Climate Solutions Explorer - hazard, impacts and exposure data
Bibliographic record
Abstract
This is a pre-release dataset and is subject to change. 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 (~30) 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 mean temperature intervals of 1.2, 1.5, 2, 2.5, 3, and 3.5 °C Tabular data (Excel .csv) is also provided, aggregating the impact indicators to the country level, for both hazards and exposure (both population and land) Further details on the methodology and indicator definitions are available on the Data Story page – www.climate-solutions-explorer.eu/story/data. Release Notes (v0.3) Changes in this version: Heatwave data calculated on an annual basis instead for the entire 31-year period Tropical nights and consecutive dry days data calculated using a 2-year window to avoid cutting consecutive periods in half when calculating from January to December only Indicator scores calculated using new bivariate scoring methodology (range 0-6 instead of 0-3) R10 associations for North Macedonia and Ukraine fixed in line with R10 definitions of the IPCC Exposure and avoided impacts data based on new scoring methodology Population exposure calculated using scaled spatial population maps to match data from IIASAPOP2.0 Exposure and avoided impact csv files for R10 regions and EU merged into one file (*_R10_EU)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.026 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".