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134 results for “global estimates”
Improved estimation of global gross primary productivity during 1981–2020 using the optimized P model
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Estimating global GPP from the plant functional type perspective using a machine learning approach
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Estimates of black carbon emissions from global biomass burning for the period 1997–2023
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Data from: Joint phylogenetic estimation of geographic movements and biome shifts during the global diversification of Viburnum
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Data from: Estimating maize harvest index and nitrogen concentrations in grain and residue using globally available data
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Estimating the global population size and highlighting conservation priority areas for the endangered Titicaca grebe
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Refining estimates of the commitment to global sea level rise over the next 500 years
<p>Within Australia alone, more than A$226 billion of coastal infrastructure is vulnerable to the anticipated rise in sea level by the end of the century. The IPCC Fifth Assessment Report concludes that the likely increase in global mean sea level during the 21st century ranges from 26-55 centimetres (under the low-end RCP2.6 climate scenario) to 45-82 centimetres (under the high-end RCP8.5 climate scenario). However, these projections do not take into account the potential for collapse of the marine-based sectors of the Antarctic Ice Sheet.</p> <p>Recent evidence has indicated that the IPCC projections may be under-estimates, with sea level increases of up to 2.5 metres possible by the end of the 21st century. Modelling studies have also demonstrated the potential for the Antarctic Ice Sheet to undergo irreversible collapse during the coming centuries. The most extreme prediction is that, under the RCP8.5 scenario, Antarctica alone could contribute 15.65±2.00 metres to global sea level by the year 2500.</p> <p>Here, we combine climate modelling and ice sheet modelling to explore the evolution of the Antarctic Ice Sheet over the next 500 years under a range of climate scenarios. We run the models many times to account for gaps in our understanding of ice sheet dynamics, using our knowledge of past changes in the Antarctic Ice Sheet to identify the configurations that are plausible. This allows us to generate robust projections of the Antarctic contribution to global sea level from the present to the year 2500, complete with quantified confidence intervals.</p> <p>We conclude that the sea level contribution during the 21st century will be modest, consistent with the IPCC Fifth Assessment Report, but that melting of the Antarctic Ice Sheet will accelerate thereafter. We also conclude that previous studies have underestimated the range of uncertainty in projections of future global sea level rise.</p>
Data from: A global estimate of terrestrial net secondary production of primary consumers
Aim: Net Secondary production (NSP) emerges from the consumption of Net Primary Production (NPP) by any heterotrophic organism. There has been sporadic interest in the importance of NSP, but no global estimates have been produced. We revisit NSP and attempt a global estimate using contemporary NPP data combined with modern metabolic scaling theory for consumption rates. We distinguish between potential NSP as the amount of secondary production that could be supported by NPP, and realized NSP as the amount remaining after anthropogenic habitat disruption. Location: Global Time period: 2000-2014 Methods: We present a model of NSP implementing a Type II functional response for consumption rates wherein search efficiency and handling time are calculated based on consumer mass and ambient temperature. We solve this model for each 0.05-decimal-degree pixel in the global terrestrial biosphere using as data inputs NPP (MOD17A3) and land-surface temperature (MOD11C3). We aggregate estimates within global land cover classifications (MCD12C1) to obtain cover-specific and global estimates of NSP. We also correct our estimates based on declines in consumer abundance reported in Living Planet Report 2014. Results: We estimate potential NSP is 4.74 PgCy-1 globally (95%~CI~=~3.75--5.75). When we correct for global consumer population declines, realized NSP declines to 2.37 PgCy-1 (95%~CI~=~1.86--2.89), a loss of 50% in the rate of carbon flux through secondary consumers. Main Conclusions: Our estimates are sufficient to suggest that the flux of carbon through consumers is of a similar magnitude to many other fluxes critical to the global carbon cycle. We view this as a hypothesis to be tested that suggests NSP deserves significantly more attention in earth systems, macroecology, and biogeochemical research.
