Find research datasets worth reusing

Data and code for "Tree species abundance changes at the edges of their climatic distribution: an interplay between climate change, plant traits, and forest management"

## Secondary data and code to accompany the research entitled "Tree species abundance changes at the edges of their climatic distribution: an interplay between climate change, plant traits, and forest management" by Padullés Cubino et al. (2024). # There are four folders with (1) "raw data", (2) "processed data", (3) "results", and (4) "scripts". # The raw and processed data folders contain the CSV and XLSX files with all the data used for analysis and produced from them # The "results" folder contains the figures and table presented in the manuscript. # The "scripts" folder contains four scripts for the analyses described in the manuscript:    01_preparation_ClimEdge.R -> data cleaning and processing    02_script_Fig1.R -> code to produce Fig1    03_script_Fig2.R -> code to produce Fig2    04_script_Table1_Fig3.R -> code to produce Table 1 and Fig3 # If anything is unclear, please contact the corresponding author for clarification (padullesj@gmail.com).

opencc-by-nc-nd-4.0Jun 2024View details →

Data for "Climate outweighs human effects on vegetation properties during the early-to-mid Holocene"

Data for analyses used in the PastHumanImpact project published in a manuscript "Climate outweighs human effects on vegetation properties during the early-to-mid Holocene".  All R code and workflows to reproduce the data analyses and figures are available at <a href="https://github.com/HOPE-UIB-BIO/PastHumanImpact" target="_blank" rel="noopener">HOPE-UIB-BIO/PastHumanImpact</a>. Usage: Each zip file should be extracted and placed into the `Data` folder, following the README file in the mentioned GitHub repo. ❗The data is under a CC BY-NC-ND licence, which requires approval from data owners before use (read <a href="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode" target="_blank" rel="noopener">here </a>for more details). Specifically, please contact Xianyong Cao (<a href="mailto:xcao@itpcas.ac.cn" target="_blank" rel="noopener">xcao@itpcas.ac.cn</a>) to share the Asian fossil pollen data.❗ Citation: If you want to use the data, please cite the mentioned paper "Felde et al. Climate outweighs human effects on vegetation properties during the early-to-mid Holocene".  DOI: <a title="Climate outweighs human effects on vegetation properties during the early-to-mid Holocene" href="https://doi.org/10.21203/rs.3.rs-4692574/v1" target="_blank" rel="noopener">10.21203/rs.3.rs-4692574/v1</a>

Data for "Elevated urban energy risks due to climate-driven biophysical feedbacks"

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 Nature Climate Change. 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. For more details, please refer to the README.md file included in the dataset.

Raw Data for Publication "Earth observations reveal impacts of climate variability on maize cropping systems in Sub-Saharan Africa"

Phenological metrics extracted for all agricultural fields used in the study. Data also includes the coordinates of the fields.

Model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model"

