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214 results for “Climatic variables”
Model output for "Climate variability leads to multiple oxygenation episodes across the Great Oxidation Event"
<div> <p>This repository contains all the model output presented in Garduno et al. (2024). Climate variability leads to multiple oxygenation episodes across the Great Oxidation Event. Submitted to Geophysical Research Letters.</p> <h1>Model output organization</h1> <p>There are seven zip files containing the model output from different simulations described in the main manuscript. See the main manuscript and <a href="https://github.com/DanyIvan/climate_goe_over_time" target="_blank" rel="noopener">code repository</a> for more details.:</p> <ul> <li>`o2_flux_constant_1.8e12.zip`: model output for simulation in which the O2 input flux is kept constant at a value of 1.8e12 molecules/cm^2/s</li> <li>`o2_flux_constant_2.2e12.zip`: model output for simulation in which the O2 input flux is kept constant at a value of 2.2e12 molecules/cm^2/s</li> <li>`linear_o2_flux_increase.zip`: model output for simulation in which the O2 input flux is linearly increased</li> <li>`linear_o2_flux_increase_change_during_glaciations.zip`: model output for simulation in which the O2 input flux is linearly increased, superimposing a 60% decrease during glaciations and a 60% increase after glaciations.</li> <li>`linear_ri_flux_decrease.zip`: model output for simulation in which the reductant input flux is linearly decreased and the O2 input flux is kept constant</li> <li>`linear_ri_flux_decrease_change_during_glaciations.zip`: model output for simulation in which the reductant input flux is linearly decreased and the O2 input flux is kept constant with a 60% decrease during glaciations and a 60% increase after glaciations.</li> <li>`stability_analysis.zip`: model output for stability analysis of steady states.</li> </ul> <p><br>Each of these folders contains model output every 1e5 years. The files are numbered from 1 to 4998. `1` corresponds to the output at 1e5 years, `2` corresponds to the output at 2e5 years, and so on.</p> <p>There are also files containing the O2 fluxes (`o2_flux.txt`) and reductant input (`ri_flux.txt`) used at each 1e5 output step.</p> <h1>Reading data</h1> <p>The data files are Fortran binary files. You can read the with the <a href="https://github.com/Nicholaswogan/PhotochemPy/blob/5bf449fa26370eb2d21d45ae59eacbfebf9705a4/PhotochemPy/io.py#L45" target="_blank" rel="noopener">read_evolve_output</a> function implemented in <a href="https://github.com/Nicholaswogan/PhotochemPy/" target="_blank" rel="noopener">PhotochemPy</a>.</p> <p>You can also use the Python functions provided in the paper's code repository: <a href="https://github.com/DanyIvan/climate_goe_over_time/blob/main/read_output.py" target="_blank" rel="noopener">https://github.com/DanyIvan/climate_goe_over_time/blob/main/read_output.py</a></p> <h1>Names and units</h1> <p>The model output files contain the mixing ratios for all modeled species species. They also contain information about:</p> <p>- 'T_time': temperature profile (K) at the output time step.<br>- 'edd_time': eddy diffusivity profile (cm^2/s) at the output time step.<br>- 'press_time': pressure profile (bar) at the output time step.<br>- 'h2osat': saturation vapor pressure profile (bar) at the output time step.<br>- 'time': time (s) at the output time step<br>- 'den': air number density (molecules/cm^3)</p> </div>
Data from: Potential breeding distributions of U.S. birds predicted with both short-term variability and long-term average climate data
Climate conditions, such as temperature or precipitation averaged over several decades strongly affect species distributions, as evidenced by experimental results and a plethora of models demonstrating statistical relations between species occurrences and long-term climate averages. However, long-term averages can conceal climate changes that have occurred in recent decades and may not capture actual species occurrence well because the distributions of species, especially at the edges of their range, are typically dynamic and may respond strongly to short-term climate variability. Our goal here was to test whether bird occurrence models can be predicted by either covariates based on short-term climate variability or on long-term climate averages. We parameterized species distribution models (SDMs) based on either short-term variability or long-term average climate covariates for 320 bird species in the conterminous U.S., and tested whether any life-history trait-based guilds were particularly sensitive to short-term conditions. Models including short-term climate variability performed well based on their cross-validated AUC score (0.85), as did models based on long-term climate averages (0.84). Similarly, both models performed well compared to independent presence/absence data from the North American Breeding Bird Survey (independent AUC of 0.89 and 0.90, respectively). However, models based on short-term variability covariates more accurately classified true absences for most species (73% of true absences classified within the lowest quarter of environmental suitability versus 68%). In addition, they have the advantage that they can reveal the dynamic relationship between species and their environment because they capture the spatial fluctuations of species potential breeding distributions. With this information we can identify which species and guilds are sensitive to climate variability, identify sites of high conservation value where climate variability is low, and assess how species' potential distributions may have already shifted due recent climate change. However, long-term climate averages require less data and processing time and may be more readily available for some areas of interest. Where data on short-term climate variability are not available, long-term climate information is a sufficient predictor of species distributions in many cases. However, short-term climate variability data may provide information not captured with long-term climate data for use in SDMs.
