The Shared Socioeconomic Pathways (SSPs) Literature Database, v1, 2014-2019 consists of biographic information, abstracts, and analysis of 1,360 articles published from 2014 to 2019 that used the SSPs. The database was generated from a Google Scholar search, followed by a manual examination of the results for papers that made substantial use of the SSPs. Each paper was then coded along a number of different dimensions, including categories of types of papers or analysis, number of subcategories for SSP Applications and SSP Extensions, particular Shared Socioeconomic Pathways (SSPs) used, particular Representative Concentration Pathways (RCPs) used, and particular SSP-RCP combinations used. Over the past ten years, the climate change research commUnity developed a scenario framework combining alternative futures of climate and society to facilitate integrated research and consistent assessment to inform policy. This framework consists of Shared Socioeconomic Pathways (SSPs), Representative Concentration Pathways (RCPs), and Shared Policy Assumptions (SPAs), which together describe alternative visions of how society and climate may evolve over the coming decades, while providing a framework for combining these pathways in integrated studies. The tracking of the use of this framework in the literature allows for assessment of how it is being used, whether it is achieving its original goals, and what improvements to the framework would benefit future research.

The Sub-global Scenarios that Extend the Global SSP Narratives: Literature Database, Version 1, 2014-2021 consists of 37 columns of bibliographic data, methodological and analytical insights, from 155 articles published from 2014 to 2021 that extended the narratives of global SSPs. Local and regional scale Shared Socioeconomic Pathways (SSPs) have grown largely in addressing Climate Change Impact, Adaptation, and Vulnerability (CCIAV) assessments at sub-global levels. Common elements of these studies, besides their focus on CCIAV, are the use of both quantitative and qualitative elements of the SSPs. To explore and learn from current literature on novel methods and insights on extending SSPs, the sub-global extended SSPs literature database is constructed in the research for analyses. The database was developed in four stages: searches; screening; data extraction; and coding. The search stage incorporated three approaches: using a search string in three academic databases (Scopus, Web of Science Core Collection, ScienceDirect); a targeted search of a specific relevant database (ICONICS); and a targeted selection in Google Scholar of all papers that cited the publication of the global SSP narratives. In the screening step, criteria were assessed for full-text papers for eligibility including relevant typologies, methodologies, and other criteria. Finally, data from eligible papers was extracted and entered in a coding framework in an Excel workbook spreadsheet. The coding framework resulted in 37 columns to systematize coding of data from the 155 papers selected along several different dimensions, including categories of papers or analysis, several subcategories for SSP Applications and SSP Extensions, specific SSPs used, specific Representative Concentration Pathways (RCPs) used, typologies of extensions of qualitative and quantitative SSPs, and the types of models and nature of the extended SSPs.

The dataset provides annual water withdrawal and consumption estimates for Europe at a spatial resolution of 5 arcminutes, covering the period from 2020 to 2100 for four SSPs (1, 2, 3, and 5). Below, we outline the procedure used to downscale the population projections to a 5-arcminute resolution and describe the main equations applied to project water withdrawal and consumption under different SSPs.

The development of the high-resolution (5 arcminute) projected water withdrawal and consumption for Europe follows the methodology outlined by Wada et al. (2011a, 2011b). This new release incorporates new projections for population, GDP per capita, and urbanization patterns from the latest SSP database (v3.0.1; available at https://data.ece.iiasa.ac.at/ssp/). Since this update is still in progress as of August 25^th, 2024, some necessary input data are sourced from an earlier version of the SSP data (SSP 2013, see Table 1). All data and methods used to generate the results provided in this dataset are described in the Readme - Data and Methods file.

Table 1: Data availability in different versions of the SSP database as of August 25^th 2024.

This climate change impact data (future scenarios on temperature-induced GDP losses) and climate change mitigation cost data (REMIND model scenarios) is published under doi: 10.5281/zenodo.3541809 and used in this paper:

Ueckerdt F, Frieler K, Lange S, Wenz L, Luderer G, Levermann A (2018) The economically optimal warming limit of the planet. Earth System Dynamics. https://doi.org/10.5194/esd-10-741-2019

Below the individual file contents are explained. For further questions feel free to write to Falko Ueckerdt (ueckerdt@pik-potsdam.de).

Climate change impact data

File 1: Data_rel-GDPpercapita-changes_withCC_per-country_all-RCP_all-SSP_4GCM.csv

Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, RCP (and a zero-emissions scenario), SSP and 4 GCMs (spanning a broad range of climate sensitivity). Negative (positive) values indicate losses (gains) due to climate change. For figure 1a of the paper, this data was aggregated for all countries.

File 2: Data_rel-GDPpercapita-changes_withCC_per-country_all-SSP_4GCM_interpolated-for-REMIND-scenarios.csv

Content: Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP and 4 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).

File 3: Data_rel-GDPpercapita-changes_withCC_per-country_SSP2_12GCM_interpolated-for-REMIND-scenarios.csv

Content: Same as file 2, but only for the SSP2 (chosen default scenario for the study) and for all 12 GCMs. Data of relative change in absolute GDP/CAP levels (compared to the baseline path of the respective SSP in the SSP database) for each country, SSP-2 and 12 GCMs (spanning a broad range of climate sensitivity). The RCP (and a zero-emissions scenario) are interpolated to the temperature pathways of the ten REMIND model scenarios used for climate change mitigation costs. Hereby the set of scenarios for climate impacts and climate change mitigation are consistent and can be combined to total costs of climate change (for a broad range of mitigation action).

