This catalogue entry provides daily and monthly global climate projections data from a large number of experiments, models and time periods computed in the framework of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). CMIP6 data underpins the Intergovernmental Panel on Climate Change 6th Assessment Report. The use of these data is mostly aimed at:

addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past.

The term "experiments" refers to the three main categories of CMIP6 simulations:

Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2014. Climate projection experiments following the combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP). The SSP scenarios provide different pathways of the future climate forcing. The period covered is typically 2015-2100.

This catalogue entry provides both two- and three-dimensional data, along with an option to apply spatial and/or temporal subsetting to data requests. This is a new feature of the global climate projection dataset, which relies on compute processes run simultaneously in the ESGF nodes, where the data are originally located. The data are produced by the participating institutes of the CMIP6 project.

This catalogue entry provides daily climate projections on single levels from a large number of experiments, models, members and time periods computed in the framework of the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The term "single levels" is used to express that the variables are computed at one vertical level which can be surface (or a level close to the surface) or a dedicated pressure level in the atmosphere. Multiple vertical levels are excluded from this catalogue entry. CMIP5 data are used extensively in the Intergovernmental Panel on Climate Change Assessment Reports (the latest one is IPCC AR5, which was published in 2014). The use of these data is mostly aimed at:

addressing outstanding scientific questions that arose as part of the IPCC reporting process; improving the understanding of the climate system; providing estimates of future climate change and related uncertainties; providing input data for the adaptation to the climate change; examining climate predictability and exploring the ability of models to predict climate on decadal time scales; evaluating how realistic the different models are in simulating the recent past.

The term "experiments" refers to the three main categories of CMIP5 simulations:

Historical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2005.; Ensemble of experiments from the Atmospheric Model Intercomparison Project (AMIP), which prescribes the oceanic variables for all models and during all period of the experiment. This configuration removes the added complexity of ocean-atmosphere feedbacks in the climate system. The period covered is typically 1950-2005. Ensemble of climate projection experiments following the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. The RCP scenarios provide different pathways of the future climate forcing. The period covered is typically, 2006-2100 some extended RCP experimental data is available from 2100-2300.

In CMIP5, the same experiments were run using different GCMs. In addition, for each model, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. Note that CMIP5 GCM data can be also used as lateral boundary conditions for Regional Climate Models (RCMs). RCMs are also available in the CDS (see CORDEX datasets). The data are produced by the participating institutes of the CMIP5 project. The latest CMIP GCM experiments will form the CMIP6 dataset, which will be published in the CDS in a later stage.

Please note this dataset supersedes previous versions on the Climate Data Portal. It has been uploaded following an update to the dataset in March 2023. This means sea level rise is approximately 1cm higher (larger) compared to the original data release (i.e. the previous version available on this portal) for all UKCP18 site-specific sea level projections at all timescales. For more details please refer to the technical note.What does the data show?The exploratory extended time-mean sea-level projections to 2300 show the amount of sea-level change (in cm) for each coastal location (grid-box) around the British Isles for several emission scenarios. Sea-level rise is the primary mechanism by which we expect coastal flood risk to change in the UK in the future. The amount of sea-level rise depends on the location around the British Isles and increases with higher emission scenarios. Here, we provide the relative time-mean sea-level projections to 2300, i.e. the local sea-level change experienced at a particular location compared to the 1981-2000 average, produced as part of UKCP18.For each grid box the time-mean sea-level change projections are provided for the end of each decade (e.g. 2010, 2020, 2030 etc) for three emission scenarios known as Representative Concentration Pathways (RCP) and for three percentiles.The emission scenarios are:RCP2.6RCP4.5RCP8.5The percentiles are:5th percentile50th percentile95th percentileImportant limitations of the dataWe cannot rule out substantial additional sea-level rise associated with ice sheet instability processes that are not represented in the UKCP18 projections, as discussed in the recent IPCC Sixth Assessment Report (AR6). These exploratory projections show sea levels continue to increase beyond 2100 even with large reductions in greenhouse gas emissions. It should be noted that these projections have a greater degree of uncertainty than the 21st Century Projections and should therefore be treated as illustrative of the potential future changes. They are designed to be used alongside the 21st Century projections for those interested in exploring post-2100 changes.What are the naming conventions and how do I explore the data?The data is supplied so that each row corresponds to the combination of a RCP emissions scenario and percentile value e.g. 'RCP45_50' is the RCP4.5 scenario and the 50th percentile. This can be viewed and filtered by the field 'RCP and Percentile'. The columns (fields) correspond to the end of each decade and the fields are named by sea level anomaly at year x, e.g. '2050 seaLevelAnom' is the sea level anomaly at 2050 compared to the 1981-2000 average.Please note that the styling and filtering options are independent of each other and the attribute you wish to style the data by can be set differently to the one you filter by. Please ensure that you have selected the RCP/percentile and decade you want to both filter and style the data by. Select the cell you are interested in to view all values.To understand how to explore the data please refer to the New Users ESRI Storymap.What are the emission scenarios?The 21st Century time-mean sea level projections were produced using some of the future emission scenarios used in the IPCC Fifth Assessment Report (AR5). These are RCP2.6, RCP4.5 and RCP8.5, which are based on the concentration of greenhouse gases and aerosols in the atmosphere. RCP2.6 is an aggressive mitigation pathway, where greenhouse gas emissions are strongly reduced. RCP4.5 is an intermediate ‘stabilisation’ pathway, where greenhouse gas emissions are reduced by varying levels. RCP8.5 is a high emission pathway, where greenhouse gas emissions continue to grow unmitigated. Further information is available in the Understanding Climate Data ESRI Storymap and the RCP Guidance on the UKCP18 website.What are the percentiles?The UKCP18 sea-level projections are based on a large Monte Carlo simulation that represents 450,000 possible outcomes in terms of global mean sea-level change. The Monte Carlo simulation is designed to sample the uncertainties across the different components of sea-level rise, and the amount of warming we see for a given emissions scenario across CMIP5 climate models. The percentiles are used to characterise the uncertainty in the Monte Carlo projections based on the statistical distribution of the 450,000 individual simulation members. For example, the 50th percentile represents the central estimate (median) amongst the model projections. Whilst the 95th percentile value means 95% of the model distribution is below that value and similarly the 5th percentile value means 5% of the model distribution is below that value. The range between the 5th to 95th percentiles represent the projection range amongst models and corresponds to the IPCC AR5 “likely range”. It should be noted that, there may be a greater than 10% chance that the real-world sea level rise lies outside this range.Data sourceThis data is an extract of a larger dataset (every year and more percentiles) which is available on CEDA at https://catalogue.ceda.ac.uk/uuid/a077f4058cda4cd4b37ccfbdf1a6bd29Data has been extracted from the v20221219 version (downloaded 17/04/2023) of three files:seaLevelAnom_marine-sim_rcp26_ann_2007-2300.ncseaLevelAnom_marine-sim_rcp45_ann_2007-2300.ncseaLevelAnom_marine-sim_rcp85_ann_2007-2300.ncUseful links to find out moreFor a comprehensive description of the underpinning science, evaluation and results see the UKCP18 Marine Projections Report (Palmer et al, 2018).For a discussion on ice sheet instability processes in the latest IPCC assessment report, see Fox-Kemper et al (2021). Technical note for the update to the underpinning data: https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/research/ukcp/ukcp_tech_note_sea_level_mar23.pdf.Further information in the Met Office Climate Data Portal Understanding Climate Data ESRI Storymap.

