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196 results for “heatwave”
Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_era5land_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_era5land_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_era5land_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
Data and scripts for the article entitled "Arctic heatwaves could significantly influence the isoprene emissions from shrubs"
<p>The package includes the data and scripts for generating the figures for the paper entitled "Arctic heatwaves could significantly influence the isoprene emissions from shrubs".</p>
Storyline Simulations Data for the paper Athanase et al.: Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean
<p>Data used for producing the Figures in the paper entitled "Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean", Athanase et al. (Communications Earth & Environment).</p> <p>The AWI-CM-1-1-MR free runs are available in the Earth System Grid Federation (ESGF) data nodes (https://esgf-data.dkrz.de/search/cmip6-dkrz/). The ERA5 reanalysis data used in the paper can be accessed from the European Centre for Medium-Range Weather Forecasts (ECMWF; https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5). Here, we provide data from the nudged storyline simulations carried out with the AWI-CM-1-1-MR coupled climate model.</p> <p>Parameters naming convention:</p> <p>- Sea Surface Temperature ("tos").</p> <p>- Radiative Fluxes ("radiations"), including net surface heat flux ("qnet"), net surface thermal radiation ("trads"), net surface solar radiation ("srads"), latent heat flux ("ahfl"), sensible heat flux ("ahfs").</p> <p>- Low Clouds Cover ("lcc").</p> <p>- Mixed Layer Depth ("mlotst").</p> <p>- Surface Air Temperature ("tas").</p> <p>- 10 m winds ("u10","v10").</p> <p>All data is provided as the 5-member ensemble mean from the nudged storyline simulations. Data is provided for the storyline simulations of the summer 2019 Northeast Pacific marine heatwave, in different background climate conditions: preindustrial ("PI"), present-day ("PD"), and +4°C warmer world ("4K"). </p> <p> </p>
Post‐processed data and analysis codes for the research "Significant reduction of potential exposure to extreme marine heatwaves by achieving carbon neutrality"
<p>[Earth's Future] Oh et al. "Significant reduction of potential exposure to extreme marine heatwaves by achieving carbon neutrality"</p> <p>1. Information for Raw datasets<br>- The data of eight global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) can be accessed at https://esgf-node.llnl.gov/search/cmip6/, <br> and can also be accessed in Eyring et al. (2016). <br>- The NOAA OISST high resolution dataset can be obtained in Reynolds et al. (2007) or via https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html. <br>- The five ocean mask dataset can be obtained from https://reccap2-ocean.github.io/regions/. </p> <p>2. Information for Software<br>- The raw data in this study were analyzed using Fortran 90, R version 4.0.3, and Grads version 2.2.1.<br>- The Fortran 90 can be accessed at https://www.intel.com/content/www/us/en/developer/articles/tool/oneapi-standalone-components.html#fortran. <br>- The R version 4.0.3 is available from https://cran.r-project.org/bin/windows/base/old/4.0.3/. <br>- The Grads version 2.2.1 can be downloaded from http://cola.gmu.edu/grads/downloads.php.</p> <p>3. Information for Post-Processed data and Codes used in this work.<br>Please find each folder and the relevant post-processed dataset and codes.</p>
CLIMATE CHANGE EFFECTS ON A SUBTROPICAL COASTAL SHALLOW LAKE FROM HEATWAVE INDEXES
<p>This zipped folder contains the files used to generate the results of this article, submitted to the journal Earth Systems and Environment.</p>
Supporting datasets for Mexico-Texas heatwave analysis
<p>This repository contains supporting data for the manuscript titled "Contributions of Atmospheric Ridging and Low Soil Moisture to the Record-Breaking June 2023 Mexico-Texas Heatwave" by Kalashnikov et al. (2025). Please contact me with any questions at dkalashnikov@ucmerced.edu. -Dmitri</p>
Data from: The 2018 European heatwave led to stem dehydration but not to consistent growth reductions in forests
<p>Heatwaves exert disproportionately strong and sometimes irreversible impacts on forest ecosystems. These impacts remain poorly understood at the tree and species level and across large spatial scales. Here, we investigate the effects of the record-breaking 2018 European heatwave on tree growth and tree water status using a collection of high-temporal resolution dendrometer data from 21 species across 53 sites. Relative to the two preceding years, annual stem growth was not consistently reduced by the 2018 heatwave but stems experienced twice the temporary shrinkage due to depletion of water reserves. Conifer species were less capable of rehydrating overnight than broadleaves across gradients of soil and atmospheric drought, suggesting less resilience toward transient stress. In particular, Norway spruce and Scots pine experienced extensive stem dehydration. Our high-resolution dendrometer network was suitable to disentangle the effects of a severe heatwave on tree growth and desiccation at large-spatial scales in situ, and provided insights on which species may be more vulnerable to climate extremes.</p>
Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production. Reproducible workflow
<p>Dataset for manuscript entitled "Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production" accepted for publication in New Phytologist. The dataset was obtained for the site Majadas del Tietar, Spain, between June/2018 and August/2018. It consists of eddy covariance data, sun-induced fluorescence data and active fluorescence data. </p>
Lethal and sublethal effects of marine heatwaves on octocorals early life history stages
<p>In this study, the effect of increased water temperature (+4 ºC and +6 ºC above ambient, 20 ºC) on larval survival and settlement was evaluated for two of the most representative Mediterranean octocoral species (<em>Eunicella singularis</em> and <em>Corallium rubrum</em>). Moreover, data on larval biomass and caloric consumption of larvae per day are also provided.Our study shows that warmer treatments did not affect the survival of symbiotic <em>E. singularis </em>larvae, but drastically reduced the survival of the non-symbiotic <em>C. rubrum</em> larvae. The results on larval biomass and caloric consumption suggest that higher mortality rates of <em>C. rubrum</em> exposed to increased temperature were not related to depletion of endogenous energy in larvae. The results also show that settlement rates of <em>E. singularis</em> did not change in response to elevated temperature after 20 days of exposure, but larvae may settle fast and close to their native population at 26 ºC (+6 ºC). Although previous experimental studies found that adult colonies of both octocoral species are mostly resistant to thermal stress, our results on early life history stages suggest that the persistence and inter-connectivity of local populations may be severely compromised under continued trends in ocean warming.</p>
What Caused the 2020 Record-Breaking Summer Marine Heatwaves in the Western North Pacific?
<p>The numerical results of tropical oceans with SST anomalies in the summer of 2020. There are five files include one control file and four tropical ocean files. </p>
Drought-heatwave compound events are stronger in drylands
<p>This data is supplementary data or code for the research article "Weather and Climate Extremes". The file name Global_CDHWs is the specific data of the six indicators of the global composite event calculated from 1961 to 2020, the granger_test file is the result data of the Granger causality test, and the lag_granger file is the lag result of the Granger causality test.</p> <p>Note: (Tsum, Tmax, Toccr, Tmean, AH, TAH,) correspond sequentially to (HDF, DHD, DHO, DHM, DHC, DHA). Inside variables correspond one to one.</p> <p>The Global CDHWs folder includes dry-heatwave compound events (state3) and wet heatwave compound events (state4), and each of the two folders contains six variables with annual and monthly scales, where the next level folder contains monthly scale data.</p>
When resilience is not enough: 2022 extreme marine heatwave threatens climatic refugia for a habitat-forming Mediterranean octocoral
<p>Climate change is impacting ecosystems worldwide, and the Mediterranean Sea is no exception. Extreme climatic events, such as marine heat waves (MHWs), are increasing in frequency, extent, and intensity during the last decades, which has been associated with an increase in mass mortality events for multiple species. Coralligenous assemblages, where the octocoral <em>Paramuricea clavata</em> lives, are strongly affected by MHWs. The Medes Islands Marine Reserve (NW Mediterranean) was considered a climate refugia for <em>P. clavata</em>, as their populations were showing some resilience to these changing conditions. In this study, we assessed the impacts of the MHWs that occurred between 2016 and 2022 in seven shallow populations of the octocoral <em>P. clavata</em> from a Mediterranean Marine Protected Area. The years that the mortality rates increased significantly were associated with the ones with strong MHWs, 2022 being the one with higher mortalities. In 2022, with 50 MHW days, the proportion of total affected colonies was almost 70%, with a proportion of the injured surface of almost 40%, reaching levels never attained in our study site since the monitoring was started. We also found spatial variability between the monitored populations. Whereas few of them showed low levels of mortality, others lost around 75% of their biomass. The significant impacts documented here raise concerns about the future of shallow <em>P. clavata</em> populations across the Mediterranean, suggesting that the resilience of this species may not be maintained to sustain these populations face the ongoing warming trends.</p>
Dataset: How do news about a heatwave affect public prioritization of climate change adaptation and mitigation behaviors?
