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2,260 results for “Climatic change”
Data from: Phenotypic interactions between tree hosts and invasive forest pathogens in the light of globalization and climate change
Invasive pathogens can cause considerable damage to forest ecosystems. Lack of coevolution is generally thought to enable invasive pathogens to bypass the defence and/or recognition systems in the host. Although mostly true, this argument fails to predict intermittent outcomes in space and time, underlining the need to include the roles of the environment and the phenotype in host–pathogen interactions when predicting disease impacts. We emphasize the need to consider host–tree imbalances from a phenotypic perspective, considering the lack of coevolutionary and evolutionary history with the pathogen and the environment, respectively. We describe how phenotypic plasticity and plastic responses to environmental shifts may become maladaptive when hosts are faced with novel pathogens. The lack of host–pathogen and environmental coevolution are aligned with two global processes currently driving forest damage: globalization and climate change, respectively. We suggest that globalization and climate change act synergistically, increasing the chances of both genotypic and phenotypic imbalances. Short moves on the same continent are more likely to be in balance than if the move is from another part of the world. We use Gremmeniella abietina outbreaks in Sweden to exemplify how host–pathogen phenotypic interactions can help to predict the impacts of specific invasive and emergent diseases. This article is part of the themed issue 'Tackling emerging fungal threats to animal health, food security and ecosystem resilience'.
Arctic Climate Response to European Radiative Forcing: A Deep Learning Study on Circulation Pattern Changes (control run daily dataset)
<p>30-year Control run with MPI-ESM1.2 coupled atmosphere-ocean–land surface model with preindustrial boundary and initial condition (for more information see the paper)<br> </p>
Arctic Climate Response to European Radiative Forcing: A Deep Learning Study on Circulation Pattern Changes (Experiment run daily dataset)
<p>0-year Experiment run with excerted negative radiative forcing over Europe with MPI-ESM1.2 coupled atmosphere-ocean–land surface model with preindustrial boundary and initial condition (for more information see the paper) </p>
A data science approach to climate change risk assessment applied to pluvial flood occurrences for the United States and Canada (Supplementary Material)
<p>This resource is tied to manuscript "A data science approach to climate change risk assessment applied to pluvial flood occurrences for the United States and Canada".</p> <p>It contains: (1) a PDF with additional details on the implementation of GLM, GAM and RF, summarized outputs for two models, a bias analysis of the CRCM and extensive tables from Section 5.2; (2) full outputs for two models; (3) high-resolution figures, and; (4) rasters for selected figures.</p> <p>**</p> <p>Version 2 adds a log-log version of Figure 9 and changes the main PDF file to remove blue section titles.</p>
Data associated to the paper: "Plant species better adapted to climate change need agricultural extensification to persist"
<p>This dataset relates to the 500-ENI network, which is funded by the French Ministry of Agriculture, in order to monitor the unintended effects of agricultural practices on biodiversity. It contains compositional and functional floristic data, as well as climatic and agricultural data between 2013 and 2021 in 555 agricultural field margins in France. These data and the R script associated, allow to reproduce the analyses described in the paper “Plant species better adapted to climate change need agricultural extensification to persist” and its Appendix, in particular the PCA on the CWM of functional traits, the temporal models and the community trajectory in the CSR triangle of Grime. The “Data” folder includes floristic, functional and environmental data by observation and site, as well as the functional traits of species. Finally, the “Output” folder contains the output files obtained from the scripts. </p>
Climate change affects desert hydrology through atmospheric water capture by soils