Open data for "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate"
<p>______________________________________________________<br> This page contains public-domain data required to reconstruct simulation results in the manuscript "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate," submitted by the following author.</p> <p>Author: Hisashi SATO (JAMSTEC) <br> email : hsatoscb_(at)_gmail.com</p> <p>______________________________________________________<br> 1. Folder "Code"<br> Detailed descriptions are available on the code. </p> <p>1-1. MachineLearningComparison.R<br> Machine learning programs using random forest (RF), naive Bayes classifier (NV), and support vector machine (SVM) algorithms.</p> <p>1-2. Analyse_MapSimilarity.R<br> Calculate coincidences of simulated potential natural vegetation (PNV) maps simulated by different models.</p> <p>1-3. Visualize_VCE.R<br> Generating VCE (Visualize Climate Image) for training CNN models.</p> <p>1-4. Visualize_Maps.R<br> Visualizing global PNV maps.</p> <p>1-5. Visualize_ClimateHistgrams.R<br> Visualizing histograms of climate datasets.</p> <p>______________________________________________________<br> 2. Folder "Input"</p> <p>2-1. Unified_BIOCLIM_WorldClim.csv<br> Input data for the current climate.<br> This file contains the following variables.<br> lon Longitude at the center of the grid<br> lat Latitude at the center of the grid<br> bio1~19 Average climate indices from BIOCLIM (AveI)<br> CDD~WSDI Extreme climate indices (CEI)<br> c1~c16 Fraction of PNV from MODIS data<br> tavg01~tavg12 Monthly mean air temperature from January to December (Ave)<br> prec01~prec12 Monthly precipitation from January to December (Ave)</p> <p>2-2. Unified_BIOCLIM_WorldClimFutureRCP85.csv<br> Input data for future climate (@RCP8.5)<br> Including variables are the same as Unified_BIOCLIM_WorldClim.csv</p> <p>2-3. BIOCLIM_RefNo.csv<br> This CSV file contains the following information for each grid.<br> lat: Latitude at the center of the grid<br> lon: Longitude at the center of the grid<br> latNo: Latitude number corresponding to the image file name<br> lonNo: Longitude number corresponding to the image file name<br> lineNo: No use. Don't mind.<br> vegNo: Most dominant PNV based on the Unified_BIOCLIM_WorldClim.csv</p> <p>______________________________________________________<br> 3. Folder "Output"</p> <p>3-1. PNV_sim<br> 3-2. PNV_sim_RCP85.csv<br> Current and future PNV maps from various models. These files are the main output files from the code MachineLearningComparison.R. For PNV maps from CNN models (m4p1~6) were supplemented. Detailed methods to build CNN models, please refer to the following manuscript.<br> Sato, H. & T. Ise (2022). "Predicting global terrestrial biomes with the LeNet convolutional neural network." Geoscientific Model Development 15(7): 3121-3132.</p> <p>Labels indicate combinations of machine-learning-algorithm and dataset for training the model. For example, In case of "m1p1", that column shows the simulation result of models trained with randomForest (RF) algorithm and Ave dataset.<br> m1: randomForest (RF)<br> m2: Support vector machine (SVM)<br> m3: Naive Bayes (NB)<br> m4: Convolutional Neural Network (CNN), which is NOT analysed in this code<br> p1: Ave<br> p2: Ave + CEI<br> p3: Ave + CEIpart<br> p4: AveI <br> p5: AveI + CEI<br> p6: AveI + CEIpart</p>
Data from: Potential breeding distributions of U.S. birds predicted with both short-term variability and long-term average climate data
Open the record for dataset details and reuse information.
