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South America Seasonal Hourly 1995-2021 IR OT Hour Count
Raster counts of anvil cloud days per month with hourly timesteps, as part of a South America convection climatology. Published by mstisdal.
This Collection provides Sentinel-3 SLSTR Level-2 Land Surface Temperature products containing data on land surface temperature measurements on a 1km grid. Radiance is measured in two channels to determine the temperature of the Earth's surface skin in the instrument field of view, where the term "skin" refers to the top surface of bare soil or the effective emitting temperature of vegetation canopies as viewed from above.
Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents mean monthly temperature values for the month of September across North America for the 1981–2010 climatological reference period, in degrees Celsius. The layer is part of a 12-raster-file (one per month) dataset derived from a kilometer-scale climate product generated using the CHELSA downscaling model, and represents long-term climatological means. The North American monthly temperature layers were derived from the CHELSA Climatologies v2.1 dataset, which is based on statistically downscaled temperature and precipitation data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a statistical downscaling approach to atmospheric temperature fields to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Karger et al. (2017). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial and seasonal patterns of monthly temperature across North America. Supporting climate, hydrological, and water-resource research and planning. Informing agricultural and ecological modeling and land-use planning. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download
The World Climate Research Programme's 6th Coupled Model Intercomparison Project (CMIP6) represents an enormous advance in the quality, detail, and scope of climate modeling. The Global Downscaled Projections for Climate Impacts Research dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the Climate Impact Lab provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid. ## Accessing the data GDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license: * Public domain (CC0-1.0) collection * Attribution (CC BY 4.0) collection * Attribution-ShareAlike (CC BY SA 4.0) collection Each modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by-sa#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by-sa#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license. ## Data format & contents The data is stored as partitioned zarr stores (see https://zarr.readthedocs.io), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 179MB in memory. Historical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM. The spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the “lon” coordinate extends from -179.875 to 179.875, and the “lat” coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc). ## Available institutions, models, and scenarios by license collection | Modeling institution | Source model | Available experiments | License collection | | -------------------- | ----------------- | ------------------------------------------ | ---------------------- | | CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets | | INM | INM-CM4-8 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets | | INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets | | BCC | BCC-CSM2-MR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] | | CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40] | | CMCC | CMCC-ESM2 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40] | | CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 | CC-BY-40] | | CSIRO | ACCESS-ESM1-5 | SSP1-2.6, SSP2-4.5, and SSP3-7.0 | CC-BY-40] | | MIROC | MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] | | MIROC | MIROC6 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] | | MOHC | HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40] | | MOHC | UKESM1-0-LL | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] | | MPI-M | MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] | | MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and SSP5-8.5 | CC-BY-40] | | NCC | NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] | | NCC | NorESM2-MM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] | | NOAA-GFDL | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 | CC-BY-40] | | NOAA-GFDL | GFDL-ESM4 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] | | NUIST | NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40] | | EC-Earth-Consortium | EC-Earth3 | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40] | | EC-Earth-Consortium | EC-Earth3-AerChem | ssp370 | CC-BY-40] | | EC-Earth-Consortium | EC-Earth3-CC | ssp245 and ssp585 | CC-BY-40] | | EC-Earth-Consortium | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40] | | EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40] | | CCCma | CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-SA-40] | *Notes:* [^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables. [^2]: The institution which ran MPI-ESM1-2-HR’s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios. ## Project methods This project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by Cannon et al. (2015), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5). We then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD). Together, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts. For further documentation, see Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts (EGUsphere, 2022 [preprint]). ## Citing, licensing, and using data produced by this project Projects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information. ### CIL GDPCIR Users are requested to cite this project in derived works. Our method documentation paper may be cited using the following: > Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. The code repository may be cited using the following: > Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794 ### ERA5 Additionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the ECMWF guide to citing a dataset on the Climate Data Store: > Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999–2049. DOI: 10.1002/qj.3803 > > Muñoz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: 10.24381/cds.e2161bac > > Muñoz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: 10.24381/cds.e2161bac ### GCM-specific citations & licenses The CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons BY-SA 4.0 or BY-NC-SA 4.0 licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here. #### Public Domain Datasets The following bias corrected and downscaled model simulations are available in the public domain using a CC0 1.0 Universal Public Domain Declaration. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0. * **FGOALS-g3** License description: data_licenses/FGOALS-g3.txt CMIP Citation: > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783 ScenarioMIP Citation: > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056 * **INM-CM4-8** License description: data_licenses/INM-CM4-8.txt CMIP Citation: > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422 ScenarioMIP Citation: > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321 * **INM-CM5-0** License description: data_licenses/INM-CM5-0.txt CMIP Citation: > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423 ScenarioMIP Citation: > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322 #### CC-BY-4.0 The following bias corrected and downscaled model simulations are licensed under a Creative Commons Attribution 4.0 International License. Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by. * **ACCESS-CM2** License description: data_licenses/ACCESS-CM2.txt CMIP Citation: > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281 ScenarioMIP Citation: > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285 * **ACCESS-ESM1-5** License description: data_licenses/ACCESS-ESM1-5.txt CMIP Citation: > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288 ScenarioMIP Citation: > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291 * **BCC-CSM2-MR** License description: data_licenses/BCC-CSM2-MR.txt CMIP Citation: > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725 ScenarioMIP Citation: > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732 * **CMCC-CM2-SR5** License description: data_licenses/CMCC-CM2-SR5.txt CMIP Citation: > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362 ScenarioMIP Citation: > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365 * **CMCC-ESM2** License description: data_licenses/CMCC-ESM2.txt CMIP Citation: > Lovato, Tomas; Peano, Daniele; Butenschön, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164 ScenarioMIP Citation: > Lovato, Tomas; Peano, Daniele; Butenschön, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168 * **EC-Earth3-AerChem** License description: data_licenses/EC-Earth3-AerChem.txt CMIP Citation: > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639 ScenarioMIP Citation: > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724 * **EC-Earth3-CC** License description: data_licenses/EC-Earth3-CC.txt CMIP Citation: > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640 ScenarioMIP Citation: > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327 * **EC-Earth3-Veg-LR** License description: data_licenses/EC-Earth3-Veg-LR.txt CMIP Citation: > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643 ScenarioMIP Citation: > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728 * **EC-Earth3-Veg** License description: data_licenses/EC-Earth3-Veg.txt CMIP Citation: > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642 ScenarioMIP Citation: > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727 * **EC-Earth3** License description: data_licenses/EC-Earth3.txt CMIP Citation: > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181 ScenarioMIP Citation: > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251 * **GFDL-CM4** License description: data_licenses/GFDL-CM4.txt CMIP Citation: > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew;
The World Climate Research Programme's 6th Coupled Model Intercomparison Project (CMIP6) represents an enormous advance in the quality, detail, and scope of climate modeling. The Global Downscaled Projections for Climate Impacts Research dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the Climate Impact Lab provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid. ## Accessing the data GDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license: * Public domain (CC0-1.0) collection * Attribution (CC BY 4.0) collection Each modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license. ## Data format & contents The data is stored as partitioned zarr stores (see https://zarr.readthedocs.io), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory. Historical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM. The spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the “lon” coordinate extends from -179.875 to 179.875, and the “lat” coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc). ## Available institutions, models, and scenarios by license collection | Modeling institution | Source model | Available experiments | License collection | | -------------------- | ----------------- | ------------------------------------------ | ---------------------- | | CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets | | INM | INM-CM4-8 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets | | INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets | | BCC | BCC-CSM2-MR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 | | CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 | | CMCC | CMCC-ESM2 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 | | CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 | CC-BY-40 | | CSIRO | ACCESS-ESM1-5 | SSP1-2.6, SSP2-4.5, and SSP3-7.0 | CC-BY-40 | | MIROC | MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 | | MIROC | MIROC6 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 | | MOHC | HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 | | MOHC | UKESM1-0-LL | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 | | MPI-M | MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 | | MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and SSP5-8.5 | CC-BY-40 | | NCC | NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 | | NCC | NorESM2-MM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 | | NOAA-GFDL | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 | CC-BY-40 | | NOAA-GFDL | GFDL-ESM4 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 | | NUIST | NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 | | EC-Earth-Consortium | EC-Earth3 | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 | | EC-Earth-Consortium | EC-Earth3-AerChem | ssp370 | CC-BY-40 | | EC-Earth-Consortium | EC-Earth3-CC | ssp245 and ssp585 | CC-BY-40 | | EC-Earth-Consortium | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 | | EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 | | CCCma | CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40[^3] | *Notes:* [^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables. [^2]: The institution which ran MPI-ESM1-2-HR’s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios. [^3]: This dataset was previously licensed as CC BY-SA 4.0, but was relicensed under CC BY 4.0 in March, 2023. ## Project methods This project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by Cannon et al. (2015), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5). We then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD). Together, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts. For further documentation, see Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts (EGUsphere, 2022 [preprint]). ## Citing, licensing, and using data produced by this project Projects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information. ### CIL GDPCIR Users are requested to cite this project in derived works. Our method documentation paper may be cited using the following: > Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. The code repository may be cited using the following: > Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794 ### ERA5 Additionally, we request you cite the historical dataset used in bias correction and downscaling, ERA5. See the ECMWF guide to citing a dataset on the Climate Data Store: > Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999–2049. DOI: 10.1002/qj.3803 > > Muñoz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: 10.24381/cds.e2161bac > > Muñoz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: 10.24381/cds.e2161bac ### GCM-specific citations & licenses The CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons BY-SA 4.0 or BY-NC-SA 4.0 licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here. #### Public Domain Datasets The following bias corrected and downscaled model simulations are available in the public domain using a CC0 1.0 Universal Public Domain Declaration. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0. * **FGOALS-g3** License description: data_licenses/FGOALS-g3.txt CMIP Citation: > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783 ScenarioMIP Citation: > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056 * **INM-CM4-8** License description: data_licenses/INM-CM4-8.txt CMIP Citation: > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422 ScenarioMIP Citation: > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321 * **INM-CM5-0** License description: data_licenses/INM-CM5-0.txt CMIP Citation: > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423 ScenarioMIP Citation: > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322 #### CC-BY-4.0 The following bias corrected and downscaled model simulations are licensed under a Creative Commons Attribution 4.0 International License. Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by. * **ACCESS-CM2** License description: data_licenses/ACCESS-CM2.txt CMIP Citation: > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281 ScenarioMIP Citation: > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285 * **ACCESS-ESM1-5** License description: data_licenses/ACCESS-ESM1-5.txt CMIP Citation: > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288 ScenarioMIP Citation: > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291 * **BCC-CSM2-MR** License description: data_licenses/BCC-CSM2-MR.txt CMIP Citation: > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725 ScenarioMIP Citation: > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732 * **CMCC-CM2-SR5** License description: data_licenses/CMCC-CM2-SR5.txt CMIP Citation: > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362 ScenarioMIP Citation: > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365 * **CMCC-ESM2** License description: data_licenses/CMCC-ESM2.txt CMIP Citation: > Lovato, Tomas; Peano, Daniele; Butenschön, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164 ScenarioMIP Citation: > Lovato, Tomas; Peano, Daniele; Butenschön, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168 * **EC-Earth3-AerChem** License description: data_licenses/EC-Earth3-AerChem.txt CMIP Citation: > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639 ScenarioMIP Citation: > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724 * **EC-Earth3-CC** License description: data_licenses/EC-Earth3-CC.txt CMIP Citation: > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640 ScenarioMIP Citation: > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327 * **EC-Earth3-Veg-LR** License description: data_licenses/EC-Earth3-Veg-LR.txt CMIP Citation: > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643 ScenarioMIP Citation: > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728 * **EC-Earth3-Veg** License description: data_licenses/EC-Earth3-Veg.txt CMIP Citation: > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642 ScenarioMIP Citation: > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727 * **EC-Earth3** License description: data_licenses/EC-Earth3.txt CMIP Citation: > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181 ScenarioMIP Citation: > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251 * **GFDL-CM4** License description: data_licenses/GFDL-CM4.txt CMIP Citation: > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Ha
This collection offers 11 whole-atmosphere parameters from the Met Office UKV 2km deterministic forecast. This is a high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, projected on to a 2km horizontal grid. The data is available as NetCDF files. ## Coverage area The forecast covers the UK and Ireland, with the following latitude and longitude coordinates for each corner of the included area: * Southwest: 48.8643°N, 10.6734°W * Northwest: 61.3322°N, 13.7254°W * Northeast: 61.6102°N, 4.3408°E * Southeast: 49.0594°N, 2.4654°E ## Timesteps The following time steps are available: * every hour from 0 to 54 hours * every 3 hours from 57 to 120 hours Exceptions: * CAPE surface (CAPE_surface) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours * Hourly lightning flash accumulation (lightning_flash_accumulation-PT01H) is only available every hour from 1 to 54 hours ## Update frequency There are three lengths of model run, each with its own update frequency: There are three lengths of model run, each with its own update frequency: * Nowcast: forecasts the next 12 hours and are at 0100, 0200, 0400, 0500, 0700, 0800, 1000, 1100, 1300, 1400, 1600, 1700, 1900, 2000, 2200 and 2300 UTC. * Short: forecasts the next 54 hours and are at 0000, 0600, 0900, 1200, 1800 and 2100 UTC. * Medium: forecasts the next 120 hours and are at 0300 and 1500 UTC. ## Archive length and latency As of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date. The data is typically available 3-6 hours after the model run time. ## Technical specs The data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components: * variables store the data * dimensions give relevant dimension information for the variables * attributes provide auxiliary information about the variables or dataset itself NetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers. Iris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data. Find further support on using Iris with NetCDF files. ## Help us improve the data services we offer Join the Met Office research panel to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities. ## How to cite UKV 2km deterministic forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897). ## License British Crown copyright 2023-2025, the Met Office, is licensed under CC BY-SA. ## Providers Met Office See all datasets managed by Met Office. ## Contact [servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.
This collection offers 14 whole-atmosphere parameters from the Met Office global deterministic 10km forecast. This is a numerical weather prediction forecast for the whole globe, with a resolution of approximately 0.09 degrees i.e. 10km (2,560 x 1,920 grid points). The data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes. ## Timesteps The following timesteps are available: * every hour from 0 to 54 hours (for most parameters, see parameter table for exceptions) * every 3 hours from 57 to 144 hours * every 6 hours from 150 to 168 hours ## Update Frequency The model is run four times each day, with forecast reference times of 00:00, 06:00, 12:00 and 18:00 (UTC). The runs at 00:00 and 12:00 provide data for the next 168 hours. The runs at 06:00 and 18:00 provide data for the next 67 hours. The forecast reference time represents the nominal data time or start time of a model forecast run, rather than the time when the data is available. ## Archive length and latency As of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date. The data is typically available 6 hours after the model run time. ## Technical specs The data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components: * Variables store the data * Dimensions give relevant dimension information for the variables * Attributes provide auxiliary information about the variables or dataset itself NetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers. Iris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data. Find further support on using Iris with NetCDF files. ## Help us improve the data services we offer Join the Met Office research panel to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities. ## How to cite Met Office global deterministic 10km forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897). ## License British Crown copyright 2023-2025, the Met Office, is licensed under CC BY-SA. ## Providers Met Office See all datasets managed by Met Office. ## Contact [servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.
Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents mean monthly temperature values for the month of July across North America for the 1981–2010 climatological reference period, in degrees Celsius. The layer is part of a 12-raster-file (one per month) dataset derived from a kilometer-scale climate product generated using the CHELSA downscaling model, and represents long-term climatological means. The North American monthly temperature layers were derived from the CHELSA Climatologies v2.1 dataset, which is based on statistically downscaled temperature and precipitation data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a statistical downscaling approach to atmospheric temperature fields to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Karger et al. (2017). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial and seasonal patterns of monthly temperature across North America. Supporting climate, hydrological, and water-resource research and planning. Informing agricultural and ecological modeling and land-use planning. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download
The Council initiated a Climate Vulnerability Assessment in response to the increased frequency of extreme heat and flooding events associated with climate change. The Land Surface Temperature raster dataset is intended for planning and alternatives analysis. This work supports regional climate mitigation and resiliency planning efforts. The Land Surface Temperature (LST) raster dataset is a dataset derived from a satellite image from Landsat 8 to show the land surface temperature for the seven-county metropolitan region of the Twin Cities, in degrees Fahrenheit. In essence, this map shows how hot the ground was to the touch at any given location. This satellite image was taken at 11:59 am CDT on July 22, 2016. At this time, the air temperature was 90F with a heat index of 90.3F, as taken at the Minneapolis-St. Paul International Airport (Minneapolis-St Paul International Airport, 2016). This was the third day of a regional heat wave, and while temperatures overnight had dipped down to around 74F, temperatures had climbed up to a maximum temperature of 97F by around 5-6pm, resulting in the hottest day in roughly three years (Midwestern Regional Climate Center, 2016). The original thermal image was taken at a 100 x 100 meter resolution, but was re-scaled and processed at the 30 x 30 meter scale. Landsat 8 collects image data at a spatial resolution of 30 meters (visible, NIR, SWIR); 100 meters (thermal); and 15 meters (panchromatic). This Landsat 8 raster image was first used to create a NDVI raster dataset. Using that NDVI dataset with Bands 10 and 11 of the Landsat 8 raster dataset, a Land Surface Temperature raster can be derived. That raster is further processed from celsius to fahrenheit, then clipped to the 7-county metropolitan area, and all water bodies removed from the final image. The LST values without regional water bodies is the primary basis for the Metropolitan Council Climate Vulnerability Assessment Report. Part of this report specifically considers the urban heat island effect, or the effect of human activity and the built environment on increases in urban temperature on human life, and as water has different heat retention properties than most land surfaces, we chose to do our analysis without regional water bodies. It should be noted that the lowest original LST values were water bodies, and so by removing the water bodies from the map the minimum LST value raised 0.6F. This is not particularly significant, except that it would minimally affect which values would be highlighted in the third layer of this package.
Overview This layer, prepared by the Commission for Environmental Cooperation (CEC), represents the month of July within a dataset of 12 raster files (one for each month) representing total precipitation values across North America for the 1981–2010 climatological reference period, in millimeters. The dataset is derived from a kilometer-scale climate product generated using the CHELSA downscaling model and represents long-term climatological means. The North American monthly precipitation layers were derived from the CHELSA Climatologies v2.1 dataset, which is based on statistically downscaled temperature and precipitation data at a spatial resolution of 30 arc-seconds (~1 km). CHELSA (Climatologies at High Resolution for the Earth's Land Surface Areas) applies a statistical downscaling approach to atmospheric precipitation fields to produce high-resolution climate surfaces. Additional technical details on the downscaling methodology and derived climate predictors are provided in: Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. https://doi.org/10.1038/sdata.2017.122 Geographic Coverage The coverage spans North America, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC). Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Karger et al. (2017). The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation with appropriate attribution. Update frequency: Irregular Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and the (CHELSA) initiative at the Swiss Federal Research Institute WSL (Land Change Science group) would be appreciated in products derived from these data. Vintage: 1981-2010 Applications (Intended Use) Analyzing spatial and seasonal patterns of monthly precipitation across North America. Supporting climate, hydrological, and water-resource research and planning. Informing agricultural and ecological modeling and land-use planning. Integration with other thematic layers in the North American Environmental Atlas for continental-scale analysis. Supporting environmental cooperation and policy-making among Canada, Mexico, and the United States. Files Download
The Prediction Of Worldwide Energy Resource (POWER) Project gathers NASA Earth Observation data and parameters related to the fields of surface solar irradiance and meteorology to serve out to the public in several free, easy-to-access and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in renewable energy development, building energy efficiency, and supporting agriculture projects. POWER is funded through the NASA Applied Sciences Program within the Earth Science Mission Directorate at NASA Langley Research Center (LaRC). ---------------------------------------------------------------------------------------------------------------------------------------------------------------- Time Interval: Yearly Time Extent: 1981 to 2021 Time Standard: Coordinated Universal Time (UTC) Grid Size: The data is vectorized from a 0.5° x 0.5° latitude and longitude global grid raster. Projection: GCS WGS84 Extent: Global Source: NASA Prediction Of Worldwide Energy Resources (POWER) For questions or issues please email: larc-power-project@mail.nasa.gov Data Source: NASA's GMAO MERRA-2 archive (Jan. 1, 1981 – Dec. 31, 2021) Building Climate Thermal Moisture Zones: This is a feature service that contains 4-Year Rolling Building Climate Thermal Zones for all 4-year periods between 1981 to 2021. Building Climate Thermal Zones are regions of the earth that have the same general climate characteristics based upon climatologically averaged heating and cooling degree days. The thermal zone values range from 0 (hottest) to 8 (coldest). The Thermal Zones are derived from the POWER project's Building Climate Thermal Zones Version 2 Application Programming Interface (API) service and are combined via a python framework. Building Climate Thermal Moisture Zones: This is a feature service that contains 4-Year Rolling Building Climate Thermal Moisture Zones for all 4-year periods between 1981 to 2021. Building Climate Thermal Moisture Zones are regions of the earth that have the same general climate characteristics based upon climatologically averaged heating and cooling degree days and moisture. The thermal zone values range from 0 (hottest) to 8 (coldest). The moisture zones are based upon precipitation data and are characterized as marine, dry, or humid with sub-zones designated as C, B, or A. The Building Climate Thermal Moisture Zones are derived from the POWER project's Thermal Zones Version 2 Application Programming Interface (API) service and are combined via a python framework.
