Climate indicators for Austria from 1961 to 2025 at 1 km resolution
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This repository provides a comprehensive dataset of 117 climate indicators for Austria on a 1-km spatial grid. The indicators are organized into seven groups based on their input variables: temperature, precipitation, snow, runoff, radiation, humidity and mixed. ℹ️ Please note: This is a static, non-operational research dataset covering the period 1961–2025. It is provided as a one-time archival release and will not be updated on this platform. The dataset is described in the corresponding Data Descriptor published in Scientific Data, which provides detailed information on the dataset structure, contents, and indicator definitions. The version provided here is based on SPARTACUS v2.1, which will be superseded by SPARTACUS v3. Following the official release of SPARTACUS v3, operational annual updates will be made available through the GeoSphere Austria DataHub. Methods The climate indicators are derived using a variety of established definitions, including those from: Klein Tank et al. (2009), Hartmann et al. (2013), Climdex, Bioclim and Chimani et al. (2019). Additionally, the dataset incorporates indicators developed through collaborative discussions with internal climate service providers at GeoSphere Austria. Temporal aggregation The climate indicators are provided in annual, seasonal, or both temporal aggregations, depending on their specific definitions. Furthermore, the dataset includes a variety of spatial and temporal aggregations: Area means: spatial averages calculated across all available fields for all temporal aggregations (seasonal/annual) Climatological fields: spatial climatologies for two time periods: 1961 to 1990 (past) 1991 to 2020 (recent) Significance fields: p-value from a two-tailed Mann-Whitney U-test between the two climatological periods Input data sources The data these indicators have been calculated from are daily climatological fields from: Temperature (minimum/mean/maximum): SPARTACUS (Hiebl. et al, 2015) [v2.1] Accumulated precipitation: SPARTACUS (Hiebl et al, 2017) [v2.1] Absolute and relative sunshine duration: SPARTACUS (Hiebl et al, 2024) [v2.1] Reference evapotranspiration: WINFORE (Haslinger and Bartsch, 2016) [v2.1] Snow and runoff variables: SNOWGRID (Olefs et al, 2020) [v2.1] The input data can be downloaded from the GeoSphere Austria Data Hub: Temperature: https://doi.org/10.60669/m6w8-s545 Precipitation: https://doi.org/10.60669/m6w8-s545 Evapotranspiration: https://doi.org/10.60669/f6ed-2p24 Sunshine duration: https://doi.org/10.60669/m6w8-s545 Snow and runoff: https://doi.org/10.60669/fsxx-6977 Visualizations The dataset includes a variety of visualizations to aid in the interpretation of the climate indicators: Trends: Anomaly timeseries plots for the area means Warming stripes for area means Spatial maps: Climatological fields for the two time periods (1961–1990 and 1991–2020). Differences between the climatological fields. p-values from significance tests. Proportion of significant changes per parameter category. Spatial coverage The dataset covers the following area of interest in the ETRS89 / Austria Lambert projection (EPSG:3416), corresponding to 105736 km² within the SPARTACUS domain: Coordinate EPSG:3416 EPSG:4326 xmin 112000 9.524316 ymin 258000 46.202145 xmax 696000 17.293778 ymax 587000 49.159911 The geographical extent of the dataset is documented in `spatial_extent.gpkg`, which contains the following layers: layer_name geometry_type features fields crs_name1 AT_border Multi Polygon 1 14 ETRS89 / Austria Lambert2 SPARTACUS_domain Multi Polygon 1 0 ETRS89 / Austria Lambert3 WINFORE_domain Polygon 1 0 ETRS89 / Austria Lambert4 SNOWGRID_domain Polygon 1 0 ETRS89 / Austria Lambert5 grid Polygon 105736 3 ETRS89 / Austria Lambert which contain the following information: the official national border of Austria (as provided by BEV, https://doi.org/10.48677/bf810ec9-3869-4290-96d2-cc1d2d5196e5) SPARTACUS domain derived from the grid WINFORE domain derived from the grid SNOWGRID domain derived from the grid the actual 1 km grid; Includes three columns for each input dataset, indicating data availability for each grid cell (1 = available, 0 = no data). How to Cite Please cite our accompanying data descriptor paper: Lehner, S., Schlögl, M. Climate indicators for Austria since 1961 at 1 km resolution. Sci Data (2026). https://doi.org/10.1038/s41597-026-06834-y References Klein Tank A.M.G., Zwiers F.W., Zhang X. (2009): Guidelines onanalysis of extremes in a changing climate in supportof informed decisions for adaptation. (WCDMP-72,WMO-TD/No. 1500) 2009, 56, https://library.wmo.int/idurl/4/48826. Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. Thorne, M. Wild and P.M. Zhai. (2013): Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Hiebl, J., & Frei, C. (2016): Daily temperature grids for Austria since 1961—concept, creation and applicability. Theor. Appl. Climatol., 124, 161–178 (2016). https://doi.org/10.1007/s00704-015-1411-4. Hiebl, J. & Frei, C. (2017): Daily precipitation grids for Austria since 1961—development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling. Theor. Appl. Climatol., 132, 327–345. https://doi.org/10.1007/s00704-017-2093-x Hiebl, J., Bourgeois, Q., Tilg, A.-M. & Frei, C. (2024): Daily sunshine grids for Austria since 1961 – combining station and satellite observations for a multi-decadal climate-monitoring dataset. Theor. Appl. Climatol., 155, 8337–8360. https://doi.org/10.1007/s00704-024-05103-5. Haslinger, K. & Bartsch, A. (2016): Creating long-term gridded fields of reference evapotranspiration in Alpine terrain based on a recalibrated Hargreaves method. Hydrol. Earth Syst. Sci., 20, 1211–1223. https://doi.org/10.5194/hess-20-1211-2016. Chimani, B., Matulla, C., Eitzinger, J., Hiebl, J., Hofstätter, M., Kubu, G., ... & Thaler, S. (2019). Guideline zur Nutzung der OeKS15-Klimawandelsimulationen sowie der entsprechenden gegitterten Beobachtungsdatensätze. CCCA Data Centre, 450. Available at: https://ccca.ac.at/wissenstransfer/starc-impact-guideline. Olefs, M., Koch, R., Schöner, W., & Marke, T. (2020): Changes in Snow Depth, Snow Cover Duration, and Potential Snowmaking Conditions in Austria, 1961–2020—A Model Based Approach. Atmosphere, 11(12), 1330. https://doi.org/10.3390/atmos11121330.
本仓库提供了一套覆盖奥地利全域的综合数据集，包含117项气候指标，空间分辨率为1千米网格。该数据集依据输入变量划分为7大类：气温、降水、积雪、径流、辐射、湿度及混合类指标。 ℹ️ 请注意： 本数据集为静态非运行型研究数据集，时间跨度为1961—2025年，以一次性存档版本发布，后续不会在此平台更新。 该数据集的详细信息已发表于《科学数据（Scientific Data）》的对应数据描述论文中，包含数据集结构、内容及指标定义的完整说明。 本版本基于SPARTACUS v2.1构建，后续将由SPARTACUS v3取代。SPARTACUS v3正式发布后，年度更新数据将通过GeoSphere Austria数据中心（GeoSphere Austria DataHub）提供。 ### 方法 本数据集的气候指标基于多项已公认的标准定义推导得到，包括Klein Tank等人（2009）、Hartmann等人（2013）、气候极端指数（Climdex）、生物气候指标（Bioclim）以及Chimani等人（2019）提出的规范。此外，数据集还纳入了与GeoSphere Austria内部气候服务机构合作研讨开发的专属指标。 #### 时间聚合方式 气候指标可按年、季或同时采用两种时间尺度聚合，具体取决于指标自身的定义要求。本数据集同时包含多种空间与时间聚合产物： 1. 区域平均值：针对所有时间聚合尺度（季/年），对全部有效网格场计算空间平均得到的结果 2. 