_and_writing_interactions.html</p>
The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates
<p>This repository contains the socioeconomic and emissions projections generated by the Resources for the Future Socioeconomic Projections (RFF-SPs) model as discussed in Rennert et al., "<a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates</a>" , forthcoming at the <em>Brookings Papers on Economic Activity</em>. The data take the form of a Monte Carlo simulation with n = 10,000 draws.</p> <p>File structure and column metadata are described here: </p> <p>--- <br> emissions/ <br> --- </p> <p>- rffsp_co2_emissions.csv <br> - rffsp_ch4_emissions.csv <br> - rffsp_n2o_emissions.csv </p> <p>Each file in this folder contains 3 columns: sample, year, and value. For each row: </p> <p>- sample contains the number identifying which draw a prediction belongs to (from 1 to 10,000). <br> - year contains the calendar year of the prediction. <br> - value contains the projected annual global emissions of the gas specified in the filename. IMPORTANT: Units are as follows: "rffsp_co2_emissions.csv" is in gigatons C (not CO2), "rffsp_ch4_emissions.csv " is in megatons CH4, and "rffsp_n2o_emissions.csv " is in megatons N2 (not N2O). </p> <p>--- <br> pop_income/ <br> --- </p> <p>- rffsp_pop_income_run_1.feather <br> - rffsp_pop_income_run_2.feather <br> - rffsp_pop_income_run_3.feather <br> . <br> . <br> . <br> - rffsp_pop_income_run_9999.feather <br> - rffsp_pop_income_run_10000.feather </p> <p>This folder contains 10,000 files in the .feather file format (https://arrow.apache.org/docs/python/feather.html), which is optimized for I/O speed and compressed to minimize storage requirements. Each file corresponds to one draw of our socioeconomic data, and contains 4 columns: Country, Year, Pop, and GDP. The number in each filename corresponds to the "sample" column in the emissions data. For each row:</p> <p>- Country contains the ISO Alpha-3 code (https://www.iso.org/iso-3166-country-codes.html) of the country whose GDP and population are projected. <br> - Year contains the calendar year of the predictions. <br> - Pop contains the projected population for a given country and year, in units of thousands of people. <br> - GDP contains the projected GDP for a given country and year, in units of millions of 2011 USD. </p> <p>--- </p> <p>The probabilistic population projections were produced by Adrian E. Raftery and Hana Ševčíková (University of Washington), using the methods described by Raftery and Ševčíková (2021). Please cite this reference in any publications using these projections. Their research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under NIH grant number R01 HD-070936. </p> <p>The probabilistic economic and emissions projections are from Rennert et al. (forthcoming), based in part on those from Müller, Stock, and Watson (forthcoming). </p> <p>---</p> <p><strong>References </strong></p> <p>Müller, U.K, Stock, J.H., and Watson, M.W. (forthcoming). An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting. The Review of Economics and Statistics, available online 30 October 2020. URL: <a href="https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth">https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth</a> </p> <p>Raftery, A.E. and Ševčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. International Journal of Forecasting, available online 7 October 2021. URL: <a href="https://www.sciencedirect.com/science/article/pii/S0169207021001394">https://www.sciencedirect.com/science/article/pii/S0169207021001394</a> </p> <p>Rennert, K., Prest, B.C., Pizer, W., Newell, R.G., Anthoff, D., Kingdon, C., Rennels, L., Cooke, R., Raftery, A.E., Ševčíková, H, and Errickson, F. (forthcoming). The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates. Brookings Papers on Economic Activity. Available online 27 October 2021. URL: <a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/ </a></p>
Probabilistic projections of mean sea level change in Finland by 2100