Data from: A global synthesis of survival estimates for microbats
Accurate survival estimates are needed to construct robust population models, which are a powerful tool for understanding and predicting the fates of species under scenarios of environmental change. Microbats make up 17% of the global mammalian fauna, yet the processes that drive differences in demographics between species are poorly understood. We collected survival estimates for 44 microbat species from the literature and constructed a model to determine the effects of reproductive, feeding and demographic traits on survival. Our trait-based model indicated that bat species which produce more young per year exhibit lower apparent annual survival, as do males and juveniles compared with females and adults, respectively. Using 8 years of monitoring data for two Australian species, we demonstrate how knowledge about the effect of traits on survival can be incorporated into Bayesian survival analyses. This approach can be applied to any group and is not restricted to bats or even mammals. The incorporation of informative priors based on traits can allow for more timely construction of population models to support management decisions and actions.
Global Carbon Monoxide (CO) Flux Estimates for 2001-2015
<p>This data set contains Global carbon monoxide (CO) flux estimates for 2001-2015 partitioned into biomass burning (BB), fossil fuel (FF) and biogenic (BG) sources. The estimates were created at JPL/Caltech by Anthony Bloom using a Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithm (Bloom et al., 2015) applied to top-down CO fluxes obtained from inverse modeling using the GEOS-Chem (with adjoint) model and data from the Terra/MOPITT satellite (Jiang et al., 2017). The spatial resolution is 4.0 x 5.0 degrees lat/lon.</p> <p>Examples of the use of this data are described in Worden, J., et al., 2017 and Worden, H. et al., 2019.</p> <p>References:</p> <p>Bloom, A. A., J. Worden, Z. Jiang, H. Worden, T. Kurosu, C. Frankenberg, D. Schimel, (2015), Remote sensing constraints on South America fire traits by Bayesian fusion of atmospheric and surface data, Geophysical Research Letters, doi:10.1002/2014GL062584</p> <p>Jiang, Z., J. R. Worden, H. Worden, M. Deeter, D. B. A. Jones, A. F. Arellano, and D. K. Henze (2017), A 15-year record of CO emissions constrained by MOPITT CO observations, Atmos. Chem. Phys., 17(7), 4565–4583, doi:10.5194/acp-17-4565-2017.</p> <p>Worden, J. R., A.A. Bloom, S. Pandey, Z. Jiang, H.M. Worden, T.W. Walker, S. Houweling, T. Röckmann, (2017), Reduced biomass burning emissions reconcile conflicting estimates of the post-2006 atmospheric methane budget, Nature Communications, 8:2227, doi:10.1038/s41467-017-02246-0.</p> <p>Worden, H. M., Bloom, A. A., Worden, J. R., Jiang, Z., Marais, E., Stavrakou, T., Gaubert, B., and Lacey, F.: New Constraints on Biogenic Emissions using Satellite-Based Estimates of Carbon Monoxide Fluxes, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2019-377, in review, 2019.</p>
Dataset: Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models
<p>This dataset corresponds to the following publication:<br>Steinert, N., J., et al. 2023: <i>Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models</i>, Environmental Research Letters, 10.1088/1748-9326/ad10d7<br>https://iopscience.iop.org/article/10.1088/1748-9326/ad10d7</p><p>Global permafrost regions are undergoing significant changes due to global warming, whose assessments often rely on permafrost extent estimates derived from climate model simulations. These assessments employ a range of definitions for the presence of permafrost, leading to inconsistencies in the calculation of permafrost area. This dataset contains permafrost area calculations using 10 different definitions for detecting permafrost presence based on either ground thermodynamics, soil hydrology, or air-ground coupling from an ensemble of 32 Earth System Models.</p><p>This dataset includes two file archives:<br>1. 32 CMIP6 models, 10 permafrost definitions, historical period (1850-2014), annual data<br>2. 