<p><span>These datasets contain survey data that was used to evaluate the effect of the exposure to heatwave news texts on people’s preference for climate mitigation and adaptation actions, as presented in the manuscript titled “<em>How do news about a heatwave affect public prioritization of climate change adaptation and mitigation behaviors?</em>”. Three versions of the dataset are available:</span></p> <ol> <li><strong>Original dataset</strong>: This version contains choice text as data points and includes all finished survey responses that passed the attention check questions (n=1209).</li> <li><strong>Original recoded dataset</strong>: This version was generated by recoding choice text into numerical values. The 'Income' variable, representing household income levels for both Canadian and US residents, was added by converting reported income ranges to a unified scale based on exchange rate equivalencies. The "Income_Canadians" and "Income_US" columns were subsequently removed to avoid repetitions. </li> <li><strong>Final dataset</strong>: This version excludes observations from participants who completed the survey in under four minutes and those who selected the same response for every item within each matrix-style question (also known as straight-lining). Additionally, responses with missing values in questions regarding political views, gender, and household income, as well as responses where participants identified as non-binary or indicated that their gender was not listed, were omitted (see “Methods” for more details). Dependent variables have been added based on the original responses, including personal-level mitigation and adaptation likelihoods, personal-level mitigation preference, and both non-weighted and weighted collective-level mitigation preference. Furthermore, the dataset includes a 'Climate Change Concern' variable, derived through principal component analysis of thirteen variables expressing participants’ climate change attitudes and efficacy beliefs concerning climate actions. Variables not used in the subsequent data analysis were removed. Age, political views, education, and income columns were standardized. The final dataset was used for the data analysis presented in the manuscript.</li> </ol> <p>The following variables/columns can be found across the three versions of the dataset:</p> <ul> <li>Dependent variables: <ul> <li>Starting with “<em>Personal_Mitigation</em>”: participant’s self-reported likelihood of taking selected personal-level climate change mitigation actions</li> <li>Starting with “<em>Personal_Adaptation</em>”: participant’s self-reported likelihood of taking selected personal-level climate change adaptation actions</li> <li>Starting with “<em>Collective_Mitigation</em>”: participant’s ranking of the collective-level climate change mitigation initiatives</li> <li>Starting with “<em>Collective_Adaptation</em>”: participant’s ranking of the collective-level climate change adaptation initiatives</li> <li><em>Personal_Mitigation_Likelihood</em>: personal-level mitigation likelihood (present only in the final dataset)</li> <li><em>Personal_Adaptation_Likelihood</em>: personal-level adaptation likelihood (present only in the final dataset)</li> <li><em>Personal_Preference</em>: personal-level mitigation preference (present only in the final dataset)</li> <li><em>Collective_Preference_Unweighted</em>: non-weighted collective-level mitigation preference (present only in the final dataset)</li> <li><em>Collective_Preference_Weighted</em>: weighted collective-level mitigation preference (present only in the final dataset)</li> </ul> </li> <li>Independent variables: <ul> <li><em>Group</em>: group that the participant was assigned to as part of the experimental intervention</li> <li><em>Distance</em>: indicates whether the participant was assigned to read about a heatwave occurring in their community or a city 6,000 km away (for experimental groups only)</li> <li><em>Severity</em>: indicates whether the participant was prompted to read about a heatwave without or with the mention of associated causalities (for experimental groups only)</li> </ul> </li> <li>Covariates and supporting variables: <ul> <li><em>Gender</em>: gender identity</li> <li><em>Identity</em>: ethnic and/or racial identity</li> <li><em>Age</em>: age</li> <li><em>Political_Views</em>: position on the liberal-conservative continuum</li> <li><em>Education</em>: highest level of education</li> <li><em>Country</em>: country of residence</li> <li><em>Canada_Province</em>: province or territory of residence (for Canadian participants only)</li> <li><em>US_State</em>: state of residence (for US participants only)</li> <li><em>Duration_Residence</em>: duration of residence in the current community</li> <li><em>Income_Canadians</em>: annual household income in Canadian dollars (for Canadian participants only)</li> <li><em>Income_US</em>: annual household income in US dollars (for US participants only)</li> <li><em>Income</em>: annual household income for both Canadian and US residents derived by converting reported income ranges to a unified scale based on exchange rate equivalencies</li> <li><em>Efficacy_Mitigation_Personal</em>: belief regarding the response efficacy of personal-level climate change mitigation actions</li> <li><em>Efficacy_Mitigation_Collective</em>: belief regarding the response efficacy of collective-level climate change mitigation actions</li> <li><em>Efficacy_Adaptation_Personal</em>: belief regarding the response efficacy of personal-level climate change adaptation actions</li> <li><em>Efficacy_Adaptation_Collective</em>: belief regarding the response efficacy of collective-level climate change adaptation</li> <li><em>Climate_Change_Importance:</em> perception of climate change as a personally important issue</li> <li>Climate_Change_Worry: level of worry about climate change</li> <li>Starting with “<em>Climate_Risk</em>”: beliefs regarding the degree of harm that climate change will cause to plants and animal species (Climate_Risk_Animals_Plants), future generations of people (Climate_Risk_Future_Generations), people in developing countries (Climate_Risk_Developing_Countries), people in participant’s country (Climate_Risk_Country), people in participant’s community (Climate_Risk_Community), and the participant personally (Climate_Risk_Personal)</li> <li>Climate_Change_Onset_Time: belief regarding when climate change will start harming people in their community</li> <li><em>Six_Americas_Segment</em>: the Global Warming's Six Americas segment participant aligns with derived based on the Six Americas Short SurveY (SASSY) Group Scoring Tool</li> <li><em>Climate_Change_Concern</em>: variable derived through PCA of thirteen variables expressing participants' climate change attitudes and efficacy beliefs pertaining to climate actions (present only in the final dataset)</li> <li><em>Survey_Duration_Seconds</em>: The amount of time it took the respondent to complete the survey</li> </ul> </li> </ul>
Simulations of urban heat island effect in Paris Region during various types of heatwaves, and in different adaptation scenarios
<p><strong>Content</strong><br> - These data present air temperature, in the shade, 2m above grounds in Paris Region (projection: RGF93/Lambert 93, EPSG:2154) at different times of the day, for various heat waves conditions, and in different prospective scenarios for the built-up evolution and adaptation actions implementations.<br> - more information can be found here : https://www.umr-cnrm.fr/ville.climat/spip.php?rubrique45</p> <p><strong>Classification of the data</strong><br> - the first 5 letters (e.g. "CDFFA") present the prospective scenario<br> - the 4 following letters (e.g. "HW34") present the type of heat wave<br> - the following 2 letters (e.g. "D8") present the length of the heat wave (number of days after the beginning of the heat wave)<br> - the final letters (e.g. H15) represent the time (UTC : one hour should be added for French time) of the day</p> <p><strong>Prospective scenarios</strong><br> - the first letter is always C<br> - the second letter represents the expansion scenario. They are presented here : Lemonsu, A., Viguié, V., Daniel, M., Masson, V., 2015. Vulnerability to heat waves: Impact of urban expansion scenarios on urban heat island and heat stress in Paris (France). Urban Climate 14, 586–605.<br> - D stands for "dense development"<br> - F for business as usual scenario ("fil de l'eau" in French)<br> - V for a scenario with 10% more parks<br> - the third letter represents the building evolution scenario<br> - F stands for business as usual scenario<br> - V for a scenario with more insulation and reflective roofs<br> - the third letter represents AC use<br> - F stands for strong AC use<br> - M for moderate AC use<br> - N for no AC use<br> - the fourth letter represents vegetation watering<br> - N stands for no watering<br> - A for watering</p> <p><strong>Heat waves</strong><br> - the figure (e.g. "34" in "HW34") represents the intensity class, in °C of the heat wave. It is more precisely the maximum daily temperature observed without the impact of the urban heat island effect. (Tmax=34, 38, 42, or 46°C).</p> <p><strong>Other information</strong><br> - see the file "aggregated data.xls" for more information and data about energy consumption for AC, and averages of temperatures in the city over the entire day.</p> <p> </p>
