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617 results for “Climate models”
Storyline data used in the paper "The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging"
<p>We provide the storyline data (in NetCDF format) used in the paper: “The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging” published in Journal of Climate. The data is structured in four .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climate. The data from the five ensemble members (E1 to E5) have been included separately in 3-months files.</p> <p>Atmospheric variables (Files are named as: {variable}_E{ensemble member}_{starting month}{year}.nc:</p> <ul> <li> <p>Latent heat flux (ahfl)</p> </li> <li> <p>Sensible heat flux (ahfs)</p> </li> <li> <p>Monthly Global Mean 2m Temperature (GMTT2mMonthly)</p> </li> <li> <p>Maximum 2m Temperature (t2max)</p> </li> <li> <p>Mean 2m Temperature (t2mean)</p> </li> <li> <p>Minimum 2m Temperature (t2max)</p> </li> <li> <p>Soil Wetness (ws)</p> </li> </ul> <p> Only for present climate:</p> <ul> <li> <p>850 hPa Temperature (T850)</p> </li> <li> <p>Total Cloud Cover (TCC)</p> </li> <li> <p>500 hPa Geopotential Height (Z500)</p> </li> </ul> <p>Five layers soil moisture (Only for present climate, Files are named as: From20172019in2017Climatessp370{ensemble member}_{year}{starting month}.01_jsbid.nc) </p> <p>Oceanic variables (from FESOM, Files are named as: {variable}_E{ensemble member}_{year}{starting month}01.nc:</p> <ul> <li> <p>Sea Ice Concentration (SIC)</p> </li> <li> <p>Sea Surface Temperature (SST)</p> </li> </ul> <p><strong>Please, note that FESOM uses an unstructured mesh.</strong></p>
Supporting information for Hausfather et al 2022, Climate simulations: recognize the 'hot model' problem, comment in Nature
<p>This is the supporting information for the figure in Hausfather et al 2022, Climate simulations: recognize the ‘hot model’ problem, <em>Nature</em>. It includes CMIP6 ECS and TCR values, the screening used in our TCR screened assessment, as well annual global mean surface temperature anomalies relative to preindustrial (1850-1899) for the AR6 assessed warming, CMIP6 multimodel mean, and TCR screened subset shown in Figure 1 in our comment. </p>
Climate trends and behavior of a model Amazonian terrestrial insectivore, Black-faced Antthrush, indicate adjustment to hot and dry conditions
<p>Rainforest loss threatens terrestrial insectivorous birds throughout the world's tropics. Recent evidence suggests these birds are declining in undisturbed Amazonian rainforest, possibly due to climate change. Here, we first asked whether Amazonian terrestrial insectivorous birds were exposed to increasingly extreme ambient conditions using 38 years of climate data. We found long-term trends in temperature and precipitation at our study site, especially in the dry season, which was ~1.3 °C hotter and 21% drier in 2019 than in 1981. Second, to test whether birds actively avoided hot and dry conditions, we used field sensors to identify periodic intervals of ambient extremes and prospective microclimate refugia within undisturbed rainforest from 2017–2019. Simultaneously, we examined how tagged Black-faced Antthrushes (Formicarius analis) used this space. We collected >1.3 million field measurements quantifying ambient conditions in the forest understory, including along elevation gradients. For 11 birds, we obtained GPS data to test whether birds adjusted their cover usage using variation in GPS fix success (<em>n</em> = 2,724) as a proxy and elevation using successful locations (<em>n</em> = 640) across seasonal and daily cycles. For four additional birds, we collected >180,000 light and temperature readings to assess exposure. Field measurements in the modern landscape revealed that temperature was higher in the dry season and highest on plateaus. Thus, low-lying areas were relatively buffered, providing microclimate refugia during hot afternoons in the dry season. At those times, birds apparently entered cover and shifted downslope. Because climate change intensifies the hot, dry conditions that antthrushes seemingly avoid, our results are consistent with the hypothesis that climate change decreases habitat quality for this species. If other terrestrial insectivores are similarly sensitive, climate-induced changes to otherwise intact rainforest may be related to their recent declines.</p>
FOCI model output used in the study by Ivanciu et al. - On the ridging of the South Atlantic Anticyclone over South Africa: the impact of Rossby wave breaking and of climate change
