This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. Those indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications. The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation. The underlying datasets hosted on the CDS are:

ERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators. Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below. Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators. Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators. Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index. Water level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.

This dataset was produced on behalf of the Copernicus Climate Change Service.

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.

Dataset for journal article "Climate Change, Extreme Events, and Their Potential Effects on Aboveground Storage Tanks" published in EM Magazine in September 2023. The dataset describes calculations for predicted emissions based on AP-42, Chapter 7, and the TankESP software from Trinity Consultants. The calculations are detailed for aboveground storage tanks at various temperatures, wind speeds, and maintenance conditions for an example tank storing gasoline in Greensboro, NC. The dataset also describes the composition of the gasoline used in the example calculations. A glossary of abbreviations is also provided. The same dataset was used to produce a two-page summary report, Community Vulnerabilities at Aboveground Storage Tanks Due to Climate Change and Extreme Events. This dataset is associated with the following publication: Smith, R.L., J. Terriquez, E. Thoma, M.A. Gonzalez, D. Johnson, H. Buenning, F. Kremer, J.D. Carpenter, and N.N. Clark. Climate Change, Extreme Events, and Their Potential Effects on Aboveground Storage Tanks. EM: AIR AND WASTE MANAGEMENT ASSOCIATION'S MAGAZINE FOR ENVIRONMENTAL MANAGERS. Air & Waste Management Association, Pittsburgh, PA, USA, 1-6, (2023).

To aid applicants with quantification and monetization of benefits of proposed water storage projects per Chapter 8 of Proposition 1 (Water Code section 79750 et. seq.), the California Water Commission (Commission) developed a Technical Reference which was released in August 2016. These data and model products are companion information to the Technical Reference and were developed to assist applicants for funding under the Water Storage Investment Program (WSIP). The WSIP required applicants for public funding to analyze their proposed projects using climate and sea level conditions for California projected at years 2030 and 2070. The data and model products were developed for the following climate and sea level conditions: Without-Project 2030 Future Conditions – Year 2030 future condition with projected climate and sea level conditions for a thirty-year period centered at 2030 (climate period 2016-2045) Without-Project 2070 Future Conditions – Year 2070 future condition with projected climate and sea level conditions for a thirty-year period centered at 2070 (climate period 2056-2085) 1995 Historical Temperature-detrended Conditions (reference) – Year 1995 historical condition with climate and sea level conditions for a thirty-year period centered at 1995 (reference climate period 1981-2010)   California Water Commission The California Water Commission consists of nine members appointed by the Governor and confirmed by the State Senate. Seven members are chosen for their expertise related to the control, storage, and beneficial use of water and two are chosen for their knowledge of the environment. The Commission provides a public forum for discussing water issues, advises the Director of the Department of Water Resources on matters within the Department’s jurisdiction, approves rules and regulations, and monitors and reports on the construction and operation of the State Water Project. Proposition 1: The Water Quality, Supply, and Infrastructure Improvement Act approved by voters in 2014, gave the Commission new responsibilities regarding the distribution of public funds set aside for the public benefits of water storage projects, and developing regulations for the quantification and management of those benefits. In 2018, the Commission approved maximum conditional funding amounts for eight projects in the Water Storage Investment Program.

land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

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.

Overview: The Essential Climate Variables for assessment of climate variability from 1979 to present dataset contains a selection of climatologies, monthly anomalies and monthly mean fields of Essential Climate Variables (ECVs) suitable for monitoring and assessment of climate variability and change. Selection criteria are based on accuracy and temporal consistency on monthly to decadal time scales. The ECV data products in this set have been estimated from climate reanalyses ERA-Interim and ERA5, and, depending on the source, may have been adjusted to account for biases and other known deficiencies. Data sources and adjustment methods used are described in the Product User Guide, as are various particulars such as the baseline periods used to calculate monthly climatologies and the corresponding anomalies. Sum of monthly precipitation: This variable is the accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. Spatial resolution: 0:15:00 (0.25°) Temporal resolution: monthly Temporal extent: 1979 - present Data unit: mm * 10 Data type: UInt32 CRS as EPSG: EPSG:4326 Processing time delay: one month