This collection offers 35 parameters at near-surface level from the Met Office UKV 2km deterministic forecast. This is a high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, projected on to a 2km horizontal grid. The data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes. ## Coverage area The forecast covers the UK and Ireland, with the following latitude and longitude coordinates for each corner of the included area: * Southwest: 48.8643°N, 10.6734°W * Northwest: 61.3322°N, 13.7254°W * Northeast: 61.6102°N, 4.3408°E * Southeast: 49.0594°N, 2.4654°E ## Data collection height There are 3 forecast heights used within the near-surface this collection: * Surface: the default collection height * Screen level: 1.5m above the surface * Wind parameters: 10m above the surface ## Timesteps For most parameters, the following time steps are available, see exceptions below: * every hour from 0 to 54 hours * every 3 hours from 57 to 120 hours Exceptions (for `rate`, `accumulation`, `min` and `max` parameters): * Hourly hail fall accumulation (hail_fall_accumulation-PT01H) is only available every hour from 1 to 54 hours * Hail fall rate (hail_fall_rate) has 15 minutely timesteps from 0 to 54 hours * Hourly precipitation accumulation (precipitation_accumulation-PT01H) is only available every hour from 1 to 54 hours * 3H precipitation accumulation (precipitation_accumulation-PT03H) is only available every three hours from 57 to 120 hours * Precipitation rate (precipitation_rate) has 15 minutely timesteps from 0 to 54 hours * Pressure at mean sea level (pressure_at_mean_sea_level) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours * Hourly rainfall accumulation (rainfall_accumulation-PT01H) is only available every hour from 1 to 54 hours * 3H rainfall accumulation (rainfall_accumulation-PT03H) is only available every three hours from 57 to 120 hours * Rainfall rate (rainfall_rate) has 15 minutely timesteps from 0 to 54 hours * Hourly snowfall accumulation (snowfall_accumulation-PT01H) is only available every hour from 1 to 54 hours * 3H snowfall accumulation (snowfall_accumulation-PT03H) is only available every three hours from 57 to 120 hours * Snowfall rate (snowfall_rate) has 15 minutely timesteps from 0 to 54 hours * Temperature at screen level (temperature_at_screen_level) has 15 minutely timesteps from 0 to 12 hours and hourly timesteps from 13 to 120 hours * Hourly temperature at screen level maximum (temperature_at_screen_level_max-PT01H) is only available every hour from 1 to 120 hours * Hourly temperature at screen level minimum (temperature_at_screen_level_min-PT01H) is only available every hour from 1 to 120 hours * Dew point temperature at screen level (temperature_of_dew_point_at_screen_level) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours * Visibility at screen level (visibility_at_screen_level) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours * Wind direction at 10m (wind_direction_at_10m) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours * Wind gust at 10m (wind_gust_at_10m) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours * Hourly wind gust at 10m maximum (wind_gust_at_10m_max-PT01H) is only available every hour from 1 to 54 hours * 3H wind gust at 10m maximum(wind_gust_at_10m_max-PT03H) is only available every three hours from 57 to 120 hours * Wind speed at 10m (wind_speed_at_10m) has 15 minutely timesteps from 0 to 12 hours, hourly timesteps from 13 to 54 hours and 3 hourly timesteps from 57 to 120 hours ## Update frequency There are three lengths of model run, each with its own update frequency: * Nowcast: forecasts the next 12 hours and are at 0100, 0200, 0400, 0500, 0700, 0800, 1000, 1100, 1300, 1400, 1600, 1700, 1900, 2000, 2200 and 2300 UTC. * Short: forecasts the next 54 hours and are at 0000, 0600, 0900, 1200, 1800 and 2100 UTC. * Medium: forecasts the next 120 hours and are at 0300 and 1500 UTC. ## Archive length and latency As of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date. The data is typically available 3-6 hours after the model run time. ## Technical specs The data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components: * variables store the data * dimensions give relevant dimension information for the variables * attributes provide auxiliary information about the variables or dataset itself NetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers. Iris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data. Find further support on using Iris with NetCDF files. ## Help us improve the data services we offer Join the Met Office research panel to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities. ## How to cite UKV 2km deterministic forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897). ## License British Crown copyright 2023-2025, the Met Office, is licensed under CC BY-SA. ## Providers Met Office See all datasets managed by Met Office. ## Contact [servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.
saf_seas_hr_ir_tropopause_difference_99th_percentile
This collection offers 1 parameter at 33 available height levels (5m to 6000m) from the Met Office global deterministic 10km forecast. This is a numerical weather prediction forecast for the whole globe, with a resolution of approximately 0.09 degrees i.e. 10km (2,560 x 1,920 grid points). The data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes. ## Height Levels Available height levels, in metres (m) above ground, are: * 5.0 10.0 20.0 30.0 50.0 75.0 100.0 150.0 200.0 250.0 300.0 400.0 500.0 600.0 700.0 800.0 1000.0 1250.0 1500.0 1750.0 2000.0 2250.0 2500.0 2750.0 3000.0 3250.0 3500.0 3750.0 4000.0 4500.0 5000.0 5500.0 6000.0 ## Timesteps The following timesteps are available: * every hour from 0 to 54 hours * every 3 hours from 57 to 144 hours * every 6 hours from 150 to 168 hours ## Update Frequency The model is run four times each day, with forecast reference times of 00:00, 06:00, 12:00 and 18:00 (UTC). The runs at 00:00 and 12:00 provide data for the next 168 hours. The runs at 06:00 and 18:00 provide data for the next 67 hours. The forecast reference time represents the nominal data time or start time of a model forecast run, rather than the time when the data is available. ## Archive length and latency As of December 2025, the archive contains data from December 2023 onwards. Forecasts will continue to be available for at least two years from their data date. The data is typically available 6 hours after the model run time. ## Technical specs The data is available as NetCDF files. NetCDF (Network Common Data Form) is an interface for array-orientated data access and a library that supports the interface. It is composed of 3 components: * Variables store the data * Dimensions give relevant dimension information for the variables * Attributes provide auxiliary information about the variables or dataset itself NetCDF is used within the atmospheric and oceanic science communities and is network transparent, allowing for it to be accessed by computers that store integers, characters and floating-point numbers. Iris supports NetCDF files through reading, writing and handling. Iris implements a model based on the CF conventions, giving a format-agnostic interface for working with data. Find further support on using Iris with NetCDF files. ## Help us improve the data services we offer Join the Met Office research panel to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities. ## How to cite Met Office global deterministic 10km forecast was accessed on DATE from the Microsoft Planetary Computer (https://zenodo.org/records/7261897). ## License British Crown copyright 2023-2025, the Met Office, is licensed under CC BY-SA. ## Providers Met Office See all datasets managed by Met Office. ## Contact [servicedesk@metoffice.gov.uk](mailto:servicedesk@metoffice.gov.uk). Service desk is only available Mon – Fri, 09:00 until 17:00 UTC (-1 hour during BST). As a non-operational service we aim to respond to any service support enquiries within 3-5 business days.