This folder contains the model data and figure code for results and figures in the manuscript submitted to Geophysical Research Letters "Hysteresis of the Antarctic ice sheet with a coupled ice sheet climate model" The code for plotting the figures is the notebook Plot_figures.ipynb Fig1/simulation_output/ : Model output necessary for plotting the first figure  The last timestep of each simulation is provided. There is one file for 1D variables (ice volume, ice volume above flotation), and one file for 2D variables (ice sheet thickness for instance). <ul> <li>melt_insoPI_output/ : melt branch, pre-industrial insolation. Results for different CO2 levels</li> <li>growth_insoPI_output/ : growth branch, pre-industrial insolation. Results for different CO2 levels</li> <li>melt_insoMAX_output/ : melt branch, maximum insolation. Results for different CO2 levels</li> <li>growth_insoMIN_output/ : growth branch, minimum insolation. Results for different CO2 levels</li> </ul> compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output Fig1/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level   Fig2/simulation_output/ : Model output necessary for plotting the second figure  The last timestep of each simulation is provided.  <ul> <li>melt_insoPI_enhancedmelt_albfb/ : melt branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</li> <li>growth_insoPI_enhancedmelt_albfb/ : growth branch, pre-industrial insolation, enhanced melt and albedo feedback. Results for different CO2 levels</li> <li>melt_insoPI_enhancedmelt_fixedalb/ : melt branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</li> <li>growth_insoPI_enhancedmelt_fixedalb/: growth branch, pre-industrial insolation, enhanced melt, no albedo feedback. Results for different CO2 levels</li> </ul> compute_SLR_equivalent.py : code to compute the ice sheet volume in SLRe based on model output Fig2/SLR_files/ : contains the equilibrium ice sheet volume of the different simulations according to the CO2 level   Fig3/simulation_output/ : Model output necessary for plotting the third figure  <ul> <li>1xCO2_nocoupling/ : simulation with pre-industrial CO2 levels and insolation and no coupling to the ice sheet model</li> <li>8xCO2_nocoupling/ : simulation with 8xpiCO2 (pre-industrial CO2) levels, pre-industrial insolation and no coupling to the ice sheet model</li> <li>8xCO2_transient_albfb/ : quasi transient simulation, 8xpiCO2 levels,  pre-industrial insolation, coupling with the ice sheet model </li> <li>8xCO2_transient_fixedalb/ : quasi transient simulation, 8xpiCO2 levels,  pre-industrial insolation, coupling with the ice sheet model excluding the albedo-melt feedback</li> </ul>

Genetic datasets, climatic conditions at sampled localities, and occurrence data to: Ice age-driven range shifts of diploids and expanding autotetraploids within a conserved niche (Grünig, Patsiou & Parisod, 2024, New Phytologist)

<div> <h3>This repository includes</h3> - An overview of the raw sequencing reads deposited in the European Nucleotide Archive (ENA) for the 370 individuals sampled in 17 diploid and 19 tetraploid field populations <div>- Scripts used to genotype diploids and autotetraploids samples of Biscutella laevigata from ddRADseq data</div> <div>- Input data (as vcf format) used in population genetic analyses</div> <div>- Scripts used to run the different genetic analyses</div> <div>- Dataset of extracted climatic conditions at sampled localities</div> <div>- Occurrence dataset used for the climatic niche modelling</div> <h3>Description of the data and file structure</h3> 00.ENA_samples_correspondance.txt: provides ENA project ID, run ID (i.e. raw fastq files), sample ID, and alias for each sample included in the study. <div> </div> <div>1.scripts_reads_to_vcf.zip: consists of the following:</div> - 1.reads_to_vcf.md: md file with scripts documenting the read quality check, demultiplexing, mapping, SNP calling using GATK4, and filtering steps <div>- Additional scripts called within 1.reads_to_vcf.md:</div> <div>-- 1.3. Mapping: 02_run_mapping_XXX.py and BWA-mem_bisc1_sg.py scripts</div> <div>-- 1.4.a. HaplotypeCaller: 03_V1_gvcf.py</div> <div>-- 1.4.b. GDBI + genotypeGVCF: 03_V3_gdbi_genotype_per100scaf.py</div> <div>2.datasets_genetics.tar.gz consists of the following</div> <div>- bisc_all370_diminDP15_tetraminDP30.vcf.gz: "Initial SNPs dataset" = biallelic SNPs fulfilling GATK quality hard filtering recommendations, present in at least 50% of samples. Genotypes with DP<15 for diploids and DP<30 for tetraploids are set to no-call. This vcf was used as basis for fastsimcoal dataset preparation, and as basis for subsequent selection of loci fulfilling requirements of each analysis. It includes 2246701 biallelic SNPs for 370 samples</div> <div>- bisc_all370_diminDP15_tetraminDP30_MD05_pruned.vcf.gz: subset of the "Initial SNPs dataset" retaining SNPs called in at least 50% of samples, and pruned for Linkage disequilibrium. This vcf includes 107574 biallelic SNPs for 370 samples and was used in the analysis of the proportion of diploids diagnostic alleles shared by tetraploids.</div> <div>- bisc_all370_diminDP15_tetraminDP30_MD01_pruned.vcf.gz: subset of the "Initial SNPs dataset", retaining SNPs called in at least 90% of samples, and pruned for Linkage disequilibrium. This vcf includes 4444 biallelic SNPs for 370 samples and was used in the analyses of Population diversity and differentiation (SpaGeDi, GenoDive, PCA), and f3-statistics.</div> <div>- bisc_all370_diminDP15_tetraminDP30_MD0.1_pruned_MAC3rm.vcf.gz: subset of the "Initial SNPs dataset", retaining SNPs called in at least 90% of samples, pruned for Linkage disequilibrium, and with a minor allele count of 3. This vcf includes 2593 biallelic SNPs for 370 samples and was used in STRUCTURE analysis</div> <div>3.pres_2x.txt: list of the 128 diploid occurrences used in climatic niche modelling</div> <div>3.pres_4x_strat_reg.txt: list of the 924 tetraploid occurrences used in climatic niche modelling</div> <div>biscall_chelsa_ordered_noDEM.txt: climatic data extracted from the CHELSA dataset at sampled localities</div> <div>4.plot_GTfreqs.md: markdown file including scripts to plot allele and genotype frequencies</div> <div> </div> <h3>Sharing/Access information</h3> Raw sequencing reads have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under the accession number PRJEB48869:<a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB48869"> https://www.ebi.ac.uk/ena/browser/view/PRJEB48869</a></div>