<p><strong>Paper describing the methods used to calculate these projections: Pellikka, H., Johansson, M. M., Nordman, M., and Ruosteenoja, K.: Probabilistic projections and past trends of sea level rise in Finland, Nat. Hazards Earth Syst. Sci., <a href="https://doi.org/10.5194/nhess-2022-230">https://doi.org/10.5194/nhess-2022-230</a>, 2023.</strong></p> <p>This dataset includes probability distributions of projected mean sea level in Finland in 2030, 2040, ... 2100, as well as time series of projected mean sea level 2005-2100. Data is provided for 13 tide gauge locations and 3 emission scenarios: low (RCP2.6 / SSP1-2.6), medium (RCP4.5 / SSP2-4.5), and high (RCP8.5 / SSP5-8.5).</p> <p>There are two data packages, <em>distributions.zip</em> and <em>timeseries.zip</em>. The data files included in these packages are tab- or space-delimited text files with the file extension .dat.</p> <p>All filenames start with a three-character code xxx that determines the tide gauge (1-letter symbol) and the emission scenario (2 digits). For example:</p> <p>v26 means Vaasa, low emission scenario (RCP2.6 / SSP1-2.6)<br> e45 means Helsinki, medium emission scenario (RCP4.5 / SSP2-4.5)<br> t85 means Turku, high emission scenario (RCP8.5 / SSP5-8.5)</p> <p>The letter symbols and locations of the tide gauges are, from north to south along the coast:</p> <p>a - Kemi (65.67 N, 24.52 E)<br> o - Oulu (65.04 N, 25.42 E)<br> b - Raahe (64.67 N, 24.41 E)<br> p - Pietarsaari (63.71 N, 22.69 E)<br> v - Vaasa (63.08 N, 21.57 E)<br> s - Kaskinen (62.34 N, 21.21 E)<br> m - Mäntyluoto (61.59 N, 21.46 E)<br> r - Rauma (61.13 N, 21.44 E)<br> t - Turku (60.43 N, 22.1 E)<br> d - Degerby (60.03 N, 20.38 E)<br> h - Hanko (59.82 N, 22.98 E)<br> e - Helsinki (60.15 N, 24.96 E)<br> f - Hamina (60.56 N, 27.18 E)</p> <p>1) <em>distributions.zip > xxx_fitdistr_yyyy.dat</em><br> These files include the probability distribution (probability density function) of projected mean sea level in year yyyy (2030, 2040, ... 2100). There are two columns: sea level and probability. Sea level values are millimetres in the Finnish N2000 height system.</p> <p>2)<em> timeseries.zip > xxx_timeseries.dat</em><br> These files include the time series of projected mean sea level in 2005-2100. The files have 8 columns: year and 7 sea level values representing different percentiles of the probability distribution. The percentiles are 1%, 5%, 17%, 50% (median), 83%, 95%, 99%. Sea level values are centimetres in the Finnish N2000 height system.</p> <p><strong>Please note that all projections for years other than 2100 are indicative and based on a simple 2nd order fit made to the current rate of mean sea level change and the projected mean sea level in 2100. In other words, the projections for intermediate years are based on the 2100 projections assuming constant acceleration in mean sea level change rates.</strong></p> <p>Example figures <em>distributions.png</em> and <em>timeseries.png</em> are included to illustrate the data. The Matlab script <em>slrfinland_figures.m</em> used to produce these figures is also included.</p>
Model data for probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under high ice-shelf basal melt conditions
<p>Model data for probabilistic projections of the Amery Ice Shelf catchment, <br>Antarctica, under high ice-shelf basal melt conditions<br>======================================================================</p> <p>This archive contains ice-sheet model output and statistical models <br>used in the manuscript:<br>"Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under<br>high ice-shelf basal melt conditions" by:<br>Sanket Jantre, Matthew Hoffman, Nathan M. Urban, Trevor Hillebrand, Mauro<br>Perego, Stephen Price, John D. Jakeman</p> <p>OVERVIEW<br>--------</p> <p>Statistical models and corresponding datasets are archived in the file<br>Amery_UQ_Study_Statistical_Models.zip. That file contains a README that<br>describes its contents.<br>Contact for statistical models:<br>Sanket Jantre, Brookhaven National Laboratory, sjantre@bnl.gov</p> <p>The remaining files in this archive contain output from ice-sheet model <br>simulations using the MPAS-Albany Land Ice (MALI) model <br>(Hoffman et al. 2018, https://github.com/MALI-Dev/E3SM)<br>applied to a regional domain of the Amery Ice Shelf catchment<br>of Antarctica with mesh resolution varying from 4 to 20 km.