In addition, reference GDP and population data (without climate change) for each country until 2100 was downloaded from the SSP database, release Version 1.0 (March 2013, https://tntcat.iiasa.ac.at/SspDb/, last accessed 15Nov 2019).

Climate change mitigation cost data

The scenario design and runs used in this paper have first been conducted in [1] and later also used in [2].

File 4: REMIND_scenario_results_economic_data.csv

File 5: REMIND_scenarios_climate_data.csv

Content: A broad range of climate change mitigation scenarios of the REMIND model. File 4 contains the economic data of e.g. GDP and macro-economic consumption for each of the countries and world regions, as well as GHG emissions from various economic sectors. File 5 contains the global climate-related data, e.g. forcing, concentration, temperature.

In the scenario description “FFrunxxx” (column 2), the code “xxx” specifies the scenario as follows. See [1] for a detailed discussion of the scenarios.

The first dimension specifies the climate policy regime (delayed action, baseline scenarios):

1xx: climate action from 2010 5xx: climate action from 2015 2xx climate action from 2020 (used in this study) 3xx climate action from 2030 4x1 weak policy baseline (before Paris agreement)

The second dimension specifies the technology portfolio and assumptions:

x1x Full technology portfolio (used in this study) x2x noCCS: unavailability of CCS x3x lowEI: lower energy intensity, with final energy demand per economic output decreasing faster than historically observed x4x NucPO: phase out of investments into nuclear energy x5x Limited SW: penetration of solar and wind power limited x6x Limited Bio: reduced bioenergy potential p.a. (100 EJ compared to 300 EJ in all other cases) x6x noBECCS: unavailability of CCS in combination with bioenergy

The third dimension specifies the climate change mitigation ambition level, i.e. the height of a global CO2 tax in 2020 (which increases with 5% p.a.).

xx1 0$/tCO2 (baseline) xx2 10$/tCO2 xx3 30$/tCO2 xx4 50$/tCO2 xx5 100$/tCO2 xx6 200$/tCO2 xx7 500$/tCO2 xx8 40$/tCO2 xx9 20$/tCO2 xx0 5$/tCO2

For figure 1b of the paper, this data was aggregated for all countries and regions. Relative changes of GDP are calculated relative to the baseline (4x1 with zero carbon price).

[1] Luderer, G., Pietzcker, R. C., Bertram, C., Kriegler, E., Meinshausen, M. and Edenhofer, O.: Economic mitigation challenges: how further delay closes the door for achieving climate targets, Environmental Research Letters, 8(3), 034033, doi:10.1088/1748-9326/8/3/034033, 2013a.

[2] Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey, V. and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 °C, Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572, 2015.

SummaryThis metadata record provides details of the data supporting the claims of the related manuscript: “Projecting future populations of urban agglomerations: around the world and through the 21st century ”.The data consist of HTML files with interactive maps for future populations projections of urban agglomerations, and HTML file displaying figures for postdictions of urban agglomerations, as well as 5 .csv files containing the raw data.The related study estimated population trends throughout the 21st century for approximately 20,000 urban agglomerations in 151 countries by working within the Shared Socioeconomic Pathways (SSPs) and using a simple urban growth model.Data accessThe following resources, which were among the sources of the data analyzed in the related study, are available from the links below.- Postdiction results for 1794 urban agglomerations http://stwww.eng.kagawa-u.ac.jp/~kii/Research/UPP_2020/UPP_2020.html#postdiction-for-1794-agglomerations-link- Temporal evolution from 2010 to 2100 of the geographical distribution of urban agglomerations, arranged by population scale, as predicted within the various SSP scenarios http://stwww.eng.kagawa-u.ac.jp/~kii/Research/UPP_2020/UPP_2020.htmlThese data are also available in raw .csv form via the 'Raw data' link on the same page, and also in the 5 files included as part of this data record.- Available urban-population data include the UN’s World Urbanization Prospects 2018 (https://population.un.org/wup/) and Gridded Population of the World, v4 (https://doi.org/10.7927/H4BC3WMT). Available settlement-point data include, in addition to the above urban population sources, World Gazetteer (https://www.arcgis.com/home/item.html?id=346ce13fa2d4468a9049f71bcc250f37) and GeoNames (https://www.geonames.org/). GDP per capita data is available from OECD.stat (https://stats.oecd.org/), Global Metro Monitor (https://www.brookings.edu/research/global-metro-monitor/), and World Development Indicators (http://datatopics.worldbank.org/world-development-indicators/). OpenStreetMap is available at https://www.openstreetmap.org/. Scenario data for SSPs are available at the IIASA-SSP database (https://doi.org/10.1016/j.gloenvcha.2016.05.009). CodeCode used for the analysis can be downloaded from the author's lab's website: http://stwww.eng.kagawa-u.ac.jp/~kii/Research/UPP_2020/UPP_2020.html#codes. These are written in R. They are provided only for the purpose of tracing the analytical procedure. They are not executable without appropriate datasets.

General Information

The Pop-AUT database was developed for the DISCC-AT project, which required subnational population projections for Austria consistent with the updated Shared Socio-Economic Pathways (SSPs). For this database, the most recent version of the nationwide SSP population projections (IIASA-WiC POP 2023) are spatially downscaled, offering a detailed perspective at the subnational level in Austria. Recognizing the relevance of this information for a wider audience, the data has been made publicly accessible through an interactive dashboard. There, users are invited to explore how the Austrian population is projected to evolve under different SSP scenarios until the end of this century.