<p>This is data supporting the paper "<span>Can dry get wetter even if rainfall declines?" by Kool and Agam.</span></p> <p><span>Data was collected at the Mashash Experimental Farm (31.07°N, 34.85°E; Mashash) and Sede-Boqer (30.86°N, 34.78°E).</span></p> <p><span>Please refer to the paper for more details.</span></p> <p><span>Please contact the authors should you want to make use of the data.</span></p> <p><span>Dilia Kool: dkool@bgu.ac.il</span></p> <p><span>Nurit Agam: agam@bgu.ac.il</span></p>
Data used to generate results for Meireles et al. 2024. The Miners of South America: impacts of climate change on the distribution of Geositta miners along elevational gradients
<p><span>Data used to generate results for Meireles et al. 2024. The Miners of South America: impacts of climate change on the distribution of <em>Geositta miners</em> along elevational gradients </span></p> <p><span> </span><span>Here we included all the data used in this paper.</span></p> <p><strong><span>Table1</span></strong><span>. Occurrence records for the seven species of <em>Geositta</em> miners, their elevation and climate suitability values per record for the present and future in different climate models (GCM: MPI-ESM1-2-HR and MRI-ESM2-0) and different scenarios (Optimistic: ssp245 and Pessimistic: ssp585).</span></p>
SECURES-Energy: Hourly electricity demand and supply profiles for historical climate and climate change projections in Europe until 2100
<p><strong>SECURES-Energy</strong></p> <p>Weather-dependent renewable electricity systems are vulnerable to climate change impacts. Electricity generation and demand profiles considering weather and climate impacts are needed in energy system modelling. We present a consistent and high-quality energy database in data formats useful for energy system modelling and keeping the high spatiotemporal complexity of climate data. The open-access dataset SECURES-Energy contains all relevant electricity demand and supply components for the EU and several additional European countries in hourly resolution covering the period 1981-2100. It is based on reanalysis data ERA5(-Land) for the historical period and two EURO-CORDEX emission scenarios (RCP 4.5 and RCP 8.5). On the generation side, impacts on onshore and offshore wind power generation, solar PV generation, and hydropower generation (run-of-river and reservoirs) – which is often missing in comparable datasets – are provided. On the demand side, all demand components relevant to future electricity systems including e-heating, e-cooling, e-mobility, and electricity demand in industry, are provided.</p> <p>The detailed methods are described in the final project report (see link below) in Chapter 2.2 and Chapter 4.3 and a related journal publication is currently in preparation.</p> <p><strong>Further information:</strong></p> <ul> <li>Project website SECURES: https://www.secures.at/</li> <li>All project-related publications: https://www.secures.at/publications</li> <li>Final SECURES project report: https://www.secures.at/fileadmin/cmc/Final_Report_SECURES.pdf and https://www.klimafonds.gv.at/wp-content/uploads/sites/16/C061007-ACRP12-SECURES-KR19AC0K17532-EB.pdf</li> </ul> <p>The SECURES-Energy dataset provides variables visible in the table.</p> <ol> <li>Hourly profiles ERA5-Land 1981-2010</li> <li>Hourly profiles RCP 4.5/RCP 8.5 2011-2100</li> </ol> <p> </p> <p><strong>Production profiles:</strong></p> <table> <tbody> <tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Temporal resolution</th> </tr> <tr> <th>Photovoltaics</th> <td>pv</td> <td>-</td> <td>hourly</td> </tr> <tr> <th>Wind onshore</th> <td>wind</td> <td>-</td> <td>hourly</td> </tr> <tr> <th>Wind offshore</th> <td>wind_offshore</td> <td>-</td> <td>hourly</td> </tr> <tr> <th>Hydro run-of-river</th> <td>hydro_ror</td> <td>-</td> <td>hourly</td> </tr> </tbody> </table> <p> </p> <p><strong>Demand profiles:</strong></p> <table> <tbody> <tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Explanation</th> </tr> <tr> <th>Temperature</th> <td>temperature</td> <td> <p>°C</p> </td> <td> <p>Population-weighted mean temperature (2 m)</p> </td> </tr> <tr> <th> <p>Rounded temperature</p> </th> <td>rounded_temperature</td> <td>°C</td> <td>Temperature values rounded to zero decimal places</td> </tr> <tr> <th>Daytype</th> <td>day type</td> <td>-</td> <td> <p>weekdays = typeday 0; Saturday or day before a holiday = typeday 1; Sunday or holiday = typeday 2</p> </td> </tr> <tr> <th>Month<strong><br></strong></th> <td> <p>month</p> </td> <td> <p>-</p> </td> <td> <p> The column “month” refers to the month of the year. 1 = January, 2 = February etc.