<p>This dataset contains the model output used in the analysis presented in the study by Ivanciu et al., 2022 - On the ridging of the South Atlantic Anticyclone over South Africa: the impact of Rossby wave breaking and of climate change. Four ensembles of three simulations each were performed with the global coupled climate model FOCI (Flexible Ocean and Climate Infrastructure, Matthes et al., 2020). Details about the ensembles can be found in the above-mentioned publication. The files containing "past" in their name belong to the ensemble "PAST", the files containing "future" in their name belong to the ensemble "FUTURE", the files containing "future_GHG" in their name belong to the ensemble "GHG" and the files containing "future_Ozone" in their name belong to the ensemble "OZONE" from the publication.</p>
Replication materials for "Effects of Urbanization in China on the East Asian Summer Monsoon as Revealed by Two Global Climate Models"
<p>The datasets are replication materials for the research "Effects of Urbanization in China on the East Asian Summer Monsoon as Revealed by Two Global Climate Models". They show urbanization-induced changes in surface air temperature (SAT), precipitation, and 850hPa atmospheric circulation from two global climate models (NCAR CESM1.2.1 and FGOALS-g3).</p>
High-resolution climate simulations using the Model for Prediction Across Scales - Atmosphere (MPAS-A; version 5.1)
<p>We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations.</p> <p>Due to storage limitations, the full dataset is much too large to be published (~50TB). Instead, a subset consisting of 6-hourly warm season (May-September) 2-meter temperature, precipitation, and 500hPa height is presented. If you wish to access the full dataset (as presented in Michaelis et al. 2019), please contact one of the authors.</p>
Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus - data set of indoor temperature and relative humidity
<p>This data supplements the journal article: </p> <p>Buechler E, Pallin S, Boudreaux P, Stockdale M. Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus. <em>Journal of Building Physics</em>. 2017;41(3):225-246. doi:<a href="https://doi.org/10.1177/1744259117701893">10.1177/1744259117701893</a></p> <p>Abstract:</p> <p>The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, heating, ventilation, and air-conditioning system performance, and occupant comfort. Therefore, indoor climate data are generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies are influenced by weather, occupant behavior, and internal loads and are generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The purpose of this study was to probabilistically model homes with the simulation engine EnergyPlus to generate indoor climate data that are widely applicable to residential buildings. Monte Carlo methods were used to perform 840,000 simulations on the Oak Ridge National Laboratory supercomputer (Titan) that accounted for stochastic variation in internal loads, air tightness, home size, and thermostat set points. The Effective Moisture Penetration Depth model was used to consider the effects of moisture buffering. The effects of location and building type on indoor climate were analyzed by evaluating six building types and 14 locations across the United States. The average monthly net indoor moisture supply values were calculated for each climate zone, and the distributions of indoor air temperature and relative humidity conditions were compared with ASHRAE 160 and EN 15026 design conditions. The indoor climate data will be incorporated into an online database tool to aid the building community in designing effective heating, ventilation, and air-conditioning systems and moisture durable building envelopes.</p> <p>This supplemental data set includes the hourly temperature and relative humidity for the 10th, 50th, and 90th percentile simulations for each building type in each climate zone. The column headings are of the following format buildingtype_climatezone_output_percentile.</p> <p>There are six building types, B1 (unfinished basement 1-story), B2 (unfinished basement 2-story), C1 (unvented crawlspace 1-story), C2 (unvented crawlspace 2-story), S1 (slab 1-story), and S2 (slab 2-story).</p>
Lagrangian Heavy Precipitation Events in convection-permitting Regional Climate Models over the Alps and in the Mediterranean
<p>The csv datafile contains a set of heavy precipitation events identified in cpRCMs.</p> <p>Each of the entries represents an event and is described with detailed properties:</p> <p>'Start Date [YYYYMMDD.HOUR/24]', 'Latitude [°]', 'Longitude [°]',<br> 'Duration [h]', 'Volume [km² h]', 'P99(pr) [mm h-1]',<br> 'P90(pr) [mm h-1]', 'P75(pr) [mm h-1]',<br> 'P50(pr) [mm h-1]', 'P25(pr) [mm h-1]',<br> 'P10(pr) [mm h-1]', 'Total Precipitation [m3]',<br> 'Maximum Precipitation [mm h-1$]',<br> 'Mean Precipitation [mm h-1$]', 'Direction [°]',<br> 'Distance Traveled [km]', 'Eccentricity [-]', 'Track Eccentricity [-]',<br> 'Mean Ellipsicity [-]', 'Track Ellipsicity [-]', 'Mean Major Angle [°]',<br> 'Track Major Angle [°]', 'Mean Major Axis [-]', 'Track Major Axis [-]',<br> 'My Orientation [°]', 'My Track Orientation [°]', 'max(Elevation) [m]',<br> 'min(Elevation) [m]', 'Start Year [YYYY]', 'Start Month [MM]',<br> 'LandFallSea [-]', 'Scenarios', 'Models', 'situations', 'Ensemble',<br> 'Speed [km h$^{-1}$]', 'Mean(Area) [km²]', 'orographic [-]',<br> 'Severity [-]', 'I/O OBS [-]', 'Region [-]', 'orographic1500 [-]',<br> 'orographic2000 [-]', 'orographic2500 [-]', 'orographic3000 [-]']</p>