Codes and source data for "Common occurrences of subsurface heatwaves and cold-spells in ocean eddies"
<p>This repository contains the MATLAB (R2022b) codes (*.m files) and the figure source data (.mat files) for the paper "Common occurrences of subsurface heatwaves and cold-spells in ocean eddies" (He et al., 2024). </p> <p>For installation of MatLab, please refer to: https://au.mathworks.com/products/matlab.html</p> <p>For queries about this repository and its contents, please contact Dr. Qingyou He (qyhe@scsio.ac.cn).</p> <p>%% Fig1.m: For plotting main Fig.1.<br>%% Fig2.m: For plotting main Fig.2.<br>%% Fig3.m: For plotting main Fig.3.<br>%% Fig4.m: For plotting main Fig.4.<br>%% Fig5.m: For plotting main Fig.5.<br>%% Fig6.m: For plotting main Fig.6.</p> <p>%% Data Fig1.mat: For main Fig.1.<br>%% Data Fig2.mat: For main Fig.2.<br>%% Data Fig3.mat: For main Fig.3.<br>%% Data Fig4.mat: For main Fig.4.<br>%% Data Fig5.mat: For main Fig.5.<br>%% Data Fig6.mat: For main Fig.6.</p> <p><br>References:</p> <p><span>He, Q., W. Zhan, M. Feng, Y. Gong, S. Cai, and H. Zhan (2024), Common occurrences of subsurface heatwaves and cold spells in ocean eddies, <em>Nature</em>, <em>634</em>, 1111–1117, doi:10.1038/s41586-024-08051-2.</span></p>
Data and Python scripts for "Manifold increase in the spatial extent of heatwaves in the terrestrial Arctic"
<p>Data and Python scripts for reproducing the figures and results included in the manuscript "Manifold increase in the spatial extent of heatwaves in the terrestrial Arctic".</p> <p>Figures 1-4 are produced via respective Python codes. Heatwave magnitude index daily (HWMId) for ERA5-Land is available from. hw_era5land.nc file. HWMId fields for CMIP6 models are included in cmip6_hwmid.zip. The underlying data behind the figures 1-4 are included in data_to_produce_figs.zip.</p> <p>The paper is published in Rantanen, M., Kämäräinen, M., Luoto, M. <em>et al.</em> Manifold increase in the spatial extent of heatwaves in the terrestrial Arctic. <em>Commun Earth Environ</em> <strong>5</strong>, 570 (2024). https://doi.org/10.1038/s43247-024-01750-8</p>
Predators mitigate the destabilising effects of heatwaves on multitrophic stream communities
<p>Abstract</p> <p>Amidst the global extinction crisis, climate change will additionally expose ecosystems to more frequent and intense extreme climatic events, such as heatwaves. Yet, whether predator species loss—a prevailing characteristic of the extinction crisis—will exacerbate the ecological consequences of extreme climatic events remains largely unknown. Here, we show that predator species loss can interact with heatwaves to affect the compositional stability of ecosystems. By exposing multitrophic stream communities to realistic current and future heatwaves—locally informed by weather station data and downscaled regional climate projections—in the presence and absence of an apex fish predator, we found that heatwaves destabilised algal communities by homogenising them in space. However, this only happened when predators were absent. Additional heatwave impacts on multiple aspects of stream communities—including changes to the structure of algal and macroinvertebrate communities, total algal biomass, and the temporal variability of algal biomass—were not apparent during heatwaves and emerged only after the heatwaves had passed. Taken together, our results suggest that, though the ecological consequences of heatwaves can amplify over time as their impacts propagate through biological interaction networks, those impacts can be mitigated by the presence of predators. These findings underscore the importance of conserving trophic structure and the integrity of biological communities, and highlight the considerable potential for species extinctions to amplify the effects of climate change and extreme events.