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82 results for “climate and heat”
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Public Transit Infrastructure and Heat Perceptions in Hot and Dry Climates (June-July, 2018; Phoenix, Arizona, USA)
Increasing the use of public transit is an important sustainability goal targeted by many cities worldwide. However, cities in hot and warming climates risk to compromise residents’ health and thermal comfort by incentivizing public transit use and, thus, subjecting them to prolonged heat exposure. This dataset contains data collected during a study on the relationships between public transit infrastructures, microclimate and heat perceptions in the hot and dry city of Phoenix, Arizona. A field campaign at six Phoenix bus stops was held between June 6 and July 27, 2018. Filed campaign consisted of surveying bus riders at bus stops and measuring microclimate variables at sun exposed and shaded locations at bus stops. Standard, advertising and art bus stop types along an arterial Phoenix road in South Mountain Village neighborhood were sampled. Standard and advertising bus stop shelters were metal with no landscaping, art stops had a larger polycarbonate canopy, integrated artwork, trees and landscaping features. Eighty-three participants filled out the survey, 241 microclimate measurements and 1003 surface temperatures at bus stops were taken. Data were collected at three intervals: 7:00-9:00am, 12:00-2:00pm, and 3:00-5:00pm. Differences between sun and shade, as well as heat perceptions were analyzed using statistical methods. The research team has found that certain infrastructure types are more effective in reducing particular microclimate variables, for instance, trees were most effective in reducing air temperature by as much as 1.3°C on average, and shade from vertical advertising sign was most effective in reducing mean radiant temperature by an average of 11°C. Many surface temperatures of sun exposed materials sampled at bus stops exceeded skin burn thresholds. Study participants perceived stops with improved infrastructure and landscaping as slightly cooler. Data collected in this study gives a glimpse of current microclimate conditions at Phoenix bus stops
2-meter Universal Thermal Climate Index (UTCI) and Human Heat Health Index (H3I) hazard for Austin, Texas
<p>Universal Thermal Climate Index (UTCI) is a physiological temperature that is widely used in biometeorological studies to assess the heat stress felt by humans. UTCI considers the shortwave and longwave radiation incident on humans from the six cubical directions as well as air temperature, humidity, wind speed and clothing. As a part of NOAA National Integrated Heat Health Information System (NIHHIS) and NASA Interdisciplinary Research in Earth Science (IDS) project, we have generated the UTCI data for Austin, Texas and surrounding peri-urban area at 2-meters spatial resolution for the year 2017. Details on data generation and methodology can be found in Kamath et al., (2023) but are summarized here. </p> <p><strong>1. Datasets and model used</strong></p> <p>The solar and longwave environmental irradiance geometry (SOLWEIG) model was used to simulate shadows, mean radiant temperature (T<sub>MRT</sub>) and the UTCI (Lindberg et al., 2008). T<sub>MRT</sub> is the equivalent temperature due to exposure to absorbed shortwave and longwave radiation from all directions in a standing position. SOLWEIG was forced using near-surface ERA-5 data available at a spatial resolution of 0.25°x 0.25°. Building, vegetation heights, and digital terrain model were again derived from 3DEP LiDAR point cloud data. SOLWEIG was run using the urban multi-scale environment predictor (UMEP) (Lindberg et al., 2018) plug-in with QGIS. </p> <p><strong>2. Data availability</strong></p> <p>Diurnal UTCI data were calculated for typical meteorological clear sky days corresponding to Summer and Fall. The typical clear sky day was selected using the 10-year Typical meteorological Year (TMY) for Austin, Texas (30.2672° N, 97.7431° W) provided by National Solar Radiation Database (NSRDB). More details on TMY files can be found at: https://nsrdb.nrel.gov/data-sets/tmy</p> <p>Additionally, data is developed for heat hazard for daytime Human Heat Health Index (H3I) calculation as defined by Kamath et al., (2023). Briefly, this heat hazard is defined as the fraction of the day when the UTCI exceeds certain threshold. The threshold used to calculate heat hazard for Summer and Fall were 35° C and 32°C, respectively that imply strong heat stress (Jendritzky et al., 2012). Note that UTCI is on a different scale compared to air temperature, and could yield different heat stress levels.