Influences of climatic and social environment on variable maternal allocation among offspring in Alpine marmots
Open the record for dataset details and reuse information.
Impacts of Climate Variability on Primary Productivity and Carbon Distributions in the Middle Atlantic Bight and Gulf of Maine (CliVEC)
Title: The Impacts of Climate Variability on Primary Productivity and Carbon Distributions in the Middle Atlantic Bight and Gulf of Maine (CliVEC)Research Team:* Antonio Mannino (PI) - NASA GSFC* Michael Novak - NASA GSFC* Margaret Mulholland (co-PI) - Old Dominion University* Peter Bernhardt - Old Dominion University* CJ Staryk - Old Dominion University* Kimberly Hyde (co-PI) - NOAA NEFSC* Jon Hare (collaborator) - NOAA NEFSC* David Lary (co-I) - University of Texas at DallasObservations from the MODIS and SeaWiFS time series (1997-2012) and measurements from an extensive field campaign are employed to examine how inter-annual and decadal-scale climate variability affects primary productivity and organic carbon distributions along the continental margin of the U.S. northeast coast. Estimates of daily primary productivity (PP) will be computed using the Ocean Productivity from Absorption of Light (OPAL) model. OPAL vertically resolves phytoplankton absorption of photosynthetically active radiation (PAR) and relates the chlorophyll-specific absorption coefficient to sea-surface temperature (SST), where SST is a proxy for seasonal changes in the phytoplankton community. OPAL will be validated with new field measurements of PP including dissolved organic carbon production.Field measurements of particulate (POC) and dissolved organic carbon (DOC) and the absorption coefficients of phytoplankton (aph) and colored dissolved organic matter (aCDOM) will allow us to extend the validation range (temporally and spatially) for our coastal algorithms and reduce the uncertainties in satellite-derived estimates of OPAL PP, POC, DOC, aph and aCDOM. Furthermore, we will apply our extensive field data to derive region-independent ocean color algorithms for PP, POC, DOC aCDOM and aph using machine learning approaches. We will rigorously validate and compare band-ratio and multivariate machine learning algorithms. Algorithms validated from this study will be applied to satellite observations to produce a time series of satellite data productsThe U.S. Middle Atlantic Bight (MAB), George's Bank (GB) and Gulf of Maine (GoM) stand at the crossroads between major ocean circulation features - the Gulf Stream and Labrador slope-sea and shelf currents - and are influenced by highly variable river discharge, summer upwelling, warm core rings, and intense seasonal stratification. Our work will focus on the impacts of variable river discharge, SST and large-scale climate indices on primary production, and POC and DOC distributions. These processes are not unique to the MAB and GoM. Consequently, the results from this activity can be applied to understanding how inter-annual and long-term variability in climate patterns can impact the carbon cycle of continental margins throughout the globe.
ClimoBase: Rouse Canadian Surface Observations of Weather, Climate, and Hydrological Variables, 1984-1998, Version 1
ClimoBase is a collection of surface climate measurements collected in Northern Canada by Dr. Wayne Rouse between 1984 and 1998 in three locations: Churchill, Manitoba; Marantz Lake, Manitoba; and Inuvik, Northwest Territories. These data are comprised of surface-climate measurements, including solar time, wind speed, wind direction, dry-bulb, wet-bulb, and vapor pressure in 24 sites focused at the three Northern Canadian locations. The sites were chosen to include a variety of terrains in the study: sedge fen wetland, willow-birch wetland, lichen-heath, bedrock boulders/heath, spruce- tamarack forest, tundra lake, creek, various (e.g. a basin: sedge, willow, lichen-heath, forest, etc.), sparse vegetation (short grass/sedge, heath spp.), and coastal marsh (tall grass, sandy soils). The measurements were taken in increments ranging from seasonally to every 15 minutes. In all, 177 different variables were measured and recorded. The data are valuable due to their unique and consistent nature.
