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The Global 0.5° gridded SPEI dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Users of the dataset are free to share, create and adapt under the conditions of attribution and share-alike. The Global SPEI database, SPEIbase, offers long-time, robust information on the drought conditions at the global scale, with a 0.5 degrees spatial resolution and a monthly time resolution. It has a multi-scale character, providing SPEI time-scales between 1 and 48 months. The Standardized Precipitatin-Evapotranspiration Index (SPEI) expresses, as a standardized variate (mean zero and unit variance), the deviations of the current climatic balance (precipitation minus evapotranspiration potential) with respect to the long-term balance. The reference period for the calculation, in the SPEIbase, corresponds to the whole study period. Being a standardized variate means that the SPEI condition can be compared across space and time. Calculation of the evapotranspiration potential in SPEIbase is based on the FAO-56 Penman-Monteith method. Data type: float; units: z-values (standard deviations). No land pixels are assigned a value of 1.0x10^30. In some rare cases it was not possible to achieve a good fit to the log-logistic distribution, resulting in a NAN (not a number) value in the database. Dimensions of the dataset: lon = 720; lat = 360; time = 1356. Resolution of the dataset: lon = 0.5º; lat = 0.5º; time = 1 month. Created in R using the SPEI package (http://cran.r-project.org/web/packages/SPEI)., Global gridded dataset of the Standardized Precipitation-Evapotranspiration Index (SPEI) at time scales between 1 and 48 months.-- Spatial resolution of 0.5º lat/lon.-- This is an update of the SPEIbase v2.6 (https://digital.csic.es/handle/10261/202305).-- What’s new in version 2.7: 1) Based on the CRU TS 4.05 dataset, spanning the period between January 1901 to December 2020. Using TLMoments::PWM instead of lmomco::pwm.ub for calculating distribution parameters. For more details on the SPEI visit http://sac.csic.es/spei, No

Repository containing derived data for the manuscript 'Global collision-risk hotspots of marine traffic and the world's largest fish, the whale shark'., Peer reviewed

Predicted genes corresponding to the four most common enzymes present in photosynthetic organisms: NADH:ubiquinone reductase (H+-translocating), N-acetyl-gamma-glutamyl-phosphate reductase, DNA-directed RNA polymerase and non-specific serine/threonine protein kinase of 174 metagenomes sequenced during the Malaspina 2010 global expedition., Peer reviewed

The dataset is comprised of: downwelling diffuse attenuation coefficients, Z10%, aCDOM and ap., Peer reviewed

The dataset is comprised of: downwelling diffuse attenuation coefficients, Z10%, aCDOM and ap, Peer reviewed

Contents of this file: Figures S1 to S8 and Table S1. -- Figure S1. CDOM absorption coefficients (aCDOM, in m-1) at UV-B wavelengths 305 nm (top panel), 313 nm (middle panel), and 320 nm (bottom panel) measured during the Malaspina 2010 Expedition. Reported values are depth-weighted averages from surface waters (3 m depth) down to the 20% PAR depth. -- Figure S2. Results of the Dunn’s tests, that were performed after Kruskal-Wallis tests to identify if aCDOM (top row), ap (middle row) and ap as % of anw (bottom row) at 305 nm (left column), 313 nm (middle column) and 320 nm (right column) varied significantly (p<0.05) between Longhurst provinces during the Malaspina 2010 Expedition. For a description of the Longhurst province codes, see Fig. 1. -- Figure S3. Particulate absorption coefficients (ap, in m-1) at UV-B wavelengths 305 nm (top panel), 313 nm (middle panel), and 320 nm (bottom panel) measured during the Malaspina Expedition. Reported values are depth-weighted averages from surface waters (3 m depth) down to the 20% PAR depth. -- Figure S4. Downwelling diffuse attenuation coefficients (Kd, in m-1) for the UV-B wavelengths 305 nm (top panel), 313 nm (middle panel), and 320 nm (bottom panel) measured during the Malaspina 2010 Circumnavigation. -- Figure S5. Downwelling diffuse attenuation coefficients (Kd, in m-1) for the UV-A wavelengths 340 nm (top panel), 380 nm (middle panel), and 395 nm (bottom panel) measured during the Malaspina 2010 Expedition. -- Figure S6. Downwelling diffuse attenuation coefficients (Kd, in m-1) for the integrated PAR spectrum (400–700 nm) measured during the Malaspina 2010 Expedition. -- Figure S7. Results of the Dunn’s tests, that were performed after Kruskal-Wallis tests to identify if the downwelling diffuse attenuation coefficient (Kd) at 305, 313, 320, 340 nm varied significantly (p <0.05) between Longhurst provinces during the Malaspina 2010 Expedition. For a description of the Longhurst provinces code, see Fig. 1. -- Figure S8. Seasonal comparison between cloud fractions in the northern and southern tropics (15.5N to 15.5S) in year 2010. Bars represent monthly averages (mean  SD) of 1 x 1 sector squares between 179.5W and 179.5E (n=5760 per bar). Data were obtained from the publicly available Aqua/MODIS satellite data set curated by NASA’s Earth Observatory (https://earthobservatory.nasa.gov/global-maps/MODAL2_M_CLD_FR). WIN, SPR, SUM and AUT refer to winter, spring, summer and autumn, respectively. WIN1 represents December for the northern latitudes and June for the southern latitudes. Asterisks indicate instances where the non-paired t-test identified significantly different means at level p <0.01. -- Table S1. Slope, correlation, 95% confidence intervals and p-values determined as part of the pairwise correlation analysis with the variables sea surface temperature, Chl-a and Kd(PAR), as well as aCDOM, ap and Kd(λ) at wavelengths 305, 313 and 320 nm. For Chl-a, aCDOM and ap, depth-weighted (3 m to 20% PAR depth) average values were used for the analysis., Peer reviewed