</p> </td> </tr> <tr> <th> Season</th> <td>season</td> <td>-</td> <td> <p>0 = Summer (15/05 - 14/09)</p> <p>1 = Winter (1/11 - 20/3)</p> <p>2 = Transition (21/3 - 14/5 & 15/9 - 31/10)</p> </td> </tr> <tr> <th>Load e-mobilty</th> <td> <p>load_emobility</p> </td> <td> <p>-</p> </td> <td> <p>E-mobility electricity demand profile, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Non-metallic minerals</th> <td> <p>non_metallic_minerals</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector non-metallic minerals, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>Paper</th> <td> <p>paper</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector paper, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>Iron and steel</th> <td> <p>iron_and_steel</p> </td> <td> <p>-</p> </td> <td>Electricity demand profile of the industrial sector iron and steel, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</td> </tr> <tr> <th>Chemicals and petrochemicals</th> <td> <p>chemicals_and_petrochemicals</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector chemicals and petrochemicals, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>Food and tobacco</th> <td> <p>food_and_tobacco</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector food and tobacco, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>SHW residential</th> <td> <p>shw_residential</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for sanitary hot water in the residential sector, normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>SHW tertiary<strong><br></strong></th> <td> <p>shw_tertiary</p> </td> <td> <p> </p> </td> <td> <p>Electricity demand profile for sanitary hot water in the tertiary sector, normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>Cooling residential<strong><br></strong></th> <td> <p>cooling_residential</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for cooling in the residential sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Heating residential<strong><br></strong></th> <td> <p>heating_residential</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for heating in the residential sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Cooling tertiary</th> <td> <p>cooling_tertiary</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for cooling in the tertiary sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Heating tertiary<strong><br></strong></th> <td> <p>heating_tertiary</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for heating in the tertiary sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Rest<strong><br></strong></th> <td> <p>rest</p> </td> <td> <p>-</p> </td> <td> <p>Rest electricity demand profile, normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>Exogenous H2<strong><br></strong></th> <td> <p>exogenous_H2</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for electrolysis (flat profile), normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>Total<strong><br></strong></th> <td> <p>total</p> </td> <td> <p>-</p> </td> <td> <p>Total electricity demand profile containing all components above (e-mobility, industry, residential heating, residential sanitary hot water, residential cooling, tertiary heating, tertiary sanitary hot water, tertiary cooling, rest, and exogenous H2 electricity demand), normalized to an annual demand of 10,000,000 in the reference year 2010</p> </td> </tr> </tbody> </table> <p>Electricity supply profiles for wind (onshore and offshore), hydro (run-of-river), and solar generation are provided for almost all European countries, namely: Andorra (AD), Albania (AL), Austria (AT), Bosnia and Herzegovina (BA), Belgium (BE), Bulgaria (BG), Switzerland (CH), Czech Republic (CZ), Germany (DE), Denmark (DK), Estonia (EE), Spain (ES), Finland (FI), France (FR), United Kingdom of Great Britain and Northern Ireland (GB), Greece (GR), Croatia (HR), Hungary (HU), Republic of Ireland (IE), Italy (IT), Liechtenstein (LI), Lithuania (LT), Luxembourg (LU), Latvia (LV), Montenegro (ME), North Macedonia (MK), Malta (MT), Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Sweden (SE), Slovenia (SI), Slovakia (SK), San Marino (SM), Ukraine (UA), Vatican (VA), and Kosovo (XK). The countries covered by the electricity demand profiles are the EU27 countries (except for Cyprus), CH, GB, and NO.