This dataset contains a 30-year average of annual average precipitation across all ten models and two greenhouse gas (RCP) scenarios in the ten model ensemble. Three named time periods are included “Historic Baseline (1961-1990)”, “Mid-Century (2035-2064)”, and “End of Century (2070-2099).”

The downscaling and selection of models for inclusion in ten and four model ensembles is described in Pierce et al. 2018, but summarized here. Thirty two global climate models (GCMs) were identified to meet the modeling requirements. From those, ten that closely simulate California’s climate were selected for additional analysis (Table 1, Pierce et al. 2018) and to form a ten model ensemble.

These data were downloaded from Cal-Adapt and prepared for use within CA Nature by California Natural Resource Agency and ESRI staff.

Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved from https://cal-adapt.org/

Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise Scenarios for the Fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.

LOCA is a statistical downscaling technique that uses past history to add improved fine-scale detail to global climate models. We have used LOCA to downscale 32 global climate models from the CMIP5 archive at a 1/16th degree spatial resolution, covering North America from central Mexico through Southern Canada. The historical period is 1950-2005, and there are two future scenarios available: RCP 4.5 and RCP 8.5 over the period 2006-2100 (although some models stop in 2099). The variables currently available are daily minimum and maximum temperature, and daily precipitation. For more information visit: http://loca.ucsd.edu/

This catalogue entry provides gridded data from global (CMIP5 and CMIP6) and regional (CORDEX) projections for the set of 22 variables and indices included in the IPCC Interactive Atlas, a novel contribution from Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6). These variables and indices are relevant for the climatic impact-drivers used in the regional assessments conducted in AR6 (Chapters 10, 11, 12 and Atlas), related to heat and cold, wet and dry, snow and ice, and wind. This dataset is particularly intended for Climate Data Store (CDS) users who want to develop customised products not directly available from the IPCC Interactive Atlas (e.g. regional information at national or subnational scales). This dataset includes gridded information with monthly/annual temporal resolution for historical experiments and climate projections based on Representative Concentration Pathways (RCP) / Shared Socioeconomic Pathways (SSP) scenarios for CMIP5/6 and CORDEX multi-model ensembles for the 22 variables and indices (computed from daily data). The ensembles are harmonised using regular grids with horizontal resolutions of 2° (CMIP5), 1° (CMIP6), 0.5° (CORDEX), and 0.25° (European CORDEX domain); details on the particular ensembles for each dataset are included in the documentation links. This dataset allows the reproduction, expansion and customisation of the climate change products displayed in the IPCC Interactive Atlas. This includes the global/continental maps of CMIP/CORDEX climate changes (for future periods across scenarios or for global warming levels, e.g. +2°C), and the regionally-aggregated time series, scatter plots, or global warming level plots. Related datasets, also available through the CDS, include the CMIP5/6 global climate projections and the CORDEX regional climate projections. The original CMIP and CORDEX data was produced by the institutions and modelling centres participating in these initiatives, as described in AR6 WGI Annex II, with partial support from different programmes, including support from Copernicus for some of the EURO-CORDEX runs and for data curation and publication of world-wide CORDEX datasets. As a result, the dataset is fully reproducible from the CDS for CORDEX, but not for CMIP (some models and versions are different in the CDS and the Atlas ensembles).
This dataset is distributed as part of the IPCC-DDC Atlas products under a Creative Commons Attribution 4.0 International License (CC-BY 4.0) and Copernicus has supported the standardisation and technical curation.

