This document presents the vocabulary/metadata describing the different terms (dataset, scenario, baseline, etc.) used to describe the products displayed in the Copernicus Interactive Climate Atlas v1.
Element | Term | Description |
scenario | scenario | Scenarios are descriptions of how the future may develop along the 21st century (and beyond) covering a range of plausible pathways determining our possible climate futures. They build on coherent socio-economic (involving demography, economy, technological innovation, etc.) or emission/concentration (greenhouse gases) pathways and provide key forcing to run climate simulations and estimate their influence in the climate system (more information; https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-1). |
scenario | historical | The historical scenario characterizes the forcing conditions corresponding to the recent past (typically from 1850 to “present”) and allows simulating and evaluating the performance of climate models (by comparing model outputs with observations and/or reanalyses). |
scenario | RCP | The Representative Concentration Pathways (RCPs) are scenarios that include time series of emissions and concentrations of the full suite of greenhouse gases (GHGs) and aerosols and chemically active gases, as well as land use/land cover. RCPs are characterized by the radiative forcing reached by the end of the 21st century (e.g. 1.9, 2.6, 4.5, 6.0, 8.5 W m–2); the word representative signifies that each RCP provides one of many possible scenarios that would lead to the specific radiative forcing and the term pathway emphasises that not only the long-term concentration levels are of interest, but also the trajectory taken over time to reach that outcome. This is the family of scenarios used in the Fifth IPCC Assessment Report, AR5 (Moss et al. 2010; https://doi.org/10.1038/nature08823). |
scenario | RCP2.6 | RCP2.6 is a low-emission pathway where radiative forcing peaks at approximately 3 W m–2 and then declines to 2.6 W m–2 in 2100. The increase of global mean surface temperature by the end of the 21st century (2081–2100) relative to 1986–2005 is likely to be 0.3°C to 1.7°C under RCP2.6 (more information; https://www.ipcc.ch/site/assets/uploads/2018/02/AR5_SYR_FINAL_SPM.pdf). |
scenario | RCP4.5 | The RCP4.5 is one of the two intermediate stabilization pathways in which radiative forcing is limited at approximately 4.5 W m–2 in 2100. The increase of global mean surface temperature by the end of the 21st century (2081–2100) relative to 1986–2005 is likely to be 1.1°C to 2.6°C under RCP4.5 (more information; https://www.ipcc.ch/site/assets/uploads/2018/02/AR5_SYR_FINAL_SPM.pdf). |
scenario | RCP6.0 | The RCP6.0 is one of the two intermediate stabilization pathways in which radiative forcing is limited at approximately 6.0 W m–2 in 2100. The increase of global mean surface temperature by the end of the 21st century (2081–2100) relative to 1986–2005 is likely to be 1.4°C to 3.1°C under RCP6.0 (more information; https://www.ipcc.ch/site/assets/uploads/2018/02/AR5_SYR_FINAL_SPM.pdf). |
scenario | RCP8.5 | The RCP8.5 is a high-emission pathway which leads to >8.5 W m–2 in 2100. The increase of global mean surface temperature by the end of the 21st century (2081–2100) relative to 1986–2005 is likely to be 2.6°C to 4.8°C under RCP8.5 (more information; https://www.ipcc.ch/site/assets/uploads/2018/02/AR5_SYR_FINAL_SPM.pdf). |
scenario | SSP | The Shared Socio-economic Pathways (SSPs) are scenarios developed to complement the Representative Concentration Pathways (RCPs), considering as a second dimension the different socio-economic development pathways followed to reach a particular concentration. The abbreviations SSP1 to 5 correspond to sustainability (taking the green road), middle of the road, regional rivalry, inequality and fossil-fueled development, respectively. This integrative SSP-RCP framework (where SSPa-RCPb is typically denoted as SSPa-b, e.g. SSP6-8.5) is widely used in the climate impact and policy analysis literature to analyse RCP scenarios against the backdrop of various SSPs (though a set of scenarios along the diagonal of the matrix, e.g. SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 are commonly used as representative ones). This is the family of scenarios used in the Sixth IPCC Assessment Report, AR6 (O’Neill et al. 2014; https://doi.org/10.1007/s10584-013-0905-2). |