Methodology

The downscaling process of the nationwide Shared Socioeconomic Pathways (SSP) population projections is a four-step procedure developed to obtain subnational demographic projections for Austria. In the first step, population potential surfaces for Austria are derived. These indicate the attractiveness of a location in terms of habitability and are obtained using machine learning techniques, specifically random forest models, along with geospatial information such as land use, roads, elevation, distance to cities, and elevation (see, e.g., Wang et al. 2023).

The population potential surfaces play a crucial role in distributing the Austrian population effectively across the country. Calculations are based on the 1×1 km spatial resolution database provided by Wang et al. (2023), covering all SSPs in 5-year intervals from 2020 to 2100.

Moving to the second step, the updated nationwide SSP population projections for Austria (IIASA-WiC POP 2023) are distributed to all 1×1 km grid cells within the country. This distribution is guided by the previously computed grid cell-level population potential surfaces, ensuring a more granular representation of demographic trends.

The base year for all scenarios is 2015, obtained by downscaling the UN World Population Prospects 2015 count for Austria using the WorldPop (2015) 1×1 km population count raster.

In the third step, the 1×1 km population projections are temporally interpolated to obtain yearly projections for all SSP scenarios spanning the period from 2015 to 2100.

The final step involves the spatial aggregation of the gridded SSP-consistent population projections to the administrative levels of provinces (Bundesländer), districts (Bezirke), and municipalities (Gemeinden).

Dashboard

The data can be explored interactively through a dashboard.

Data Inputs

Updated nationwide SSP population projections: IIASA-WiC POP (2023) (https://zenodo.org/records/7921989)

Population potential surfaces: Wang, X., Meng, X., & Long, Y. (2022). Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways. Scientific Data, 9(1), 563.

Shapefiles: data.gv.at

WorldPop 2015: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647

Version

This is version 1.0, built upon the Review-Phase 2 version of the updated nationwide SSP population projections (IIASA-WiC POP 2023). Once these projections are revised, this dataset will be accordingly updated.

File Organization

The SSP-consistent population projections for Austria are accessible in two formats: .csv files for administrative units (provinces = Bundesländer, districts = Politische Bezirke, municipalities = Gemeinden) and 1×1 km raster files in GeoTIFF and NetCDF formats. All files encompass annual population counts spanning from 2015 to 2100.

We developed and presented a set of comparable spatially explicit global gridded gross domestic product (GDP) for both historical period (2005 as representative) and for future projections from 2030 to 2100 at a ten-year interval for all five SSPs. The DMSP-OLS nighttime light (NTL) images and the LandScan Global Population database were used to generate LitPop map, which reduces the limitations of saturation problem of using NTL images alone or the assumption of even GDP per capita within an administrative boundary of gridded data set in GDP disaggregation. We used the LitPop maps to disaggregate national GDP and over 800 provincial gross regional product (GRP, in 2005 PPP USD) across the globe in 2005 and to downscaled to a spatial resolution of 30 arc-seconds (~1 km at equator). National and supranational GDP growth rate projections in 2030-2100 under five SSPs were then downscaled to 1-km grids based on the LitPop approach, which used NPP-VIIRS product as fixed NTL image in 2015 and the population projections of 0.125 arc-degreee (Jones and O'Neill, 2016), which are downscaled to 1-km based on LandScan population distribution pattern in 2015. We then upscaled this gridded GDP dataset to 0.25 arc-degree and provided here.

There are 41 tif files (2005 and 2030 - 2100 at a ten-year interval for five SSPs) for each spatial resolution. The gridded GDP are distributed over land with value of zero filled in the Antarctica, oceans and some desert or wilderness areas (non-illuminated and depopulated zones). The spatial extents are 60S - 90N and 180E - 180W in standard WGS84 coordinate system.

For more details, please refer to the corresponding article: Global gridded GDP data set consistent with the shared socioeconomic pathways by Wang and Sun (2022).

Recommended citation

Article citation will be added once the article is available.

Content

• Use of the dataset and full description
• Abstract
• Support
• Files included in the dataset
• Notes
• Data format description (columns)
• Data sources
• Changelog
• References

Use of the dataset and full description

Before using the dataset, please read this document and the article describing the methodology, especially the "Discussion and limitations" section.

The article will be referenced here as soon as it is published.

Please notify us (johannes.guetschow@pik-potsdam.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the RCP-SSP-dwn dataset. See the full citations in the References section further below.

Support

If you encounter possible errors or other things that should be noted or need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@pik-potsdam.de.

Abstract

This dataset provides country scenarios, downscaled from the RCP (Representative Concentration Pathways) and SSP (Shared Socio-Economic Pathways) scenario databases, using results from the SSP GDP (Gross Domestic Product) country model results as drivers for the downscaling process harmonized to and combined with up to date historical data.

Files included in the dataset

The repository comprises several datasets. Each dataset comes in a csv file. The file name is constructed from dataset properties as follows:

The "Source" flag indicates which input scenarios were used.

• PMRCP: RCP scenarios downscaled using the SSPs: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.
• PMSSP: Downscaled SSP IAM scenarios: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.

the "Bunkers" flag indicates if the input emissions scenarios have been corrected for emissions from international shipping and aviation (bunkers) before downscaling to country level or not. The flag is "B" for scenarios where emissions from bunkers have been removed before downscaling and "" (no flag) where they have not been removed.

The "Downscaling" flag indicates the downscaling technique used.

• IE: Convergence downscaling with exponential convergence of emissions intensities and convergence before transition to negative emissions.
• IC: Regional emission intensity growth rates for all countries.
• CS: Constant emission shares as a reference case independent of the socio-economic scenario.