Fire weather refers to weather conditions that are conducive to fire. These conditions determine the fire season, which is the period(s) of the year during which fires are likely to start, spread and do sufficient damage to warrant organized fire suppression. The length of fire season is the difference between the start- and end-of-fire-season dates. These are defined by the Canadian Forest Fire Weather Index (FWI; http://cwfis.cfs.nrcan.gc.ca/) start-up and end dates. Start-up occurs when the station has been snow-free for 3 consecutive days, with noon temperatures of at least 12°C. For stations that do not report significant snow cover during the winter (i.e., less than 10 cm or snow-free for 75% of the days in January and February), start-up occurs when the mean daily temperature has been 6°C or higher for 3 consecutive days. The fire season ends with the onset of winter, generally following 7 consecutive days of snow cover. If there are no snow data, shutdown occurs following 7 consecutive days with noon temperatures lower than or equal to 5°C. Historical climate conditions were derived from the 1981–2010 Canadian Climate Normals. Future projections were computed using two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century. Provided layer: difference in projected fire season length for the medium-term (2041-2070) under the RCP 8.5 (continued emissions increases) compared to reference period across Canada.

The data describes future land use projections at 1 km^2 resolution developed by CRAFTY-GB. For each of six Shared Socioeconomic Pathways (SSP-RCP) scenarios, gridded land use maps for Great Britain are provided, each as a stacked raster file with seven bands representing land use at each decadal timestep, from 2020 to 2080. CRAFTY-GB is a new agent-based model of the British land system operating at a 1 km^2 resolution and based on a broad range of available land system data . The model is based on linked UK-RCP climate scenarios and UK-SSPs socio-economic pathway (SSP) scenarios, based on global SSPs developed by the Intergovernmental Panel on Climate Change (IPCC). It extrapolates the impact of these on the British Land system at decadal timesteps from 2020-2080.

Climate variations on seasonal-to-decadal (S2D) timescales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales an invaluable tool for policymakers and stakeholders. Such variations ... modulate the likelihood and intensity of extreme weather events including, tropical cyclones (TCs), heat waves, winter storms, atmospheric rivers (ARs), and floods, which have all been associated with (1) increases in human morbidity and mortality rates; (2) severe impacts on agriculture, energy use, and industrial activity; and (3) economic costs in the billions of dollars. Changes in prevailing climate patterns are also responsible for prolonged droughts, which can have profoundly negative effects on large segments of the world population. Enhancing our foreknowledge of climate variability on S2D time scales and understanding its influence on extreme weather events could help mitigate negative impacts on human and biological populations, making climate predictions an exceptionally important climate and social science frontier. Over the past six years, our research team consisting of scientists at Texas A&M University (TAMU) and the U.S. National Science Foundation National Center for Atmospheric Research (NSF NCAR) has made major breakthroughs in advancing high-resolution global climate modeling and prediction. We have completed an unprecedented 10-member ensemble of Community Earth System Model (CESM) historical and future climate simulations at a tropical cyclone-permitting and ocean-eddy-rich resolution (hereafter simply referred to as CESM-HR). This CESM-HR ensemble was completed as part of our NSF-funded project entitled "Understanding the Role of MESoscale Atmosphere-Ocean Interactions in Seasonal-to-Decadal CLImate Prediction (MESACLIP)". This ensemble is particularly timely, following the April 2023 report entitled "Extreme Weather Risk in a Changing Climate: Enhancing Prediction and Protecting Communities" from the U.S. President's Council of Advisors on Science and Technology (PCAST). Indeed, this report made several recommendations on how climate science can support the provision of information about future risks from extreme weather and highlight the urgent need for high-resolution simulations to improve predictions of extreme weather events and guide risk management strategies. More specifically, the report recognized that high-resolution simulations, in the range of 10 to 25 km horizontal resolution, would capture extreme events more accurately than typical low-resolution (approximately 100 km) climate projections, and it goes on to recommend "a focused federal effort to provide estimates of the risk that a weather event of a given severity will occur in any location and year between now and mid-century". Our 10-member CESM-HR ensemble is able to meet some of the key aspects of this PCAST report. The CESM-HR configuration is based on an earlier CESM version, CESM1.3, with many additional modifications and improvements. CESM-HR uses a 0.25 degree grid in the atmosphere and land components and a 0.1 degree grid in the ocean and sea-ice components. The primary reason for using an older model version, instead of the latest CESM2, is that CESM2 does not support a high-resolution version per the decision by the CESM Scientific Steering Committee. The component models within CESM1.3 are the Community Atmosphere Model version 5 (CAM5; Neale et al., 2012), the Parallel Ocean Program version 2 (POP2; Danabasoglu et al., 2012; Smith et al., 2010), the Community Ice Code version 4 (CICE4; Hunke & Lipscomb, 2008), and the Community Land Model version 4 (CLM4; Lawrence et al., 2011). The CESM-HR ensemble experimental design follows a similar approach as the CESM LENS1 large ensemble. We started with a 500-year preindustrial control (PI-CTRL) simulation forced by a perpetual climate forcing that corresponds to the year 1850 conditions. The first ensemble member is branched at year 250 of the PI-CTRL simulation and then integrated forward from year 1850 to 2100 (Figure 1). Ensemble members 2-10 are subsequently started from the year 1920 of ensemble member 1 and integrated forward to 2100 (Figure 1). Spread in the ensemble is generated by applying round-off level perturbations in the atmospheric potential temperature initial conditions for members 2-10. All 10 members use the same specified external climate forcing. Following the CMIP5 protocol for the Coupled Model Intercomparison Project phase 5 (CMIP5) experiments, historical forcing is used from 1920 to 2005 followed by the representative concentration pathway 8.5 (RCP 8.5) forcing from 2006 to 2100. RCP 8.5 is a high-emissions scenario and is frequently referred to as the "business as usual" scenario. It refers to the concentration of carbon that delivers global warming at an average of 8.5 W/m^2 across the planet by 2100. All 10 members produce a warming of approximately 4.5K at the end of 2100 in response to the applied historical and RCP 8.5 external forcing (Figure 1). The warming produced by CESM-HR is consistent with the warming from the standard low-resolution (approximately 1 degree) version of the model. The rate of warming simulated by CESM-HR over the observed period agrees very well with the observed rate of warming derived from the Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (Figure 1). Citation: The two papers linked below are the most appropriate references for the CESM-HR ensemble. To cite the dataset, use Chang et al. (2025). We ask that you also cite the dataset itself using the reference Castruccio et al. (2024) in any documents or publications using these data. Chang et al. (2020) describes the initial CESM-HR simulations, including the 500-year pre- industrial control simulation and the first 250-year historical and future climate simulation from 1850 to 2100. We would also appreciate receiving a copy of the relevant publications. This will help us to justify keeping the data freely available online in the future. Thank you!