scenario | SSP1-1.9 | SSP1-1.9 represents the low end of future emissions pathways, with radiative forcing limited at approximately 1.9 W m–2 in 2100 achieved with a sustainable socio-economic pathway with net zero CO2 emissions around the middle of the century. This scenario holds warming to approximately 1.5°C above 1850–1900 in 2100 after slight overshoot (more information; https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-1/). |
scenario | SSP1-2.6 | SSP1-2.6 represents a low end emission pathway, with radiative forcing limited at approximately 2.6 W m–2 in 2100 reached through a sustainable socio-economic pathway with net zero CO2 emissions in the second half of the century. This scenarios holds warming below 2.0°C (relative to 1850–1900) (more information; https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-1/). |
scenario | SSP2-4.5 | SSP2-4.5 is an intermediate reference scenario with radiative forcing limited at approximately 4.5 W m–2 in 2100 resulting from a middle of the road socio-economic development pathway with net zero CO2 emissions in the second half of the century. The increase of global mean surface temperature by the end of the 21st century (2081–2100) relative to 1850-1900 is likely to be 2.1°C to 3.5°C under SSP2-4.5 (more information; https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-1/). |
scenario | SSP3-7.0 | SSP3-7.0 is an intermediate-to-high reference scenario with radiative forcing limited at approximately 7.0 W m–2 in 2100 resulting from no additional climate policy under the SSP3 socio-economic development narrative. CO2 emissions roughly double from current levels by 2100. SSP3-7.0 has particularly high non-CO2 emissions, including high aerosols emissions. The increase of global mean surface temperature by the end of the 21st century (2081–2100) relative to 1850-1900 is likely to be 2.8°C to 4.6°C under SSP3-7.0 (more information; https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-1/). |
scenario | SSP5-8.5 | SSP5-8.5 represents the high end of future emissions pathways with radiative forcing limited at approximately 8.6 W m–2 in 2100 resulting from a fossil-fueled socio-economic development with no additional climate policy (note that this forcing is only compatible with fossil-fueled SSP5 socio-economic development pathway). CO2emissions roughly double from current levels by 2050. The increase of global mean surface temperature by the end of the 21st century (2081–2100) relative to 1860-1900 is likely to be 3.3°C to 5.7°C under SSP5-8.5 (more information; https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-1/). |
GWL | GWL | The global mean surface temperature change, or ‘global warming level’ (GWL), is an analysis dimension alternative to the use of future periods across scenarios which is highly relevant from a policy making perspective (since it aligns with the warming thresholds used in the Paris Agreement, e.g. the commitment to limit global temperature increase to well below 2 degrees Celsius). Under a particular scenario, a GWL determines a (20-year) period when global mean surface temperature change will first reach a given threshold (e.g. 1.5 degrees Celsius for GWL1.5), relative to 1850–1900, which is the period used as a proxy for pre-industrial levels. When computed from an ensemble of global climate model simulations this period may be different for different models and scenarios, but the regional change patterns have been found to be similar across models and scenarios for many variables thus making it suitable as a simple ’dimension of integration’ for climate change assessment (https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-1/#1.6). |
GWL | GWL1.5 | GWL1.5 corresponds to 1.5 degrees Celsius global warming level relative to the 1850-1900 period (more information; https://github.com/IPCC-WG1/Atlas/tree/main/warming-levels#readme). |
GWL | GWL2 | GWL2 corresponds to 2 degrees Celsius global warming level relative to the 1850-1900 period (more information; https://github.com/IPCC-WG1/Atlas/tree/main/warming-levels#readme). |
GWL | GWL3 | GWL3 corresponds to 3 degrees Celsius global warming level relative to the 1850-1900 period (more information; https://github.com/IPCC-WG1/Atlas/tree/main/warming-levels#readme). |
GWL | GWL4 | GWL4 corresponds to 4 degrees Celsius global warming level relative to the 1850-1900 period (more information; https://github.com/IPCC-WG1/Atlas/tree/main/warming-levels#readme). |