All files contain data for all countries and variables. For detailed methodology descriptions we refer to the paper this dataset is a supplement to. A reference to the paper will be added as soon as it is published.

Finally the data description including detailed references is included: RCP-SSP-dwn_v1.0_data_description.pdf.

Notes

If you encounter problems with the size of the csv files please let us know, so we can find solutions for future releases of the data.

Data format description (columns)

"source"

For PMRCP files source values are

• RCPSSP
• PMRCP
• PMRCPMISC

For PMSSP files source values are

• SSPIAM
• PMSSP
• PMSSPMISC

For possible values of

"scenario"

For PMRCP files the scenarios have the format

For PMSSP files the scenarios have the format

Model codes in scenario names

• AIMCGE: AIM-CGE
• IMAGE: IMAGE
• GCAM4: GCAM
• MESGB: MESSAGE-GLOBIOM
• REMMP: REMIND-MAGPIE
• WITGB: WITCH-GLOBIOM

"country"

ISO 3166 three-letter country codes or custom codes for groups:

Additional "country" codes for country groups.

• EARTH: Aggregated emissions for all countries
• ANNEXI: Annex I Parties to the UNFCCC
• NONANNEXI: Non-Annex I Parties to the UNFCCC
• AOSIS: Alliance of Small Island States
• BASIC: BASIC countries (Brazil, South Africa, India and China)
• EU28: European Union (still including the UK)
• LDC: Least Developed Countries
• UMBRELLA: Umbrella Group

"category"

Category descriptions.

• IPCM0EL: Emissions: National Total excluding LULUCF
• ECO: Economical data
• DEMOGR: Demographical data

"entity"

Gases and gas baskets using global warming potentials (GWP) from either Second Assessment Report (SAR) or Fourth Assessment Report (AR4).

Gases / gas baskets and underlying global warming potentials

• CH4: Methane (CH₄)
• CO2: Carbon Dioxide (CO₂)
• N2O: Nitrous Oxide (N₂O)
• FGASES: Fluorinated Gases (SAR): HFCs, PFCs, SF₆, NF₃
• FGASESAR4: Fluorinated Gases (AR4): HFCs, PFCs, SF₆, NF₃
• KYOTOGHG: Kyoto greenhouse gases (SAR)
• KYOTOGHGAR4: Kyoto greenhouse gases (AR4)

"unit"

The following units are used:

• Million2011GKD: Million 2011 international dollars
• ThousandPers: Thousand persons
• kt: kilotonnes
• Mt: Megatonnes
• Gg: Gigagrams
• MtCO2eq: Megatonnes of CO₂ equivalents using the GWPs defined by "entity"
• GgCO2eq: Gigagrams of CO₂ equivalents using the GWPs defined by "entity"

Remaining columns

Years from 1850-2100.

Data Sources

The following data sources were used during the generation of this dataset:

Scenario data

• RCP scenarios website/data
• SSP basic elements website/data
• SSP IAM scenarios website/data
• SSP CMIP6 scenarios website/data

Historical data

• CDIAC data
• CEDS CMIP6 data paper/data
• EDGAR version 4.3.2: data, paper
• IMO GHG report report
• PRIMAP-hist v2.1 paper, <a

Global fraction of land cover type from 1970 to 2100 per land grid cell: cropland; pasture; forest; other nature; urban area. The data is licensed under CC-BY. The IMAGE-team would appreciate to be involved in projects using the data. SSP scenarios are documented in: Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm, https://doi.org/10.1016/j.gloenvcha.2016.05.008

What does the data show?

Population from the UK Climate Resilience Programme UK-SSPs project. The data is available for the end of each decade. Provided on a 2km Transverse Mercator Grid (prj4string: “+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +a=6377563.396 +rf=299.324975315035 +units=m +no_defs”).

The source data was originally at a 1km resolution, but for usability it has been converted to 2km resolution.

This dataset contains SSP1, SSP2, SSP3, SSP4 and SSP5. For more information see the table below.

Indicator

Population

Metric

Population

Unit

Headcount

Spatial Resolution

2km grid (sourced from 1km grid)

Temporal Resolution

Decadal

Sectoral Categories

N/A

Baseline Data Source

ONS 2019; LCM 2015, Worldpop 2020

Projection Trend Source

IIASA; UK SSP urbanisation

What are the naming conventions and how do I explore the data?

This data contains a field for each SSP scenario and the year at the end of each decade. For example, 'SSP1_2040' is the projection for 2040 in the SSP1 scenario.

There are a small number of features in this data with much higher population values than the majority of features. This can skew the styling, and so if you want to emphasise areas of high density population you may wish to adjust the style settings to account for this.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘SSP1_2020’ values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links:Further information on the UK SSPs can be found on the UK SSP project site and in this storymap.Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project is comprised of two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. The towed Surface Salinity Profiler (SSP) platform is a converted paddleboard with a keel and surfboard outrigger that is tethered to the ship and skims the sea surface beyond the ships wake. Below the paddleboard are salinity and temperature sensors at depths of 10, 30, 50 and 100cm, and microstructure sensors that measure turbulence. The SSP was deployed 19 times throughout the first SPURS-2 cruise, totaling over 200 hours of measurements, and a further 15 times during the 2017 cruise. SSP deployment is most informative when there is a rain event leading to near-surface ocean stratification. The SSP then measures how the ocean changes over the periods before, during, and after rain, and how rainwater mixes into the ocean during recovery. All SSP data files are in netCDF format with standards compliant metadata.

What does the data show?