</p> <p>Industrial, heating, and cooling demand profiles are based on regressions developed in the H2020 Hotmaps project [1] [2]. </p> <p>SECURES-Energy is available in a tabular csv format for the historical period (1981-2010) created from ERA5 and ERA5-Land and two future emission scenarios (<strong>RCP 4.5 </strong>and <strong>RCP 8.5</strong>, both 2011-2100) created from one CMIP5 EURO-CORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E) on the<strong> </strong>spatial aggregation level<strong> NUTS0 </strong>(country-wide).</p> <p>The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shapefiles of the different NUTS levels.</p> <p>Hydro reservoir profiles are also published and can be found in the related dataset SECURES-Met: https://zenodo.org/records/7907883.</p> <p>The project SECURES and corresponding publications are funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.</p> <p>[1] Fallahnejad M. Hotmaps-data-repository-structure 2019. https://wiki.hotmaps.eu/en/Hotmaps-open-data-repositories.</p> <p>[2] Pezzutto S, Zambotti S, Croce S, Zambelli P, Garegnani G, Scaramuzzino C, et al. HOTMAPS - D2.3 WP2 Report – Open Data Set for the EU28. 2019.</p>
Supplementary material 1 from: Fernandez D, Millán A, Rizzo V, Comas J, Lleopard E, Pastor J, Pallarés S, Abellán P, Spada M, Bilton DT, Ribera I (2018) The CAVEheAT project: climate change, thermal niche and conservation of subterranean biodiversity. ARPHA Conference Abstracts 1: e30105. https://doi.org/10.3897/aca.1.e30105
The CAVEheATproject: climate change, thermal niche and conservation of subterranean biodiversity
Dataset used to assess the vulnerability of chondrichthyan fishes of the Eastern Tropical Pacific to climate change
<p><span>Information and data on ETP chondrichthyan species distribution, life history, ecology, and physiology were gathered from the IUCN Red List, FishBase, published literature, unpublished data/grey literature, and expderts advice.</span></p>
Supported Datasets for the Research Titled: "Impacts of Climate and Land Use Changes on Streamflow in the Mun-Chi River Basin, the Largest Tributary of the Mekong River"
<p><strong>Supported Datasets for the Research Titled: "Impacts of Climate and Land Use Changes on Streamflow in the Mun-Chi River Basin, the Largest Tributary of the Mekong River"</strong></p> <p>Abstract: </p> <p><span>The impact of climate change and human activities poses significant challenges in the tropical region of Southeast Asia, specifically within the Mun-Chi River Basin, the largest tributary of the Mekong River in Thailand. The bias-corrected MPI-ESM1-2-LR, the most appropriate Global Climate Model (GCM) under the Coupled Model Intercomparison Project Phase 6 (CMIP6) for projecting Mun-Chi River flow, represent future climate variations in this basin. The analysis reveals forthcoming transformations in future land use, with cropland areas transitioning into forests and urban areas. While the projected annual streamflow contributing to the Lower Mekong River is expected to slightly increase by up to 4%, with 67% attributed to climate change and 33% to land-use change, temporal variations in the future flow regime reveal a wetter wet season and a drier dry season in this catchment. During the wet season, streamflow is projected to rise by 5% to 18% in 2023-2035 and 10% to 24% in 2036-2050. In contrast, the dry season is expected to experience a decrease of -3% to -9% in 2023-2035 and -6% to -17% in 2036-2050. Projected streamflow fluctuations are more pronounced in mountainous areas and upstream tributaries. These seasonal contrasts highlight the potential impact of more severe drought during the dry season and more severe flooding during the wet season. These potential increases in extreme hydrological events present challenges for efficient water resource management in this watershed and downstream countries. Consequently, effective water regulation and land-use policies are deemed crucial for sustainable management in the Mun-Chi River Basin.</span></p>
The dataset of predicting the potential habitat suitability of Saussurea species in China under future climate change using the optimized Maximum Entropy (MaxEnt) model