World Terrestrial Ecosystems use a combination of landform, land cover, and climate region information to objectively characterize ecosystem types. Using global climate models, land cover and climate region can be projected into the future. The latest global climate models, part of the 6th Coupled Model Intercomparison Program (CMIP6), use a variety of developmental and emissions scenarios called the Shared Socioeconomic Pathways, or SSPs. This layer shows the projected World Terrestrial Ecosystems in 2050 using SSP1-2.6, which includes deep cuts in CO2 emissions and a transition to more sustainable development. This layer can be directly compared to the 2015 World Terrestrial Ecosystems v2 and the projected World Terrestrial Ecosystems in 2050 for SSP3-7.0 and SSP5-8.5. Those layers and others can be found in the WTE 2015 to 2050 Comparison Project Layers and Maps ArcGIS Online Group. To learn more about this work, read our open access peer-reviewed journal article in Global Ecology and Conservation, Volume 57, January 2025, e03370: Potential 2050 distributions of World Terrestrial Ecosystems from projections of changes in World Climate Regions and Global Land Cover.MethodologyEcosystems are mapped by combining remotely-sensed and field methods. From remote sensing, data derived from satellites are combined with landforms derived from elevation, land cover derived from multi-sensor imagery, and climate variables modeled into annual averages and indicators. From the field, scientists find patterns and measurements which delineate regions that cannot be derived from imagery, most notably in differentiating savanna from dry tropical rain forests. The most subtle boundaries require ground truthing to identify tricky vegetation differences, especially when telling apart species of grasses in the tropics.CHELSA Climate DataGlobal climate models are quite coarse in resolution, so downscaling techniques often are applied to provide more detailed spatial resolution. CHELSA version 2.1 provides a set of downscaled (1-km) climate models from CMIP6. We obtained five different downscaled projections for 2041-2070 and three SSP scenarios (1-2.6, 3-7.0, and 5-8.5), along with a historical climatology for 1981-2010 The v2.1 data was accessed in May of 2023 from CHELSA's data download site (Karger, et. al., 2017). We classified the CHELSA models according to the climate region definitions in Sayre, et. al., 2020. This layer represents an ensemble of the five different models for SSP1-2.6.An older version of World Terrestrial Ecosystems 2015 used a different source for downscaled climate data (WorldClim version 2). CHELSA leverages more accurate downscaling techniques for both historical and projected climate information. References: Sayre, Roger, Karagülle, Deniz, Frye, Charlie, Boucher, Timothy, Wolff, Nicholas H., Breyer, Sean, Wright, Dawn, Martin, Madeline, Butler, Kevin, Van Graafeiland, Keith, Touval, Keith, Sotomayor, Leonardo , McGowan, Jennifer , Game, Edward T., Possingham, Hugh. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems. Global Ecology and Conservation, v21. DOI: 10.1016/j.gecco.2019.e00860.Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. 2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122.Land CoverWe obtained three Plant Functional Type-based SSP land cover models for the year 2050 and one for 2015 from: https://zenodo.org/records/4584775 on Jun 2, 2023. The SSP models were for 1-2.6, 3-7.0, and 5-8.5. The land cover model for this layer was the SSP1-2.6 model.References: Chen, G., Li, X. & Liu, X. Global land projection based on plant functional types with a 1-km resolution under socio-climatic scenarios. Sci Data 9, 125 (2022). https://doi.org/10.1038/s41597-022-01208-6.Chen, G., Li, X., & Liu, X. (2021). Future global land datasets with a 1-km resolution based on the SSP-RCP scenarios [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4584775.World Ecological Facets (Hammond) Landform ClassesWe used the data from the image service in ArcGIS Living Atlas of the World.Reference: Karagulle, D., Frye, C., Sayre, R., Breyer, S., Aniello, P., Vaughan, R., & Wright, D. (2017). Modeling global Hammond landform regions from 250-m elevation data. Transactions in GIS, 21(5), 1040–1060. https://doi.org/10.1111/tgis.12265.

