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697 results for “thresholds”
Thresholds and Regime Shifts at Four LTER Sites (CCE, JRN, PAL, SBC) 1951-2009
The existence and causes of abrupt transitions, thresholds, or regime shifts between ecosystem states is of great concern because the likelihood of such transitions is predicted to increase. The science for measuring and responding to state changes, however, is not well developed. This limitation stems from a lack of data-supported case studies of abrupt transitions in all but a few well-studied ecosystems. We used 30-60 years of data on biological responses and putative drivers from ocean, coastal, polar, and dryland ecosystems to illustrate general approaches to analysis of abrupt transitions. The analyses indicate one case in which the state or response variable (krill abundance) tracked abrupt changes in the driver (Pacific Decadal Oscillation) in a linear fashion. Response variables in other cases (sea cucumber abundance, penguin abundance, and perennial grass production) exhibited hysteretic relationships to drivers (wave intensity, sea ice duration, and monsoonal rainfall amounts, respectively) through a variety of response mechanisms. The analyses illustrate that 1) a suite of common concepts and approaches can be used across disparate systems, 2) there are generally insufficient data for the use of leading indicators, particularly considering the abruptness of transition relative to the lifespan of long-lived organisms, 3) information on spatiotemporal context is useful for comparing transitions in similar systems, and 4) ancillary information from associated experiments and observations is critical for interpreting response-driver relationships.
Thresholds and Tipping Points in a Sarracenia Microecosystem at Harvard Forest 2012-2013
The primary goal of this project is to determine experimentally the amount of lead time required to prevent a state change. To achieve this goal, we will (1) experimentally induce state changes in a natural aquatic ecosystem - the Sarracenia microecosystem; (2) use proteomic analysis to identify potential indicators of states and state changes; and (3) test whether we can forestall state changes by experimentally intervening in the system. This work uses state-of-the art molecular tools to identify early warning indicators in the field of aerobic to anaerobic state changes driven by nutrient enrichment in an aquatic ecosystem. The study tests two general hypotheses: (1) proteomic biomarkers can function as reliable indicators of impending state changes and may give early warning before increasing variances and statistical flickering of monitored variables; and (2) well-timed intervention based on proteomic biomarkers can avert future state changes in ecological systems.
Data from the Forest Resilience Threshold Experiment, University of Michigan Biological Station, 2024
During the 2024 field season, data collection efforts led by the FoRTE crew centered on understanding forest ecosystem dynamics and carbon cycling processes in a temperate forest landscape. Comprehensive datasets were gathered to evaluate structural and functional responses across multiple forest strata. Measurements included diameter at breast height (DBH) for canopy, subcanopy, and seedling layers, alongside a detailed subcanopy census to assess understory composition and diversity. Soil respiration (Rs) was monitored to quantify carbon fluxes, while fern density and distribution were documented to explore their role in forest microclimates and nutrient cycling. Photosynthetically active radiation (PAR) readings provided insights into light availability and its impact on primary production. Advanced remote sensing tools, including LiDAR and normalized difference vegetation index (NDVI), were employed to characterize canopy structure, vegetation health, and spatial heterogeneity. These diverse datasets collectively contribute to a robust framework for analyzing forest resilience, recovery, and carbon sequestration potential following disturbance, advancing our understanding of ecosystem processes in the face of environmental change.