6 pages. -- Supplementary Figure 1. Current mean maximum summer temperature (average 𝑇!"# """""" for the period 1980-2005) across potential seagrass distribution. -- Supplementary Figure 2. Difference between current mean maximum summer temperature ( 𝑇!"# """""" ) and the Tlimit as a function of latitude. Negative and positive latitude values for southern and northern hemispheres, respectively. -- Supplementary Figure 3. Uncertainty associated to the time (in years) for mean maximum summer temperature to reach seagrass upper thermal limit (Tlim) at the warming rates projected under the RCP8.5 scenario around potential seagrass sites. -- Supplementary Figure 4. Time (in years) for mean maximum summer temperature to reach the upper thermal limits (Tlim) of temperate and tropical affinity seagrass flora at the warming rates projected under the RCP8.5 scenario around potential seagrass sites in the Mediterranean Sea and Queensland (Australia) coastal areas. -- Supplementary Figure 5. The time (in years) to reach Tlimit at the warming rates predicted under the RCP4.5 scenario around potential seagrass sites. -- Supplementary Figure 6. Time (in years) for mean maximum summer temperature to reach the upper thermal limits (Tlim) of temperate and tropical affinity seagrass flora at the warming rates projected under the RCP4.5 scenario around potential seagrass sites in the Mediterranean Sea and Queensland (Australia) coastal areas., Seagrasses have experienced major losses globally mostly attributed to human impacts. Recently they are also associated with marine heat waves. The paucity of information on seagrass mortality thermal thresholds prevents the assessment of the risk of seagrass loss under marine heat waves. We conducted a synthesis of reported empirically- or experimentally-determined seagrass upper thermal limits (Tlimit) and tested the hypothesis that they increase with increasing local annual temperature. We found that Tlimit increases 0.42± 0.07°C per°C increase in in situ annual temperature (R2 = 0.52). By combining modelled seagrass Tlimit across global coastal areas with current and projected thermal regimes derived from an ocean reanalysis and global climate models (GCMs), we assessed the proximity of extant seagrass meadows to their Tlimit and the time required for Tlimit to be met under high (RCP8.5) and moderate (RCP4.5) emission scenarios of greenhouse gases. Seagrass meadows worldwide showed a modal difference of 5°C between present Tmax and seagrass Tlimit. This difference was lower than 3°C at the southern Red Sea, the Arabian Gulf, the Gulf of Mexico, revealing these are the areas most in risk of warming-derived seagrass die-off, and up to 24°C at high latitude regions. Seagrasses could meet their Tlimit regularly in summer within 50-60 years or 100 years under, respectively, RCP8.5 or RCP4.5 scenarios for the areas most at risk, to more than 200 years for the Arctic under both scenarios. This study shows that implementation of the goals under the Paris Agreement would safeguard much of global seagrass from heat-derived mass mortality and identifies regions where actions to remove local anthropogenic stresses would be particularly relevant to meet the Target 10 of the Aichi Targets of the Convention of the Biological Diversity., Peer reviewed

[EN] SPI01 grid: plain text. 5219 rows (excluding the header) and 1261 columns (excluding the X and Y coordinates). SPI12 grid: plain text. 5219 rows (excluding the header) and 1250 columns (excluding the X and Y coordinates). Episode descriptive files: duration and intensity integral maps, SPI01 and SPI12 averages, and spatial propagation maps., [ES] Malla SPI01: texto plano. 5219 filas -descontando el encabezado- y 1261 columnas -descontando las coordenadas X e Y-. Malla SPI12: texto plano. 5219 filas -descontando el encabezado- y 1250 columnas -descontando las coordenadas X e Y-. Archivos descriptivos de los episodios: mapas integrales de duración e intensidad, promedios de SPI’1 y SPI12 y mapas de la propagación espacial., Open Data Commons Attribution (ODC-By 1.0)., [EN] The database consists of two files in .txt format with the precipitation anomaly grids (Standardized Precipitation Index) calculated at 1 and 12 months over the Spanish peninsular domain, covering the period 2015/12_2020/12. These have been calculated from the monthly data of the MOPREDAScentury precipitation grid (https://doi.org/10.20350/digitalCSIC/15136). In addition, a descriptive analysis of the 40 drought episodes identified according to the criteria of drought intensity (SPI12 =< -0.84) and affected area (>20 % of the grid area) is included. For each episode we include the time series of the SPI01 and SPI12 average of the whole grid (expressed in anomalies); the area of the grid under drought conditions (SPI12 =< -0.84) (expressed in percent per one); the integral maps of the episode according to its duration (expressed in number of months) and intensity (average of the cells under drought conditions); and the maps representing the spatial propagation of the episode. This record corresponds to version 1.0.0 of the dataset. The database is distributed under an open license (Open Data Commons Attribution, ODC-By)., [ES] La base de datos consta de dos archivos en formato .txt con las mallas de anomalías de precipitación (Standardized Precipitation Index) calculadas a 1 y 12 meses sobre el dominio peninsular español, cubriendo el periodo 12/2015_12/2020. Estas han sido calculadas a partir de los datos mensuales de la malla de precipitación MOPREDAScentury (https://doi.org/10.20350/digitalCSIC/15136). Además, se incluye un análisis descriptivo de los 40 episodios de sequía identificados según los criterios de intensidad de la sequía (SPI12 =< -0.84) y superficie afectada (>20 % de la superficie de la malla). Para cada episodio se incluyen las series temporales del SPI01 y SPI12 promedio de toda la malla (expresadas en anomalías); el área de la malla en condiciones de sequía (SPI12 =< -0.84) (expresada en tanto por uno); los mapas integrales del episodio atendiendo a su duración (expresada en número de meses) e intensidad (promedio de las celdas en condiciones de sequía); y los mapas que representan la propagación espacial del episodio. Este registro se corresponde con la versión 1.0.0 del conjunto de datos. La base de datos se distribuye bajo una licencia abierta (Open Data Commons Attribution, ODC-By)., Project PID2020-116860RB-C22: Extremos térmicos y pluviométricos en la España peninsular 1916-2020), funded by the Spanish Ministry of Science., Peer reviewed

[EN] It contains a collection of .csv files by country and a netCDF file. Last one need specific data analyse software. [ES] Contiene una colección de archivos .csv por país y un archivo netCDF. Este último necesita software de análisis de datos específico., [EN] This database includes the representative Standardized Precipitation Evapotranspiration Index (SPEI) series for the different countries of the world from 1901 at the time scales from 1 to 48 months. The data is based on the average precipitation and reference evapotranspiration series from the Climatic Research Unit (last version) for the different world countries., [ES] Esta base de datos incluye la serie representativa del Índice Estandarizado de Precipitación y Evapotranspiración (SPEI) para los diferentes países del mundo desde 1901 en las escalas de tiempo de 1 a 48 meses. Los datos se basan en las series de precipitación media y evapotranspiración de referencia de la Unidad de Investigación Climática (última versión) para los diferentes países del mundo., Peer reviewed

[EN] It contains a netCDF file which needs specific data analysis software. [ES] Contiene un fichero netCDF que necesita software de análisis de datos específico., [EN] It is a gridded dataset for the whole of Europe, which employed a set of 125 climate indices from 1950. Climate indices were computed at different temporal scales (i.e. monthly, seasonal and annual) and mapped at a grid interval of 0.25°., [ES] Es una rejilla de 125 índices climáticos con una resolución espacial de 0.25 grados calculados para toda Europa desde 1950. Los índices climáticos han sido calculados a diferentes escalas temporales (mensual, estacional y anual)., Spanish Commission of Science and Technology and FEDER by the research projects PCIN-2015-220, CGL2017-82216-R and CGL2017-83866-C3-1-R, AXIS (Assessment of Cross(X) - sectorial climate Impacts and pathways for Sustainable transformation), JPI-Climate co-funded call of the European Commission by the project CROSSDRO, FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462) by the reserach project INDECIS which is part of ERA4CS, an ERA-NET initiated by JPI Climate, Peer reviewed