The data shows projections of population age structure (thousands of people per age class) from the UK Climate Resilience Programme UK-SSPs project. The data is available for each Office for National Statistics Local Authority District (ONS LAD) shape simplified to a 10m resolution.

The age structure is split into 19 age classes e.g. 10-14 and is available for the end of each decade. For more information see the table below.

This dataset contains only SSP2, the 'Middle of the Road' scenario.

Indicator

Demography

Metric

Age Structure

Unit

Thousands per age class

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

19 age classes

Baseline Data Source

ONS 2019

Projection Trend Source

IIASA

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Age Class' allow the data to be filtered e.g. by age class '10-14'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links:

Further information on the UK SSPs can be found on the UK SSP project site and in this storymap. Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

V1 dataset:Under the global framework of Shared Socioeconomic Pathways (SSPs), based on localized population and economic parameters, a Population Development Environment (PDE) model is adopted to construct population grid data for SSPs from 2020 to 2100; Using the Cobb Douglas model, construct economic data for SSPs from 2020 to 2100.The v1 dataset includes:Population grid data of the world, The Belt and Road region, and China, with a spatial resolution of 0.5°GDP grid data of the world, The Belt and Road region, and China, with a spatial resolution of 0.5 °Grid data on the output value of three industries in the Chinese region, with a spatial resolution of 0.1 °V2 dataset:Based on the data from the 7th National Population Census of China, starting from 2020, the parameters such as fertility rate, mortality rate, migration rate, and education level in the Population Development Environment (PDE) model were updated. Under the Shared Socioeconomic Pathways (SSP1-5), a new version (v2) of the total population and age and gender specific population projection dataset for China and its provinces from 2020 to 2100 was created. Based on the data from the 7th National Population Census and the 4th Economic Census of China, with 2020 as the starting year, the parameters of total factor productivity, capital stock, labor input, and capital elasticity coefficient in the Cobb Douglas model were updated. Under the shared SSP1-5, a new version (v2) of China and its provincial GDP projectiondataset from 2020 to 2100 was created.The v2 (2024 version) dataset includes:Total Population Data of China and Provinces (2020-2100)Population data by age and gender in China (2020-2100)China and Provincial GDP Data (2020-2100)

What does the data show?

Life expectancy at birth (years) from the UK Climate Resilience Programme UK-SSPs project. The data is available for each Office for National Statistics Local Authority District (ONS LAD) shape simplified to a 10m resolution.

The data is available for the end of each decade. This dataset contains SSP1, SSP2, SSP3, SSP4 and SSP5. For more information see the table below.

Indicator

Health

Metric

Life expectancy at birth

Unit

Years

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

N/A

Baseline Data Source

ONS 2018

Projection Trend Source

Stakeholder process

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Scenario' allows the data to be filtered, e.g. by scenario 'SSP3'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links: Further information on the UK SSPs can be found on the UK SSP project site and in this storymap.Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