South Africa Climatology Full 2005-2019 IR OT Day Count
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ArcGIS Image ServiceTime Interval: AnnualVersion: 1 Spatial Resolution: 3 km Time Extent: 2005 to 2025 Projection: GCS WGS84 Extent: Regional | South Africa Other Formats: OGC WMS, OGC WCS, REST CollectionThis service contains South African Anvil Cloud Days/Year statistics averaged Annually.
South America CLimatology Full 1995 2021 IR Tropopause Difference 99th Percentile
This raster dataset provides a full climatology of anvil cloud days per month for South America, based on data from 1995 to 2021. It covers multiple countries including Aruba, Barbados, Grenada, Martinique, Saint Lucia, Saint Vincent and the Grenadines, Trinidad and Tobago, Costa Rica, El Salvador, and Nicaragua. Published by mstisdal.
saf_seas_full_2005_2019_cloud_top_height_99th_percentile
This Collection provides Sentinel-5P Level-2 O3 PR products, which contains ozone concentration for 33 levels in the atmosphere.
smash_gpm_trmm_combo_climatology_norm_hail_density
ArcGIS Image ServiceTime Interval: Full Climatology With Hourly Timesteps Version: 1 Spatial Resolution: 3 km Time Extent: 2005 to 2025 (With 24 Hours) Projection: GCS WGS84 Extent: Regional | South Africa Other Formats: OGC WMS, OGC WCS, REST CollectionThis collection contains South African OT detection climatological diurnal statistics averaged over 2005-2025
Annual hourly infrared overshooting top hour count for South Africa, presented as overshooting top days per month. Published by mstisdal.
Annual climatology of hourly overshooting top counts per month for South America convection, derived from infrared imagery. Published by mstisdal with the Langley Research Center.
Monthly raster climatology of 99th percentile cloud top height with hourly timesteps, covering South Africa. Published by NASA Langley Research Center as part of the SMASH project.
Monthly counts of anvil cloud days per month with hourly timesteps, forming a convection climatology for South Africa. Published by mstisdal.
ArcGIS Image ServiceTime Interval: Seasonal Climatology With Hourly Timesteps Version: 1 Spatial Resolution: 3 km Time Extent: 2005 to 2025 (Dec-Feb, Mar-May, Jun-Aug, Sep-Nov) (With 24 Hours) Projection: GCS WGS84 Extent: Regional | South Africa Other Formats: OGC WMS, OGC WCS, REST CollectionThis collection contains South African IR detection climatological diurnal statistics averaged over: Dec-Feb, Mar-May, Jun-Aug, Sep-Nov 2005-2025
Overview and Intended Use GPM IMERG Early Precipitation Rate L3 V07 (GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHE 07)) is an image service derived from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) Early dataset. This image service is intended for visualizing, mapping, and analyzing near-real-time global precipitation rate estimates from the GPM IMERG Early dataset at 30-minute temporal resolution and 0.1° x 0.1° spatial resolution. To access the REST endpoint for the service, input the URL into a browser or select View just above the URL. Limitations of Use: The image service shows precipitation rate (mm/hr), approximately four hours after observation. NoData values, and values less than 0.03 mm/hr (the current threshold value for the IMERG algorithm) have been removed. The image service provides global coverage with a temporal span from 06/01/2000 0:00 UTC to present at 30-minute intervals. The service is updated every three hours to incorporate the new granules. IMERG is an algorithm that estimates precipitation rate from multiple passive microwave sensors in the GPM constellation, the GPM Dual-Frequency Radar, and infrared (IR) sensors mounted on geostationary satellites. Currently, the near-real-time Early estimates have no concluding calibration. Briefly describing the Early Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the Combined Radar-Radiometer Algorithm (CORRA) product (because it is presumed to be the best snapshot Tropical Rainfall Measuring Mission (TRMM)/GPM estimate after adjustment to the monthly Global Precipitation Climatology Project Satellite-Gauge (GPCP SG)), then "forward morphed" and combined with microwave precipitation-calibrated geo-IR fields to provide half-hourly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is computed using analyses of surface temperature, humidity, and pressure. Dataset at a Glance Shortname: GPM_3IMERGHHE DOI: 10.5067/GPM/IMERG/3B-HH-E/07 Version: 07 Coverage: -180.0,-90.0,180.0,90.0 Temporal Coverage: 2000-06-01 to Present Data Resolution Spatial: 0.1 ° x 0.1 ° Temporal: 30 minutes Symbology The default symbology in the Map Viewer may be changed to accommodate other color schemes using the settings in the Image Display panel from the layer settings menu. NoData values, and values less than 0.03 mm/hr (the current threshold value for the IMERG algorithm) have been removed. Ensure that pop-ups are enabled to view pixel values (select Modify Map first). Temporal Coverage The source dataset is in UTC time but the service is displayed in the Map Viewer in local time. The data is available in 30-minute intervals, and the map visualization may be modified by opening the Time Slider Settings menu from the icon on the time slider bar. The total temporal coverage may be limited to the desired range and the time interval may also be changed. The options in the time interval units are based on the total time range input, so a shorter time range will enable shorter time units to be selected from the time interval drop-down menu. If the time settings are set to more than 30-minute intervals, the first time slice in the time interval is visible. Portal Options Select Modify Map to customize the layer visualization. More information about the image service capabilities may be found in the REST endpoint. In the portal, the basemap may be changed by selecting the desired option from the Basemap menu.