<br>The contents of these files are described below.<br>Contact for MALI simulations:<br>Matthew Hoffman, Los Alamos National Laboratory, mhoffman@lanl.gov</p> <p>ENSEMBLES<br>---------</p> <p>The ensembles consist of all or a subset of 200 model runs with values for 6<br>parameters sampled from a Sobol' sequence over a specified range.</p> <p>Parameters and Sampled Ranges:<br>1. Ice stiffness scaling factor, Cφ: (0.8, 1.2)<br>2. Basal friction scaling factor, Cμ: (0.8, 1.2)<br>3. Basal slip exponent, q: (0.1, 0.333)<br>4. Calving yield stress, σmax: (80, 180) kPa<br>5. Ice-shelf melt coefficient, γ0: (9620, 471000) m yr−1<br>6. Ice-shelf basal melt rate, m: (12, 58) Gt yr−1</p> <p>SCENARIOS<br>---------</p> <p>The archive contains output from 4 scenarios described in the manuscript:</p> <p>Historical relaxation (RELX): For each ensemble member, we conducted a 50 year<br>relaxation from the initial condition using historical climate forcing to<br>integrate out fast transient behavior. For surface mass balance, we applied a<br>1995–2017 climatological average from RACMO2.3p1 (Van Wessem et al., 2014; van<br>den Broeke, 2019). The ocean thermal forcing was the observation-based<br>climatology compiled for ISMIP6-Antarctica, which uses data from 1995–2018<br>(Jourdain et al., 2020; Nowicki et al., 2020). The 50 year relaxation duration<br>was chosen as the most rapid adjustments occur in the first few decades of<br>integration, while the long term adjustment to a fully steady state takes<br>thousands of years. Relaxation to full steady state would require<br>substantially more computing resources than our entire set of ensembles and<br>also leads to the complication of different runs having potentially very<br>different initial states. Future improvements to model initialization that<br>account for surface elevation change (Perego et al., 2014) may reduce model<br>drift and adjust this requirement. The final model state in each run at the<br>end of RELX was given the nominal date of January 1, 2015, and all three<br>projection ensembles were branched from these states.</p> <p>Control projection (CTRL): The CTRL projection ensemble were an extension of<br>the RELX configurations, continuing the same surface mass balance and ocean<br>thermal forcing from January 1, 2015, to January 1, 2300. This ensemble was<br>used to assess model drift relative to the forced response of the climate<br>scenarios. </p> <p>SSP1-2.6 projection (SSP1): Our SSP1 projection used annual surface mass<br>balance and ocean thermal forcing derived from a UKESM SSP1-2.6 climate<br>scenario (expAE10 from Seroussi et al., 2024). Surface mass balance and ocean<br>thermal forcing were applied as anomalies relative to the climatological mean<br>forcings in RELX/CTRL to avoid issues related to climate model bias and abrupt<br>changes in forcing. This ensemble was also run from January 1, 2015, to<br>January 1, 2300.</p> <p>SSP5-8.5 projection (SSP5) Our SSP5 projection used the UKESM SSP5-8.5<br>projection forcings (expAE05 from Seroussi et al., 2024), again applied as<br>anomalies and from 2015 to 2300.</p> <p><br>OUTPUT<br>------</p> <p>Each ensemble directory contains at the base level:<br>* branch_ensemble.cfg - configuration file for the ensemble_generator test<br> case (https://mpas-dev.github.io/compass/latest/users_guide/landice/test_groups/ensemble_generator.html) <br> of COMPASS (Configuration Of Model for Prediction Across Scales Setups, https://github.com/MPAS-Dev/compass)<br> used to set up the ensemble.<br>* mesh_vars.nc - netCDF file containing the mesh variables. This file is the<br> same for all ensembles. These fields need to be appended to an output file to<br> visualize in, e.g., Paraview. This can be done with the command:<br> ncks -A mesh_vars.nc output.nc</p> <p>Each ensemble contains subdirectories for each run numbered 000-199. Each run<br>directory contains the files:<br>* globalStats.nc - scalar metrics at every time step<br>* output.nc - spatial fields at 10 year intervals<br>* run_info.cfg - summary of parameter values</p> <p>Notes:<br>* The SSP1 and SSP5 ensembles contain a subset of the full 200 runs<br> because some runs were filtered out as being unnecessary during model<br> calibration (see manuscript).