Global Aridity Index and Potential Evapotranspiration Database: CMIP_6 Future Projections(Future_Global_AI_PET)Robert J. Zomer 1, 2, 3, Antonio Trabucco1,41. Euro-Mediterranean Center on Climate Change, IAFES Division, Sassari, Italy. 2. Centre for Mountain Futures, Kunming Institute of Botany, Chinese Academy of Science, Kunming, Yunnan, China3. CIFOR-ICRAF China Program, World Agroforestry (ICRAF), Kunming, Yunnan. China4. National Biodiversity Future Center (NBFC), Palermo, ItalyThe Global Aridity Index and Potential Evapotranspiration (Global AI-PET) Database: CMIP_6 Future Projections – Version 1 (Future_Global_AI_PET) provides a high-resolution (30 arc-seconds) global raster dataset of average monthly and annual potential evapotransipation (PET) and aridity index (AI) for two historical (1960-1990; 1970-2000) and two future (2021-2040; 2041-2060) time periods for each of 22 CMIP6 Earth System Models across four emission scenarios (SSP: 126, 245, 370, 585). The database also includes three averaged multi-model ensembles produced for each of the four emission scenarios:· All Models: includes all of the 22 ESM, as available within a particular SSP.· High Risk: includes 5 ESM identified as projecting the highest increases in temperature and precipitation and lying outside and significantly higher than the majority of estimates.· Majority Consensus: includes 15 ESM, that is, all available ESM excluding the ESM in the “High Risk” category, and those missing data across all of the 4 SSP. Further herein referred to as the “Consensus” category.These geo-spatial datasets have been produced with the support of Euro-Mediterranean Center on Climate Change, IAFES Division; Centre for Mountain Futures, Kunming Institute of Botany, Chinese Academy of Science; CIFOR-ICRAF China Program, World Agroforestry (CIFOR-ICRAF) and the National Biodiversity Future Center (NBFC).These datasets are provided under a CC_BY 4.0 License (please attribute), in standard GeoTiff format, WGS84 Geographic Coordinate System, 30 arc seconds or ~ 1km at the equator, to support studies contributing to sustainable development, biodiversity and environmental conservation, poverty alleviation, and adaption to climate change, among other global, regional, national, and local concerns.The Future_Global_AI_PET is available online from the Science Data Bank (ScienceDB) at: https://doi.org/10.57760/sciencedb.nbsdc.00086Previous versions of the Global Aridity Index and PET Database are available online here:https://figshare.com/articles/dataset/Global_Aridity_Index_and_Potential_Evapotranspiration_ET0_Climate_Database_v2/7504448/6Technical questions regarding the datasets can be directed to Robert Zomer: r.zomer@mac.com or Antonio Trabucco: antonio.trabucco@cmcc.it Methods:Based on the results of comparative validations, the Hargreaves model has been evaluated as one of the best fit to model PET and Aridity index globally with the available high resolution downscaled and bias corrected climate projections and chosen for the implementation of the Global-AI_PET- CMIP6 Future Projections. This method performs almost as well as the Penman-Monteith method, but requires less parameterization, and has significantly lower sensitivity to error in climatic inputs (Hargreaves and Allen, 2003). The currently available downscaled CMIP6 projections (available from WorldClim) do provide fewer climate variables idoneous for implementation of temperature-based evapotranspiration methods, such as the Hargreaves method. Hargreaves (1985, 1994) uses mean monthly temperature (Tmean), mean monthly temperature range (TD) and extraterrestrial radiation (RA, radiation on top of the atmosphere) to calculate ET0, as shown below: PET = 0.023 * RA * (Tmean + 17.8) * TD0.5where RA is extraterrestrial radiation at the top of the atmosphere, TD is the difference between mean maximum temperatures and mean minimum temperatures (Tmax - Tmin), and Tmean is equal to Tmax + Tmin divided by 2. The Hargreaves equation has been implemented globally on a per grid cell basis at 30 arc seconds resolution (~ 1km2 at the equator), in ArcGIS (v11.1) using Python v3.2 (see code availability section) to estimate PET/AI globally using future projections provided by the CMIP6 collaboration. The data to parametrize the equation were obtained from the Worldclim (worldclim.org) online data repository, which provides bias-corrected downscaled monthly values of minimum temperature, maximum temperature, and precipitation for 25 CMIP6 Earth System Models (ESMs), across four Shared Socio-economic Pathways (SSPs): 126, 245, 370 and 585. PET/AI was estimated for two historical periods, WorldClim 1.4 (1960-1990) and WorldClim 2.1 (1970-2000), representing on average a decades change, by applying the Hargreaves methodology described above. Similarly, PET/AI was estimated for two future time periods, namely 2021-2040 and 2041-2060, for each of the 25 models across their respective four SSP scenarios (126, 245, 370,585). Aridity Index Aridity is often expressed as an Aridity Index, comprised of the ratio of precipitation over PET, and signifying the amount of precipitation available in relation to atmospheric water demand and quantifying the water (from rainfall) availability for plant growth after ET demand has been met, comparing incoming moisture totals with potential outgoing moisture. The AI for the averaged time periods has been calculated on a per grid cell basis, as: AI = MA_Prec/MA_PETwhere: AI = Aridity Index MA_Prec = Mean Annual Precipitation MA_PET = Mean Annual Reference EvapotranspirationUsing the mean annual precipitation (MA_Prec) values obtained from the CMIP6 climate projections, while ET0 datasets estimated on a monthly average basis by the method described above were aggregated to mean annual values (MA_PET). Using this formulation, AI values are unitless, increasing with more humid condition and decreasing with more arid conditions.Multi-Model Averaged EnsemblesBased upon the distribution of the various scenarios along a gradient of their projected temperature and precipitation estimates for the each of the four SSP and two future time period, three multi-model ensembles, each articulated by their four respective SSPs, were identified. The three parameters of monthly minimum temperature, monthly maximum temperature and monthly precipitation for ESM’s included within each of these ensemble categories were averaged for each of their respective SSPs. These averaged parameters were then used to calculate the PET/AI as per the above methodology.Code Availablity:The algorithm and code in Python used to calculate PET and AI is available on Figshare at this link below:https://figshare.com/articles/software/Global_Future_PET_AI_Algorithm_Code_Python_-_Calculate_PET_AI/24978666DATA FORMATPET datasets are available as monthly averages (12 datasets, i.e. one dataset for each month, averaged over the specified time period) or as an annual average (1 dataset) for the specified time period. Aridity Index grid layers are available as one grid layer representing the annual average over the specified period. The following nomenclature is used to describe the dataset: Zipped Files - Directory Names refer to: Model_SSP_Time-PeriodFor example: ACCESS-CM2_126_2021-2040.zip Model: ACCESS-CM2 / SSP:126 / Time-Period: 2021-2040Prefix of Files (TIFFs) is either:pet_ for PET layers aridity_index for Aridity Index (no suffix)Suffix For PET Files is either:1, 2, ... 12 Month of the yearyr Yearly averagesd Standard DeviationExamples:pet_02.tif is the PET average for the month of February.pet_yr.tif is the PET annual average.’pet_sd.tif is the standard deviation of the annual PETaridity_index.tif is the annual aridity index. The PET values are defined as total mm of PET per month or per year. The Aridity Index values are unit-less.The geospatial dataset is in geographic coordinates; datum and spheroid are WGS84; spatial units are decimal degrees. The spatial resolution is 30 arc-seconds or 0.008333 degrees. Arc degrees and seconds are angular distances, and conversion to linear units (like km) varies with latitude, as below:The Future-PET and Future-Aridity Index data layers have been processed and finalized for distribution online as GEO-TIFFs. These datasets have been zipped (.zip) into monthly series or individual annual layers, by each combination of climate model/scenarios, and are available for online access. Data Storage HierarchyThe database is organized for storage into a hierarchy of directories (see ReadMe.doc):( Individual zipped files are generally about 1 GB or less) Associated Peer Reviewed Journal Article:Zomer RJ, Xu J, Spano D and Trabucco A. 2024. CMIP6-based global estimates of future aridity index and potential evapotranspiration for 2021-2060. Open Research Europe 4:157 https://doi.org/10.12688/openreseurope.18110.1For further info, please refer to these earlier paper describing the database and methodology:Zomer, R.J.; Xu, J.; Trabucco, A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Scientific Data 9, 409.Zomer, R. J; Bossio, D. A.; Trabucco, A.; van Straaten, O.; Verchot, L.V. 2008. Climate Change Mitigation: A Spatial Analysis of Global Land Suitability for Clean Development Mechanism Afforestation and Reforestation. Agric. Ecosystems and Environment. 126:67-80.Trabucco, A.; Zomer, R. J.; Bossio, D. A.; van Straaten, O.; Verchot, L.V. 2008. Climate Change Mitigation through Afforestation / Reforestation: A global analysis of hydrologic