VCF datasets and analysis scripts for: The combination of genomic offset and niche modelling provides insights into climate change-driven vulnerability
<p>Global warming is increasingly exacerbating biodiversity loss. Populations locally adapted to spatially heterogeneous environments may respond differentially to climate change, but this intraspecific variation has only recently been considered when modelling vulnerability under climate change. Here, we incorporate intraspecific variation in genomic offset and ecological niche modelling to estimate climate change-driven vulnerability in two bird species in the Sino-Himalayan Mountains. We found that the cold-tolerant populations show higher genomic offset but risk less challenge for niche suitability decline under future climate than the warm-tolerant populations. Based on a genome-niche index estimated by combining genomic offset and niche suitability change, we identified the populations with the least genome-niche interruption as potential donors for evolutionary rescue, i.e., the populations tolerant to climate change. We evaluated potential rescue routes via a landscape genetic analysis. Overall, we demonstrate that the integration of genomic offset, niche suitability modelling, and landscape connectivity can improve climate change-driven vulnerability assessments and facilitate effective conservation management.</p>
Data for: Modeling the distribution of the endangered Jemez Mountains salamander (Plethodon neomexicanus) in relation to geology, topography, and climate
<p>The Jemez Mountains salamander (<em>Plethodon neomexicanus</em>; hereafter JMS) is an endangered salamander restricted to the Jemez Mountains in north-central New Mexico, United States. This strictly terrestrial species requires moist surface conditions for mating and foraging. Threats to its current habitat include fire suppression and ensuing severe fires, changes in forest composition, habitat fragmentation, and climate change. Forest composition changes resulting from reduced fire frequency and increased tree density suggest that its current aboveground habitat does not mirror its historically successful habitat regime. We hypothesized that geology and topography might play a significant role in the current distribution of the salamander. We modeled the distribution of the JMS using a machine learning algorithm to assess how geology, topography, and climate variables influence its distribution. Our habitat suitability map reveals low uncertainty in model predictions, and we found slight discrepancies between the designated critical habitat and the most suitable areas for the JMS. Because geological features are important to its distribution, we recommend that geological and topographical data are considered, both during survey design and in the description of localities of JMS records once detected.</p>
Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning
<p>Storm-resolving climate models (SRMs) have gained international interest for their unprecedented detail with which they globally resolve convection. However, this high resolution also makes it difficult to quantify the emergent differences or similarities among complex atmospheric formations induced by different parameterizations of sub-grid information. This paper uses modern unsupervised machine learning methods to analyze and intercompare SRMs based on their high-dimensional simulation data, learning low-dimensional latent representations and assessing model similarities based on these representations. To quantify such inter-SRM ``distribution shifts'', we use variational autoencoders in conjunction with vector quantization. Our analysis involving nine different global SRMs reveals that only six of them are aligned in their representation of atmospheric dynamics. Our analysis furthermore reveals regional and planetary signatures of the convective response to global warming in a fully unsupervised, data-driven way. In particular, this approach can help elucidate the effects of climate change on rare convection types, such as ``Green Cumuli''. Our study provides a path toward evaluating future high-resolution global climate simulation data more objectively and with less human intervention than has historically been needed.</p>
Climate change effects on deep-water corals – habitat suitability model input data