</p> <p><strong>3. Data format</strong></p> <p>The georeferenced UTCI and heat hazard data are available in the geoTIFF file format. The files can be readily visualized using GIS software such as QGIS and ArcGIS, as well as programing languages such as Python.</p> <p> <strong>4. Companion dataset</strong></p> <p>Based on the calculated UTCI here, the potential locations for tree planting were calculated to increase the shade to reduce heat vulnerability for Austin, Texas. [https://doi.org/10.5281/zenodo.6363494]</p> <p><strong>References</strong></p> <ol> <li>Kamath, H. G., Martilli, A., Singh, M., Brooks, T., Lanza, K., Bixler, R. P., ... & Niyogi, D. (2023). Human heat health index (H3I) for holistic assessment of heat hazard and mitigation strategies beyond urban heat islands. Urban Climate, 52, 101675.</li> <li>Lindberg, F., Holmer, B., & Thorsson, S. (2008). SOLWEIG 1.0–Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. <em>International journal of biometeorology</em>, <em>52</em>, 697-713.</li> <li>Lindberg, F., Grimmond, C. S. B., Gabey, A., Huang, B., Kent, C. W., Sun, T., ... & Zhang, Z. (2018). Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. <em>Environmental modelling & software</em>, <em>99</em>, 70-87.</li> <li>Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI—why another thermal index?. <em>International journal of biometeorology</em>, <em>56</em>, 421-428.</li> <li>Bixler, R. P., Coudert, M., Richter, S. M., Jones, J. M., Llanes Pulido, C., Akhavan, N., ... & Niyogi, D. (2022). Reflexive co-production for urban resilience: Guiding framework and experiences from Austin, Texas. Frontiers in Sustainable Cities, 4, 1015630.</li> <li>Lanza, K., Jones, J., Acuña, F., Coudert, M., Bixler, R. P., Kamath, H., & Niyogi, D. (2023). Heat vulnerability of Latino and Black residents in a low-income community and their recommended adaptation strategies: A qualitative study. <em>Urban Climate</em>, <em>51</em>, 101656.</li> </ol>
Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models (Ocean and Climate variables from Simple Climate Models)
<p>This dataset provide global ocean and climate variables from 8 Simple Climate Models used in the study "Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models"</p>
Urban heat island: temperature climate trends in central Arizona-Phoenix: period 1948 to 2007
The question was to what degree are summer minimum temperature climate trends in the latter half of the 20th and early part of the 21st century attributed to local urban development as opposed to global climate change? The approach was to select a range of towns/cities in CA, NV, and AZ for which a pairing of sites from a town/city and a site outside that town/city was possible. Climate records for the period 1948 to 2007 were accessed, and statistical time trends determined for the urban vs. rural locations for towns/cities over a considerable range of population (i.e., from 3.5K to 3.2M). The urban heat island effect increased with the natural log of the population, ranging from a total change in minimum monthly temperatures of ca. 1.5F to over 12F over the population range of 3.5K to 3.2M. These rates of change in the 1948-2007 period overwhelm any background global climate change, with the exception of the rural sites and smaller towns. This study for the first time identified the temperature trends of a range of towns and cities in the Sonoran and Mojave deserts to unravel the impact of urban warming from that of global warming in the contemporary global warming era sometimes called the Anthropocene era. Previous literature investigatin these sites were only up to 1984 or did not address the urban warming contribution. The impact depends on land cover and extent of population development over time.
Infilled climate and heat flux data for Tvan towers data loggers (CR3000), 2008 - ongoing.
Two identical 3-meter towers were installed near T-Van in 2007, and continuous meteorological and eddy covariance data are presented beginning in 2008. The sampling interval was 5 seconds for the meteorological data and 10 Hz for the eddy covariance data, and 30-minute means of all variables were calculated using a Campbell Scientific CR3000 datalogger. The 30-minute mean data were subsequently averaged to create this 24-hour mean dataset. Information about specific sensors, instrumental orientation, units, and data post-processing and infilling procedures are contained in the metadata for this file.