Data reported in the csv files are gridded monthly time-series used in the article “Sohoulande, C.D., Martin, J., Szogi, A. and Stone, K., 2020. Climate-Driven Prediction of Land Water Storage Anomalies: An Outlook for Water Resources Monitoring Across the Conterminous United States. Journal of Hydrology, p.125053”. The study focused on the conterminous United States (CONUS) which extends over a region of contrasting climates with an uneven distribution of freshwater resources. Under climate change, an exacerbation of the contrast between dry and wet regions is expected across the CONUS and could drastically affect local ecosystems, agriculture practices, and communities. Hence, efforts to better understand long-term spatial and temporal patterns of freshwater resources are needed to plan and anticipate responses. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite observations provide estimates of large-scale land water storage changes with an unprecedented accuracy. However, the limited lifetime and observation gaps of the GRACE mission have sparked research interest for GRACE-like data reconstruction. The study developed a predictive modeling approach to quantify monthly land liquid water equivalence thickness anomaly (LWE) using climate variables including total precipitation (PRE), number of wet day (WET), air temperature (TMP), and potential evapotranspiration (PET). The approach builds on the achievements of the GRACE mission by determining LWE footprints using a multivariate regression on principal components model with lag signals. Methods are described in the manuscript https://doi.org/10.1016/j.jhydrol.2020.125053. Descriptions corresponding to each figure and table in the manuscript are placed in the Read Me.docx file that is included as part of the Dryad dataset. Resources in this dataset:Resource Title: Link to Climate-driven prediction of land water storage anomalies dataset at datadryad.org. File Name: Web Page, url: https://doi.org/10.5061/dryad.qnk98sfdz These research data are associated with the manuscript entitled “Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States” (https://doi.org/10.1016/j.jhydrol.2020.125053). The study focused on the conterminous United States (CONUS) which extends over a region of contrasting climates with an uneven distribution of freshwater resources. Under climate change, an exacerbation of the contrast between dry and wet regions is expected across the CONUS and could drastically affect local ecosystems, agriculture practices, and communities. Hence, efforts to better understand long-term spatial and temporal patterns of freshwater resources are needed to plan and anticipate responses. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite observations provide estimates of large-scale land water storage changes with an unprecedented accuracy. However, the limited lifetime and observation gaps of the GRACE mission have sparked research interest for GRACE-like data reconstruction. This study developed a predictive modeling approach to quantify monthly land liquid water equivalence thickness anomaly (LWE) using climate variables including total precipitation (PRE), number of wet day (WET), air temperature (TMP), and potential evapotranspiration (PET). The approach builds on the achievements of the GRACE mission by determining LWE footprints using a multivariate regression on principal components model with lag signals. The performance evaluation of the model with a lag signals consideration shows 0.5 ≤ R2 ≤ 0.8 for 41.2% of the CONUS. However, the model’s predictive power is unevenly distributed. The model could be useful for predicting and monitoring freshwater resources anomalies for the locations with high model performances. The processed data used as inputs in the study are here provided including the GIS files of the different maps reported.

The dataset provides climate extreme indices related to temperature and precipitation as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), as well as selected heat stress indicators (HSI). The indices are provided for historical and future climate projections (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) included in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and used in the 6th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). This dataset provides a comprehensive source of pre-calculated and consistent ETCCDI and heat stress indicators commonly used by the climate science and impact communities. The majority of models used in this catalogue entry are now available in the Climate Data Store though the indices offered in this entry additionally include ensemble members obtained from the Earth System Grid Federation. The indices are calculated from CMIP6 models that have the necessary daily resolved data for both historical and at least two of the future projections. In addition, four of the chosen models contained a large number of ensemble members in order to enable the estimation of the associated uncertainty in the spread of model outcomes when calculating the ETCCDI indices (CanESM5, EC-Earth3, MIROC6 and MPI-ESM1-2-LR). All the ETCCDI indices in this dataset are calculated using the climdex.pcic R package, which was developed, evaluated and approved by the ETCCDI. To facilitate the usage of heat stress indicators in combination with thresholds on absolute values, this dataset additionally provides bias-adjusted heat stress indicators. Bias adjustment is carried out using the ISIMIP3b bias-adjustment method and employs the WATCH Forcing Data methodology applied to ERA5 (WFDE5) dataset as reference. Providing both pre-calculated bias-adjusted data and data without bias adjustment is of great value for climate and impact studies since the calculation of these datasets also are computationally expensive. The WFDE5 dataset is also available in the Climate Data Store. The heat stress indicators combine near-surface air temperature, near-surface specific humidity, and surface air pressure to give indications of adverse effects of heat on human health. Other variables like wind or solar radiation are not considered, and the selected heat stress indicators thus represent indoor conditions or calm conditions in the shade. This dataset was produced on behalf of the Copernicus Climate Change Service.