[ Derived from parent entry - See data hierarchy tab ]

These data sets contain the assessed Global Surface Air Temperature (GSAT) projections and all input data and instructions necessary to reproduce the assessed GSAT projections in the Intergovernmental Panel on Climate Change Sixth Assessment Report (Figure 4.11, IPCC AR6 WGI). The constrained CMIP6 projections are based on the methods from three publications calculating the global mean near surface air temperature relative to the average over the period 1995–2014. They are described in box 4.1. The Effective Radiative Forcing (ERF) time series are reproduced from IPCC WGI chapter 7 and included to facilitate reproduction of the analysis. They are uncoupled to any GSAT change. ERF quantifies the energy gained or lost by the earth system following an imposed perturbation (for instance in green house gases, aerosols or solar irradiance). As such it is a fundamental driver of changes in the earth’s TOA energy budget. ERF is determined by the change in the net downward radiative flux at the top of atmosphere (box 7.1) after the system has adjusted to the perturbation but excluding the radiative response to changes in surface temperature (Figure 7.3, IPCC AR6 WGI). For a detailed description of the ERF time series, please refer to chapter 7 (https://github.com/IPCC-WG1/Chapter-7).

Disclaimer: The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.

Required Acknowledgements and Citation: In order to document the impact of assessed Global Surface Air Temperature, users of the data are obligated to cite chapter 4 of WGI contribution to the IPCC AR6.

The Climate Solutions Explorer website maps and presents information about mitigation pathways, avoided climate impacts, vulnerabilities and risks arising from development and climate change. www.climate-solutions-explorer.eu

Using the latest data, state-of-the-art models were used to assess the future trends of indicators of development- and climate-induced challenges.

Updated gridded global climate and impact model data are based on CMIP6 and CMIP5 projections, using a subset of models from the ISIMIP project that have been consistently downscaled and bias-corrected. The data includes various indicators (~42) relating to extremes of precipitation and temperature (e.g. from Expert Team on Climate Change Detection and Indices), hydrological variables including runoff and discharge, heat stress (from wet bulb temperature) events (multiple statistics and durations), and cooling degree days, as well as further indicators relating to air pollution (PM2.5 from the GAINs model), and crop yields and natural habitat land-use change (biodiversity pressure) from the GLOBIOM model.

Indicators were calculated at a spatial resolution of 0.5° (approximately 50km at the equator), and subsequently spatially aggregated to the country level – from which population and land area exposure to the impacts were calculated. This has enabled the country-by-country comparison of national climate impacts and avoided exposure. Impacts were calculated at global mean temperature intervals, i.e. 1.2, 1.5, 2, 2.5, 3, and 3.5 °C, compared to a pre-industrial climate.

The dataset includes:

Global gridded projections (in netCDF format) of all the climate impact indicators at 0.5° spatial resolution, at global warming levels of 1.2, 1.5, 2, 2.5, 3, and 3.5 °CFor each GWL, maps for the absolute indicator values, the relative difference, and the scores are provided. The naming format is: cse_[short_indicator_name]_[ssp]_[gwl]_[metric].nc4. Please note that the Greenland ice sheet and the desert areas have been masked out for the hydrology indicators for these datasets.

Intermediate output data, including gridded maps of absolute values, relative differences, and scores for all ensemble members, as well as gridded maps of the multi-model ensemble statistics for the global warming levels and the reference period For the ensemble member data, the naming format is [gcm]_[ssp/rcp]_[gwl]_[short_indicator_name]_global_[start_year]_[end_year].nc4 or [ghm]_[gcm]_[ssp/rcp]_[gwl]_[soc]_[short_indicator_name]_global_[start_year]_[end_year]_[metric].nc4 for the hydrology indicators.

Tabular data (.csv) aggregating the indicators to country (or region) level, for both hazards and exposure, population and land-area weightedThe .zip archives ‘table_output_climate_exposure_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_climate_exposure_land_air_pollution.zip’ contains the table data for theland and air pollution indicators.

Tabular data (.csv) for avoided impacts by mitigating to 1.5 °C (land and population exposure)The .zip archives ‘table_output_avoided_impacts_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_avoided_impacts_land_air_pollution.zip’ contains the table data for the land and air pollution indicators.

Further details are available on the Data Story page – www.climate-solutions-explorer.eu/story/data. A detailed description of the methodology and the calculation of the ISIMIP-derived indicators has been published in Werning, M. et al. (2024).

