OSCARS - Regional State of the Climate: Data and Indices

This dataset provides harmonised regional climate data and climate-extreme indices developed within the framework of the Regional State of the Climate (RSOTC) project. This work was supported by the European Union's Horizon Europe research and innovation programme through the OSCARS (Open Science Cluster of EOSC Regional State of the Climate) project, Grant Agreement No. 101058571. The dataset uses the ERA5 reanalysis as its foundational data source. ERA5 is the fifth-generation atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) and distributed via the Copernicus Climate Change Service (C3S) (DOI: https://doi.org/10.24381/cds.4991cf48). ERA5 provides hourly estimates of a wide range of atmospheric, land-surface, and sea-state variables at a spatial resolution of 0.25° x 0.25° (approximately 31 km), covering the period from 1940 to the present. These data form the basis for deriving the consistent and high-resolution regional climate information presented here. The dataset provides regional information spatially aggregated as time series over European regions defined by the Nomenclature of Territorial Units for Statistics (NUTS) classification: NUTS 0: Country level NUTS 1: Major socio-economic regions NUTS 2: Basic regions for regional policies NUTS 3: Small regions for specific diagnoses (Reference: Eurostat - NUTS classification) The dataset is designed to support climate monitoring, impact assessment, policy analysis, and downstream climate services across multiple administrative scales. It is updated on a monthly basis. Core variables The core variables are derived from the state-of-the-art ERA5 reanalysis and represent fundamental quantities for climate and meteorological applications: Near-surface air temperature (tas, tasmin, tasmax) Daily mean (tas), maximum (tasmax), and minimum (tasmin) air temperature at 2 m. Total precipitation (pr) Daily accumulated liquid and frozen precipitation, relevant for hydrology, drought, and flood analysis. 10-m wind speed (sfcWind) Magnitude of the horizontal wind vector at 10 m, relevant for wind energy and atmospheric transport studies. Derived indices In addition to the core variables, the dataset includes a comprehensive set of derived climate indices commonly used in climate-impact and risk assessments. Indices are calculated for multiple temporal aggregations (annual, seasonal, and monthly). Hot days tx30: Number of days with maximum temperature (tasmax) > 30 °C tx35: Number of days with maximum temperature (tasmax) > 35 °C tx40: Number of days with maximum temperature (tasmax) > 40 °C Example: the tx35 value for January 2020 represents the number of days in that month with tasmax exceeding 35 °C. Tropical nights tr20: Number of nights with minimum temperature (tasmin) > 20 °C tr25: Number of nights with minimum temperature (tasmin) > 25 °C Frost days fd: Number of frost days with minimum temperature (tasmin) < 0 °C Precipitation indicators r1mm: Number of wet days (pr ≥ 1 mm) r20mm: Number of very heavy precipitation days (pr ≥ 20 mm) r95ptot: Total precipitation from very wet days (pr > 95th percentile) All indices follow established climate-index definitions, ensuring consistency and comparability across regions and time periods. Data format All datasets are stored as Zarr data stores and distributed as compressed ZIP archives for long-term archival and Zenodo compatibility. Filenames follow the convention: {variable}_{pressure_level}_{dataset}_{region_set}.zarr.zip For surface variables, the pressure level is specified as None. Example usage The following Python example demonstrates how to open a core variable and a derived index using xarray: import xarray as xr ds_tasmin = xr.open_dataset( "tasmin_None_ERA5_NUTS-3.zarr", engine="zarr" ) ds_fd = xr.open_dataset( "fd_None_ERA5_NUTS-3.zarr", engine="zarr" ) <xarray.Dataset> Dimensions: (region: 1345, time: 31412) Coordinates: * region (region) object 'CZ020' 'CZ031' ... 'NL327' 'NL328' * time (time) datetime64[ns] 1940-01-01 ... 