Subjective human thresholds over computer generated images
<p>Realistic image computation mimics the natural process of acquiring pictures by simulating the physical interactions of light between all the objects, lights and cameras lying within a modelled 3D scene. This process is known as global illumination and was formalised by Kajiya with the following rendering Equation:<br> <span class="math-tex">\(\begin{equation} \label{eq:rendering_equation} L_o(x, \omega_o) = {L_e(x, \omega_o)} + \int_{\Omega}^{} {L_i(x, \omega_i)} \cdot f_r(x, \omega_i \rightarrow \omega_o) \cdot \cos \theta_i d\omega_i \end{equation}\)</span></p> <p>where:</p> <ul> <li> <span class="math-tex">\(L_o(x, \omega_o)\)</span> is the luminance traveling from point <span class="math-tex">\(x\)</span> in direction <span class="math-tex">\(\omega_o\)</span>;</li> <li><span class="math-tex">\(L_e(x, \omega_o)\)</span> is point <span class="math-tex">\(x\)</span> emitted luminance (it is null if point x does not lie on a ligth source surface);</li> <li>the integral represents the set of luminances <span class="math-tex">\(L_i\)</span>incident in <span class="math-tex">\(x \)</span> from the hemisphere of the directions <span class="math-tex">\(\Omega\)</span> and reflected in the direction <span class="math-tex">\(\omega_o\)</span>. The reflected luminances are weighted by the materials reflecting properties (bidirectionnal reflectance function <span class="math-tex">\(f_r(x, \omega_i \rightarrow \omega_o)\)</span>) and the cosinus of the incident angle.</li> </ul> <p>This equation cannot be analytically solved and Monte Carlo approaches are generally used to estimate the value of the pixels of the final image.</p> <p>This proposed dataset is composed of 80 points of view of photo realistics images with different level of samples (following the Monte Carlo approach) for each. Each image is 800 x 800 pixels in size. The most noisy image is of 20 samples and the reference one (the most converged image obtained) is of 10000 samples. The <a href="https://www.pbrt.org/index.html">pbrt</a> rendering engine (version 3) was used to generate these images.</p> <p>By exploiting these levels of samples obtained and therefore of noise perceptible in the images, average subjective human thresholds were collected. For this purpose, the images were divided into 16 areas of 200 x 200 pixels in size for each point of view.</p> <p>The proposed image database is composed of the following files:</p> <ul> <li><strong>human-thresholds.csv</strong> : the set of human subjective thresholds obtained on 40 points of view. A line is composed of the name of the point of view followed by all the thresholds obtained for each of the 16 zones;</li> <li><strong>SIN3D_dataset.tar.gz</strong> : is an archive containing all the images from 20 to 10000 samples in steps of 20 samples for each point of view (i.e. 500 images per point of view). Each folder in the archive corresponds to a point of view.</li> </ul> <p><em>This image database has been exploited in order to propose an objective model for noise detection in photo-realistic computer-generated images (article referenced to this image database).</em></p> <p><strong>Note:</strong> Some of the proposed scenes come from:</p> <ul> <li><a href="https://pbrt.org/scenes-v3">https://pbrt.org/scenes-v3</a></li> <li><a href="https://benedikt-bitterli.me/resources/">https://benedikt-bitterli.me/resources/</a></li> </ul> <p><strong>Funding:</strong> This research was funded by ANR support: project ANR-17-CE38-0009.</p> <p> </p>
NGE01 Chronic Addition of Nitrogen Gradient Experiment (ChANGE): Assessing threshold responses of plant community composition and ecosystem processes at Konza Prairie
Chronic nutrient additions can lead to drastic shifts in the plant community through time, both within tallgrass prairie in other grassland ecosystems worldwide. Nutrient addition experiments have answered many questions about patterns of diversity loss and community shifts; however, the level of nutrients which must be added to cause community shifts is unknown. To date, all nitrogen (N) addition experiments at Konza have added 10 g m-2 (e.g., NutNet Plots; Phosphorus (P) Plots; Belowground Plots), yet current rates of N deposition are one-tenth of that level. Even predicted rates of future N deposition in grasslands are not expected to exceed 5 g m-2 by the year 2050 and will likely be around 2 g m-2 for most of the US. This mismatch begs the question will 10 g/m2 affect grasslands the same way 2 or 5 g m-2 will? There are two main goals for this long-term experiment (1) to identify the nutrient threshold needed to drive plant community change with nutrient additions, and (2) to determine what factors underlie those threshold responses (build up of nutrients, mycorrhizal loss, invertebrate herbivory). Konza ChANGE is part of a multi-site experiment spanning grasslands on two different continents: North America – tallgrass prairie (KNZ) and shortgrass steppe (SGS), and China – three sites in Inner Mongolia. By including multiple grasslands, we expand our ability to make generalizations about how grasslands are affected by N additions, and whether thresholds, if they exist, vary with precipitation, natural nutrient availability, and species identity/composition. Research Questions: (1) Do ecosystems have N tolerance thresholds above which community composition will change, and does that differ between grassland types (i.e. mesic and xeric grasslands)? (2) Does adding a large amount of nutrients in one season result in an equivalent community change as adding a small amount over multiple years? (For example does 5 g m-2 for 6 years create the same community change as
Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in Dec 2023.