GPM IMERG Final precipitation rate(GES DISC Dataset: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHH 07))is derived from the Integrated Multi SatellitE Retrievals for GPM (Global Precipitation Measurement) algorithm. Precipitation rate measurements are taken from multiple satellite based passive microwave sensors in the GPM constellation. The Final Run IMERG data product is distinct in that it combines data from ground sources and satellite-based observations to provide research quality precipitation rate data. Precipitation rate is depicted in mm/hr in 30-minute intervals, beginning on January 1, 1998. The "Final", satellite-gauge product is available ~3.5 months after the observation month, and includes both forward and backward morphing and monthly gauge analyses. In the post-real-time Final Run, the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. Briefly describing the Final Run, the input precipitation estimates computed from the various satellite passive microwave sensors are inter-calibrated to the Combined Radar-Radiometer Algorithm (CORRA) product (because it is presumed to be the best snapshot Tropical Rainfall Measuring Mission (TRMM)/GPM estimate after adjustment to the monthly Global Precipitation Climatology Project Satellite-Gauge (GPCP SG)), then "forward/backward morphed" and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with seasonal GPCP SG surface precipitation data to provide half-hourly and monthly precipitation estimates. Dataset at a Glance Shortname: GPM_3IMERGHH DOI: 10.5067/GPM/IMERG/3B-HH/07 Version: 07 Coverage: -180.0,-90.0,180.0,90.0 Temporal Coverage: 1998-01-01 to Present Data Resolution Spatial: 0.1 ° x 0.1 ° Temporal: 30 minutes Symbology The default symbology in the Web Map Viewer may be changed to accommodate other color schemes using the settings in the Image Display panel from the layer settings menu. Ensure that pop-ups are enabled to view pixel values (select Modify Map first). Temporal Coverage The source dataset is in UTC time, but the service is displayed in the Map Viewer in local time. The data is available in 30-minute intervals, and the map visualization may be modified by opening the Time Slider Settings menu from the icon on the time slider bar. The total temporal coverage may be limited to the desired range and the time interval may also be changed. The options in the time interval units are based on the total time range input, so a shorter time range will enable shorter time units to be selected from the time interval drop-down. If the time settings are set to more than 30-minute intervals, the first time slice in the time interval is visible. Portal Options Select Modify Map to customize the layer visualization. More information about the image service capabilities may be found in the REST endpoint. In the portal, the basemap may be changed by selecting the desired option from the Basemap menu.
Abstract and Limitations of Use: The Annual Global High-Resolution Extreme Heat Estimates (GEHE), 1983-2016 data set provides global 0.05 degrees (~5 km) gridded annual counts of the number of days where the maximum Wet Bulb Globe Temperature (WBGTmax) exceeded dangerous hot-humid heat thresholds for the period 1983 to 2016. This historical data set does not have a routine update frequency. The thresholds are based on the International Standards Organization (ISO) criteria for occupational heat-related risk, defined as days where WBGTmax > 28, 30, and 32 degrees Celsius. This data set also includes the annual rate of change in the number of extreme humid-heat days that exceeded these thresholds. GEHE has a wide array of applications for mapping and quantifying extreme humid-heat dynamics over a 34-year time period, and is the highest resolution data set of its kind to date. GEHE provides scientific researchers and decision makers from a wide range of arenas, including climate change, public and occupational health, urban planning and design, hazards risk reduction, and food security, insights into how humid-heat has impacted human and environmental systems worldwide. The data set can be used to pinpoint how changes in extreme humid-heat impact human health and well-being, as well as ecological systems, across scales of analysis, from local, to national, to global. Use the Multidimensional Slider in ArcGIS Map Viewer to select a WBGT variable and explore annual slices from 1983–2016. This is the recommended method for viewing changes over time in this multidimensional dataset. Trend variables represent change across the full 1983–2016 period and do not have individual yearly slices. Intended Use: To provide a high-resolution, longitudinal global data set of annual extreme humid-heat. Variables: Wet Bulb Globe TemperatureWBGTmax28 WBGTmax28-trend WBGTmax30 WBGTmax30-trend WBGTmax32 WBGTmax32-trend LegendSymbol Pixel Value Description High: 366 A high-resolution, longitudinal global data set of annual extreme humid-heat Low: 0 Citation: Tuholske, C., P. Peterson, C. Funk, and K. Caylor. 2023. Annual Global High-Resolution Extreme Heat Estimates (GEHE), 1983-2016. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/hff0-k565. Publication References:Tuholske, C., K. Caylor, C. Funk, A. Verdin, S. Sweeney, K. Grace, P. Peterson, and T. Evans. 2021. Global Urban Population Exposure to Extreme Heat. Proceedings of the National Academy of Sciences 118(41), e2024792118. DOI: https://doi.org/10.1073/pnas.2024792118 Contact:For inquiries about this service, please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum.
