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212 results for “Climate Simulation”
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Uccle KMI, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Uccle KMI (50°47'49"N 4°21'29" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Leuven City centre, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of city centre of Leuven (50°52'48"N, 4°42'0"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Building consensus for ambitious climate action through the World Climate Simulation
<p>Sociopolitical values are an important driver of climate change beliefs, attitudes, and policy preferences. People with 'individualist-hierarchical' values favor individual freedom, competition, and clearly defined social hierarchies, while communitarian-egalitarians value interdependence and equality across gender, age, heritage, and ethnicity. In the US, individualist-hierarchs generally perceive less risk from climate change and express lower support for actions to mitigate it than communitarian-egalitarians. Exposure to scientific information does little to change these views. Here, we ask if a widely-used experiential simulation, World Climate, can help overcome these barriers. World Climate combines an engaging role-play with an interactive computer model of the climate system. We examine pre- and post-World Climate survey responses from 2,080 participants in the US and use a general linear mixed model approach to analyze interactions among participants' sociopolitical values and gains in climate change knowledge, affect, and intent to take action. As expected, prior to the simulation, participants holding individualist-hierarchical values had lower levels of climate change knowledge, felt less urgency, and expressed lower intent to act than those holding communitarian-egalitarian values. However, individualist-hierarchs made significantly larger gains across all constructs, particularly urgency, than communitarian-egalitarians. Participants' sociopolitical values also shifted: those with individualistic-hierarchical values before the simulation showed a substantial, statistically significant shift toward a communitarian-egalitarian worldview. Simulation-based experiences like World Climate may help reduce polarization and build consensus towards science-based climate action.</p>
Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations
<p>Data for article "Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations"</p>
Widespread glacier advances across the Tian Shan during Marine Isotope Stage 3 not supported by climate-glaciation simulations
<p>This dataset shows the modeled ice extent over the Tian Shan during the present, MIS 2, MIS 3 interstadial, and MIS 3 stadial based on a 250m resolution of PISM model forced by the outputs from the NorESM1-F climate model.</p>
Glacier model simulations of moraine building forced by interannual variability in climate
<p>A set of 2,000-year simulations of moraine building by a glacier flowing through a synthetic alpine landscape forced by interannual variability in weather imposed on an otherwise stable climate. Moraine relief is shown for a standard deviation in mean annual air temperature (dT) of 0.5°C, 1.5°C, and 3.0°C around a long-term mean of 7.0°C. Simulations were made using the ice-flow model iSOSIA (Egholm et al., 2011, <em>Geomorphology</em>).</p>
Determination of the Optimal Parameters for Self-Healing Efficiency of Encapsulated bacteria in Concrete Simulated Subtropical Climate
<p>Concrete is a remarkable construction material. However, its low tensile strength makes it prone to cracking, which negatively affects its durability. To address this issue, bacterial concrete has been implemented as a self-healing alternative due to its capability to seal microcracks through microbial-induced calcium carbonate precipitation (MICCP). In this study, a bacterial strain (i.e, Bacillus Pseudiformus) was encapsulated through three different methods: encapsulation through hydrogel beads, vacuum impregnation on lightweight aggregates, and attachment to cellulose nanocrystals. Furthermore, three precursor types were used, magnesium acetate, calcium lactate, and sodium lactate were implemented. Compressive strength tests and flexural strength tests were performed on mortar specimens to characterize their mechanical properties. Once the crack was induced, samples were subjected to 28 days of wet/dry cycles in which the corresponding crack width was monitored. At the end of this period, the beams were retested to determine the strength recovery of the specimens. The results showed that the specimen groups in which calcium lactate was added to the cementitious matrix displayed the highest values in compressive strength. In terms of flexural strength, no major difference was found among the specimens. Moreover, the flexural strength recovery of the specimens did not show any significant difference as well. In terms of the healing efficiency, the sample that displayed the best results was the one containing calcium lactate as a precursor along with bacteria and yeast extract encapsulated in hydrogel beads. In addition, scanning electron microscopy (SEM) along with x-ray energy dispersive spectroscopy (EDS) was performed on the cracked specimens to characterize the healing products. Furthermore, a scale study was performed on concrete samples to determine the long-term implications of adding encapsulated bacteria along with calcium lactate and yeast extract in concrete.</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>
Code for APJAS publication - Numerical errors in ice microphysics parameterizations and their effects on simulated regional climate