Data and code for: Acute heat priming promotes short-term climate resilience of early life stages in a model sea anemone
<p>Across diverse taxa, sublethal exposure to abiotic stressors early in life can lead to benefits such as increased stress tolerance upon repeat exposure. This phenomenon, known as hormetic priming, is largely unexplored in early life stages of marine invertebrates, which are increasingly threatened by anthropogenic climate change. To investigate this phenomenon, larvae of the sea anemone and model marine invertebrate <em>Nematostella vectensis</em> were exposed to control (18°C) or elevated (24°C, 30°C, 35°C, or 39°C) temperatures for 1 hour at 3 days post-fertilization (DPF), followed by return to control temperatures (18°C). The animals were then assessed for growth, development, metabolic rates, and heat tolerance at 4, 7, and 11 DPF. Priming at intermediately elevated temperatures (24°C, 30°C, or 35°C) augmented growth and development compared to controls or priming at 39°C. Indeed, priming at 39°C hampered developmental progression, with around 40% of larvae still in the planula stage at 11 DPF, in contrast to 0% for all other groups. Total protein content, a proxy for biomass, and respiration rates were not significantly affected by priming, suggesting metabolic resilience. Heat tolerance was quantified with acute heat stress exposures, and was significantly higher for animals primed at intermediate temperatures (24°C, 30°C, or 35°C) compared to controls or those primed at 39°C at all time points. To investigate a possible molecular mechanism for observed changes in heat tolerance, the expression of heat shock protein 70 (HSP70) was quantified at 11 DPF. Expression of HSP70 significantly increased with increasing priming temperature, with the presence of a doublet band for larvae primed at 39°C, suggesting persistent negative effects of priming on protein homeostasis. Interestingly, primed larvae in a second cohort cultured to 6 weeks post-fertilization continued to display hormetic growth responses, whereas benefits for heat tolerance were lost; in contrast, negative effects of short-term exposure to extreme heat stress (39°C) persisted. These results demonstrate that some dose-dependent effects of priming waned over time while others persisted, resulting in heterogeneity in organismal performance across ontogeny following priming. Overall, these findings suggest that heat priming may augment the climate resilience of marine invertebrate early life stages via the modulation of key developmental and physiological phenotypes, while also affirming the need to limit further anthropogenic ocean warming.</p>
Datasets used for "Heat Pump - Heating Electrification and Climate Change - Grid Impact Studies"
<h2> Summary</h2> <p> </p> <p>In this work, we explore long term patterns in electricity demand driven by the dual effects of space heating electrification and climate change. We use an open source nodal power system model of the Electric Reliability Council of Texas (ERCOT) system to investigate a wide range of future climate and technology scenarios that evolve over time, and report results in terms of market prices, reliability and corresponding relative capacity requirements </p> <h2> About </h2> <p>The technical analysis aimed to:</p> <h3>1) Understand the Long-Term Patterns:</h3> <p>We aim to analyze patterns in peak load, total load, loss of load, and the seasonality of these phenomena, driven by widespread heat pump adoption alongside climate change.</p> <h3>2) Use Extensive Scenario Analysis:</h3> <p>Explore a wide range of future scenarios, including variations in climate pathways, to capture the uncertainty associated with these long-term changes. In total, 1280 simulation years.</p> <h3>3) Use a validated open source DC OPF model(reproducibility)</h3> <p>Use an open-source nodal power system model of the ERCOT system to simulate and understand the potential impacts on market prices, reliability, and relative capacity requirements. Similar models are available for all interconnections of the conterminous US.</p> <h3>4) Assess Grid Vulnerability:</h3> <p>Assess the vulnerability of the grid to these simultaneous changes, identify potential vulnerability.</p> <h3>5) Provide Insights for System Planners:</h3> <p>Offer results that can assist long-term system planners in anticipating and preparing for potential shifts in grid reliability.</p>
Urban Heat: Forward-Looking Climate Modeling for Nis, Serbia
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for <strong>present-day and future conditions</strong> under selected climate scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>). The study domain focuses on Nis, Serbia.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2021-2040, and 2041-2060</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>All indicators are available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats.</li> <li>The indicators are calculated at a resolution of <strong>150 m</strong>, consistent with the UrbClim and WBGT simulations. Additionally, downscaled versions of the indicators are provided at a resolution of <strong>30 m</strong>.</li> <li>The UrbClim and WBGT simulations, as well as the postprocessing, are conducted using the regional projection <strong>E</strong><strong>PSG 32634</strong>. The NetCDF and GeoTiff data also adopt this projection. Furthermore, a GeoTiff data file with <strong>EPSG 4326</strong> projection is included.</li> <li>All indicators are calculated as <strong>yearly averages</strong>. Some indicators also have additional calculations for <strong>seasonal averages</strong>, including Spring (MAM), Summer (JJA), Autumn (SON), and Winter (DJF).</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. Present denotes the period 2001-2020; 2030 denotes the period 2021-2040; & 2050 denotes the period 2041-2060.</li> <li>The NetCDF and GeoTiff data can be found in the data.zip; The PNG files for quick viewing can be found in quickview.zip; more information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the Technical_Annex_Nis.docx</li> </ul>
Data and code for: Acute heat priming promotes short-term climate resilience of early life stages in a model sea anemone
Open the record for dataset details and reuse information.