Climate Variability and Predictability (CLIVAR)
Climate Variability and Predictability (CLIVAR)
Climate variability, heat distribution, and polar amplification in the warm unipolar "icehouse" of the Oligocene
<p>Supplementary Data for the Climate variability, heat distribution and polar amplification in the unipolar 'doubthouse' of the Oligocene paper. Oligocene mean annual temperature (MAT) and mean annual precipitation (MAP) Data is based on a nearest living relative (NRL) analysis. </p><p>Compiled sea surface temperatures (SSTs) for the Oligocene include alkenone based Uk'37, isoprenoidal glycerol dialkyl glycerol tetraether (isoGDGT) TEX86, biogenic calcite δ18O and clumped isotope (D47) data. All SST data are associated with the publication "The enigma of Oligocene climate and global surface temperature evolution" by C.L. O'Brien, M. Huber, E. Thomas, M. Pagani, J.R. Super, L.E. Elder, and P. M. Hull, in Proceedings of the National Academy of Science, 2020. https://www.pnas.org/lookup/doi/10.1073/pnas.2003914117 . </p>
Seasonal and interannual variability of the wave climate at a wave energy hotspot off the southwestern coast of Australia
<p>This dataset contains the output from a 38-year wave hindcast for the Albany region of Western Australia. The methods and analysis are contained within the publication "Seasonal and interannual variability of the wave climate at a wave energy hotspot off the southwestern coast of Australia" by Cuttler et al.</p> <p>Please see publication for full details: Cuttler, MVW, Hansen, JE, and Lowe RJ (2019) Seasonal and interannual variability of the wave climate at a wave energy hotspot off the southwestern coast of Australia. <em>Renewable Energy</em>, 146, 2337-2350.Cuttler, MVW, Hansen, JE, and Lowe RJ (2019) Seasonal and interannual variability of the wave climate at a wave energy hotspot off the southwestern coast of Australia. <em>Renewable Energy</em>, 146, 2337-2350.</p> <p>Files contained within this dataset include:</p> <ul> <li>Hourly spectral output files from the 50m-resolution domains at the proposed development site (30 m depth) in Torbay, Western Australia </li> <li>Hourly spectral output files from the 165-m resolution domain at the WA Dept.of Transport wave buoy (60 m depth) and proposed development site in Torbay, Western Australia</li> <li>Example Matlab scripts for reading 2D spectral data from the 50m- and 165m-resolution grids</li> </ul> <p>For other data requests, comments, or questions please contact Michael Cuttler at michael.cuttler@uwa.edu.au</p>
Global SPEI over the last millennium calculated from monthly climate variables of isotope-enabled climate model simulations
<p>This data is Standardized precipitation evapotranspiration index (SPEI) for different time scales from 851 to 2000. The time scale is partially omitted due to the upload capacity, but it is from 1 month to 48 months at maximum.</p> <p>[Structure]</p> <p>Spatial resolution: 1.9(Treated the earth as a 94x192 grid)</p> <p>Time resolution: 1 month(1150 year = 13800 month)</p> <p>This data is one-dimensional. When using, please slice to (13800,94,192) using python, etc.</p> <p>The method for obtaining the grid for the survey area is as follows.</p> <p>north latitude: (94/180)*(90+lat)</p> <p>south latitude: (94/180)*(90-lat)</p> <p>east longitude: (192/360)*longitude</p> <p>west longitude: (192/360)*(360-longitude)</p> <p> </p> <p>This SPEI is calculated using the SPEI package in R. The settings for this package are as follows.</p> <p>Setting</p> <p>kernel: type = rectangular, shift =0</p> <p>distribution: log-Logistic</p> <p>fit: ub-pwm</p> <p> </p> <p>The data used for this SPEI calculation are climate data reconstructed by data assimilation using isotope ratios. Please refer to the following page for details of the data.</p> <p>Details</p> <p>[Title]</p> <p>Data assimilation products by using multiple climate model simulations and different combinations of proxies</p> <p>[url]</p> <p>https://zenodo.org/record/5760209#.ZAs2qxXP1D9</p>
Mirador - Climate Variability and Change
Earth Science data access made simple. NASA's role in climate variability study is centered around providing the global scale observational data sets on oceans and ice, their forcings, and the interactions with the entire Earth system.
EAMv1 outputs Macquarie Island- Long-term variability in immersion-mode marine ice-nucleating particles from climate model simulations and observations
<p>EAMv1 outputs for the ACP publication </p> <p>https://acp.copernicus.org/articles/23/5735/2023/acp-23-5735-2023.pdf</p> <p>We have archived the outputs from the EAMv1 control simulations. </p>
Global Leaf Area Index, and Climatic Variables, 1982-2015
<p>This dataset contains a group of global, half-degree, monthly climate covariates, and leaf area index records, for the period of 1982-2015.</p>
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.