<p>Deep-water corals are protected in the seas around New Zealand by legislation that prohibits intentional damage and removal, and by marine protected areas where bottom trawling is prohibited. However, these measures do not protect them from the impacts of a changing climate and ocean acidification. To enable adequate future protection from these threats we require knowledge of the present distribution of corals and the environmental conditions that determine their preferred habitat, as well as the likely future changes in these conditions, so that we can identify areas for potential refugia.</p> <p>In this study, we built habitat suitability models for 12 taxa of deep-water corals using a comprehensive set of sample data and predicted present and future seafloor environmental conditions from an earth system model specifically tailored for the South Pacific. These models predicted that for most taxa there will be substantial shifts in the location of the most suitable habitat and decreases in the area of such habitat by the end of the 21st century, driven primarily by decreases in seafloor oxygen concentrations, shoaling of aragonite and calcite saturation horizons, and increases in nitrogen concentrations. The current network of protected areas in the region appear to provide little protection for most coral taxa, as there is little overlap with areas of highest habitat suitability, either in the present or the future. We recommend an urgent re-examination of the spatial distribution of protected areas for deep-water corals in the region, utilising spatial planning software that can balance protection requirements against value from fishing and mineral resources, take into account the current status of the coral habitats after decades of bottom trawling, and consider connectivity pathways for colonisation of corals into potential refugia.</p>
ModelE simulation output used in the study "Severe Global Cooling After Volcanic Supereruptions? The Answer Hinges on Unknown Aerosol Size" in Journal of Climate (2024)
<p>The included files are the GISS ModelE output needed to replicate the figures in McGraw et al 2023, "Severe Global Cooling After Volcanic Supereruptions? The Answer Hinges on Unknown Aerosol Size"</p> <p>Most of the data herein is output from GISS ModelE2.2 simulations that did not include interactive aerosol microphysics and chemistry. Instead, aerosol extinction and effective radius were input into the model from scaled Easy Volcanic Aerosol [Toohey et al, GMD 2016] output, as described in this study's Methods section. To calculate volcanic temperature impacts and forcings at combinations of injected sulfur mass and peak effective radius (Reff) that were not simulated, we used 2D linear interpolation with the scipy function 'Rbf'.</p> <p>Separately included is output from GISS ModelE2.1 with MATRIX interactive aerosol microphysics and chemistry [Bauer et al, ACP 2008]. Note that the injections were scaled to match that a 6.5 Tg sulfur (S) injection in ModelE2.1/MATRIX best replicated the aerosol optical depth (AOD) and effective radius observations of the 1991 Pinatubo event despite this injection being most commonly considered an 9 Tg S injection. Hence, to produce the 1000 Tg S eruption, a 722 Tg S injected was simulated. Such a mismatch has been found in other GCMs (eg Mills et al, JGRA 2016) and may be due to aerosol quick-removal processes not represented in these models.</p> <p>Please note that simulated eruption masses are in this dataset listed in units of Tg S, but in the publication are in Tg SO2 (Tg S x 2).</p> <p>Data from other modeling studies included in Fig. 1 and tree ring estimates in Figs. S2 & S4 can be found within the cited studies.</p> <p>For additional information, please contact zachary.mcgraw@columbia.edu</p>
The June 2012 North American Derecho: A testbed for evaluating regional and global climate modeling systems at cloud-resolving scales
<p>This is the companion data for the manuscript titled 'The June 2012 North American Derecho: A testbed for evaluating regional and global climate modeling systems at cloud-resolving scales', submitted to the Journal of Advances in Modeling Earth Systems in September 2022.</p> <p>derecho_simulation_result: this folder contains part of the SCREAM RRM outputs I ran on NERSC Cori in 2021-2022 corresponding to the simulation in Table 1 of the manuscript.</p> <p>wrf_simulation_result: this folder contains part of the WRF outputs run by Jianfeng Li from PNNL (jianfeng.li@pnnl.gov) in 2022 corresponding to the simulations in Table 4 of the manuscript.</p> <p>plot_script: this folder includes python scripts to plot figures shown in the manuscript.</p> <p>ASOS_station: this txt file contains the processed ASOS station wind speed used in the manuscript.</p> <p><br> Unfortunately, all model outputs are large (~ 3.8 TB for SCREAM RRM and 3.1 TB for WRF). Therefore, I only provide the variables (i.e., OLR, precipitation, composite radar reflectivity, and 10-m wind speed) used directly to generate figures in this repository. All model outputs are archived on tape at NERSC.</p> <p>For more details, refer to the manuscript, or contact me (wrliu@ucdavis.edu).</p>