气候态场：针对两个时段计算的空间气候态结果： - 1961—1990年（历史参考时段） - 1991—2020年（近期参考时段） 3. 显著性检验场：基于双尾曼-惠特尼U检验（Mann-Whitney U-test）得到的两个气候态时段差值的p值 #### 输入数据源 本数据集的指标计算基于以下逐日气候场数据： - 气温（最低/平均/最高气温）：SPARTACUS（Hiebl等人，2015）[v2.1] - 累积降水量：SPARTACUS（Hiebl等人，2017）[v2.1] - 绝对日照时长与相对日照时长：SPARTACUS（Hiebl等人，2024）[v2.1] - 参考蒸散量：WINFORE（Haslinger与Bartsch，2016）[v2.1] - 积雪与径流变量：SNOWGRID（Olefs等人，2020）[v2.1] 上述输入数据可通过GeoSphere Austria数据中心下载，具体链接如下： - 气温：https://doi.org/10.60669/m6w8-s545 - 降水：https://doi.org/10.60669/m6w8-s545 - 蒸散量：https://doi.org/10.60669/f6ed-2p24 - 日照时长：https://doi.org/10.60669/m6w8-s545 - 积雪与径流：https://doi.org/10.60669/fsxx-6977 ### 可视化内容 本数据集包含多种可视化产物，以辅助气候指标的解读与分析： 1. 趋势分析模块： - 区域平均值的异常值时间序列图 - 区域平均值的变暖条纹图（Warming stripes） 2. 空间地图模块： - 两个参考时段（1961—1990年与1991—2020年）的气候态场分布图 - 不同时段气候态场的差值对比图 - 显著性检验的p值分布图 - 各参数类别中存在显著变化的网格占比图 ### 空间覆盖范围 本数据集采用ETRS89 / 奥地利兰伯特投影（EPSG:3416），覆盖SPARTACUS域内105736 km²的奥地利研究区域，坐标信息如下： | 坐标系参数 | EPSG:3416 | EPSG:4326 | |------------|------------|------------| | xmin（最小X坐标） | 112000 | 9.524316 | | ymin（最小Y坐标） | 258000 | 46.202145 | | xmax（最大X坐标） | 696000 | 17.293778 | | ymax（最大Y坐标） | 587000 | 49.159911 | 本数据集的地理范围已存储于`spatial_extent.gpkg`文件中，该文件包含以下5个图层： | 图层ID | 图层名称 | 几何类型 | 要素数量 | 字段数量 | 坐标系名称 | |--------|------------------|------------------------|----------|----------|--------------------------------| | 1 | AT_border | 多面（Multi Polygon） | 1 | 14 | ETRS89 / Austria Lambert | | 2 | SPARTACUS_domain | 多面（Multi Polygon） | 1 | 0 | ETRS89 / Austria Lambert | | 3 | WINFORE_domain | 面（Polygon） | 1 | 0 | ETRS89 / Austria Lambert | | 4 | SNOWGRID_domain | 面（Polygon） | 1 | 0 | ETRS89 / Austria Lambert | | 5 | grid | 面（Polygon） | 105736 | 3 | ETRS89 / Austria Lambert | 各图层包含的信息如下： - 奥地利官方国界（由奥地利联邦地貌测量局BEV提供，https://doi.org/10.48677/bf810ec9-3869-4290-96d2-cc1d2d5196e5） - 基于网格推导得到的SPARTACUS研究域范围 - 基于网格推导得到的WINFORE研究域范围 - 基于网格推导得到的SNOWGRID研究域范围 - 实际的1千米分辨率网格：每个输入数据集对应三列数据，用于标识各网格单元的数据可用性（1表示数据可用，0表示无有效数据） ### 引用方式 请引用本数据集配套的数据描述论文： Lehner, S., Schlögl, M. Climate indicators for Austria since 1961 at 1 km resolution. Sci Data (2026). https://doi.org/10.1038/s41597-026-06834-y ### 参考文献 1. Klein Tank A.M.G., Zwiers F.W., Zhang X. (2009): 《气候变化极端事件分析指南：支持适应决策的知情参考》（WCDMP-72, WMO-TD/No. 1500）, 2009, 56, https://library.wmo.int/idurl/4/48826. 2. Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. Thorne, M. Wild 与 P.M. Zhai. (2013): 《观测：大气与地表》，收录于《气候变化2013：物理科学基础》——政府间气候变化专门委员会第五次评估报告第一工作组贡献 [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex 与 P.M. Midgley (eds.)]. 剑桥大学出版社，英国剑桥及美国纽约州纽约市。 3. Hiebl, J., & Frei, C. (2016): 1961年以来奥地利逐日气温网格数据：概念、构建与适用性. 《理论与应用气候学》, 124, 161–178 (2016). https://doi.org/10.1007/s00704-015-1411-4. 4. Hiebl, J. & Frei, C. (2017): 1961年以来奥地利逐日降水网格数据：水文气候监测与建模的空间数据集开发与评估. 《理论与应用气候学》, 132, 327–345. https://doi.org/10.1007/s00704-017-2093-x 5. Hiebl, J., Bourgeois, Q., Tilg, A.-M. & Frei, C. (2024): 1961年以来奥地利逐日日照网格数据：结合台站与卫星观测构建的数十年气候监测数据集. 《理论与应用气候学》, 155, 8337–8360. https://doi.org/10.1007/s00704-024-05103-5. 6. Haslinger, K. & Bartsch, A. (2016): 基于重新校准的Hargreaves方法构建阿尔卑斯山区长期参考蒸散量网格场. 《水文与地球系统科学》, 20, 1211–1223. https://doi.org/10.5194/hess-20-1211-2016. 7. Chimani, B., Matulla, C., Eitzinger, J., Hiebl, J., Hofstätter, M., Kubu, G., 等. (2019). 《OeKS15气候变化模拟及对应网格化观测数据集使用指南》. CCCA数据中心, 450. 可获取于: https://ccca.ac.at/wissenstransfer/starc-impact-guideline. 8. Olefs, M., Koch, R., Schöner, W., & Marke, T. (2020): 1961–2020年奥地利积雪深度、积雪持续时间与潜在造雪条件变化——基于模型的研究方法. 《大气》, 11(12), 1330. https://doi.org/10.3390/atmos11121330.
