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The zipped file of this repository contains code and data to reproduce the results of the publication: Kotz et al, Nature, The effect of rainfall changes on economic production. (2021). https://doi.org/10.1038/s41586-021-04283-8 Economic data are derived from the DoSE database: https://doi.org/10.5281/zenodo.4681305 Climate data are derived from the ERA-5 reanalysis: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 Please see the README document for detailed: - Descriptions of the code and data provided - Lists of the required dependencies - Naming conventions for variables

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

Velocity-based macrorefugia for boreal passerine birds Citation for dataset -------------------- Stralberg, D. Velocity-based macrorefugia for boreal passerine birds. Boreal Avian Modelling Project. Edmonton, Alberta, Canada. DOI: 10.5281/zenodo.1299880 https://doi.org/10.5281/zenodo.1299880 Data layers ----------------- Refugia layers represent mid-century (2041-2070) and end-of-century (2071-2100) conditions for the SRES A2 emissions scenario at 4-km resolution ----------------- Combined index for 53 species (clipped to Brandt's boreal region): _refbrandt53_YYYYZZZZ Species-specific indices: XXXX_refYYYY where: YYYY = Time period (2050s or 2080s) ZZZZ = weighted or unweighted XXXX = Songbird Species Code (see Birdlookup.csv) Percentile values of refugia indices for mapping purposes 0.01 0.1 0.25 0.5 0.75 0.9 0.99 "2050s, weighted " 0.032 0.243 0.317 0.399 0.484 0.589 0.779 "2080s, weighted" 0.002 0.09 0.137 0.2 0.281 0.386 0.675 "2050s, unweighted" 0.006 0.108 0.159 0.218 0.292 0.358 0.421 "2080s, unweighted" 0.001 0.055 0.083 0.123 0.185 0.241 0.297 Projection information ------------------- """+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs""" ------------------- Projection LAMBERT Spheroid GRS80 Units METERS Zunits NO Xshift 0.0 Yshift 0.0 Parameters 49 0 0.0 /* 1st standard parallel 77 0 0.0 /* 2nd standard parallel -95 0 0.0 /* central meridian 0 0 0.0 /* latitude of projection's origin 0.0 /* false easting (meters) 0.0 /* false northing (meters)

This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

Climate-informed conservation priorities in British Columbia (Version 1.0) Territorial acknowledgement: We respectfully acknowledge that we live and work across diverse unceded territories and treaty lands and pay our respects to the First Nations, Inuit and Métis ancestors of these places. We honour our connections to these lands and waters and reaffirm our relationships with one another. Suggested citation: Stolar, J., D. Stralberg, I. Naujokaitis-Lewis, S.E. Nielsen, and G. Kehm. 2023. Spatial priorities for climate-change refugia and connectivity for British Columbia (Version 1.0). Place of publication: University of Alberta, Edmonton, Canada. doi: 10.5281/zenodo.8333303 Corresponding author: stolar@ualberta.ca Summary: The purpose of this project is to identify spatial locations of (a) vulnerabilities within British Columbia’s current network of protected areas and (b) priorities for conservation and management of natural landscapes within British Columbia under a range of future climate-change scenarios. This involved adaptation and implementation of existing continental- and provincial-scale frameworks for identifying areas that have potential to serve as refugia from climate change or corridors for species migration. Outcomes of this work include the provision of practical guidance for protected areas network design and vulnerabilities identification under climate change, with application to other regions and jurisdictions. Project results, in the form of multiple spatial prioritization scenarios, may be used to evaluate the resilience of the existing protected area network and other conservation designations to better understand the risks to British Columbia’s biodiversity in our changing climate. Description: These raster layers represent different scenarios of Zonation rankings of conservation priorities for climate resilience and connectivity between current and 2080s conditions for a provincial-scale analysis. Input conservation features included metrics of macrorefugia (forward and backward climate velocity (km/year), overlapping future and current habitat suitability for ~900 rare species in BC), microrefugia (presence of old growth ecosystems, drought refugia, glaciers/cool slopes/wetlands, and geodiversity), and connectivity. Please see details in the accompanying report. File nomenclature: .zip folder (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.zip): Contains the files listed below. Macrorefugia (2080s_macrorefugia.tif): Scenarios for each taxonomic group (equal weightings for all species) (Core-area Zonation Function) Climate-type velocity + species scenarios from above (Core-area Zonation; equal weightings) Microrefugia (microrefugia.tif): Scenario with old growth forest habitat, landscape geodiversity, wetlands/cool slopes/glaciers, drought refugia (Core-area Zonation; equal weightings) Overall scenario (2080s_macro_micro_connectivity.tif): Inputs from above (with equal weightings) + connectivity metrics (each weighted at 0.1) (Additive Benefit Function Zonation) Conservation priorities (Conservation_priorities_2080s.tif): Overall scenario from above extracted to regions of low human footprint. Restoration priorities (Restoration_priorities_2080s.tif): Overall scenario from above extracted to regions of high human footprint. Accompanying report (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.pdf): Documentation of rationale, methods and interpretation. READ_ME file (READ_ME_PLEASE.txt): Metadata. Legend interpretation: Ranked Zonation priorities increase from 0 (lowest) to 1 (highest). Raster information: Columns and Rows: 1597, 1368 Number of Bands: 1 Cell Size (X, Y): 1000, 1000 Format: TIFF Pixel Type: floating point Compression: LZW Spatial reference: XY Coordinate System: NAD_1983_Albers Linear Unit: Meter (1.000000) Angular Unit: Degree (0.0174532925199433) false_easting: 1000000 false_northing: 0 central_meridian: -126 standard_parallel_1: 50 standard_parallel_2: 58.5 latitude_of_origin: 45 Datum: D_North_American_1983 Extent: West -139.061502 East -110.430823 North 60.605550 South 47.680823 Disclaimer: The University of Alberta (UofA) is furnishing this deliverable "as is". UofA does not provide any warranty of the contents of the deliverable whatsoever, whether express, implied, or statutory, including, but not limited to, any warranty of merchantability or fitness for a particular purpose or any warranty that the contents of the deliverable will be error-free. Funding: We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation. We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation.