Datasets for The Dynamics of Megafire Smoke Plumes in Climate Models: Why a Converged Solution Matters for Physical Interpretations
<p>The data in this repository contains the information needed to reproduce the core results of the paper, "The Dynamics of Megafire Smoke Plumes in Climate Models: Why a Converged Solution Matters for Physical Interpretations" submitted to the Journal of Advances in Modeling Earth Systems (JAMES) on 10/3/2022.</p> <p> </p> <p>The "intsmoke*.txt" files are text files with the globally integrated smoke mass above 150 hPa for all simulations in the paper. A header is provided in each file.</p> <p>The "spectra*.mat" files are matlab files that contain fields used to plot the kinetic energy spectra for all simulations. The variable "kes" is the kinetic energy as a function of wavelength, "wvl" are the associated wavelengths of "kes". The variables "hcut" and "lcut" represent the indices of the data "kes" and "wvl" that are used to make the kinetic energy spectra plots in the paper.</p> <p>The "budget*.nc" files contain the budget terms for the relative, vertical vorticity evolution equation at the appropriate date/time. The fields in the netcdf file are self-describing. These budget terms are for the "optimal simulation" described in the paper.</p> <p>The "vort*.nc" file contains the vorticity fields just before the plume forcing starts based on the date/time on the file.</p>
How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation? - Observed precipitation data
<p>The dataset contains the rain gauge hourly rainfall series used in the paper "How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation?". Each rain gauge series is saved in one Matlab variable, organized as a structure S with five fields:</p> <p>S.name: the identification name of the rain gauge station</p> <p>S.vals_mm: series of hourly rainfall in millimeter</p> <p>S.time_utc: time steps series, in UTC time</p> <p>S.elev_m: elevation of the station, in m a.s.l.</p> <p>S.xy_utm: station coordinates X and Y in meter in the Reference system WGS84/UTM zone 32N</p>
Preprocessed Data for "Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning"
<p>Preprocessed Data (training and test) for 3 SRMs used in "Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning". Here we included ICON, SPCAM, and SPCAM with sea surface tem[eratures warmed by +4K. Additionally we include lat/lon information for the test data.</p>
Dataset: Selecting tree species to restore forest under climate change conditions: complementing species distribution models with field experimentation
<p>This repository contains the files associated with the following article:</p> <p>Jesús Sandoval-Martínez, Ernesto I. Badano, Francisco A. Guerra-Coss, Jorge A. Flores Cano, Joel Flores, Sandra Milena Gelviz-Gelvez, Felipe Barragán-Torres, “Selecting tree species to restore forest under climate change conditions: complementing species distribution models with field experimentation”, submitted to <em>Journal of Environmental Management</em>.</p> <p><strong>Supplementary material 01 </strong>is a compressed file that contains two Microsoft Excel files with data that support the results of the study. A file correspond to <em>Vachellia pennatula</em> and the another file correspond to <em>Prosopis laevigata</em>. In both files, the first spreadsheet shows the occurrence data (latitude and longitude) used to calibrate the distribution model (SDM) of the corresponding species, the current values of the 19 bioclimatic variables associated with these coordinates and the Spearman correlation coefficients used to select the variables included in the SDM (selected variables are indicated in green). The second spreadsheet shows the current habitat occupancy probabilities of the target species estimated with the SDM at the geographic coordinates of occurrence points, while the table on the side shows the fraction of true presences dropping at the following probability categories: (1) habitat occupancy probabilities below 0.1 = unsuitable spatial units for the species, (2) habitat occupancy probabilities between 0.1 and 0.4 = barely suitable spatial units for the species, (3) habitat occupancy probabilities between 0.4 and 0.7 = moderately suitable spatial units for the species, and (4) habitat occupancy probabilities above 0.7 = highly suitable spatial units for the species. The third spreadsheet shows the one-thousand random geographic coordinates and the corresponding current and future habitat occupancy probabilities of each species. Future habitat occupancy probabilities are provided for three time periods (2041-2060, 2061-2080 and 2081-2100) at four radiative forcing levels each (2.6, 4.5, 7.0 and 8.5 W/m<sup>2</sup>).</p> <p><strong>Supplementary material 02 </strong>is a compressed file that contains a folder for <em>Vachellia pennatula</em> and another folder for <em>Prosopis laevigata</em>. Each of these folders contains the summaries of the MaxEnt outputs that support the results of the corresponding SDM.