32 CMIP6 models, 10 permafrost definitions, SSP5-85 period (2015-2100), annual data</p><p>This dataset represents source data for the following publication. Please refer to this reference for a more detailed description of the definitions used in this dataset:<br>Steinert, N., J., et al. 2023: Evaluating permafrost definitions for global permafrost area estimates in CMIP6 climate models, https://iopscience.iop.org/article/10.1088/1748-9326/ad10d7</p><p>The results show that variations between permafrost-presence definitions result in substantial differences of up to 18 million km2, where any given model could both over- or underestimate the present-day permafrost area. Ground-thermodynamic-based definitions are, on average, comparable with observations but are subject to a large inter-model spread. The associated uncertainty of permafrost area estimates is reduced in definitions based on ground-air coupling. However, their representation of permafrost area strongly depends on how each model represents the ground-air coupling processes. The definition-based spread in permafrost area can affect estimates of permafrost-related impacts and feedbacks, such as quantifying permafrost carbon changes. For instance, the definition spread in permafrost area estimates can lead to differences in simulated permafrost-area soil carbon changes of up to 28%. This dataset therefore supports an emphasis on the importance of consistent and well-justified permafrost-presence definitions for robust projections and accurate assessments of permafrost from climate model outputs.</p><p>For any questions regarding the dataset, please free feel to contact Norman J. Steinert (nste@norceresearch.no, normanst@ucm.es)</p>
Datasets for "Reconciling global terrestrial evapotranspiration estimates from multi-product intercomparison and evaluation"
<p>Datasets for "Reconciling global terrestrial evapotranspiration estimates from multi-product intercomparison and evaluation"</p>
Global environmental flow requirement estimates based on multimodel simulations
<p><a href="../api/files/5d8d266f-99bd-4351-8c7a-684f0bc0df90/global_natural_discharge_multimodel_medians.nc4">global_natural_discharge_multimodel_medians.nc4</a>: multimodel medians of simulations of monthly naturalized streamflow during 1971-2010 provided by six global hydrological models (DBH, H08, LPJmL, MATSIRO, PCR-GLOBWB, and WaterGAP), derived from the ISIMIP2a dataset (https://doi.org/10.5880/PIK.2017.010).</p> <p>Global environmental flow requirement estimated with different methods: Qxx (Q90, Q50), Smakhtin (Smakhtin et al. 2004), Tennant (Tennant 1976), Tessmann (Tessmann 1980), VMF (variable monthly flow, Pastor et al. 2014). The unit for the EFR data is m3 s-1.</p> <p>Related reference<br>Liu, X., Liu, W., Liu, L., Tang, Q., Liu, J., & Yang, H. (2021). Environmental flow requirements largely reshape global surface water scarcity assessment. Environmental Research Letters, 16(10), 104029.</p>
Global canopy top height estimates from GEDI LIDAR waveforms for 2019
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S. The map is based on the first four months of L1B Version 1 data (April-July 2019). The sparse footprint level predictions are averaged at 0.5 degree resolution (approx. 55 km raster cells at the equator) to obtain a dense map. We refer to the original research article below for further information, especially on how the predictions were filtered before the aggregation.</p> <p>The footprint level RH98 predictions are stored in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data. The file <a href="https://zenodo.org/api/files/0a9300b5-2dea-4791-a019-319ed6209713/load_pred_RH98_files.py?versionId=6af41185-f13b-44aa-9042-a59efd4abb82">load_pred_RH98_files.py </a>contains more information on how to parse and load the prediction orbit files.</p> <p><strong>GEDI mission website</strong>: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>.</p> <p><strong>Citation: </strong>Use of these data require citation of this dataset and the original research article. These citations are as follows:</p> <p>Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. <em>Remote Sensing of Environment</em>, <em>268</em>, 112760.</p> <p>Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Global canopy top height estimates from GEDI LIDAR waveforms for 2019 (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5704852</p> <p> </p>
Genomic Resources for Global and Local Ancestry Estimation in a Captive Baboon Colony
<p>VCF files mapped to <em>Panubis1.0</em> with 881 olive (<em>Papio anubis</em>) and yellow (<em>Papio cynocephalus</em>) baboons from the Southwest National Primate Research Center. VCF files were generated in two separate pipelines, first using Beagle 4.1 and Beagle 5.4 and additionally SHAPEIT5/IMPUTE5 to test if a pedigree-aware software reduced the number of evident phase switch errors. VCFs here have been phased and imputed in their respective pipelines, filtered for imputation accuracy with markers with less than 0.7 confidence removed using BCFTools, and then phase switch corrected using Tractor. Genomic resources (AIMs and Fixed markers) are based off of <em>Panubis1.0 </em>coordinates. Local ancestry estimation completed by RFMix and then phase-switch-corrected using Tractor. </p>