Data and figure production code for 'Hydrological cycle amplification imposes spatial pattern on climate change response of ocean pH and carbonate chemistry'

Time mean data, and python code, used to create figures in 'Hydrological cycle amplification imposes spatial pattern on climate change response of ocean pH and carbonate chemistry', Biogeosciences, Hogikyan and Resplandy 2024

opencc-by-4.0Mar 2018View details →

Data for "The influence of internal variability on Earth's energy balance framework and implications for estimating climate sensitivity"

This archive contains processed model output necessary for reproducing the figures in Dessler, Maurtisen, Stevens, ACP, 2018 historicalEnsemble.nc contains data from the 100-member MPI historical ensemble forcing_aed_ensemble.nc contains the forcing for the historical runs model0001.nc is the control run of the MPI model model0003.nc is an abrupt 4xCO2 run of the MPI model cmip5 contains data from individual CMIP5 models  the folder "fig 5" contains zonal average fields necessary for that figure

opencc-by-4.0Jan 2018View details →

Data for: Projected effects of climate-change-induced flow alterations on stream macroinvertebrate abundances

Global change has the potential to affect river flow conditions which are fundamental determinants of physical habitats. Predictions of the effects of flow alterations on aquatic biota have mostly been assessed based on species ecological traits (e.g., current preferences), which are difficult to link to quantitative discharge data. Alternatively, we used empirically derived predictive relationships for species’ response to flow to assess the effect of flow alterations due to climate change in two contrasting central European river catchments. Predictive relationships were set up for 294 individual species based on (1) abundance data from 223 sampling sites in the Kinzig lower-mountainous catchment and 67 sites in the Treene lowland catchment, and (2) flow conditions at these sites described by five flow metrics quantifying the duration, frequency, magnitude, timing and rate of flow events using present-day gauging data. Species’ abundances were predicted for three periods: (1) baseline (1998–2017), (2) horizon 2050 (2046–2065) and (3) horizon 2090 (2080–2099) based on these empirical relationships and using high-resolution modeled discharge data for the present and future climate conditions. We compared the differences in predicted abundances among periods for individual species at each site, where the percent change served as a proxy to assess the potential species responses to flow alterations. Climate change was predicted to most strongly affect the low-flow conditions, leading to decreased abundances of species up to −42%. Finally combining the response of all species over all metrics indicated increasing overall species assemblage responses in 98% of the studied river reaches in both projected horizons and were significantly larger in the lower-mountainous Kinzig compared to the lowland Treene catchment. Such quantitative analyses of freshwater taxa responses to flow alterations provide valuable tools for predicting potential climate-change impacts on species abundances and can be applied to any stressor, species, or region.

opencc-by-4.0May 2019View details →

Data used in "Communicating climate change in a 'post-factual' society: Lessons learned from the Pole to Paris campaign"