Data from: Assessing uncertainties and approximations in solar heating of the climate system
Open the record for dataset details and reuse information.
A climate database with varying drought-heat signatures for climate impact modelling
<p>To allow for impact modelling for a wide range of sectors, we provide six scenarios with differing drought-heat signatures. Each scenario is 100 years long and we provide temperature variables (mean, minimum, maximum), precipitation, radiation (short- and longwave downward radiation and shortwave net radiation) and wind (zonal and meridional) at daily timescales. All variables are available at a regular 1◦×1◦grid over land, except Antarctica and large parts of Greenland. Leap days were removed, so there are 365×100 time steps for each scenario.</p>
Synchronization of seasonal acclimatization and short-term heat hardening improves physiological resilience in a changing climate
<p><b>Summary</b></p> <p>1. Animal survival and species distribution in the face of global warming and increasing occurrences of heatwave largely depend on how heat tolerance shifts with plastic responses at different spatiotemporal scales, including long-term acclimation/acclimatization and short-term heat hardening. However, knowledge about the interaction of these plastic responses is still unclear.</p> <p>2. To understand how plastic responses at different timescales work together to adjust heat tolerance of organisms, we examined the effect of heat hardening on the upper thermal limits of an intertidal mudflat bivalve, the razor clam <i>Sinonovacula constricta</i>, for different seasons by using heart rate as a proxy.</p> <p>3. We observed a stronger heat hardening response of<i> S. constricta</i> in warm seasons, implying that heat hardening worked synchronously with seasonal acclimatization to increase resistance of the clams to high temperatures in warm seasons. In warm seasons, heat hardening increased heat tolerance by 2-4<sup>°</sup>C and showed a 24-h temporal dependence, suggesting an adaptation to the diel fluctuation of thermal regimes in summer.</p> <p>4. Furthermore, thermal stress resembling seasonal maximum environmental temperature induced stronger heat hardening effects, indicating that heat hardening is an essential plastic response to extreme hot weather, complementing seasonal acclimatization.</p> <p>5. Our results suggest that high temperature risk can be alleviated jointly by seasonal acclimatization and heat hardening, and emphasize the importance of considering physiological plasticity on both long-term and short-term temporal scales in evaluating and forecasting vulnerability of organisms to climate change.</p>
Data to the Supporting Information to "Radiative Heating of High-Level Clouds and its Impacts on Climate"
<p><strong>Author:</strong> Kerstin Haslehner kerstin.haslehner@univie.ac.at</p> <p>This archive includes the ICON-ESM output files used to study the radiative heating of high-level clouds.</p> <p>This dataset is related to the manuscript "Radiative Heating of High-Level Clouds and its Impacts on Climate" by Kerstin Haslehner, Blaž Gasparini and Aiko Voigt, which will be submitted to the Journal of Geophysical Research: Atmospheres.</p> <p>The output data was originally created as part of a Master's thesis by Kerstin Haslehner at the Department of Meteorology and Geophysics at University Vienna. This thesis is available here: https://utheses.univie.ac.at/detail/67015/#</p> <p> </p> <p>Descriptions of names:</p> <p>"clouds off": radiative heating of all clouds turned off</p> <p>"ice-off": radiative heating of when clouds at temperatures colder than -35°C are turned off</p> <p>"ice-off-warmbase": radiative heating of high-level clouds turned off</p> <p>"ice-on": radiative heating of all clouds active, reference run</p> <p>"diagice-cirrus": contains output variables that diagnose the radiative heating of clouds at temperatures colder than -35°C</p> <p>"hl": height levels</p>
Data to "Radiative Heating of High-Level Clouds and its Impacts on Climate"