period | period | The common dimension of analysis for future climate projections is considering different future periods in the 21st century and analyse the information across different scenarios. Typical periods used to characterize future climate change are 20- or 30-year periods characterizing climate conditions for near-, middle- or future-term. |
period | 2021-2040 | Period used in the IPCC AR6 to convey climate change information in the near term, relative to a particular baseline (typically the pre-industrial period 1850-1900 (more information; https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf). |
period | 2041-2060 | Period used in the IPCC AR6 to convey climate change information in the mid-term, relative to a particular baseline (typically the pre-industrial period 1850-1900 (more information; https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf). |
period | 2081-2100 | Period used in the IPCC AR6 to convey climate change information in the long term, relative to a particular baseline (typically the pre-industrial period 1850-1900 (more information; https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf). |
baseline | baseline | A baseline period is a time period against which differences or changes are calculated (e.g., expressed as changes relative to a baseline). Different historical and modern periods are typically used as baselines to characterize current climate (e.g. WMO for climate normals). |
baseline | 1850-1900 | This period represents the earliest period of sufficiently globally complete observations to estimate global surface temperature and is used as an approximation for pre-industrial conditions (a proxy for conditions with no human influence). This is a common choice for the baseline to estimate global warming levels (more information; https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf). |
baseline | 1991-2020 | This period represents the current WMO period for climate normals: The World Meteorological Congress recommended that the new 30-year baseline, 1991-2020, should be adopted globally and pledged support to Members to help them update their figures (WMO climate normals; https://www.ncei.noaa.gov/products/wmo-climate-normals). |
visuals | climate stripes | The climate stripes are plots resembling barcodes that transform annual/seasonal climate data for a particular location or region into sequences of colourful bands (stripes), indicating annual temperature or precipitation in relation to the historical average (e.g. for temperature red stripes represent warm years, while blue denotes cooler ones). Climate stripes are implemented in the Interactive Atlas using chronologically ordered vertical bars along the 1950-2100 period (spanning historical and projected simulations) to represent annual/seasonal raw values or changes (anomalies relative to the selected baseline in this case) of the selected variable and scenario. In the case of ensembles, the stripes corresponding to different models are displayed in different rows to provide a visual indication of model uncertainty. They were first introduced by Ed Hawkins (more information; https://showyourstripes.info). |
visuals | seasonal stripes | The seasonal stripes are a variation of the climate stripes using vertical colour bands (stripes) to depict the intra-annual variability. The monthly values are represented in different rows in the vertical (from January in the bottom to December in the top). In the case of ensembles the multi-model median is represented in the plot (more information; https://doi.org/10.1017/9781009157896.021). |
visuals | scatter plots | The scatter plots show model projections comparing selected variables (e.g., temperature change) against an additional variable (e.g., precipitation change) for the same season and baseline. Points represent pairs of values from different ensemble members across the three different periods for the selected scenario (more information; https://doi.org/10.1017/9781009157896.021). |
dataset | CMIP6 | The Coupled Model Intercomparison Project (CMIP) is a project of the World Climate Research Programme (WCRP) providing global climate projections to understand past, present and future climate changes. CMIP and its associated data infrastructure have become essential to the Intergovernmental Panel on Climate Change and other international and national climate assessments. CMIP6 is the sixth phase of the project and underpins the Intergovernmental Panel on Climate Change 6th Assessment Report (IPCC AR6). The CMIP6 phase involves a suite of common model experiments as well as an ensemble of CMIP-endorsed Model Intercomparison Projects (MIPs). The information included in the Interactive Atlas corresponds to ScenarioMIP where future projections are produced for different scenarios at a typical resolution of 100 km (SSPs in this case) (O'Neill et al. 2016; https://gmd.copernicus.org/articles/9/3461/2016, CDS catalogue; https://doi.org/10.24381/cds.c866074c). |