<br>* The RELX ensemble is run for 50 years with an arbitrary start year of 2000.<br> The CTRL, SSP1, and SSP5 ensembles are started in 2015 from the final state<br> of the corresponding RELX run (nominally 2050). Because spatial data has<br> been saved in this archive at 10 year intervals, the first output for the<br> three projection ensembles is 2020. To get the initial condition for the<br> projection ensembles, use the 2050 state from RELX for the corresponding<br> model run.<br>* Spatial field output at 1 year intervals, as well as run input, log, and<br> restart files are available from the corresponding contact. The complete<br> run data is about 2 TB.</p> <p><br>CITATION<br>--------<br>If you find these data useful, please cite the DOI for this <br>archive (10.5281/zenodo.11166628), as well as our manuscript<br>submitted to The Cryosphere.</p> <p>This archive is approved by Los Alamos National Laboratory<br>for public release under LA-UR-24-24969; distribution is unlimited.</p> <p> </p> <p>REFERENCES<br>----------</p> <p>Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T.,<br>Jacobsen, D., Tezaur, I., Salinger, A. G., Tuminaro, R., and Bertagna, L.:<br>MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth<br>system modeling using Voronoi grids, Geoscientific Model Development, 11,<br>3747–3780, 2018.</p> <p>Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H.,<br>Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in<br>the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134,<br>https://doi.org/10.5194/tc-14-3111-2020, 2020.</p> <p>Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H.,<br>Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A.,<br>Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J. M.,<br>Hattermann, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C.<br>M., Morlighem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R.<br>S., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R.:<br>Experimental protocol for sea level projections from ISMIP6 stand-alone ice<br>sheet models, The Cryosphere, 14, 2331–2368,<br>https://doi.org/10.5194/tc-14-2331-2020, 2020.</p> <p>Perego, M., Price, S., and Stadler, G.: Optimal Initial Conditions for<br>Coupling Ice Sheet Models to Earth System Models, Journal of Geophysical<br>Research: Earth Surface, 119, 1894–1917, https://doi.org/10.1002/2014JF003181,<br>2014.</p> <p>Seroussi, H., et al. 2024. ISMIP6 Projections 2300 Antarctica Protocol.<br>https://theghub.org/groups/ismip6/wiki/ISMIP6-Projections2300-Antarctica. </p> <p>van den Broeke, M.: RACMO2.3p1 annual surface mass balance Antarctica<br>(1979-2014), PANGAEA - Data Publisher for Earth & Environmental Science,<br>https://doi.org/10.1594/PANGAEA.896940, 2019.</p> <p>van Wessem, J., Reijmer, C., Morlighem, M., Mouginot, J., Rignot, E., Medley,<br>B., Joughin, I., Wouters, B., Depoorter, M., Bamber, J., Lenaerts, J., Van De<br>Berg, W., Van Den Broeke, M., and Van Meijgaard, E.: Improved Representation<br>of East Antarctic Surface Mass Balance in a Regional Atmospheric Climate<br>Model, Journal of Glaciology, 60, 761–770,<br>https://doi.org/10.3189/2014JoG14J051, 2014.</p>
Data for "Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"
<p>Data for "<strong>Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"</strong></p>
[Review version] Probabilistic projections of granular energy technology diffusion at subnational level
<p>This is a version for review prior to the publication of the article:<br>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, <em>PNAS Nexus</em>, 2023;, pgad321, <a href="https://doi.org/10.1093/pnasnexus/pgad321">https://doi.org/10.1093/pnasnexus/pgad321</a></p> <p>You find the public data set to the article here:<br><a href="https://doi.org/10.5281/zenodo.8414845">https://doi.org/10.5281/zenodo.8414845 </a></p>
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