</p> <p>Methods</p> <p>The dataset contains the Chlorophyll a concentrations of mesocosms measured during a multifactorial experiment from terracotta tiles with a Benthotorch fluorometer, as well as data on leaf dry mass decomposition, and macroinvertebrate community composition, richness, and abundance. We conducted an experiment looking at the combined effects of predator loss and heatwaves on multiple aspects of aquatic communities in semi-open mesocosms with immigration and emigration from the adjacent Horonai stream, in Tomakomai experimental forest, Hokkaido, Japan. Our focal predator was <em>Cottus nozawae</em>, a freshwater sculpin. We exposed 2/3 of our 48 mesocosms to heatwaves based on either observed current or projected future heatwaves and tested for interactions between predator presence/absence and heatwaves. Our dataset also contains algal time series for different functional groups (based on fluoroprobe chlorophyll a data) across our experiment, including dates that were not the focus of our analyses. See Methods section in the associated manuscript for details on data processing and the measurement of different experimental variables.</p> <p>Usage notes</p> <p>See readme file for further details and 'data descriptions.csv' file for descriptions of data structure.</p>
WRF simulations of 2017 Europe Heatwave
<p>The WRF model (version 4.1.4) domain was configured with a 9 km grid spacing (547x462 grid cells) with 51 vertical levels covering Europe. National Centers provide the initial and boundary conditions for Environmental Prediction Final Operational Model Global Tropospheric Analyses (NCEP FNL) data, which is available at a 6-hourly and 1-degree temporal and spatial resolution, respectively. The simulations started on 2017-06-08 at 00:00 UTC and ended on 2017-06-25 at 00:00 UTC and with 3-hour steps. The following parameterization schemes were used: the Rapid Radiative Transfer model scheme for longwave radiation, Goddard shortwave scheme, Bougeault-Lacarrère for the planetary boundary layer, WRF single moment 6-class scheme as microphysics, Kain-Fritsch as cumulus scheme, and multi-layer Building Energy Parameterization (BEP) as the urban scheme. The simulations are performed using two LSMs, i.e., Noah and Noah-MP. For the LULC, the Moderate Resolution Imaging Spectroradiometer (MODIS) data is used in two ways: i) the default MODIS LULC which has one urban class `Urban and Built-up Lands'; and ii) the MODIS LULC product in which its original urban land cover is replaced by the 10 urban LCZ classes, available from the European LCZ map at 100 m spatial resolution (hereafter denoted as WUDAPT LULC).</p> <p>The data format is NetCDF containing detailed metadata. Two-dimensional variables are included, horizontal wind components at 10 m AGL, atmospheric pressure at the surface, water vapor mixing ratio at 2 m AGL, the temperature at 2 m AGL, land use/ land cover, sensible heat flux, latent heat flux, and ground heat flux, the height of the terrain, and land mask.</p> <p>These WRF simulations are used to analyze the results of the "Modelling Large-Scale Heatwave by Incorporating Enhanced Urban Representation" manuscript submitted to JGR-Atmospheres.</p>
Data and code for "Hotspots and drivers of compound marine heatwave and low net primary production extremes"
<p>This repository provides the code and data for the study "Hotspots and drivers of compound marine heatwave and low net primary production extremes". All processed data required to produce the figures in this study are provided. However, not all raw data are provided, because of too large file sizes. For more information, please contact natacha.legrix@unibe.ch. </p>
Mediterranean Marine Heatwaves (MHWs) as detected from ESA CCI SST 0.05°x0.05° covering 1982-2021
<p>Daily records of Mediterranean Marine Heatwaves (MHW) resulting from detection applied to the European Space Agency (ESA) Climate Change Initiative (CCI) Sea Surface Temperature (SST) satellite product on a regular 0.05°x0.05° grid, covering the period 01/01/1982-31/12/2021.</p> <p>The MHW detection has been carried out via Hobday's method (Hobday et al. 2016).</p> <p>The <strong>mhw_original</strong> field provides the intensity of anomaly [°C] of MHW events detected on the original SST data, while the <strong>mhw_detrended</strong> field describes the intensity of anomaly [°C] of MHW events detected on detrended SST data, via X-11 seasonal adjustment procedure. </p> <p>The production of this dataset has been sustained with the support of the European Space Agency (ESA) "deteCtion and threAts of maRinE Heat waves" project (CAREHeat; grant number: 4000137121/21/I-DT).</p> <p>References:<br> Hobday, A. J., Alexander, L. V., Perkins, S. E., Smale, D. A., Straub, S. C., Oliver, E. C., ... & Wernberg, T. (2016). <br> A hierarchical approach to defining marine heatwaves. Progress in Oceanography 141:227–238, https://doi.org/10.1016/j.pocean.2015.12.014.</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.