dataset | CMIP5 | The Coupled Model Intercomparison Project (CMIP) is a project of the World Climate Research Programme (WCRP) providing global climate projections to understand past, present and future climate changes. CMIP and its associated data infrastructure have become essential to the Intergovernmental Panel on Climate Change and other international and national climate assessments. CMIP5 is the fifth phase of the project and underpins the Intergovernmental Panel on Climate Change 5th Assessment Report (IPCC AR5). The CMIP5 phase involves a suite of common model experiments as well as an ensemble of CMIP-endorsed Model Intercomparison Projects (MIPs). The information included in the Interactive Atlas corresponds to ScenarioMIP where future projections are produced for different scenarios at a typical resolution of 200km (RCPs in this case) (Taylor et al. 2012; https://doi.org/10.1175/BAMS-D-11-00094.1, CDS catalogue; https://doi.org/10.24381/cds.d3513dbf). |
dataset | CORDEX | The Coordinated Regional Climate Downscaling Experiment (CORDEX) is a project of the World Climate Research Programme (WCRP) providing regional climate projections to understand past, present and future climate changes. CORDEX provides spatially detailed climate change projections from a plethora of regional climate models applied over 14 large continental areas, at horizontal grid spacing ranging from ∼12 to 50 km. These projections are driven at the boundaries by the output of global projections from the CMIP experiments (currently CMIP5). The resulting matrix of GCM-RCM simulations (of variables sizes for the different domains) represents the regional and global model uncertainties characterized by this dataset. These simulations have been used as a new line of evidence to assess regional climate projections in the latest contribution of the Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6), particularly in the regional chapters and the Atlas (Diez-Sierra et al. 2022; https://doi.org/10.1175/BAMS-D-22-0111.1, CDS catalogue; https://doi.org/10.24381/cds.bc91edc3). |
dataset | CORDEX-CORE | CORDEX-CORE is a CORDEX initiative developed with the aim of producing homogeneous regional climate model projections across all domains covering major inhabited regions worldwide at a 25 km resolution. This sub-ensemble of the worldwide CORDEX dataset constitutes a minimum homogeneous ensemble (six members) for worldwide climate change impact and adaptation studies and is composed by two regional models driven by three CMIP5 global models covering high, medium and low climate sensitivities. It also constitutes the main source of high-resolution regional climate projections to analyze climate change (CORDEX web; https://cordex.org/experiment-guidelines/cordex-core/cordex-core-simulations/, https://doi.org/10.24381/cds.bc91edc3, CORDEX-CORE is part of the CORDEX CDS catalogue (0.22 resolution); https://doi.org/10.24381/cds.bc91edc3). |
dataset | CORDEX-EUR-11 | CORDEX-EUR is the European branch of the Coordinated Regional Climate Downscaling Experiment (CORDEX). CORDEX-EUR-11 consists of coordinated high-resolution regional climate model (RCM) simulations for the European domain, conducted at the finer resolution of 0.11 degree (~12.5 km) driven by a subset of CMIP5 global models. The resulting matrix of GCM-RCM simulations (with approximately 60 members) represents the regional and global model uncertainties characterized by this dataset (Jacob et al. 2020; https://doi.org/10.1007/s10113-020-01606-9, CORDEX-EUR11 is part of the CORDEX CDS catalogue (0.11 resolution, European domain); https://doi.org/10.24381/cds.bc91edc3,). |
dataset | ERA5 | The flagship ERA5 dataset is the 5th generation of ECMWF reanalysis created by C3S to provide authoritative and quality-assured data and information about the past based on a unique combination of observations and state-of-the art weather and Earth system models. ERA5 now covers the period from 1940 to present and provides a physically-consistent view of the hourly evolution of a large set of atmospheric, land and oceanic variables at a resolution of ~31 km (more information; https://climate.copernicus.eu/what-copernicus-climate-change-services-era5-reanalysis-dataset-and-what-can-it-do-you, CDS catalogue; https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview). |