This catalogue entry provides Regional Climate Model (RCM) data on single levels from a number of experiments, models, domains, resolutions, ensemble members, time frequencies and periods computed over several regional domains all over the World in the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). The term "single levels" is used to express that the variables are 2D-matrices computed on one vertical level which can be surface (or a level close to the surface) or a dedicated pressure level in the atmosphere. Multiple vertical levels are excluded from this catalogue entry. High-resolution Regional Climate Models (RCMs) can provide climate change information on regional and local scales in relatively fine detail, which cannot be obtained from coarse scale Global Climate Models (GCMs). This is manifested in better description of small-scale regional climate characteristics and also in more accurate representation of extreme events. Consequently, outputs of such RCMs are indispensable in supporting regional and local climate impact studies and adaptation decisions. RCMs are not independent from the GCMs, since the GCMs provide lateral and lower boundary conditions to the regional models. In that sense RCMs can be viewed as magnifying glasses of the GCMs. The CORDEX experiments consist of RCM simulations representing different future socio-economic scenarios (forcings), different combinations of GCMs and RCMs and different ensemble members of the same GCM-RCM combinations. This experiment design through the ensemble members allows for studies addressing questions related to the key uncertainties in future climate change. These uncertainties come from differences in the scenarios of future socio-economic development, the imperfection of regional and global models used and the internal (natural) variability of the climate system. This experiment design allows for studies addressing questions related to the key uncertainties in future climate change:

what will future climate forcing be? what will be the response of the climate system to changes in forcing? what is the uncertainty related to natural variability of the climate system?

The term "experiment" in the CDS form refers to three main categories:

Evaluation: CORDEX experiment driven by ECMWF ERA-Interim reanalysis for a past period. These experiments can be used to evaluate the quality of the RCMs using perfect boundary conditions as provided by a reanalysis system. The period covered is typically 1980-2010; Historical: CORDEX experiment which covers a period for which modern climate observations exist. Boundary conditions are provided by GCMs. These experiments, that follow the observed changes in climate forcing, show how the RCMs perform for the past climate when forced by GCMs and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1950-2005; Scenario: Ensemble of CORDEX climate projection experiments using RCP (Representative Concentration Pathways) forcing scenarios. These scenarios are the RCP 2.6, 4.5 and 8.5 scenarios providing different pathways of the future climate forcing. Boundary conditions are provided by GCMs. The period covered is typically 2006-2100.

In CORDEX, the same experiments were done using different RCMs (labelled as “Regional Climate Model” in the CDS form). In addition, for each RCM, there is a variety of GCMs, which can be used as lateral boundary conditions. The GCMs used are coming from the CMIP5 (5th phase of the Coupled Model Intercomparison Project) archive. These GCM boundary conditions are labelled as “Global Climate Model” in the form and are also available in the CDS. Additionally, the uncertainty related to internal variability of the climate system is sampled by running several simulations with the same RCM-GCM combination. On the forms, these are indexed as separate ensemble members (the naming convention for ensemble members is available in the documentation). For each GCM, the same experiment was repeatedly done using slightly different conditions (like initial conditions or different physical parameterisations for instance) producing in that way an ensemble of experiments closely related. More details behind these sequential ensemble numbers is available in the detailed documentation. The data are produced by the institutes and modelling centres participating in the different CORDEX domains with partial support from different international and national contributions including support from COPERNICUS for some of the EURO-CORDEX runs. The data can be used for commercial purposes (unrestricted use) with the exception of the simulations from the following RCMs: BOUN-RegCM4-3 model (for Central Asia and Middle East and North Africa domains) and RU-CORE-RegCM4-3 model (for South-East Asia domain). Precise terms of use are provided in the CORDEX licence.