Layer Overview The Tropospheric Emissions: Monitoring of Pollution (TEMPO) Cloud Fraction layer provides information on the effective cloud fraction at 466 nm derived from the O2-O2 cloud retrieval algorithm used in TEMPO data processing. The effective cloud fraction is provided on a scale of zero (0) to one (1), with higher values indicating cloudier pixels and is a unitless quantity. The TEMPO cloud product is used to derive vertical columns of TEMPO trace gases (e.g., nitrogen dioxide, formaldehyde). Thus, this cloud fraction layer can be used to identify cloud coverage as observed by the TEMPO instrument, as well as used in conjunction with the TEMPO nitrogen dioxide and formaldehyde image services to provide context and additional information for those data. TEMPO cloud fraction Level 3 files provide cloud fraction information on a regular grid. Level 3 files are derived by combining information from all Level 2 files constituting a TEMPO East-West scan cycle, using an area-weighted re-gridding approach. The data have been converted from their native file format (netCDF4) to Cloud Raster Format (CRF). Temporal Coverage The temporal resolution of a nominal scan is approximately one hour during daylight hours, with more frequent scans in the morning over the eastern portion of the field of regard and in the evenings over the western portion of the field of regard. Each image is presented with the starting timestamp of the corresponding TEMPO scan. Due to the nature of the TEMPO instrument’s east to west scanning pattern, each image is a composite of measurements taken over a period of 40-60 minutes, depending on the spatial coverage of the scan. Data are available from September 17, 2025 to the present and updated daily with the previous day’s data. These data correspond to the Version 04 (V04) product operationally generated by the TEMPO science data processing center. All the observations since the beginning of the TEMPO mission (August 2023) are being reprocessed with to V04, and data will be added to this service upon their generation. Geographic Coverage Imagery is available for the TEMPO field of regard covering the continental United States (CONUS), and much of Mexico, Canada, and the Caribbean. The imagery in this image service is regridded onto a 0.02º longitude x 0.02º latitude grid and presented in the geographic coordinate system WGS 1984. The sensor’s native spatial resolution is about 2 km x 4.75 km at the center of TEMPO’s field of regard (33.5°N, 89.2°W) in WGS 1984. Data Filtering The layer is filtered to remove pixels at high solar zenith angles, which typically have low quality. The applied filter is set to remove pixels based on the following criterion: solar_zenith_angle > 80°. Cloud fractions outside the range between 0.0 and 1.0 represent missing values. Data Validation These data should be considered as provisional products per the Provisional Product Maturity level defined in the TEMPO validation plan. The provisional maturity level means that product performance has been demonstrated through a large, but still (seasonally or otherwise) limited number of independent measurements. The analysis is sufficient for limited qualitative determinations of product fitness-for-purpose, and the product is potentially ready for testing by operational users and may be suitable for scientific publication. Users may consult the TEMPO User Guide for descriptions of the data and associated known issues. Recommended Usage Notes When viewing the image service in the Esri online map viewer, it is recommended to use the multidimensional slider rather than the default time slider. The multidimensional slider can be accessed via the “multidimensional” icon in the right-hand menu. TEMPO Mission Overview The Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument is a grating spectrometer, sensitive to the visible (VIS) and ultraviolet (UV) wavelengths of light in the spectral range of 290-490 nm and 540-740 nm with about 0.6 nm spectral resolution. The TEMPO instrument is attached to the Earth-facing side of a commercial telecommunications satellite (Intelsat 40e) in a geostationary orbit over 91° W longitude (about 22,000 miles above Earth’s equator). This allows TEMPO to maintain a continuous view of North America so that the instrument's light-collecting mirror can make a complete east-to-west scan of the field of regard hourly during daylight hours. By measuring sunlight reflected and scattered from the Earth's surface and atmosphere back to the instrument's detectors, TEMPO's ultraviolet and visible light sensors provide measurements of ozone, nitrogen dioxide, formaldehyde, and other constituents of Earth’s atmosphere. The primary mission objective of TEMPO is to improve our understanding of air quality and its impacts. By providing near real-time data and comprehensive atmospheric composition measurements, TEMPO helps advance knowledge of the Earth system and improve air quality forecasting. These data also aid in quantifying how air quality affects vegetation, agriculture, and human health, among others. Contact For inquiries about this service, please contact larc-dl-asdc-tempo@mail.nasa.gov or post/view questions on the Earthdata Forum.
Point-based air monitoring observations from the City of Minneapolis Community Air Monitoring project. Each point records sensor readings such as timestamp, humidity, temperature, pressure, and particulate matter counts and concentrations. Published by the City of Minneapolis.
Raster climatology of annual overshooting top day counts per month over South Africa, derived from full-disk infrared imagery. Published by mstisdal.
HRDPA is ECCC’s high-resolution precipitation analysis, merging gauge, radar, and HRDPS model data. This layer aggregates the final (or preliminary) HRDPA accumulations to sub-basin polygons. Each record indicates the average precipitation that truly occurred over each watershed, vital for verifying model forecasts, calibrating hydrological models, and conducting post-event analyses of flood or drought severity.