<p>Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in NHESS journal in Dec 2023.</p> <p>For further technical details read the file READMEdata in the zipfile. The script MAKEFIG remakes all the figures from the data.</p> <p>For scientific details read the associated article preprint on the NHESS egusphere website.</p>
Dataset for "Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation"
<p>This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 187223) in association with the recent publication entitled “Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation”. This work aims to study the capacitive response of a resonating capacitive device coated with phase changing material (jojoba oil) as it melts when crossing its melting temperature. Several configuration were simulated with different electrode spacing, oil volume and encapsulation thickness and the induced changes in capacitance were tested experimentaly. An eco-friendly implementation of the optimized spiral resonating devices was tested wirelessly over a custom made near field antenna and the frequency of resonance was measured as the oil melted over the structure, irreversibly changing its resonance frequency. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.</p>
Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities
<p>Simulation output files for 'Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities'.</p> <p>Simulating COVID-19 transmission and hospital burden to assess at which intensive care unit (ICU) occupancies mitigation, that reduces transmission, needs to be triggered to avoid exceeding ICU capacity limits, using the city of Chicago, Illinois as an example.</p> <p>Manuscript is under review for scientific publication, (see <a href="https://www.medrxiv.org/content/10.1101/2021.06.27.21259530v1">preprint on medRxiv</a>) and scripts are available from the GitHub repository at https://github.com/numalariamodeling/ICUtrigger_covid_chicago_paper_2021. </p> <p>Simulation output files uploaded per scenario including projected COVIID-19 transmission and burden trajectories for Chicago city for March 2020 to May 2021 per day.</p> <p>Simulation scenarios:</p> <p><reopening % above ICU capacity>_<delay after reaching ICU threshold>_<%mitigation>_<common simulation name> i.e. `50perc_1daysdelay_pr6_triggeredrollback_reopen`</p> <ul> <li>`emodl` file <ul> <li>required file for COVID-19 transmission model in the <a href="https://docs.idmod.org/projects/cms/en/latest/index.html">Compartmental Modeling Software</a> (see <a href="https://github.com/numalariamodeling/ICUtrigger_covid_chicago_paper_2021">GitHub repository</a> for details)</li> </ul> </li> <li>sampled_parameters.csv <ul> <li>simulation input and scenario parameters, (nrow=4400, 400 unique parameter combinations * 11 scenario values)</li> </ul> </li> <li>rt_trajectoriescovidregion_11.csv <ul> <li>estimated reproductive numbers per trajectory for complete timeline per day</li> </ul> </li> <li>trajectoriesDat_region_11_traces.csv <ul> <li>filtered to include top 100 trajectories fitted to ICU data</li> </ul> </li> <li>trajectoriesDat_region_trimfut.csv <ul> <li>truncated to only include projections after September 1st 2020</li> </ul> </li> </ul> <p>The folder `mainfigures_csvs.zip` includes processed simulation output data for the publication figures.</p>
Testing of AgReFed FAIR data Minimum Thresholds and Stretch Targets
<p>This dataset is a testing of the FAIR thresholds for participation in The Australian Research Federation (AgReFed). The participants in the project assessed their data products before and after project works to improve the maturity of their datasets. The technology and information employed to progress the FAIR maturity of the data was recorded here.</p> <p>This data was used in the testing of the Minimum Thresholds and Stretch Targets developed by Box et al. (2019). Box, Paul, Levett, Kerry, Simons, Bruce, & Wong, Megan. (2019). Guidelines for the development of a Data Stewardship and Governance.</p>
Data from 'Deoxygenation lowers thermal threshold of coral bleaching.'