Evaluating multiple signals of climate change across the conterminous United States during three 30-year periods (2010�2039, 2040�2069, 2070�2099) during this century to a baseline period (1980�2009) emphasizes potential changes for growing degree days (GDD), plant hardiness zones (PHZ), and heat zones. These indices were derived using the CCSM4 and GFDL CM3 models under the representative concentration pathways 4.5 and 8.5, respectively, and included in Matthews et al. (2018). Daily temperature was downscaled by Maurer et al.�(https://doi.org/10.1029/2007EO470006 at a 1/8 degree grid scale and used to obtain growing degree days, plant hardiness zones, and heat zones.�Each of these indices provides unique information about plant health related to changes in climatic conditions that influence establishment, growth, and survival. These data and the calculated changes are provided as 14 individual IMG files for each index to assist with management planning and decision making into the future. For each of the four indices the following are included: two baseline files (1980�2009), three files representing 30-year periods for the scenario CCSM4 under RCP 4.5 along with three files of changes, and three files representing 30-year periods for the scenario GFDL CM3 under RCP 8.5 along with three files of changes. Growing degree days address an important component to general patterns of plant growth by accumulating the degree days across the growing season. This metric provides a level of detail related to defining the growing season potential. Here, we evaluate the accumulation of growing degree days at or above 5 �C (41 �F), assuming that limited growth occurs below 5 �C.�Specifically, we calculate growing degree days by first calculating the average daily temperature, based on the maximum and minimum projected daily temperature. We then subtract 5 �C from each mean value and then accumulate the positive difference values for all days within each year. The mean GDD values for the conterminous United States during the baseline period ranged from less than 100 to over 7,000 degree days, increasing from north to south with highest values in the Florida panhandle, southern Texas, southwestern Arizona, and southeastern California. GDD projections throughout the century suggest a ubiquitous increase across the United States with slightly less change in the Northeast and much greater increases throughout the southern United States under the high scenario. Original data and associated metadata can be downloaded from this website:�https://www.fs.usda.gov/rds/archive/Product/RDS-2019-0001This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The SnowClim Model and Dataset address the need for climate and snow data products that are based on physical principles, that are simulated at high spatial resolution, and that cover large geographic domains.

The SnowClim Model is a physics-based snow model that incorporates key energy balance processes necessary for capturing snowpack spatiotemporal variability, including under future climate scenarios, while optimizing computational efficiency throughout several empirical simplifications. The model code can be downloaded or run in the cloud using MATLAB Online through HydroShare.

The SnowClim Dataset consists of climate forcing data for and snow outputs from the SnowClim Model. Climate forcing data was downscaled from 4 km climate data from the Weather Research and Forecasting (WRF) model (Rasmussen and Liu, 2017) to ~210 m across the contiguous western United States. Climate forcings were downscaled from WRF directly for a present day (2000-2013) period and a thirteen year pseudo global warming scenario reflecting conditions between 2071-2100 under RCP 8.5. Climate forcings were prepared for a third time period by perturbing present-day downscaled climate data by the multi-model mean from CMIP5 to reflect conditions under pre-industrial conditions (1850-1879).

Additional details regarding the SnowClim model physics, model calibration, climate data downscaling, model application to the western US, and model performance are available in: Lute, A. C., Abatzoglou, J., and Link, T.: SnowClim v1.0: high-resolution snow model and data for the western United States, Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, 2022.

This data-set is structured such that each row corresponds to a combination of a Local Authority, an emissions scenario (RCP 2.6, RCP 4.5 and RCP 8.5) and a percentile among the model projections (5th, 50th, 95th). To display the data, you must first filter by the "RCP and Percentile" column. The columns (fields) correspond to each decade. The fields are named by sea level anomaly, in cm, at decade x, e.g. "seaLevelAnom_2050s" is the anomaly in 2051-2060 compared to the 1981-2000 average. Note, some Local Authorities will show as "Null", this means there is no sea level data available. To understand the data, refer to the LACS Scientific Detail.To understand how to explore the data, see the User Guides available on the Climate Data Portal.

This dataset is a component of a complete package of products from the Connect the Connecticut project. Connect the Connecticut is a collaborative effort to identify shared priorities for conserving the Connecticut River Watershed for future generations, considering the value of fish and wildlife species and the natural ecosystems they inhabit. Click here to download the full data package, including all documentation.

This dataset represents the climate response index for Moose. Climate response is one of several different measures of landscape capability that reflect different decisions (or assumptions) regarding how to incorporate current versus future land use and climate changes. The climate response index is based on the current landscape capability and predicted climate conditions in 2080 (averaged between RCP 4.5 and 8.5 scenarios). Specifically, this index is based on (1) current habitat conditions (reflecting current land use patterns) and (2) the average of current and future climate conditions. The climate response index is an attempt to emphasize areas that provide the best habitat and climate conditions today and where future climate conditions through 2080 are likely to remain suitable.

The climate response index for Moose, along with the 13 other representative terrestrial wildlife species, was not used as an input to the building of terrestrial cores, but is provided as an overlay to help inform the design with respect to potential climate change impacts.

This dataset is a component of a complete package of products from the Connect the Connecticut project. Connect the Connecticut is a collaborative effort to identify shared priorities for conserving the Connecticut River Watershed for future generations, considering the value of fish and wildlife species and the natural ecosystems they inhabit. Click here to download the full data package, including all documentation.