The dataset accompanies the Nature Energy publication by Figgener et al. (2024), Multi-year field measurements of home storage systems and their use in capacity estimation, DOI 10.1038/s41560-024-01620-9. In addition, we use the dataset in Figgener et al. (2024), Degradation mode estimation using reconstructed open circuit voltage curves from multi-year home storage field data, DOI 10.48550/arXiv.2411.08025 The ISEA / CARL of RWTH Aachen University measured 21 private home storage systems in Germany over up to eight years from 2015 to 2022. All these storage systems are combined with residential photovoltaic systems to increase self-consumption. The measured quantities published are system-level battery current, voltage, power, battery pack housing temperature, and room temperature. The sample rate is one second. The dataset consists of 106 system years, 14 billion data points, and 1,270 monthly files stored in 21 system folders. Use the data as follows:1. Download the data (Data_ID_01.zip to Data_ID_21.zip) and the belonging repository (Metadata_and_Code.zip) 2. Uncompress the files so that the uncompressed folders have the same name as the .zip files. 3. Copy all data folders in folder "Metadata_and_Code/00_Data/01_Operational_Data". Read and execute the file "StartUp_Read_and_Execute.m" and stay in this folder for any script you execute. In addition, a detailed description of the dataset and how to use it can be found in the supplementary information of the publication.

This data set describes the population dynamics of Wilson's Storm Petrels (Oceanites oceanicus) at King George Island (Isla 25 de Mayo, Antarctica) over a forty year period (1978 – 2020). It includes all available data on Wilson's Storm Petrels from two colonies: around the Argentinian Base Carlini (62°14′S, 58°40′W; CA, formerly called Base Jubany) and the Henryk Arctowski Polish Antarctic Station (62°09′S, 58°27′W; HA). Data on population productivity (number of nests, eggs, chicks and fledglings) was collected by regular visits to the colonies and searching for nest burrows, or monitoring of the egg or chick if found. Data on adult abundance and estimated age categories (i.e., presence of foot spots; Quillfeldt et al. (2000, doi:10.1007/s003000000167) were collected at CA by using the same size mistnet every study year in the same location within the breeding colony. Chicks were measured regularly (varying intervals depending on the study) at both CA and HA. Chick tarsus was measured using callipers (vernier or digital depending on the study year) to the nearest 0.1 mm, chick wing length was measured using wing rulers to the nearest 1 mm, and chick body mass was measured using mechanical or digital scales depending on the study year to the nearest 0.1 g. Chick growth rates were calculated based on the linear growth period following Ausems et al. (2020, doi:10.1016/j.scitotenv.2020.138768). Chick food loads (g) were recorded at CA and determined based on changes in chick body mass on consecutive days (Gladbach et al. (2009, doi:10.1007/s00300-009-0628-z); Kuepper et al. (2018, doi:10.1016/j.cbpa.2018.06.018). This study was further supported by the Erasmus+ programm and thee German Academic Exchange Service (DAAD)

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.