<p><strong>Description:</strong></p> <p>This dataset accompanies the study on the Saussurea species, renowned for its biodiversity and medicinal significance in high-elevation regions, which faces endangerment due to climate change and human activities. Despite its importance, conservation research on Saussurea has been limited. To address this gap, the study employed the optimized MaxEnt model to simulate Saussurea's habitat suitability and analyze key environmental factors influencing its distribution.</p> <p>The dataset includes:</p> <ol> <li><strong>Model and Parameter Optimization Code</strong>: The code used for optimizing the MaxEnt model parameters, ensuring reproducibility of the habitat suitability models.</li> <li><strong>Saussurea Distribution Points</strong>: Georeferenced points indicating the observed locations of Saussurea species.</li> <li><strong>Current Environmental Variables</strong>: Data on key environmental factors influencing Saussurea distribution, such as Elevation, Isothermality (Bio3), and Temperature Annual Range (Bio7).</li> <li><strong>Future Environmental Variables: </strong>Data on key environmental factors influencing Saussurea distribution under SSP126, SSP245, SSP370 and SSP585 in 2020-2100s.</li> </ol>
Code and data used for findings and figures in the manuscript "Land cover change-climate interactions amplified the diminishment of spring ecosystem productivity in the Arctic-Boreal region"
<p><span>This is the code and data used in the manuscript "Land cover change-climate interactions amplified the diminishment of spring ecosystem productivity in the Arctic-Boreal region" to generate all findings and figures.</span></p>
Dataset for McClelland et al. "Crop-yield tradeoffs reduce the climate change mitigation potential of soil"
<p>The zip file in this record contains the post-processed model data for recreating analyses and figures from the manuscript "Crop-yield tradeoffs reduce the climate change mitigation potential of soil." The associated R scripts can be found at a separate Zenodo record, 10.5281/zenodo.13327482.</p>
Data supporting the publication of "Interactive effects of climate change and land-use change on mammal range retraction in Great Britain"
<p><strong>Table S1 (Species records) provided as a separate .xlsx file in Supporting Information. </strong>List of species included in the sample with corresponding attributes, number of records and rates of change over time.</p> <p>Column A (Scientific name): species’ accepted scientific name (n = 43 species).</p> <p>Column B (Common name): species’ common name in Great Britain (n = 43 names).</p> <p>Column C (Order): species’ taxonomical Order (n = 6 Orders).</p> <p>Column D (Family): species’ taxonomical Family (n = 14 Families).</p> <p>Column E (Guild): species’ sampling guild (n = 3 Guilds, either Bats, Midlarge, or Small</p> <p>Column F (Distribution): species’ distribution status in Great Britain (n = 3 Statuses, either Native, Naturalised, or Non-Native).</p> <p>Column G (Habitat): species’ habitat preference (n = 2 Habitats, either Terrestrial or Freshwater).</p> <p>Column H (Records): total number of records per species from 1960 to 2016 (average = 10,931).</p> <p>Column I (1960s): total number of records per species from 1960 to 1969 (average = 420).</p> <p>Column J (1970s): total number of records per species from 1970 to 1979 (average = 457).</p> <p>Column K (1980s): total number of records per species from 1980 to 1989 (average = 423).</p> <p>Column L (1990s): total number of records per species from 1990 to 1999 (average = 641).</p> <p>Column M (2000s): total number of records per species from 2000 to 2010 (average = 943).</p> <p>Column N (2010s): total number of records per species from 2011 to 2016 (average = 870).</p> <p>Column O (Hectads TP1): number of hectads where the species has been recorded in Time Period 1, from 1960 to 1992 (average = 892).</p> <p>Column P (Hectads TP2): number of hectads where the species has been recorded in Time Period 2, from 2000 to 2016 (average = 1,117).</p> <p>Column Q (Hectads Total): number of hectads where the species has been recorder from 1960 to 2016 (average = 1,315).</p> <p>Column R (Extirpation rate): species’ extirpation rate, calculated as the ratio of extirpations over the sum of extirpations and persistences (average = 0.24). The sum of extirpation and persistence rates is always equal to 1.