<p><strong>Author:</strong> Kerstin Haslehner kerstin.haslehner@univie.ac.at</p> <p>This archive includes the ICON-ESM output files used to study the radiative heating of high-level clouds.</p> <p>This dataset is related to the manuscript "Radiative Heating of High-Level Clouds and its Impacts on Climate" by Kerstin Haslehner, Blaž Gasparini and Aiko Voigt, which will be submitted to the Journal of Geophysical Research: Atmospheres.</p> <p>The output data was originally created as part of a Master's thesis by Kerstin Haslehner at the Department of Meteorology and Geophysics at University Vienna. This thesis is available here: https://utheses.univie.ac.at/detail/67015/#</p> <p> </p> <p>Descriptions of names:</p> <p>"clouds off": radiative heating of all clouds turned off</p> <p>"ice-off-warmbase": radiative heating of high-level clouds turned off</p> <p>"ice-on": radiative heating of all clouds active, reference run</p> <p>"diagice-warmbase": contains output variables that diagnose the radiative heating of high-level clouds</p> <p>"diagice-cirrus": contains output variables that diagnose the radiative heating of clouds at temperatures colder than -35°C</p> <p>"ua700": zonal wind at 700hPa</p> <p>"mastrfu": mass stream function, calculated with CDO</p> <p>"hl": height levels</p> <p>"wap500hPa": omega at 500hPa</p>
NIOO-QingZ/Geertruidenberg_Mesocosms: Towards climate-robust water quality management: testing the efficacy of different eutrophication control measures during a heat
<p>Data and Codes used in the following open-access publication: </p> <p>https://www.sciencedirect.com/science/article/pii/S0048969722015145</p>
CESM1-SOM Climatologies used for "Climate Sensitivity is Sensitive to Changes in Ocean Heat Transport" (published in Journal of Climate, Mar 2022)
<p>CESM1-SOM climatologies.</p> <p>Pre-industrial control run = SOM_Control.cam5.0030-0059.ann.nc</p> <p>CO2-doubling experiments:</p> <ul> <li>OHT + 30% = SOM_OHFC_P30_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>OHT + 15% = SOM_OHFC_P15_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>Control OHT = SOM_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>OHT - 15% = SOM_OHFC_M15_2XCO2_032019.cam5.0030-0059.ann.nc</li> <li>OHT - 30% = SOM_OHFC_M30_2XCO2_032019.cam5.0030-0059.ann.nc</li> </ul>
Climate Impacts of Parameterizing Subgrid Partitioning of Land Surface Heat Fluxes to the Atmosphere with the NCAR CESM1.2
<p>The modified code as well as the CAM5 output for all the simulations in this study (V0 for the CTL run, CON1 for the EXP run, and PCON1R for EXP_COR run).</p> <p>The CESM1.2.1-CAM5.3 source code can be downloaded through the CESM official website https://www.cesm.ucar.edu/models/cesm1.2/cesm/doc/usersguide/x290.html#download_ccsm_code. Its output files are named in V0*.nc.</p> <p>The modified code for the EXP run in the study is in CON1.tar, with its CAM5 output files named in CON1*.nc.</p> <p>The modified code for the EXP_COR run in the study is in PCON1R.tar, with its CAM5 output files named in PCON1R*.nc</p>
Data for: Temperate and tropical lizards are vulnerable to climate warming due to increased water loss and heat stress
<p><span>Climate warming has imposed profound impacts on species globally. Understanding the vulnerabilities of species from different latitudinal regions to warming climates is critical for biological conservation. </span><span>Using five species of <em>Takydromus </em>lizards as a study system, we quantified physiological and life-history responses and geography range change across latitudes under climate warming. Using integrated biophysical models and hybrid species distribution models, w</span><span>e found: (1) thermal safety margin is larger at high latitudes, and is predicted to decrease under climate warming for lizards at all latitudes; (2) climate warming will speed up embryonic development and increase annual activity time of adult lizards, but will exacerbate water loss of adults across all latitudes; and (3) species across latitudes are predicted to experience habitat contraction under climate warming due to different limitations: tropical and subtropical species are vulnerable due to increased extremely high temperatures, whereas temperate species are vulnerable due to both extremely high temperatures and increased water loss. This study provides a comprehensive understanding of the vulnerability of species from different latitudinal regions to climate warming in ectotherms and also highlights the importance of integrating environmental factors, behavior, physiology, and life-history responses in predicting the risk of species to climate warming.</span></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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.