This dataset represents the climate response index for Wood Thrush. Climate response is one of several different measures of landscape capability that reflect different decisions (or assumptions) regarding how to incorporate current versus future land use and climate changes. The climate response index is based on the current landscape capability and predicted climate conditions in 2080 (averaged between RCP 4.5 and 8.5 scenarios). Specifically, this index is based on (1) current habitat conditions (reflecting current land use patterns) and (2) the average of current and future climate conditions. The climate response index is an attempt to emphasize areas that provide the best habitat and climate conditions today and where future climate conditions through 2080 are likely to remain suitable.

The climate response index for Wood Thrush, along with the 13 other representative terrestrial wildlife species, was not used as an input to the building of terrestrial cores, but is provided as an overlay to help inform the design with respect to potential climate change impacts.

What does the data show?

Railway lines per area (m/km2) 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

Rail Infrastructure

Metric

Railway lines per area

Unit

m/km2

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

N/A

Baseline Data Source

WFP 2014

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

The need for a research tool that integrates the many physical and biological processes on a farm has led to the development of the Integrated Farm System Model (IFSM). The model has been used to evaluate a wide variety of technologies and management strategies, and these analyses have been reported in the scientific and farm-trade literature. Systems research in dairy and beef production remains as the primary purpose of this tool, but the model also provides an effective teaching aid. With the model, students gain a better appreciation for the complexity of livestock forage systems. The learn how small changes affect many parts of the system, causing unanticipated results. They may also use the model to develop a more optimum food production system. When used in extension type teaching, producers can learn more about their farms and obtain information useful in strategic planning. By testing and comparing different options with the model, those offering the greatest economic benefit with acceptable environmental impact can be found. Input information is supplied to the program through three parameter files. The farm parameter file contains data describing the farm such as crop areas, soil type, equipment and structures used, numbers of animals at various ages, harvest, tillage, and manure handling strategies, and prices for various farm inputs and outputs. The machinery file includes parameters for each machine available for use on a simulated farm. Simulation output is available in four files, which contain summary tables, report tables, optional tables, and parameter tables. The summary tables provide average performance, environmental impact, costs, and returns for the years simulated. These values consist of crop yields, feeds produced, feeds bought and sold, manure produced, nutrient losses to the environment, production costs, income from products sold, and the net return or profitability of the farm. Values are provided for the average and standard deviation of each over all simulated years. The report tables provide extensive output information including all the data given in the summary tables. In these tables, values are given for each simulated year of weather as well as the mean and variance over all simulated years. Optional tables are available for a closer inspection of how the components of the full simulation are functioning. These tables include very detailed data, often on a daily basis. Parameter tables summarize the input parameters specified for a given simulation. These tables provide a convenient method of documenting the parameter settings used for a simulation. Resources in this dataset:Resource Title: Projected Climate Data for IFSM. File Name: Web Page, url: https://res1wwwd-o-tarsd-o-tusdad-o-tgov.vcapture.xyz/research/software/download/?softwareid=497&modecode=80-70-05-00 Downscaled climate data (1950 to 2100) are available for 78 locations across the United States formatted for use in IFSM. Each location includes 18 climate files created using 9 general circulation models (GCM) and 2 projected emission scenarios. Emission scenarios include Representative Concentration Pathways (RCP) 4.5 and 8.5 where RCP 4.5 represents a somewhat optimistic outlook for reducing greenhouse gas emissions and 8.5 represents continuing the current trend for emissions.

This catalogue entry provides the gridded climate data (monthly/annual timeseries) used for the Copernicus Climate Change Service Atlas (C3S Atlas). The gridded datasets consist of in-situ and satellite observation-based datasets, reanalyses (CERRA, ERA5, ERA5-Land, and ORAS5) and global (CMIP5 and CMIP6) and regional (CORDEX) climate projections for the variables and indices included in the C3S Atlas. This dataset complements the Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas (IPCC Atlas dataset hereafter), including new datasets, variables and indices. The variables and indices describe various types of climatic impact characteristics: heat and cold, wet and dry, snow and ice, wind and radiation, ocean, circulation and drought characteristics of the climate system. All data sources included in this entry are available in the Climate Data Store (CDS, see “Related data” in the sidebar). Contrary to the frozen IPCC Atlas dataset, this entry will update adding new data on a regular basis. This dataset includes gridded information with monthly/annual temporal resolution for observations/reanalyses of the recent past and climate projections for the 35 variables and indices computed from daily/monthly data across the different datasets. The climate projections are based on Representative Concentration Pathways (RCP) / Shared Socioeconomic Pathways (SSP) scenarios. The datasets are harmonised using regular latitude-longitude grids. Bias correction is available for threshold-based indices. Two methods are available, depending on the variable; linear scaling and the ISIMIP method. This dataset allows the reproduction, expansion and customisation of the climate change products provided interactively by the Copernicus Interactive Climate Atlas. This is an interactive web application displaying global/regional maps of observed trends and climate changes for future periods across scenarios or for global warming levels, and regionally aggregated time series, seasonal cycle plots and climate stripes.