dataset | ERA5-Land | Global reanalysis dataset based on the land component of ECMWF ERA5 providing a physically-consistent view of the evolution of a large set of land variables over several decades at an enhanced horizontal resolution of 9 km. It is the first dataset of its kind able to describe the water and energy cycles (more information; https://climate.copernicus.eu/what-copernicus-climate-change-services-era5-reanalysis-dataset-and-what-can-it-do-you, CDS catalogue; https://doi.org/10.24381/cds.e2161bac). |
dataset | E-OBS | Gridded dataset obtained by interpolating (ordinary kriging) observations from a pan-European station network providing daily data for mean, minimum, and maximum, temperature, precipitation and sea level pressure. The data set covers the period back to 1950 and provides gridded fields at a spacing of 0.25 × 0.25 degree in regular latitude/longitude coordinates. E-OBS is used for model validation (the gridbox values represent areal averages) and, more generally, for monitoring the climate in Europe, particularly for the magnitude and frequency of daily extremes (Cornes et al. 2018; https://doi.org/10.1029/2017JD028200, CDS catalogue; https://doi.org/10.24381/cds.151d3ec6). |
dataset | ORAS5 | Ocean and sea-ice reanalysis (ORAS, or ocean syntheses) are reconstructions of the ocean and sea-ice states using an ocean–sea-ice coupled model driven by atmospheric surface forcing and constrained by ocean observations via a data assimilation method. ORAS5 is a global eddy-permitting ocean and sea-ice ensemble reanalysis providing historical ocean and sea-ice conditions from 1958 onwards (Zuo et al. 2019; https://doi.org/10.5194/os-15-779-2019, CDS catalogue; https://doi.org/10.24381/cds.67e8eeb7). |
magnitude | change | The change magnitude for a reference (typically future) period relative to a baseline period is computed for each grid point as the arithmetic difference between the climatology in the two time periods. |
magnitude | relative change | The relative change magnitude for a reference (typically future) period relative to a baseline period is computed for each grid point as the fraction of the change relative to the baseline value (for regionally aggregated results, relative changes are computed for the regionally aggregated values). |
magnitude | trend | The trend of a variable in a given reference period is the average rate of increase or decrease at a specific grid point over the specified time period. It is determined through simple linear regression using the available data for each grid point. Red indicates a positive trend, while blue indicates a negative one. In this dataset, trends are calculated for the observed variables (observational and reanalysis datasets). |
uncertainty | uncertainty | Mapping some estimates of robustness/uncertainty together with future climate change signals is standard practice to convey comprehensive climate change projections. This information is typically overlaid on the climate change signal using hatching (diagonal lines or crosses obscuring areas with uncertain signals). |
uncertainty | simple method | The simple method for representing the ensemble robustness of a climate change signal is the approach proposed in AR6. It consists of two categories: 1) agreement and 2) no agreement, based on whether or not at least 80% of the models agree on the sign of change of the ensemble mean (IPCC AR6-WGI Atlas; Cross-Chapter Box Atlas.1; https://doi.org/10.1017/9781009157896.021). |
uncertainty | advanced method | The advanced method for representing the ensemble robustness of the climate change signal is the approach proposed in AR6. It consists of three categories: 1) robust signal, 2) conflicting signals (crosses overlying the signals), and 3) no change or no robust signal (diagonal lines overlaid on the signal). The first two categories indicate significant changes, i.e. areas where the (20-year mean) climate change model signal likely emerges (individually in >=66% of the models) from the internal variability of 20-year mean values; these changes are defined as robust if at least 80% of the models agree on the sign of change, and as conflicting if less than 80% of the models agree on the sign of change. The third category indicates areas of low change values and/or low significance, where less than 66% of the models exhibit emergent signals. The level of Internal variability (or emergence threshold) was computed as 1.645*sqrt(2/20)*sigma, where 1.645 corresponds to a 90% confidence level and sigma represents the inter-annual standard deviation computed from the linearly detrended baseline period 1850-1900 (IPCC AR6-WGI Atlas; Cross-Chapter Box Atlas.1; https://doi.org/10.1017/9781009157896.021). |