<p>In this repository, we include the source codes for WRF microphysics parameterization used in the APJAS publication "Numerical errors in ice microphysics parameterizations and their effects on simulated regional climate"</p> <p>There are three WDM6 codes for simulations. The original WDM6 code (ORG) using parameter defined by Hong et al (2004), the revised WDM6 code (NEW) those revised by removing the numerical errors, and the additional WDM6 code (SEN) for sensitivity experiment adopting the column-shaped parameters.</p> <p>In supplement, several cloud-ice characteristics presented in the paper were induced in detail and compared with Hong et al (2004) and this study.</p>
The simulated monthly runoff data in the historical period and under future climate scenarios of the Yarlung Zangbo River Basin
<p>This data provides the simulated monthly runoff data under the historical period (1979-2014) and future (2049-2084) climate scenarios for four sub-basins of the Yarlung Zangbo River Basin, including Nugexia, Nuxia, Lasha, and Rikaze.<br> This runoff data is simulated based on the GR4J model coupled with a simple degree-day snow module. The GR4J_SNOW performs parameterization and calculates runoff on each grid cell, and the gridded simulated runoff then converges to the outlet of the sub-basin.<br> Time series of the daily records for meteorological forcing data (precipitation, air temperature, vapor pressure, wind speed, downward long-wave radiation, and downward short-wave radiation) from 1979-2014 was provided by China Meteorological Forcing Dataset (CMFD). <br> Future climate scenarios were generated using the combined climate forcing data together with scaling factors obtained from empirical downscaling of 30 available CMIP5 models (28 GCMs for RCP4.5 and 29 GCMs for RCP8.5). The simulated runoff under RCP4.5 and RCP8.4 are the ensemble averages of 28 and 29 simulated runoff results, respectively.</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>
Files associated with: Migration-based simulations for Canadian trees show limited tracking of suitable climate under climate change
<p><strong>Aim</strong></p> <p>Species distribution models typically project climatically suitable habitat for trees in eastern North America to shift hundreds of kilometers this century. We simulated potential migration considering species' life history and traits for 10 tree species and their ability to track climatically suitable habitat.</p> <p><strong>Location</strong></p> <p>Eastern Canada, covering ~3.7 million km<sup>2</sup></p> <p><strong>Methods</strong></p> <p>We simulated migration-constrained range shifts through 2100 using a hybrid approach combining projections of climatically suitable habitat based on two Representative Concentration Pathways (RCP4.5, RCP8.5) for three time periods and two species distribution modelling approaches with process-based models parameterized using data related to <span>dispersal ability and generation time</span>. We developed a unique 'migration kernel' that uses seed dispersal traits and observed migration velocities to obtain kernel shape and dispersal probabilities. We then calculated lags between the migration-constrained range limits obtained through simulations and limits of climatically suitable habitat.</p> <p><strong>Results</strong></p> <p>All species demonstrated northward range shifts at the leading edge of their simulated distribution through 2100, but the magnitude and rate of that shift varied by species and time period. Climatically suitable habitat limits were found to be north of simulated distribution limits across both RCPs, with lags increasing through time. On average, the simulated distribution that remained within climatically suitable habitat showed higher decreases under RCP8.5 than RCP4.5, with large areas of the rear edge of the simulated distribution becoming partially or completely climatically unsuitable for many species.</p> <p><strong>Main conclusions</strong></p> <p><span>Climatically suitable habitat limits projected for 2100 far exceeded migration-constrained range limits for all 10 species, particularly for temperate species. This study underlines the limited extent to which species will track climate change via natural migration. Integrating observed migration velocities, seed dispersal and generation time with SDM outputs allows for more realistic evaluations of tree migration ability under climate change and may help orient forest conservation and restoration efforts.</span></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>
Wave climate simulations for Denmark - for paper 'Coinciding storm surge and wave setup: A regional assessment of sea level rise impact'
<p>This wave climate dataset are the results for the paper titled 'Coinciding storm surge and wave setup: A regional assessment of sea level rise impact'.</p> <p>The operational wave forecasting service provided by DMI-WAM uses the WAM Cycle version 4.5.4, a third-generation spectral wave model. DMI-WAM is used for the wave climate simulations. The meteorological forcing used in this study was obtained from the regional climate model DMI-HIRHAM, developed by the Danish Meteorological Institute (DMI). It is a component of the CORDEX (Coordinated Regional Climate Downscaling Experiment) ensemble in Europe. Regarding the selection of the time frame and IPCC scenarios in our study, we adhered to the recommendations provided by municipalities. Municipalities are keenly interested in obtaining near-future wind wave data for the specific purpose of using them for risk management. Therefore, the examination of forthcoming weather extremes in the near future within the context of the high greenhouse gas emission scenario (RCP8.5 scenario) is of significance within this investigation. We conduct simulations that encompass two distinct time periods: the historical period spanning from 1976 to 2005, and the near-future period from 2041 to 2070. We analyse the WAM model results for wave climate under both present climate conditions (1976-2005) and future climate scenarios (2041-2070) under the RCP8.5 scenario. Furthermore, note that while our wave climate simulations provide valuable insights into the dynamics of wind-induced waves, the mean SLR is not explicitly taken into account. The mean SLR component is considered in the storm surge simulations.