[Metadata] Tropical storms, hurricanes, and tsunamis create waves that flood low-lying coastal areas. The National Flood Insurance Program (NFIP) produces flood insurance rate maps (FIRMs) that depict flood risk zones referred to as Special Flood Hazard Areas (SFHA) based modeling 1%-annual-chance flood event also referred to as a 100-year flood. The purpose of the FIRM is twofold: (1) to provide the basis for application of regulatory standards and (2) to provide the basis for insurance rating.SFHAs identify areas at risk from infrequent but severe storm-induced wave events and riverine flood events that are based upon historical record. By law (44 Code of Federal Regulations [CFR] 60.3), FEMA can only map flood risk that will be utilized for land use regulation or insurance rating based on historical data, therefore, future conditions with sea level rise and other impacts of climate change are not considered in FIRMs. It is important to note that FEMA can produce Flood Insurance Rate Maps that include future condition floodplains, but these would be considered “awareness” zones and not to be used for regulatory of insurance rating purposes.The State of Hawai‘i 2018 Hazard Mitigation Plan incorporated the results of modeling and an assessment of vulnerability to coastal flooding from storm-induced wave events with sea level rise (Tetra Tech Inc., 2018). The 1% annual-chance-coastal flood zone with sea level rise (1%CFZ) was modeled to estimate coastal flood extents and wave heights for wave-generating events with sea level rise. Modeling was conducted by Sobis Inc. under State of Hawaiʻi Department of Land and Natural Resources Contract No: 64064. The 1%CFZ with 3.2 feet of sea level rise was utilized to assess vulnerability to coastal event-based flooding in mid to - late century.The 1%CFZ with sea level rise would greatly expand the impacts from a 100-year flood event meaning that more coastal land area will be exposed to damaging waves. For example, over 120 critical infrastructure facilities in the City and County of Honolulu, including water, waste, and wastewater systems and communication and energy facilities would be impacted in the 1%CFZ with 3.2 feet of sea level rise (Tetra Tech Inc., 2018). This is double the number of facilities in the SFHA which includes the impacts of riverine flooding.A simplified version of the Wave Height Analysis for Flood Insurance Studies (WHAFIS) extension (FEMA, 2019b) included in Hazus-MH, was used to create the 1% annual chance coastal floodplain. Hazus is a nationally applicable standardized methodology that contains models for estimating potential losses from earthquakes, floods, tsunamis, and hurricanes (FEMA, 2019a). The current 1%-annual-chance stillwater elevations were collected using the most current flood insurance studies (FIS) for each island conducted by FEMA (FEMA, 2004, 2010, 2014, 2015). The FIS calculates the 1%-annual-chance stillwater elevation, wave setup, and wave run-up (called maximum wave crest) at regularly-spaced transects around the islands based on historical data. Modeling for the 1%CFZ used the NOAA 3-meter digital elevation model (DEM) which incorporates LiDAR data sets collected between 2003 and 2007 from NOAA, FEMA, the State of Hawaiʻi Emergency Management Agency, and the USACE (NOAA National Centers for Environmental Information, 2017).Before Hazus was run for future conditions, it was run for the current conditions and compared to the FEMA regulatory floodplain to determine model accuracy. This also helped determine the stillwater elevation for the large gaps between some transects in the FIS. Hazus was run at 0.5-foot stillwater level intervals and the results were compared to the existing Flood Insurance Rate Map (FIRM). The interval of 0.5-feet was chosen as a small enough step to result in a near approximation of the FIRM while not being too impractically narrow to require the testing of dozens of input elevations. The elevation which matched up best was used as the current base flood elevation.Key steps in modeling the projected 1%CFZ with sea level rise include: (1) generating a contiguous (no gaps along the shoreline) and present-day 1%-annual-chance stillwater elevation based on the most recent FIS, (2) elevating the present-day 1%-annual-chance stillwater elevation by adding projected sea level rise heights, and (3) modeling the projected 1%-annual-chance coastal flood with sea level rise in HAZUS using the 1%-annual-chance wave setup and run-up from the FIS. The 1%CFZ extent and depth was generated using the HAZUS 3.2 coastal flood risk assessment model, 3-meter DEM, the FIS for each island, and the IPCC AR5 upper sea level projection for RCP 8.5 scenario for 0.6 feet, 1.0 feet, 2.0 feet, and 3.2 feet of sea level rise above MHHW (IPCC, 2014). The HAZUS output includes the estimated spatial extent of coastal flooding as well as an estimated flood depth map grid for the four sea level rise projections.Using the current floodplain generated with Hazus, the projected 1%-annual-chance stillwater elevation was generated using the four sea level rise projections. This stillwater elevation with sea level rise was used as a basis for modeling. The projected 1%-annual coastal flood with sea level rise was modeled in Hazus using the current 1%-annual-chance wave setup and run-up from the FIS and the projected 1%-annual-chance stillwater elevation with sea level rise. Statewide GIS Program staff extracted individual island layers for ease of downloading. A statewide layer is also available as a REST service, and is available for download from the Statewide GIS geoportal at https://geoportal.hawaii.gov/, or at the Program's legacy download site at https://planning.hawaii.gov/gis/download-gis-data-expanded/#009. For additional information, please refer to summary metadata at https://files.hawaii.gov/dbedt/op/gis/data/coastal_flood_zones_summary.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov.