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This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

Related paper containing detailed description: Moesinger et al. (2020) Vegetation optical depth (VOD) describes the attenuation of radiation by plants. VOD a function of frequency as well as vegetation water content, and by extension biomass. VOD has many possible applications in studies of the biosphere, such as biomass monitoring, drought monitoring, phenology analyzes or fire risk management. We merged VOD observations from various spaceborne sensors (SSM/I, TMI, AMSR-E, AMSR2, WindSat) to create global long-term vod time series. Prior to aggregation the data has been rescaled to AMSR-E, removing systematic differences between them. There is a product for C-band (~6.9 GHz, 2002 - 2018), X-band (10.7 GHz, 1997 - 2018) and Ku-band (~19 GHz, 1987 - 2017). The data is global sampled on a regular 0.25 degrees grid. Each product is available as daily global netcdf4 files. Currently there is an issue with opening the file using ESA SNAP. As an alternative Panoply can be used to quickly visualize the data. An update of VODCA, addressing this issue and potentially including an extension of the dataset, is foreseen to be published on Zenodo early 2020. Please contact us if you have any questions, problems or suggestions for improvement! Files: "VODCA_C-band_2002-2018_v01.0.0.zip" (unzipped size: ~140 GB): VODCA C-band files, sorted into yearly folders "VODCA_X-band_1997-2018_v01.0.0.zip" (unzipped size: ~180 GB): VODCA X-band files, sorted into yearly folders "VODCA_Ku-band_1987-2017_v01.0.0.zip" (unzipped size: ~270 GB) : VODCA Ku-band files, sorted into yearly folders "vodca_v01-0_K-band_2007-06-01.nc" sample file of the Ku-band product "ESA-CCI-SOILMOISTURE-LAND_AND_RAINFOREST_MASK-fv04.2.nc" Contains a global land mask, VODCA only has data for land locations. Source: https://github.com/TUW-GEO/smecv-grid Variables of data in VODCA files: "VOD": Unitless, Vegetation Optical Depth of the respective band "sensor_flag": Bit-flag indicating which sensors contributed to each observation. Values: 1 = AMSR-E 2 = AMSR2 3 = SSM/I F8 4 = SSM/I F11 5 = SSM/I F13 6 = TMI 7 = WindSat "processing_flag": Bit-flag indicating irregularities during processing affecting the quality of the observations Values: 0 = Everything is fine 10 = AMSR-2 7.3 GHz band is used instead of 6.9 GHz 11 = Sensor is scaled to matched TMI instead of AMSR-E 12 = Sensor scaled without temporally overlapping observations "time"/"lon"/"lat": Dimensions of the data.

Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Earth_Heat_Inventory_Ocean_Heat_Content_data.nc” contains a consistent long-term Earth system heat inventory over the period 1960-2020. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory published in von Schuckmann et al. (2020), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2020. The dataset also contains estimates for global ocean heat content over 1960-2020 for different depth layers, i.e., 0-300m, 0-700m, 700-2000m, 0-2000m, 2000-bottom, which are described in von Schuckmann et al. (2022). This version includes an update of heat storage of global ocean heat content, where one additional product (Li et al., 2022) had been included to the initial estimate. The Earth heat inventory had been updated accordingly, considering also the update for continental heat content (Cuesta-Valero et al., 2023).

Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. This dataset features a detailed map of material stocks for the whole of Germany on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Temporal extent The map is representative for ca. 2018. Data format Per federal state, the data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. The dataset features area and mass for different street types area and mass for different rail types area and mass for other infrastructure area, volume and mass for different building types Masses are reported as total values, and per material category. Units area in m² height in m volume in m³ mass in t for infrastructure and buildings Further information For further information, please see the publication or contact Helmut Haberl (helmut.haberl@boku.ac.at). A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Publication Haberl, H., Wiedenhofer, D., Schug, F., Frantz, D., Virág, D., Plutzar, C., Gruhler, K., Lederer, J., Schiller, G. , Fishman, T., Lanau, M., Gattringer, A., Kemper, T., Liu, G., Tanikawa, H., van der Linden, S., Hostert, P. (accepted): High-resolution maps of material stocks in buildings and infrastructures in Austria and Germany. Environmental Science & Technology Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). ML and GL acknowledge funding by the Independent Research Fund Denmark (CityWeight, 6111-00555B), ML thanks the Engineering and Physical Sciences Research Council (EPSRC; project Multi-Scale, Circular Economic Potential of Non-Residential Building Scale, EP/S029273/1), JL acknowledges funding by the Vienna Science and Technology Fund (WWTF), project ESR17-067, TF acknowledges the Israel Science Foundation grant no. 2706/19.

Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Atmosphere_Heat_Content_data.nc” presents an updated estimate of the atmospheric heat content (AHC) from 1960-2020 calculated using observational data and reanalyses. The estimate is given for the AHC relative to 1960. This represents an update to the record described in von Schuckmann et al. (2020) with the ENSO signal removed. The data are used in von Schuckmann et al. (2022).

AbstractThe future distribution of river fishes will be jointly affected by climate and land use changes forcing species to move in space. However, little is known whether fish species will be able to keep pace with predicted climate and land use‐driven habitat shifts, in particular in fragmented river networks. In this study, we coupled species distribution models (stepwise boosted regression trees) of 17 fish species with species‐specific models of their dispersal (fish dispersal model FIDIMO) in the European River Elbe catchment. We quantified (i) the extent and direction (up‐ vs. downstream) of predicted habitat shifts under coupled “moderate” and “severe” climate and land use change scenarios for 2050, and (ii) the dispersal abilities of fishes to track predicted habitat shifts while explicitly considering movement barriers (e.g., weirs, dams). Our results revealed median net losses of suitable habitats of 24 and 94 river kilometers per species for the moderate and severe future scenarios, respectively. Predicted habitat gains and losses and the direction of habitat shifts were highly variable among species. Habitat gains were negatively related to fish body size, i.e., suitable habitats were projected to expand for smaller‐bodied fishes and to contract for larger‐bodied fishes. Moreover, habitats of lowland fish species were predicted to shift downstream, whereas those of headwater species showed upstream shifts. The dispersal model indicated that suitable habitats are likely to shift faster than species might disperse. In particular, smaller‐bodied fish (<200 mm) seem most vulnerable and least able to track future environmental change as their habitat shifted most and they are typically weaker dispersers. Furthermore, fishes and particularly larger‐bodied species might substantially be restricted by movement barriers to respond to predicted climate and land use changes, while smaller‐bodied species are rather restricted by their specific dispersal ability.

Abstract Background Around the Cretaceous-Paleogene (K-Pg) boundary, an obvious global cooling occurred, which resulted in dramatic changes in terrestrial ecosystems and the evolutionary trends of numerous organisms. However, how plant lineages responded to the cooling has remained unknown until now. Between ca. 70–60 Ma Mesocyparis McIver & Basinger (Cupressaceae), an extinct conifer genus, was distributed from eastern Asia to western North America and provides an excellent opportunity to solve this riddle. Results Here we report a new species, Mesocyparis sinica from the early Paleocene of Jiayin, Heilongjiang, northeastern China. By integrating lines of evidence from phylogeny and comparative morphology of Mesocyparis, we found that during ca.70–60 Ma, the size of seed cone of Mesocyparis more than doubled, probably driven by the cooling during the K-Pg transition, which might be an effective adaptation for seed dispersal by animals. More importantly, we discovered that the northern limit of this genus, as well as those of two other arboreal taxa Metasequoia Miki ex Hu et Cheng (gymnosperm) and Nordenskioldia Heer (angiosperm), migrated ca.4–5° southward in paleolatitude during this time interval. Conclusions Our results suggest that the cooling during the K-Pg transition may have been responsible for the increase in size of the seed cone of Mesocyparis and have driven the migration of plants southwards.

Seagrass meadows are among the most valuable habitats in the world as they are used by a wide range of marine organisms and provide important ecosystem services. With increasing human populations and coastal development, seagrasses are under increased stress and coverage is declining worldwide. This is the first experiment to test the effects of two known seagrass stressors, increased temperature and reduced light availability, on the composition of seagrass blade surface microbial communities, which is a relatively understudied community. Analysis of 16S rRNA amplicon (iTag) sequence data revealed that both of these stressors significantly altered microbial community structure, including both taxonomy and abundance, on the blade surfaces of the tropical seagrass Thalassia testudinum. The highest temperature and lowest light treatments showed higher abundances of phyla not commonly reported as indigenous members of seagrass phyllosphere communities, including members of the bacterial phyla Ca. PAUC34f, Ca. Modulibacteria, and Chlamyidae. Despite these compositional difference among treatments, no significant differences in overall microbial diversity or richness were found. These results suggested seagrass phyllosphere microbial communities have the capacity to change significantly and relatively quickly in response to changing environmental conditions due to anthropogenic activity. Further studies are needed to determine if these direct environmental effects on the microbial community or indirect effects that feedback through the seagrass host.