<p>Increasing exposure to deoxygenation as a result of climate warming and localised pollution is emerging as an important contributing factor for widespread coral bleaching and mortality. Some corals have been observed to thrive under multiple extreme conditions including very low O<sub>2</sub> levels and high temperatures yet the combined effects of heating and deoxygenation on coral bleaching susceptibility remain unknown. Here, we incorporated deoxygenated seawater into short-term heat assays (CBASS) to show that deoxygenation can lower the thermal limit of an <em>Acropora</em> coral species. </p> <p>Data files provided:</p> <p>'CBASS_deoxy_ref_transcriptome.fasta' contains the reference transcriptome assembled <em>de novo</em> using SOAPdenovo-Trans using non-bleached samples of the CBASS assay (i.e., 30°C and 33°C) and then filtered to keep only reads ≥ 500 bp in length (20,115 contigs).</p> <p>'CBASS_deoxy_BIS_photos_Initial_and_poststress.pdf' contains images of coral fragments alongside a colour reference chart for estimating bleaching index scores (BIS) before experiment starts (initial) and post 6hr stress phase. Sample IDs are denoted as: H0/HH = normoxia/deoxygenation treatment, 30/33/36/39 = temperature in °C, T1 = time point 1, R1/2/3/4 = replicate 1/2/3/4.</p> <p>'CBASS_deoxy_SymPortal_output_files.zip' contains all SymPortal (https://symportal.org) output files.</p>
DATASET : Thresholds of fire response to moisture and fuel load differ between tropical savannas and grasslands across continents
<p><strong>Abstract </strong></p> <p><strong>Aim:</strong> An emerging framework for tropical ecosystems states that fire activity is either ‘<em>fuel build-up limited</em>’ or ‘<em>fuel moisture limited</em>’ i.e. as you move up along rainfall gradients, the major control on fire occurrence switches from being the amount of fuel, to the moisture content of the fuel. Here we used remotely sensed datasets to assess whether interannual variability of burned area is better explained by annual rainfall totals driving fuel build-up, or by dry season rainfall driving fuel moisture.</p> <p><strong>Location:</strong> Pantropical savannas and grasslands</p> <p><strong>Time period:</strong> 2002-2016</p> <p><strong>Methods:</strong> We explored the response of annual burned area to interannual variability in rainfall. We compared several linear models to understand how <em>fuel moisture </em>and <em>fuel build-up effect </em>(accumulated rainfall during 6 and 24 months prior to the end of the burning season respectively) determine the interannual variability of burned area and explore if tree cover, dry season duration and human activity modified these relationships.</p> <p><strong>Results:</strong> Fuel and moisture controls on fire occurrence in tropical savannas varied across continents. Only 24% of South American savannas were <em>fuel build-up limited</em> against 61% of Australian savannas and 47% of African savannas. On average, South America switched from fuel limited to moisture limited at 500 mm yr<sup>-1</sup>, Africa at 800 mm yr<sup>-1</sup> and Australia at 1000 mm yr<sup>-1 </sup>of mean annual rainfall.</p> <p><strong>Main conclusions:</strong> In 42% of tropical savannas (accounting for 41% of current area burned) increased drought and higher temperatures will not increase fire, but there are savannas, particularly in South America, that are likely to become more flammable with increasing temperatures. These findings highlight that we cannot transfer knowledge of fire responses to global change across ecosystems/regions – local solutions to local fire management issues are required, and different tropical savanna regions may show contrasting responses to the same drivers of global change.</p>
Data for "Quantum error correction below the surface code threshold"
<p>Datasets of the surface code and repetition code memory experiments executed on Google's quantum processor.</p> <p>See README at the root of each zip archive for detailed description of each dataset.</p>
Potentially toxic trace metal (Cu, Cd) threshold concentrations for phytoplankton at given open and coastal locations
<p>This data compilation contains previously published threshold concentrations of copper and cadmium of phytoplankton in open and coastal oceans. The data was recalculated to nmol/L for consistency, assuming the following molar masses of copper and cadmium as 63.546 and 112.411, respectively, and salinity as 1.025 kg/L. The temperature and salinity provided by the authors were also included, in case there is a desire for future users to utilise different conversion methods to recalculate original data. Only data with information on whether the authors measured the trace metal concentrations in open or coastal marine environments were included, along with the name of the phytoplankton. The oceans were divided into geographical sections, namely the Atlantic Ocean, Indian Ocean, Pacific Ocean and Southern Ocean, and subsequently further subdivided according to the information authors have given in their publications. In this context, several chemically diverse seas were included in geographical regions in order to limit the number of broad ocean regimes. Chemically diverse sub-regimens were, however, considered within each geographical grouping.</p>