Office Open XML Workbooks and an Adobe Portable Document Format containing all the data used in the publication "Communicating climate change in a 'post-factual' society: Lessons learned from the Pole to Paris campaign" in Geoscience Communication by Erlend Moster Knudsen and Oria Jamar de Bolsée, published in 2019.

opencc-by-4.0Oct 2019View details →

Data for: Global coupled climate response to polar sea ice loss: evaluating the effectiveness of different ice-constraining approaches

This is the data for the paper titled "Global coupled climate response to polar sea ice loss: Evaluating the effectiveness of different ice-constraining approaches".

opennotspecifiedJul 2021View details →

Fig. 4 Species distribution models for Vaejovis carolinianus. Results were projected onto LGM conditions from MIROC a and CCSM4 b data sources invoking the model generated using current climates data c in Pliocene origins, Pleistocene refugia, and postglacial range expansions in southern devil scorpions (Vaejovidae: Vaejovis carolinianus)

Fig. 4 Species distribution models for Vaejovis carolinianus. Results were projected onto LGM conditions from MIROC a and CCSM4 b data sources invoking the model generated using current climates data c. Localities used to test and train the model are indicated by

Data used in "Fire Weather Compromises Forestation-reliant Climate Mitigation Pathways" (Jaeger et al. 2024a)

Open the record for dataset details and reuse information.

Data for the publication Climate and nutrition drive gut microbiome variation in a fruit-specialist primate

Open the record for dataset details and reuse information.

Data for manuscript "Substantial Contraction of Dense Shelf Water in the Ross Sea under Future Climate Scenarios"

Relevant data shown in figures of the manuscript "Substantial Contraction of Dense Shelf Water in the Ross Sea under Future Climate Scenarios"

Data for "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: A case study on the Italian grid"

Data employed for the paper "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: A case study on the Italian grid" Abstract This study explores compounding impacts of climate change on power system's load and generation, emphasising the need to integrate adaptation and mitigation strategies into investment planning. We combine existing and novel empirical evidence to model impacts on: i) air-conditioning demand; ii) thermal power outages; iii) hydro-power generation shortages. Using a power dispatch and capacity expansion model, we analyse the Italian power system's response to these climate impacts in 2030, integrating mitigation targets and optimising for cost-efficiency at an hourly resolution. We outline different meteorological scenarios to explore the impacts of both average climatic changes and the intensification of extreme weather events. We find that addressing extreme weather in power system planning will require an extra 5-8 GW of  photovoltaic  (PV) capacity, on top of the 50 GW of the additional solar PV capacity required by the mitigation target alone. Despite the higher initial investments, we find that the adoption of renewable technologies, especially PV, alleviates the power system's vulnerability to climate change and extreme weather events. In fact, renewable energy sources are generally less vulnerable to the impacts of climate change, such as rising temperatures and shifting precipitation patterns, compared to thermal power and hydropower generation. Furthermore, enhancing short-term storage with lithium-ion batteries is crucial to counterbalance the reduced availability of dispatchable hydro generation.

Data for paper "Diagnosing the factors that contribute to the intermodel spread of climate feedback in CMIP6"

This data deposit includes the model simulation data and intermediate data.  All data is annunal mean to reduce the size. The globalmean.zip includes the global mean radiative repsonse data.  The annualmean.zip includes the annual mean data of all model simulations done and have been regridded to 2x2.5 degree resolution.

opencanada-crownSep 2024View details →

Montreal climate data for building simulations with urban heat island effects and nature-based solutions