</p> <p>Description of files:</p> <p><a href="../api/records/11052226/draft/files/sla.swh.slope.hist.final.max.nc/content" target="_blank" rel="noopener noreferrer">sla.swh.slope.hist.final.max.nc</a> - Maximum sea level, significant wave height, wave length and slope for the historical period.</p> <p><a href="../api/records/11052226/draft/files/sla.swh.slope.rcp85.final.max.MSLR35.nc/content" target="_blank" rel="noopener noreferrer">sla.swh.slope.rcp85.final.max.MSLR35.nc</a> - Maximum sea level, significant wave height, wave length and slope for the RCP8.5 period.</p> <p><a href="../api/records/11052226/draft/files/wavesetup.hist.final.max.nc/content" target="_blank" rel="noopener noreferrer">wavesetup.hist.final.max.nc</a> - Maximum wave setup for the historical period.</p> <p><a href="../api/records/11052226/draft/files/wavesetup.rcp85.final.max.MSLR35.nc/content" target="_blank" rel="noopener noreferrer">wavesetup.rcp85.final.max.MSLR35.nc</a> - Maximum wave setup for the RCP8.5 period.</p> <p><a href="../api/records/11052226/draft/files/wam.grib.his.swh.98p.nc/content" target="_blank" rel="noopener noreferrer">wam.grib.his.swh.98p.nc</a> - 2% exceedence of significant wave height for the historical period.</p> <p><a href="../api/records/11052226/draft/files/wam.grib.rcp8.swh.98p.nc/content" target="_blank" rel="noopener noreferrer">wam.grib.rcp8.swh.98p.nc</a> - 2% exceedence of significant wave height for the RCP8.5 period.</p>
Effect of Soil Moisture on Future Heatwaves over Eastern China: Convection-Permitting Regional Climate Simulations
<p>Data used in the manuscript "Effect of Soil Moisture on Future Heatwaves Over Eastern China: Convection-Permitting Regional Climate Simulations" which will be submitted to Journal of Geophysical Research: Atmospheres.</p>
Ottawa climate data for building simulations with urban heat island effects and nature-based solutions
<p>As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Ottawa airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions.</p> <p>This dataset contains hourly historical and future weather files for use in building simulations for the city of Ottawa, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned. </p> <p>The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R. </p> <p> </p> <p>The historical and future global warming thresholds and their corresponding time periods are as follows:</p> <table> <tbody> <tr> <td> <p><strong>Global Warming Scenario</strong></p> </td> <td> <p><strong>Time Period</strong></p> </td> </tr> <tr> <td> <p><strong>Historical</strong></p> </td> <td> <p>1991-2021</p> </td> </tr> <tr> <td> <p><strong>Global Warming 0.5ºC</strong></p> </td> <td> <p>2003-2033</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.0ºC</strong></p> </td> <td> <p>2014-2044</p> </td> </tr> <tr> <td> <p><strong>Global Warming 1.5ºC</strong></p> </td> <td> <p>2024-2054</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.0ºC</strong></p> </td> <td> <p>2034-2064</p> </td> </tr> <tr> <td> <p><strong>Global Warming 2.5ºC</strong></p> </td> <td> <p>2042-2072</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.0ºC</strong></p> </td> <td> <p>2051-2081</p> </td> </tr> <tr> <td> <p><strong>Global Warming 3.5ºC</strong></p> </td> <td> <p>2064-2094</p> </td> </tr> </tbody> </table> <p> </p> <p>The following variables are included in the files:</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>RUN</strong></td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td><strong>YEAR</strong></td> <td>Year associated with the record</td> </tr> <tr> <td><strong>MONTH</strong></td> <td>Month associated with the record</td> </tr> <tr> <td><strong>DAY</strong></td> <td>Day of the month associated with the record</td> </tr> <tr> <td><strong>HOUR</strong></td> <td>Hour associated with the record</td> </tr> <tr> <td><strong>YDAY</strong></td> <td>Day of the year associated with the record</td> </tr> <tr> <td><strong>DRI_kJPerM2</strong></td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DHI_kJperM2</strong></td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>DNI_kJperM2</strong></td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the <em>HOUR</em> indicated)</td> </tr> <tr> <td><strong>GHI_kJperM2</strong></td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>TCC_Percent</strong></td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td><strong>RAIN_Mm</strong></td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td><strong>WDIR_ClockwiseDegFromNorth</strong></td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td><strong>WSP_MPerSec</strong></td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td><strong>RHUM_Percent</strong></td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td><strong>TEMP_K</strong></td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td><strong>ATMPR_Pa</strong></td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td><strong>SnowC_Yes1No0 </strong></td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td><strong>SNWD_Cm</strong></td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>
Datasets used for the paper "Enhanced Blocking Frequencies in Very-high Resolution Idealized Climate Model Simulations"