</p> <p><strong>Supplementary material 03 </strong>is a compressed Keyhole Markup Language file (KMZ) that contains interactive maps that are optimized for the desktop version of Google Earth. To accelerate visualization of maps, we recommend installing this software in a computer meeting the following requirements: CPU Intel Core i5 9<sup>th</sup> generation or higher, CPU clock speed 1.8 GHz or higher, random-access memory (RAM) 8 GB or higher, and video random access memory (VRAM) 1 GB or higher. Otherwise, opening this file may take several minutes. These maps are organized in a folder for <em>Vachellia pennatula</em> and another folder for <em>Prosopis laevigata</em>, which must be expanded for accessing the following information (click on the arrow on the left of folders to expand them):</p> <ul> <li><strong>Current climate </strong>– Activating this folder (click the fox on the left of the folder) display the map of habitat occupancy probabilities of species across Mexico under the current climate.</li> <li><strong>Period 2041-2060, 2061-2080 and 2081-2100 </strong>– Expanding each of these folders (click on the arrow on the left of folders) shows four subfolders that correspond to different radiative forcing levels (2.6, 4.5, 7.0 and 8.5 W/m<sup>2</sup>). Activating each of these sub folders (click the fox on the left of subfolders) display the map of habitat occupancy probabilities of species across Mexico expected on the corresponding time period and radiative forcing level. These maps also show the areas classified as climatically unsuitable in the multivariate environmental similarity surface (MESS) analysis. Clicking on the names of subfolders displays a figure showing the relationship between current and future habitat occupancy probabilities of the species on the corresponding time period and radiative forcing level. In these figures, the red line is the empirical relationship between these variables and the solid blue line is the theoretical relationship with intercept = 0 and slope = 1. The statistical results that support these relationships are also shown in these figures.</li> </ul> <p><strong>Supplementary material 04 </strong>is a compressed file that contains two Microsoft Excel files with data that support the results of the study. the file labeled as “Microclimate data” contains two spreadsheets, which correspond to the temperature and rainfall values measured in controls under the current climate and climate change simulation plots located of the field experiments. The file levelled as “Seedling emergence and survival” contains a spreadsheet for <em>Vachellia pennatula</em> and another one for <em>Prosopis laevigata</em>, which contains the data used to estimate the seedling emergence and survival rates in controls and climate change simulation plots.</p>
Limits of the Habitable Zone CO2 atmosphere with 3D Climate Modelling
<p>Water is crucial for life, regardless of the environment. That's why in search of extraterrestrial life, we focus on planets in the habitable zone (HZ), where liquid water can exist. The size of this zone depends on factors like the star type and planet size. Using the Generic PCM model (https://lmdz-forge.lmd.jussieu.fr/mediawiki/Planets/index.php/Overview_of_the_Generic_PCM), we've defined the limits of the HZ for atmospheres dominated by CO2 in 3D for the first time. You can access the dataset from the simulations in ".nc" file format. Temporal evolution of variables such as surface temperature, surface pressure, water vapour, liquid water, ice etc. are in a 3D grid for different orbital distances and with different surface pressures are present in the dataset. We have used the correlated-k table published in Zenodo for the simulations (https://doi.org/10.5281/zenodo.10978791).</p>
Correlated-k table for H2 dominated atmospheres for 3D Climate Modelling
<p>Correlated-k table for H2 dominated atmospheres with a variable amount of water vapour built by incorporating absorption data files from the HITRAN database and using the exo\_k code by J.Leconte. This correlated-k tables are created to use with Generic-PCM model to simulate the atmospheres of H2 dominated planets.</p> <p>The calculation of radiative transfer can be performed using the correlated-k method, which efficiently determines net radiative fluxes by categorizing spectral lines into infrared and visible bands (IR x VI) and assigning the average absorption coefficients to each band. These coefficients are pre-calculated based on detailed line-by-line radiative transfer calculations. The correlated-k method is an economic alternative to the line-by-line method, making it possible to accurately and rapidly compute atmospheric radiation.</p> <p> </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.