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Montreal airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions. This dataset contains hourly historical and future weather files for use in building simulations for the city of Montreal, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.  The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.     The historical and future global warming thresholds and their corresponding time periods are as follows: <table> <tbody> <tr> <td> Global Warming Scenario </td> <td> Time Period </td> </tr> <tr> <td> Historical </td> <td> 1991-2021 </td> </tr> <tr> <td> Global Warming 0.5ºC </td> <td> 2003-2033 </td> </tr> <tr> <td> Global Warming 1.0ºC </td> <td> 2014-2044 </td> </tr> <tr> <td> Global Warming 1.5ºC </td> <td> 2024-2054 </td> </tr> <tr> <td> Global Warming 2.0ºC </td> <td> 2034-2064 </td> </tr> <tr> <td> Global Warming 2.5ºC </td> <td> 2042-2072 </td> </tr> <tr> <td> Global Warming 3.0ºC </td> <td> 2051-2081 </td> </tr> <tr> <td> Global Warming 3.5ºC </td> <td> 2064-2094 </td> </tr> </tbody> </table>   The following variables are included in the files: <table> <tbody> <tr> <td>Variable</td> <td>Description</td> </tr> <tr> <td>RUN</td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td>YEAR</td> <td>Year associated with the record</td> </tr> <tr> <td>MONTH</td> <td>Month associated with the record</td> </tr> <tr> <td>DAY</td> <td>Day of the month associated with the record</td> </tr> <tr> <td>HOUR</td> <td>Hour associated with the record</td> </tr> <tr> <td>YDAY</td> <td>Day of the year associated with the record</td> </tr> <tr> <td>DRI_kJPerM2</td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DHI_kJperM2</td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DNI_kJperM2</td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>GHI_kJperM2</td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>TCC_Percent</td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td>RAIN_Mm</td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>WDIR_ClockwiseDegFromNorth</td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td>WSP_MPerSec</td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td>RHUM_Percent</td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td>TEMP_K</td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td>ATMPR_Pa</td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td>SnowC_Yes1No0 </td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td>SNWD_Cm</td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>

opencanada-crownSep 2024View details →

Toronto climate data for building simulations with urban heat island effects and nature-based solutions

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Toronto airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions. This dataset contains hourly historical and future weather files for use in building simulations for the city of Toronto, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.  The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.     The historical and future global warming thresholds and their corresponding time periods are as follows: <table> <tbody> <tr> <td> Global Warming Scenario </td> <td> Time Period </td> </tr> <tr> <td> Historical </td> <td> 1991-2021 </td> </tr> <tr> <td> Global Warming 0.5ºC </td> <td> 2003-2033 </td> </tr> <tr> <td> Global Warming 1.0ºC </td> <td> 2014-2044 </td> </tr> <tr> <td> Global Warming 1.5ºC </td> <td> 2024-2054 </td> </tr> <tr> <td> Global Warming 2.0ºC </td> <td> 2034-2064 </td> </tr> <tr> <td> Global Warming 2.5ºC </td> <td> 2042-2072 </td> </tr> <tr> <td> Global Warming 3.0ºC </td> <td> 2051-2081 </td> </tr> <tr> <td> Global Warming 3.5ºC </td> <td> 2064-2094 </td> </tr> </tbody> </table>   The following variables are included in the files: <table> <tbody> <tr> <td>Variable</td> <td>Description</td> </tr> <tr> <td>RUN</td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td>YEAR</td> <td>Year associated with the record</td> </tr> <tr> <td>MONTH</td> <td>Month associated with the record</td> </tr> <tr> <td>DAY</td> <td>Day of the month associated with the record</td> </tr> <tr> <td>HOUR</td> <td>Hour associated with the record</td> </tr> <tr> <td>YDAY</td> <td>Day of the year associated with the record</td> </tr> <tr> <td>DRI_kJPerM2</td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DHI_kJperM2</td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DNI_kJperM2</td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>GHI_kJperM2</td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>TCC_Percent</td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td>RAIN_Mm</td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>WDIR_ClockwiseDegFromNorth</td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td>WSP_MPerSec</td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td>RHUM_Percent</td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td>TEMP_K</td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td>ATMPR_Pa</td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td>SnowC_Yes1No0 </td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td>SNWD_Cm</td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>

Data Package: Investors Reward Countries for Participating in Climate Agreements

This package contains the processed dataset containing publicly available data, log files, and codes for the manuscript 'Investors Reward Countries for Participating in Climate Agreements'. The log files and codes correspond to the key regression output from the manuscript. All analysis was conducted using Stata version 18.5.