<p>Atmospheric model and processed data for reproducing the results of "Enhanced Blocking Frequencies in Very-high Resolution Idealized Climate Model Simulations" currently submitted to Geophysical Research Letters.</p>
Data from: Connections between the Southern Ocean and the Eastern tropical Pacific in unforced and forced climate model simulations
<p>The sea surface temperature (SST) over the eastern tropical Pacific significantly influences global-mean climate feedback and may be driven in part by the SST over the Southern Ocean. Previous studies demonstrated a teleconnection from the Southern Ocean to the eastern tropical Pacific by perturbing the Southern Ocean climate. We investigate if this teleconnection holds in a fully coupled, freely running climate system using CMIP6 models. We assess the relationship between the Southern Ocean (SO) and the eastern tropical Pacific (SEP) by calculating correlations between SO and SEP SST timeseries within each model and regressions between mean SO and SEP SSTs across models. We show robust, positive SO-SEP relationships in an unforced climate using pre-industrial SSTs, in a forced climate using SST anomalies between pre-industrial and quadrupled CO<sub>2</sub> simulations, and in the SST pattern of the forced response relative to the global-mean SST anomaly. The strength of SO-SEP correlations is positively related to the stratocumulus cloud feedback off the west coast of South America, and negatively related to ocean heat uptake in the same region. As both shortwave cloud feedback and ocean heat uptake are underestimated in climate models, understanding their effects on SO-SEP teleconnections and their interactions is crucial for determining the strength of SO-SEP teleconnection in the real world and its trustworthiness in climate model projection.</p>
Data for: A harmonized database of European forest simulations under climate change
<p>This repository contains the database presented in the publication "A harmonized database of European forest simulations under climate change". It contains a collection of harmonized forest simulation model outputs from 17 different models covering 1.1 million individual simulation runs, over 136 million simulation years across over 13,599 unique locations in Europe.</p> <p>Detailed description can be found in the publication (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.110384" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dib.2024.110384</a>). The file "forest_simulation_db_v1.7z" contains all raw simulation outputs and a metadata table of all simulations including information about locations and harmonized soil conditions for those locations. Simulation outputs with harmonized climate data are stored in one SQLite database per climate scenario.</p> <p>The code that was used to create the database, as well as to access and explore the data can be found here: https://github.com/magrueni/forest_simulation_database.git</p> <p>Note: Please be cautious with the use of the simulations with unique identifiers 1037-1047. There were some abrupt species compositions changes reported that suggest that in a small number of the original simulations there was an underlying issue in the compilation of the raw simulation data.</p> <p> </p> <p>--- Please use the updated version 1.1 ---</p> <p>Unfortunately we found an bug in the daily climate extraction process of the previous version, leading to inconsistencies in the harmonized climate data. We corrected the harmonized daily climate data for all scenarios. Additionally, the LAI values in the raw data of the simulations with the unique identifier 1016 were calculated wrongly and therefore corrected in this version. Please use the updated version for all analyses. We apologize for any inconveniences.</p> <p> </p> <p>--- Please use the updated version 1.1 ---</p>
Self-Healing Concrete using Encapsulated Bacterial Spores in a Simulated Hot Subtropical Climate
<p>Corresponding data set for Tran-SET Project No. 18CLSU02. Abstract of the final report is stated below for reference:</p> <p>"Bacterial concrete has become one of the most promising self-healing alternatives due to its capability to seal crack widths through microbial induced calcite precipitation (MICP). In this study, two bacterial strains were embedded at varying dosages (by weight of cement) in concrete. Beam specimens were used to identify the maximum crack-sealing efficiency, while cylinder samples were used to determine their effects on the intrinsic mechanical properties, as well as its stiffness recovery over time after inducing damage. The concrete specimens were cured in wet-dry cycles to determine their feasibility in Region 6. The results showed that the specimen groups with the highest calcium alginate concentrations (including the control specimens with embedded alginate beads but no bacteria) resulted in higher increases in stiffness recovery. Similarly, the beam samples containing alginate beads (also including the Control 3%C specimen group) had superior crack-healing efficiencies than the control samples without alginate beads (Control NC). This was attributed to the fact that the alginate beads act as a reservoir that can further enhance the autogenous healing capability of concrete. Overall, further research is recommended to verify whether the promising results reported in the literature (relating to self-healing mortar) correlate with concrete proportionally. In addition, there is a need to explore the factors that can maximize the self-healing mechanism of bio concrete through MICP, whether an alternative encapsulation mechanism, nutrient selection, curing regime, or bacterial strain is desired."</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.