Data for threshold plot in "Finite-rate sparse quantum codes aplenty"
<p>This dataset contains the quantum error correcting codes that were sampled to render the threshold plot under the erasure noise channel in the article. They are sorted by batch corresponding to different jobs on the computing cluster. Each batch contains many codes sorted by length. There are also three extra metadata files. The inputs that were given to sample codes (inputs.json), the CSP solver output (output.csv) and the result of the decoding simulation (decoding.json).</p> <p>Each file named code.json contains the sparse list of X and Z stabilizers. That is, each stabilizer is given by a list of integers corresponding to the index of the qubits supporting the stabilizer.</p>
Psychophysical thresholds for figure-ground discrimination driven by binary textures
<p>Documentation and matlab data files containing psychophysical data for figure-ground segregation driven by binary textures. Methodological details of measurements and analyses (along with interpretation) are provided in Victor, J.D., and Conte. M.M. (2022) Functional recursion of orientation cues in figure-ground separation. Vision Research 197, 108047, doi.org/10.1016/j.visres.2022.108047.</p>
CAELUS: Classification of sky conditions from 1-min time series of global solar irradiance using variability indices and dynamic thresholds
<p>CAELUS, a novel classification algorithm that relies on various thresholds to separate all possible sky conditions into six classes, is presented in Ruiz-Arias and Gueymard (2023, doi: <a href="https://doi.org/10.1016/j.solener.2023.111895">10.1016/j.solener.2023.111895</a>).</p> <p>This dataset was used to develop, validate and benchmark CAELUS. It is made up by 1-min quality-assured observations of global horizontal irradiance (GHI) and diffuse horizontal irradiance at 54 stations of the Baseline Surface Radiation Network (BSRN) archive, which is publicly available (see download instructions in https://bsrn.awi.de/data). The dataset includes 5 years of data per station, except in two of them (Petrolina, Brazil, and Solar Village, Saudi Arabia), combined with other variables that are required to run CAELUS, namely: solar zenith angle (sza), extraterrestrial horizontal solar irradiance (eth), clear-sky GHI (ghics) and GHI in a clean and dry atmosphere (ghicda). In addition, the dataset also provides the sky classification obtained with CAELUS.</p> <p>Further details about CAELUS and the dataset compilation is available in Ruiz-Arias and Gueymard (2023, doi: <a href="https://doi.org/10.1016/j.solener.2023.111895">10.1016/j.solener.2023.111895</a>). A Python implementation of CAELUS is available in https://github.com/jararias/caelus.</p>
Overshooting the critical threshold for the Greenland ice sheet
<p>Model output of PISM-dEBM-simple and Yelmo-REMBO used in the paper <a href="https://doi.org/10.1038/s41586-023-06503-9"><strong>Overshooting the critical threshold for the Greenland ice sheet</strong></a>. Code for analysis/recreating the main figures of the paper is provided as well as an example script of how to run PISM-dEBM-simple.</p><p>The models, methods and the used parameters are described in the paper.</p><p>Contact: nils.bochow@uit.no</p>
Temperature Thresholds for Aquatic Species in the Sacramento San-Joaquin Delta
The effects of increasing water temperature on species in the Sacramento San-Joaquin Delta is of growing importance for conservation and resource management; however, species metrics of temperature sensitivity vary across agency groups and can result in varied understanding and predictions of species vulnerability and modeling. Here, we provide a catalogue of key temperature metrics for several species in the Sacramento San Joaquin Delta that may be used as a standard for scientists studying temperature effects and resource management of native and non-native species. The dataset includes both documented physiological thresholds for suboptimal and upper temperature tolerances from metrics such as growth or critical and lethal limits, as well as calculated minimum, maximum and mean temperature values of species catch in the Delta from long-term monitoring surveys from 1954-2022. Species include critical estuarine fishes such as Delta Smelt, Longfin Smelt, Chinook Salmon, and sturgeon, as well as the species they interact with such as invasive fishes and invertebrates, cyanobacteria, and aquatic vegetation. The dataset includes values for different life-stages where possible and references for information. This information can help guide conservation measures and species management needed to lessen the impact stressful temperature conditions.