Angular momentum estimates for global geophysical fluids, 1995--2015
<p>Data supplement for manuscript Börger, L., Schindelegger, M., Zhao, M., Ponte, R. M., Löcher, A., Uebbing, B., Molines, J.-M., and Penduff, T.: Chaotic oceanic excitation of low-frequency polar motion variability, Earth System Dynamics, 16, 75–90, <a href="https://doi.org/10.5194/esd-16-75-2025"> https://doi.org/10.5194/esd-16-75-2025</a>.</p> <p> </p> <div> <p>Provided are the following monthly angular momentum time series, 1995-2015:</p> <ul> <li>Atmospheric angular momentum: AAM_ERA_Int_monthly_1995-2015.aam</li> <li>Oceanic angular momentum: <ul> <li>OAM_OCCIPUT_EnsMean_monthly_1995-2015.oam</li> <li>OAM_OCCIPUT_ens*_monthly_1995-2015.oam</li> </ul> </li> <li>Hydrologic angular momentum: HAM_SLR_DORIS_monthly_1995-2015.asc</li> <li>Cryospheric angular momentum: <ul> <li>Cryo_AM_Greenland_SLR_DORIS_monthly_1995-2015.asc</li> <li>Cryo_AM_Antarctica_SLR_DORIS_monthly_1995-2015.asc</li> </ul> </li> <li>Gravitational attraction and loading angular momentum: GAL_SLR_DORIS_monthly_1995-2015.asc</li> </ul> </div> <p> </p> <p>For content see <em>ReadMe.txt</em>.</p> <p> </p> <p>Terms of usage:</p> <p>If you use the OAM time series, please cite: </p> <p>Börger, L., Schindelegger, M., Zhao, M., Ponte, R. M., Löcher, A., Uebbing, B., Molines, J.-M., and Penduff, T.: Chaotic oceanic excitation of low-frequency polar motion variability, Earth System Dynamics, 16, 75–90, <a href="https://doi.org/10.5194/esd-16-75-2025"> https://doi.org/10.5194/esd-16-75-2025</a>.</p> <p>Bessières, L., Leroux, S., Brankart, J.M., Molines, J.M., Moine, M.P., Bouttier, P.A., Penduff, T., Terray, L., Barnier, B., Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: Application at eddying resolution. Geosci. Model Dev. 10, 1091–1106. doi:10.5194/gmd-10-1091-2017.</p> <p>Hogg, A.M., Penduff, T., Close, S.E., Dewar, W.K., Constantinou, N.C., Mart ́ınez-Moreno, J., 2022. Circumpolar variations in the chaotic nature of Southern Ocean eddy dynamics. J. Geophys. Res. Oceans 127, e2022JC018440. doi:10.1029/2022JC018440.</p> <div>Penduff, T., Bernier, B., Terray, L., Bessières, L., Sérazin, G., Gregorio, S., Brankart, J.M., Moine, M.P., Brankart, J.M., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate. CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling 19, 26–29.</div> <div> </div> <div> </div> <div> <p>If you use the angular momentum estimates of the other geophysical fluids, please cite: </p> <p>Börger, L., Schindelegger, M., Zhao, M., Ponte, R. M., Löcher, A., Uebbing, B., Molines, J.-M., and Penduff, T.: Chaotic oceanic excitation of low-frequency polar motion variability, Earth System Dynamics, 16, 75–90, <a href="https://doi.org/10.5194/esd-16-75-2025"> https://doi.org/10.5194/esd-16-75-2025</a>.</p> </div> <p> </p> <p>Contact: L. Börger (lboerger@igg.uni-bonn.de)</p>
Data for: Global GPP estimates at 8-day/monthly/annual temporal resolution generated by the PTEC model
<p>PTEC provides spatiotemporally estimates of Gross Primary Productivity based on a two-leaf light use efficiency model incorporating plant water status and phenology. PTEC integrates a set of satellite and climate variables within a parsimonious modeling framework to be simple yet robust and grounded on eco-physiological principles. Available at 8-day/monthly/annual and 0.05° resolution from 2001 to 2021, PTEC shows superior performance compared to benchmark products.</p>
Global surface O3, NO2, HCHO, and PM2.5 concentrations estimated from deep learning from 2019 to 2023
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Soil organic carbon formation efficiency from straw/stover and manure input and its drivers: Estimates from long-term data in global croplands
<p><span>The supporting data for raw data, geographic location of the experimental sites, grid-level maps showing the predicted NCE (%) of global cropland</span></p>
Soil organic carbon formation efficiency from straw/stover and manure input and its drivers: Estimates from long-term data in global croplands
<p>In-situ observations collected from publications, grid-level maps showing the predicted NCE (%) of global cropland and data-driven model codes</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.