China Convenience Store Climate Index: Store: Store's Own Sales data was reported at 70.500 % in Sep 2018. This records a decrease from the previous number of 87.500 % for Jun 2018. China Convenience Store Climate Index: Store: Store's Own Sales data is updated quarterly, averaging 82.900 % from Mar 2017 (Median) to Sep 2018, with 7 observations. The data reached an all-time high of 88.100 % in Jun 2017 and a record low of 68.500 % in Sep 2017. China Convenience Store Climate Index: Store: Store's Own Sales data remains active status in CEIC and is reported by Ministry of Commerce. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HSA: Convenience Store Climate Index.

China Convenience Store Climate Index: Store: Service Item data was reported at 64.200 % in Sep 2018. This records a decrease from the previous number of 65.000 % for Jun 2018. China Convenience Store Climate Index: Store: Service Item data is updated quarterly, averaging 80.700 % from Mar 2017 (Median) to Sep 2018, with 7 observations. The data reached an all-time high of 85.600 % in Mar 2018 and a record low of 64.200 % in Sep 2018. China Convenience Store Climate Index: Store: Service Item data remains active status in CEIC and is reported by Ministry of Commerce. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HSA: Convenience Store Climate Index.

This dataset includes processed climate change datasets related to climatology, hydrology, and water operations. The climatological data provided are change factors for precipitation and reference evapotranspiration gridded over the entire State. The hydrological data provided are projected stream inflows for major streams in the Central Valley, and streamflow change factors for areas outside of the Central Valley and smaller ungaged watersheds within the Central Valley. The water operations data provided are Central Valley reservoir outflows, diversions, and State Water Project (SWP) and Central Valley Project (CVP) water deliveries and select streamflow data. Most of the Central Valley inflows and all of the water operations data were simulated using the CalSim II model and produced for all projections.

These data were originally developed for the California Water Commission’s Water Storage Investment Program (WSIP). The WSIP data used as the basis for these climate change resources along with the technical reference document are located here: https://data.cnra.ca.gov/dataset/climate-change-projections-wsip-2030-2070. Additional processing steps were performed to improve user experience, ease of use for GSP development, and for Sustainable Groundwater Management Act (SGMA) implementation. Furthermore, the data, tools, and guidance may be useful for purposes other than sustainable groundwater management under SGMA.

Data are provided for projected climate conditions centered around 2030 and 2070. The climate projections are provided for these two future climate periods, and include one scenario for 2030 and three scenarios for 2070: a 2030 central tendency, a 2070 central tendency, and two 2070 extreme scenarios (i.e., one drier with extreme warming and one wetter with moderate warming). The climate scenario development process represents a climate period analysis where historical interannual variability from January 1915 through December 2011 is preserved while the magnitude of events may be increased or decreased based on projected changes in precipitation and air temperature from general circulation models.

2070 Extreme Scenarios Update, September 2020

DWR has collaborated with Lawrence Berkeley National Laboratory to improve the quality of the 2070 extreme scenarios. The 2070 extreme scenario update utilizes an improved climate period analysis method known as "quantile delta mapping" to better capture the GCM-projected change in temperature and precipitation. A technical note on the background and results of this process is provided here: https://data.cnra.ca.gov/dataset/extreme-climate-change-scenarios-for-water-supply-planning/resource/f2e1c61a-4946-4863-825f-e6d516b433ed.

Note: the original version of the 2070 extreme scenarios can be accessed in the archive posted here: https://data.cnra.ca.gov/dataset/sgma-climate-change-resources/resource/51b6ee27-4f78-4226-8429-86c3a85046f4

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.

Climate maps (raster layers .tif) of derived-ecvs with a spatial resolution of 5.5 km (1 km for Azores) obtained by statistically downscaling a set of CMIP6 simulations for different IPCC climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (reference, short time-horizon, medium time-horizon, long time-horizon). Data are representative of specific climate normals (yearly averaged values) and created by RethinkAction. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data: Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c. Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac Acknowledgement also to: DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (https://portal.azores.gov.pt/en/web/draac) SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1

Monthly timeseries of basic-ecvs (.csv) spatially averaged over Case Studies for different climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (1985-2014, 2015-2100). Data are created by RethinkAction project using statistical downscaling method from CMIP6 simulations.

We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.

Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data:

Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c.

Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47

Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac

Acknowledgement also to:

DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (https://portal.azores.gov.pt/en/web/draac)

SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1

This dataset includes urban tree inventories for 22 urban areas across Europe spanning 5 Köppen-Geiger climate zones. These datasets are the base for the research paper 'Climate change threatens carbon storage in Europe’s urban trees'. It also includes a summary table of each city '22.city.data' required in further analysis and a dataset of 'species niches' that shows niche data for all 188 study species (see methodology for how these were calculated). R code that shows how these datasets are tidied and the rest of the analysis is carried out can be found on GitHub : Climate-change-and-European-urban-trees/R at main · GeorgeLloyd300/Climate-change-and-European-urban-trees (github.com)Outputs of each stage of the GitHub page and any further info can be obtained by contacting us.

Goals: Explore OH yield, product formation (carbonyls, peroxides, organic acids, and others), and Ozone-related GTHOS interference. Investigate any instrumental interferences of high levels. Summary: Experiment went well, lots of products were made, both by OH and O3 + isoprene chemistry. A significant amount of the isoprene mass was converted to hydroxymethylhydroperoxide (HMHP) at this RH, as expected based on previous CIT measurements. Experiment time was shorter than hoped, due to bag volume depleting too quickly. LIF OHR instrument was taken off bag at 1.5 hour mark to conserve bag volume. The "end" of the experiment was when CF3O- CIMS saw that product formation stabilized and when PTRMS and GC-FID saw that isoprene was all gone - this happened at approximately 16:15. At reaction end (although it's technically still going in the bag), we did a test for some instruments to see if sampling directly from a port into the chamber gives the same answer as from a common sampling line. For I/A CIMS, CF3O- CIMS, and PTRMS, there does not appear to be significant differences. For O3/NOx, there appears to be a difference in the beginning of sampling, and these instruments now will be on their own line when O3 measurements are more critical. AMS reports losing ~30% of particle mass through a longer inlet. GC-FID tested out trapping methods, with limited success but will continue to work on it. The flow rate through LIF OHR was higher than expected, but this will be worked on tomorrow when this instrument will be off. At the end of the experiment, UV lights were also turned on at 100% for ~40 minutes. Photolysis of some species seen by CF3O- CIMS was observed. CRM OHR was not on the bag today, but at the end of the day reported success in measuring OHR of pyrrole and expects to be on tomorrow. Preparations for upcoming experiment, starting this evening, started approximately 19:00. ... Organization: 2014 FIXCIT Study Lab Affiliation: California Institute of Technology Chamber: Seinfeld chambers Experiment Category: Gas phase chemical reaction Oxidant: Hydroxyl radical Reactants: Isoprene, Ozone, Water Reaction Type: Photooxidation Relative Humidity: 5 Temperature: 25 Seed Name: No Seed Pressure: 750 Torr

US forest C storage potential - Wu et al. (2023) code to reproduce the results. Contact Chao Wu (chaowu.thu@gmail.com) with questions. Both the R codes and Python codes were included. Citation: Wu C, Coffield SR, Goulden ML, Randerson JT, Trugman, AT, Anderegg WRL (2023) Uncertainty in US forest carbon storage potential due to climate risks. Nature Geoscience. doi: 10.1038/s41561-023-01166-7 The data include (i) all raw data generated in this paper; (ii) the code for figures 1-4 in the main text; and (iii) the code for the climate niche model.

Detailed information: (1) source code 1. FutureUSforestC_manuscript_published_mainFigures2.R: the code for generating figures in the main text. 2. RF_carbon_models.py: the source code for building the climate niche model. 3. RF_forestgroups_models.py: the source code for building the tree species niche model. (2) raw data 1. cmip6_post/: results from the 22 CMIP6 ESMs. 2. em_results/: results from the climate niche model. 3. semi_empirical_fiaregression_esm/: results from the growth-mortality model. 4. sourceData/: Source data for figures 1-4 in the main text and 4 Extended Data figures. 5. figure/: generated figures using the code above. 6. cb_2018_us_state_500k/: US boundary shapefile. 7. other dependent data used in the code above, more details can be found in the code.

This catalogue entry provides monthly climate projections on single levels from a large number of experiments, models, members and time periods computed in the framework of 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.

Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per day. Unit: mm day-1.

The Precipitation flux variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5.

References: https://doi.org/10.24381/cds.6c68c9bb

The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide.

ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Data publication: 2021-01-30

Data revision: 2021-10-05

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Acknowledgement

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Citation:

Boogaard, H., Schubert, J., De Wit, A., Lazebnik, J., Hutjes, R., Van der Grijn, G., (2020): Agrometeorological indicators from 1979 to present derived from reanalysis. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.6c68c9bb (Accessed on DD-MMM-YYYY).

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Data lineage:

Agrometeorological data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model.

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