2025-12-31 Data variables: tasmin (time, region) float64 <xarray.Dataset> Dimensions: (time: 1021, time_filter: 17, region: 1345) Coordinates: * region (region) object 'CZ020' ... 'NL328' * time (time) datetime64[ns] 1940-01-01 ... 2025-01-01 * time_filter (time_filter) 'Annual' 'Apr' ... 'SepNov' Data variables: fd (time, time_filter, region) timedelta64[ns] Core variables: daily data with time and region dimensions Derived indices: aggregated data with an additional time_filter dimension indicating the aggregation period Data production and reproducibility All data products are generated using the open-source RSOTC Ingestion Pipeline, which implements a fully automated and reproducible workflow for data retrieval, preprocessing, aggregation, and index calculation. The pipeline follows FAIR principles (Findable, Accessible, Interoperable, Reusable), ensuring transparent provenance, standardised metadata, and long-term usability. Intended use This dataset is intended for researchers, climate service providers, policymakers, and other stakeholders requiring consistent regional climate information across Europe. Typical use cases include: Trend analysis Regional climate diagnostics Climate-impact indicators Integration into dashboards and decision-support tools Users are encouraged to cite this dataset when using it in scientific publications, reports, or operational climate services.本数据集提供经统一协调的区域气候数据，以及在区域气候状况（Regional State of the Climate，RSOTC）项目框架下研发的气候极端指数。 本工作获欧盟地平线欧洲（Horizon Europe）研究与创新计划资助，通过OSCARS（EOSC区域气候状况开放科学集群，Open Science Cluster of EOSC Regional State of the Climate）项目提供支持，资助协议编号为101058571。 本数据集以第五代大气再分析数据集（ERA5）作为基础数据源。ERA5是由欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts, ECMWF）制作、哥白尼气候变化服务局（Copernicus Climate Change Service, C3S）分发的第五代大气再分析数据集，其数字对象标识符（DOI）为https://doi.org/10.24381/cds.4991cf48。 ERA5可提供1940年至今、空间分辨率为0.25°×0.25°（约31千米）的多类大气、陆面及海表状态变量逐时估算值。上述数据为本数据集所呈现的一致性高分辨率区域气候信息提供了核心支撑。 本数据集提供按统计区域命名法（Nomenclature of Territorial Units for Statistics, NUTS）划分的欧洲区域空间聚合时序数据： NUTS 0：国家级单元 NUTS 1：主要社会经济区域 NUTS 2：区域政策基本区域 NUTS 3：用于特定诊断分析的小型区域 （参考来源：欧盟统计局——NUTS分类） 本数据集旨在支撑多行政尺度下的气候监测、影响评估、政策分析及下游气候服务业务，且每月更新一次。 ## 核心变量 核心变量由当前先进的ERA5再分析数据衍生而来，为气候与气象应用提供基础物理量： - 近地面气温（tas、tasmin、tasmax）：2米高度处的日平均气温（tas）、日最高气温（tasmax）与日最低气温（tasmin）。 - 总降水量（pr）：日累计液态与固态降水量，适用于水文、干旱及洪涝分析。 - 10米风速（sfcWind）：10米高度处水平风矢量的模长，适用于风能开发及大气输送研究。 ## 衍生指数 除核心变量外，本数据集还包含一套适用于气候影响与风险评估的全面衍生气候指数。指数基于多种时间聚合方式（年、季、月）计算得到。 ### 热日天数 tx30：日最高气温（tasmax）超过30℃的天数 tx35：日最高气温（tasmax）超过35℃的天数 tx40：日最高气温（tasmax）超过40℃的天数 示例：2020年1月的tx35值代表该月内tasmax超过35℃的天数总和。 ### 热带夜天数 tr20：日最低气温（tasmin）超过20℃的夜晚天数 tr25：日最低气温（tasmin）超过25℃的夜晚天数 ### 霜日天数 fd：日最低气温（tasmin）低于0℃的霜日天数 ### 降水指标 r1mm：降水日数（pr≥1mm） r20mm：强降水日数（pr≥20mm） r95ptot：极湿日（pr>95百分位数）的总降水量 所有指数均遵循已确立的气候指数定义标准，确保不同区域与时段间的结果具有一致性与可比性。 ## 数据格式 所有数据集均存储为Zarr数据存储格式，并以压缩ZIP归档形式分发，以适配长期存档与Zenodo平台兼容需求。 文件名遵循如下命名规范： {变量}_{气压层}_{数据集}_{区域集合}.zarr.zip 对于地表变量，气压层字段以None表示。 ## 示例用法 以下Python代码示例演示了如何使用xarray打开核心变量与衍生指数： import xarray as xr ds_tasmin = xr.open_dataset( "tasmin_None_ERA5_NUTS-3.zarr", engine="zarr" ) ds_fd = xr.open_dataset( "fd_None_ERA5_NUTS-3.zarr", engine="zarr" ) <xarray.Dataset> Dimensions: (region: 1345, time: 31412) Coordinates: * region (region) object 'CZ020' 'CZ031' ... 'NL327' 'NL328' * time (time) datetime64[ns] 1940-01-01 ... 2025-12-31 Data variables: tasmin (time, region) float64 <xarray.Dataset> Dimensions: (time: 1021, time_filter: 17, region: 1345) Coordinates: * region (region) object 'CZ020' ... 'NL328' * time (time) datetime64[ns] 1940-01-01 ... 2025-01-01 * time_filter (time_filter) 'Annual' 'Apr' ... 'SepNov' Data variables: fd (time, time_filter, region) timedelta64[ns] 核心变量：包含时间与区域两个维度的逐日数据 衍生指数：为聚合后的数据，新增了time_filter维度以标识时间聚合周期 ## 数据生产与可复现性 所有数据产品均通过开源RSOTC数据摄入管道生成，该管道实现了数据检索、预处理、聚合与指数计算的全自动化可复现工作流。 该管道遵循FAIR原则（Findable, Accessible, Interoperable, Reusable，即可发现、可访问、可互操作、可复用），确保数据溯源透明、元数据标准化且具备长期可用性。 ## 预期用途 本数据集面向研究人员、气候服务提供商、政策制定者及其他需要欧洲区域一致化气候信息的利益相关者。典型应用场景包括： - 趋势分析 - 区域气候诊断 - 气候影响指标研究 - 集成至仪表盘与决策支持工具 鼓励用户在科学出版物、报告或业务化气候服务中使用本数据集时进行引用。