Data set: "A 21st Century Warming Threshold for Sustained Greenland Ice Sheet Mass Loss"
<p>This data set includes the materials required to reproduce the figures and tables presented in the study: "A 21<sup>st</sup> Century Warming Threshold for Sustained Greenland Ice Sheet Mass Loss". The data consist of:</p> <ul> <li>Maps of annual historical and projected surface mass balance (SMB) of the Greenland ice sheet (GrIS) from RACMO2.3p2 at 1 km spatial resolution in NetCDF format.</li> </ul> <ol> <li><strong>smb_rec.1950-2014.BN_RACMO2.3p2-CESM2-Historical.1km.YY.nc</strong>: annual cumulative GrIS SMB (kg m<sup>-2</sup> or mm w.e. per year) from RACMO2.3p2 forced by CESM2 for the historical period 1950-2014, further statistically downscaled to 1 km spatial resolution.</li> <li><strong>smb_rec.2015-2099.BN_RACMO2.3p2-CESM2-SSP5-85.1km.YY.nc</strong>: annual cumulative GrIS SMB (kg m<sup>-2</sup> or mm w.e. per year) from RACMO2.3p2 forced by CESM2 under a high-end warming scenario SSP5-8.5 for the period 2015-2099, further statistically downscaled to 1 km spatial resolution.</li> <li><strong>Icemask_Topography_lon_lat_average_1km_GrIS.nc</strong>: mask file including an ice mask (Promicemask) separating Greenland's peripheral glaciers and ice caps (values 1 and 2) from the main ice sheet (value 3), surface topography derived from the GIMP DEM, and longitude/latitude coordinates on the 1 km grid. </li> </ol> <p>NB: the NetCDF files above use a Polar Stereographic North (EPSG:3413) projection with a horizontal resolution of 1 km x 1 km. The reference point is located at 45ºW longitude and 70ºN latitude.</p> <ul> <li>Time series of historical and projected annual GrIS-integrated SMB (Gigatons or Gt per year) and annual mean GrIS temperature (TGrIS; K) from RACMO2.3p2 at 1 km spatial resolution in ASCII format.</li> </ul> <ol> <li><strong>SMB_TGrIS_RACMO2.3p2-CESM2_Historical_1950-2014.dat</strong>: time series of annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) and mean TGrIS (K) from the CESM2-forced RACMO2.3p2 simulation for the historical period 1950-2014.</li> <li><strong>SMB_TGrIS_RACMO2.3p2-CESM2_SSP5-8.5_2015-2099.dat</strong>: time series of annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) and mean TGrIS (K) from the CESM2-forced RACMO2.3p2 projection under a high-end warming scenario SSP5-8.5 (2015-2099).</li> </ol> <ul> <li>Time series of historical and projected reconstruction of annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) and mean TGrIS (K) from 12 CESM2 historical members and 10 CESM2 projections under various warming scenarios, namely SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.</li> </ul> <ol> <li><strong>Reconstructed_SMB_CESM2_Historical_1950-2014.dat</strong>: time series of reconstructed annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) from the parent historical CESM2 simulation (HIST-parent) used to force RACMO2.3p2 and 11 additional historical CESM2 members (HIST-X) for 1950-2014. </li> <li><strong>Reconstructed_SMB_CESM2_Projections_2015-2099.dat</strong>: time series of reconstructed annual GrIS-integrated SMB (Gt yr<sup>-1</sup>) from the parent CESM2 projection (SSP5-8.5-parent) used to force RACMO2.3p2 and 9 additional CESM2 projection members (SSP1-2.6-X to SSP5-8.5-X ) for 2015-2099. </li> <li><strong>TGrIS_CESM2_Historical_1950-2014.dat</strong>: time series of annual mean TGrIS (K) from the parent historical CESM2 simulation (HIST-parent) used to force RACMO2.3p2 and 11 additional historical CESM2 members (HIST-X) for 1950-2014.