Release notes (v1.1)

Changes in this version:

Only table output data for the land and air pollution indicators have been changed, all other indicator data remain unchanged from v1.0

Updated land and air pollution indicators to use scaled population data to match the latest SSP population projections from the Wittgenstein Center from 2023

Fixed issue with the region mask for the EU

Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions

Release notes (v1.0)

Changes in this version:

Fixed calculation of the indicator “Drought intensity” (both for the version using discharge and run-off)

Masked out the Greenland ice sheet and the desert areas for the global gridded projections for the hydrology indicators in the final output files

Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions

Used scaled population data to match the latest SSP population projections from the Wittgenstein Center from 2023

Added the indicator ‘Heatwave days’

Added intermediate outputs for all ensemble members for energy, hydrology, precipitation, and temperature indicators

Release Notes (v0.4)

Changes in this version:

Removed ssp and metric from variable name in netCDF files

Removed obsolete coordinates in netCDF files for 'Drought intensity'

Added intermediate outputs for energy, hydrology, precipitation, and temperature indicators

Data Set Overview This archive contains science data products from observations acquired by the Huygens Probe Surface Science Package (SSP) instrument during the Descent of the Huygens Probe on 2005/01/15 as well as on the surface. SSP data products include raw data and calibration tables. This volume contains both. Descent data can be found in the /DATA/DESCENT/RAW directory. All data are organised by sensor (housekeeping data being treated like all other sensors), with the exception of the impact event which, for reasons of preserving the original data structure, is in a separate directory. SSP consists of nine sensors which are described in INST.CAT. In addition, housekeeping data and a special data packet on impact were created. The corresponding subdirectories are therefore ACCE ACCI APIS APIV DEN HK IMPACT PER REF THP TIL The /CALIB directory contains the necessary calibration information to convert the raw data from Voltages or A/D counts respectively to scientifically meaningful quantities. Given the calibration information alone, a conversion is possible. Should a clarification be required, please consult EAICD.DOC or SSP_SUM.DOC, both in the DOCUMENTS directory. DEN and PER (and, to some extent, also REF) have worked throughout the descent, but these sensors were not designed with the objective of atmospheric measurements in mind. Therefore the related data may only be of limited use. TIMING SSP times are given with with reference to the DDB T0 event, based on the Huygens onboard clock (see INSTHOST.CAT). At the time of archiving there appears to be an additional 250ms offset w.r.t. UTC pending verification. DATASET ORGANISATION Files on this volume are organized into a series of subdirectories below the toplevel directory. The following table shows the structure and content of these directories. In the table, directory names are enclosed in square brackets (). FILE CONTENTS Toplevel di truncated!, Please [truncated!, Please see actual data for full text]

This repository contains the data and scripts required to reproduce the results of the manuscript "Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages" submitted to the Environmental Research Climate Journal (ERCL).

Brief description of project

This project has two main goals:

1. Examine the key factors influencing global economic wildfire damages
2. Projecting future damages under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP370)

Repository structure

• /data directory: contains the data to reproduce the regression analyses and plot the figures presented in the manuscript
  • /data/historical: contains the historical (training) data that was used for fitting the linear regression model
  • /data/ssp: contains the SSP projection data for all predictors, as well as the projected model output for future wildfire damages
  • /data/source: contains all raw data used in this study
• /scripts directory: contains the python scripts to run the regression model and to plot the figures presented in the manuscript
  • /scripts/linregress: contains the scripts for running the linear regression model and to conduct various model validation steps
    • run_linregress.py: script to run the linear regression model
    • run_nonlinregress.py: script to run the nonlinear models (preliminary)
    • inspect_model.py: script to conduct model validation
  • /scripts/plotting: contains the scripts to plot all figures presented in the manuscript
    • plot_map_y_X_hist.py: script to plot Figure 1 (world map of historical wildfire damage and predictors used in this study)
    • plot_residual_plots.py: script to plot Figure 2 (residual and partial residual plots of the fitted regression model)
    • plot_beta_coef_model_prediction.py: script to plot Figure 3 (standardized beta coefficients of the fitted regression model and the scatterplots for reported vs. model-estimated wildfire damages)
    • plot_predictor_ssp_timeseries_global.py: script to plot Figure 4 (time-series of the SSP projections of the predictors)
    • plot_map_y_X_ssp.py: script to plot Figure 5 (world map of predictor values for the three SSPs explored in this study)
    • plot_ssp_damage_projection_by_region.py: script to plot Figure 6 (projected wildfire damages under the three SSPs and for the six IPCC AR6 regions)
    • plot_ssp_damage_projection_per_predictor.py: script to plot Figure 7 (time-series of global mean projected wildfire damage with all predictors changing and only individual predictors changing)
    • plot_ssp3_ssp1_difference.py: script to plot Figure 8 (time-series of mean avoided wildfire damage in SSP126 compared to SSP370)
    • SI_plot_ssp_damage_projection_lin_vs_nonlin.py: script to plot Figure S1 (comparison of time-series of mean projected wildfire damage with the linear and nonlinear models)
    • SI_plot_beta_coef_pop_wui.py: script to plot Figure S2 (same as Figure 3 but for the model using pop_wui instead of PDforest)
    • SI_plot_ssp_population.py: script to plot Figure S3 (population projection under the three SSP scenarios)
    • SI_plot_ssp_map_pop_wui.py: script to plot Figure S4 (world map of the pop_wui predictor under three SSP scenarios)
    • SI_plot_ssp_damage_projection_pop_wui.py: script to plot Figure S5 (comparison of the time-series of projected wildfire damage using pop_wui vs PDforest as predictor)
    • SI_plot_predictor_ssp_trend_by_dev_region.py: script to plot Figure S6 (time-series of the SSP projections of the predictors by developmental regions)

Global monthly temperature from 1970 to 2100 per land grid cell. The data is licensed under CC-BY. The IMAGE-team would appreciate to be involved in projects using the data.