</p> <p>Column S (Persistence rate): species’ persistence rate, calculated as the ratio of persistences over the sum of extirpations and persistences (average = 0.76). The sum of persistence and extirpation rates is always equal to 1.</p> <p>Column T (Occupancy TP1): species’ occupancy estimate in Time Period 1, from 1960 to 1992, as calculated in Frescalo (average = 0.395).</p> <p>Column U (Occupancy TP2): species’ occupancy estimate in Time Period 2, from 2000 to 2016, as calculated in Frescalo (average = 0.403).</p> <p>Column V (Occupancy change): change in the species’ occupancy estimates between Time Periods 1 and 2, as calculated in Frescalo (average = 0.076).</p> <p>Column W (Occupancy change slope): average yearly change in the species’ occupancy estimates from 1960 to 2016, as calculated in Frescalo (average = -0.001).</p> <p>Column X (Frequency TP1): adjusted frequency of occurrence in Time Period 1, from 1960 to 1992, as calculated in Frescalo (average = 0.527).</p> <p>Column Y (Frequency TP2): adjusted frequency of occurrence in Time Period 2, from 2000 to 2016, as calculated in Frescalo (average = 0.461).</p> <p>Column Z (Frequency change): change in the adjusted frequency of occurrence between Time Periods 1 and 2, as calculated in Frescalo (average = -0.066).</p>
Codes for "Intensifying Inverse KE Cascade Over Energetic Oceans Under Global Warming" By Geng et al. Submitted to Nature Climate Change
<p>This repository contains the necessary codes for the study of "Intensification of Oceanic Inverse Energy Cascade Under Global Warming" .</p> <p>Specifically, this repository contains the following items:</p> <p>(1) The codes for computing the global kinetic energy cascade, the four metrics of inverse KE cascade and their trends.</p> <p>(2) The function codes needed for coarse-graining filtering and trend analysis.</p> <p>(3) Necessary data for running the programs at MATLAB.</p>
The relative importance of nitrogen deposition and climate change in driving plant diversity decline in roadside grasslands
<p>Data and scripts for the manuscript "The relative importance of nitrogen deposition and climate change in driving plant diversity decline in roadside grasslands"</p>
Storyline data used in the paper "Storylines reveal contrasting thermodynamic effects of climate change on 2020/21 East Asian cold extremes"
<p>We provide the storyline data (in NetCDF format) used in the paper:”<strong>Storylines reveal contrasting thermodynamic effects of climate change on 2020/21 East Asian cold extremes”. </strong>The data is structured five .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climates. The data includes the five ensemble members (E1 to E5) and ensemble mean variables at winter season (DJF) in 2020/2021.</p> <p>Files of simulation ensemble member data are named as:</p> <p><span> </span>“AWICM1_ssp370/hist_f{begin year}_n2017_T20e24_{variable name}_E{ensemble member}_DJF-{years}_dailymean.nc”</p> <p>Files of simulation ensemble-mean data are names as:</p> <p>“AWICM1_ssp370/hist_f{begin year}_n2017_T20e24_{variable name}_DJF-{years}_ensmean.nc”</p> <p>Files of free-run (CMIP6) data are names as:</p> <p>“freerun_{variable name}_DJF-{year}_ensmean_31days-runmean_11years-ydaymean.nc”</p> <p>Variables includes:<span> </span></p> <ul> <li>Mean 2m Temperature (t2m)</li> </ul> <ul> <li>Downward net surface solar radiation (srads)</li> </ul> <ul> <li>Total cloud cover (aclcov)</li> <li>Downward solar radiation at clear sky (rsdscs)</li> <li>sea ice concentration (friac)</li> </ul> <p>Only for present climate:</p> <ul> <li>Zonal/meridional wind at 850hPa (u850,v850)</li> <li>500 hPa Geopotential Height (z500)</li> </ul> <p><span> </span></p>
Natural Environment Research Council (NERC) - Climate Change
Natural Environment Research Council (NERC) - Climate Change
Artificial intelligence reveals potential Arctic whale aggregation disruption due to climate change
<p>Climate change has been shown to alter the spatial distribution of whales and other marine mammals. Fast changing ocean temperatures may also affect the spatial distribution of whales at a finer scale, namely within populations, including aggregation behaviour. Here, we analyze the impact of climate change on whale aggregation behavior.</p>
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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.