</li> <li><strong>TGrIS_CESM2_Projections_2015-2099.dat</strong>: time series of annual mean TGrIS (K) from the parent CESM2 projection (SSP5-8.5-parent) used to force RACMO2.3p2 and 9 additional CESM2 projection members (SSP1-2.6-X to SSP5-8.5-X ) for 2015-2099. </li> </ol> <p>NB: the file <strong>Crossref_sim_names.txt </strong>cross-references the simulation abbreviations in the above .dat files to official simulation names from the National Center for Atmospheric Research (NCAR).</p> <p>The daily downscaled SMB data set from the CESM2-forced RACMO2.3p2 historical simulation and SSP5-8.5 projection are freely available from the authors upon request and without conditions (contact: <strong>b.p.y.noel@uu.nl</strong>). Besides SMB, the data set includes daily total precipitation (snow and rain), snowfall, total melt (snow and ice), meltwater runoff, retention and refreezing, total sublimation (surface and drifting snow), snow drift erosion, as well as 2 m air temperature at 1 km horizontal resolution. </p> <p>Abstract: "Under anticipated future warming, the Greenland ice sheet (GrIS) will pass a threshold when meltwater runoff exceeds the accumulation of snow, resulting in a negative surface mass balance (SMB < 0) and sustained mass loss. In spite of several recent warm summers with high melt rates, SMB < 0 has not been reached since at least the year 1958. Here we dynamically and statistically downscale the outputs of an Earth system model to 1 km resolution to infer that a Greenland near-surface atmospheric warming of 4.5 ± 0.3 °C—relative to pre-industrial—is required for GrIS SMB to become persistently negative. Climate models from CMIP5 and CMIP6 translate this regional temperature change to a global warming threshold of 2.7 ± 0.2 °C. Under a high-end warming scenario, this threshold may be reached around 2055, while for a strong mitigation scenario it will likely not be passed."</p>
Wildfires and climate change push low-elevation forests across a critical climate threshold for tree regeneration
Climate change is increasing fire activity in the western United States, which has the potential to accelerate climate-induced shifts in vegetation communities. Wildfire can catalyze vegetation change by killing adult trees that could otherwise persist in climate conditions no longer suitable for seedling establishment and survival. Recently documented declines in postfire conifer recruitment in the western United States may be an example of this phenomenon. However, the role of annual climate variation and its interaction with long-term climate trends in driving these changes is poorly resolved. Here we examine the relationship between annual climate and postfire tree regeneration of two dominant, low-elevation conifers (ponderosa pine and Douglas-fir) using annually resolved establishment dates from 2,935 destructively sampled trees from 33 wildfires across four regions in the western United States. We show that regeneration had a nonlinear response to annual climate conditions, with distinct thresholds for recruitment based on vapor pressure deficit, soil moisture, and maximum surface temperature. At dry sites across our study region, seasonal to annual climate conditions over the past 20 years have crossed these thresholds, such that conditions have become increasingly unsuitable for regeneration. High fire severity and low seed availability further reduced the probability of postfire regeneration. Together, our results demonstrate that climate change combined with high severity fire is leading to increasingly fewer opportunities for seedlings to establish after wildfires and may lead to ecosystem transitions in low-elevation ponderosa pine and Douglas-fir forests across the western United States.
ScienceDex guides
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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.