variable | mean temperature | Mean of daily mean near-surface (2 metre) air temperature |
variable | minimum temperature | Mean of daily minimum near-surface (2 metre) air temperature |
variable | maximum temperature | Mean of daily maximum near-surface (2 metre) air temperature |
variable | minimum of minimum temperature | Minimum of daily minimum near-surface (2 metre) air temperature |
variable | maximum of maximum temperature | Maximum of daily maximum near-surface (2 metre) air temperature |
variable | days with maximum temperature above 35 °C | Count of days with maximum near-surface (2 metre) temperature above 35 °C |
variable | days with bias adjusted maximum temperature above 35 °C | Count of days with bias adjusted (simple linear scaling: mean adjustment; reference period: 1971-2010; reference dataset: WFDE5 bias adjusted ERA5) maximum near-surface (2-metre) temperature above 35 °C |
variable | days with maximum temperature above 40 °C | Count of days with maximum near-surface (2 metre) temperature above 40 °C |
variable | days with bias adjusted maximum temperature above 40 °C | Count of days with bias adjusted (simple linear scaling: mean adjustment; reference period: 1971-2010; reference dataset: WFDE5 bias adjusted ERA5) maximum near-surface (2-metre) temperature above 40 °C |
variable | frost days | Count of days with minimum near-surface (2 metre) temperature below 0 °C |
variable | heating degree-days | Energy consumption to heat the deficit of temperature below 15.5 °C |
variable | cooling degree-days | Energy consumption to cool the excess of temperature above 22 °C |
variable | precipitation | Mean of daily accumulated precipitation of liquid water equivalent from all phases |
variable | snowfall | Mean of daily accumulated liquid water equivalent thickness snowfall |
variable | maximum of 1-day precipitation | Maximum of 1-day accumulated precipitation of liquid water equivalent from all phases |
variable | maximum of 5-day precipitation | Maximum of 5-day accumulated precipitation of liquid water equivalent from all phases |
variable | consecutive dry days | Maximum of consecutive days when daily accumulated precipitation amount is below 1 mm |
variable | Standardised Precipitation Index (SPI-6) | Monthly index that compares accumulated precipitation for 6 months with the long-term distribution (reference period: 1971-2010) for the same location and accumulation period, as the number of standard deviations from the median |
variable | Standardised Precipitation Evapotranspiration Index (SPEI-6) | Monthly index that compares accumulated precipitation minus potential evapotranspiration (Hargreaves method) for 6 months with the long-term distribution (reference period: 1971-2010) for the same location and accumulation period, as the number of standard deviations from the median |
variable | wind speed | Mean of daily mean near-surface (10 metre) wind speed |
variable | sea surface temperature | Mean temperature of sea water near the surface |
variable | sea-ice area | Mean percentage of sea grid cell area covered by ice |
variable | specific humidity | Amount of moisture in the air near the surface divided by amount of air plus moisture at that location. |
variable | evaporation | Mean of daily amount of water in the atmosphere due to conversion of both liquid and solid phases to vapor including sublimation and transpiration (from underlying surface and vegetation) |
variable | soil moisture | Soil shallow moisture content, as the vertical sum per unit area of water in all phases contained in the upper soil portion to a depth of 7 to 10 cm (depending on the dataset) |
variable | runoff | Mean of daily amount per unit area of surface and subsurface liquid water which drains from land |
variable | cloud cover | Mean fraction (%) of the sky covered by clouds |
variable | surface solar radiation downwards | Incident solar (shortwave) radiation that reaches a horizontal plane at the surface |
variable | surface thermal radiation downwards | Incident thermal (longwave) radiation at the surface (during cloudless and overcast conditions) |
variable | sea level pressure | Average air pressure at mean sea level |