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75 results for “urban heat”
Data from: Current inequality and future potential of US urban tree cover for reducing heat-related health impacts
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Data from: Evolution of thermal tolerance and its fitness consequences: parallel and non-parallel responses to urban heat islands across three cities
The question of parallel evolution—what causes it, and how common it is—has long captured the interest of evolutionary biologists. Widespread urban development over the last century has driven rapid evolutionary responses on contemporary timescales, presenting a unique opportunity to test the predictability and parallelism of evolutionary change. Here we examine rapid urban evolution in an acorn-dwelling ant species, focusing on the urban heat island signal and the ant's tolerance of these altered urban temperature regimes. Using a common-garden experimental design with acorn ant colonies collected from urban and rural populations in three cities and reared under five temperature treatments in the laboratory, we assessed plastic and evolutionary shifts in the heat and cold tolerance of F1 offspring worker ants. In two of three cities, we found evolved losses of cold tolerance, and compression of thermal tolerance breadth. Results for heat tolerance were more complex: in one city, we found evidence of simple evolved shifts in heat tolerance in urban populations, though in another, the difference in urban and rural population heat tolerance depended on laboratory rearing temperature, and only became weakly apparent at the warmest rearing temperatures. The shifts in tolerance appeared to be adaptive, as our analysis of the fitness consequences of warming revealed that while urban populations produced more sexual reproductives under warmer laboratory rearing temperatures, rural populations produced fewer. Patterns of natural selection on thermal tolerances supported our findings of fitness tradeoffs and local adaptation across urban and rural acorn ant populations, as selection on thermal tolerance acted in opposite directions between the warmest and coldest rearing temperatures. Our study provides mixed support for parallel evolution of thermal tolerance under urban temperature rise.
Potential impacts of future warming and land use changes on intra-urban heat exposure in Houston Texas
<p>Extreme heat events in the United States are projected to become more frequent and intense as a result of climate change. We investigated the individual and combined effects of land use and warming on the spatial and temporal distribution of daily minimum temperature (T<sub>min</sub>) and daily maximum heat index (HI<sub>max</sub>) during summer in Houston, Texas. Present-day (2010) and near-future (2040) parcel-level land use scenarios were embedded within 1-km resolution land surface model (LSM) simulations. For each land use scenario, LSM simulations were conducted for climatic scenarios representative of both the present-day and near-future periods. LSM simulations assuming present-day climate but 2040 land use patterns led to spatially heterogeneous temperature changes characterized by warmer conditions over most areas, with summer average increases of up to 1.5°C (T<sub>min</sub>) and 7.3°C (HI<sub>max</sub>) in some newly developed suburban areas compared to simulations using 2010 land use patterns. LSM simulations assuming present-day land use but a 1°C temperature increase above the urban canopy (consistent with warming projections for 2040) yielded more spatially homogeneous metropolitan-wide average increases of about 1°C (T<sub>min</sub>) and 2.5°C (HI<sub>max</sub>), respectively. LSM simulations assuming both land use and warming for 2040 led to summer average increases of up to 2.5°C (T<sub>min</sub>) and 8.3°C (HI<sub>max</sub>), with the largest increases in areas projected to be converted to residential, industrial and mixed-use types. Our results suggest that urbanization and climate change may significantly increase the average number of summer days that exceed current threshold temperatures for initiating a heat advisory for metropolitan Houston, potentially increasing population exposure to extreme heat.</p>
Replication package for: "Heat and observed economic activity in the rich urban tropics"
<p>The data and code shared here pertain to an article that is forthcoming in the Economic Journal. Please consult the README file herein for details.</p>
Urban Heat Islands of Small Settlements
<p>R scripts and data associated with the manuscript "Urban Heat Islands of Small Settlements".<br><br>The .zip-folder contains two subfolders, <em>data </em>and <em>r</em>. All files stored in <em>data </em>are accessed via scripts found in <em>r</em>, thus the explanation of the structure follows only for the R scripts. </p> <p><strong>00_Municipalities</strong>: here the GIS data underlying Figure 1 is processed and general descriptions of the natural landscape of the municipalities are based on analyses in this script.. Since Figure 1 was visualized in QGIS, no script for Figure 1 itself exists.<br><strong>01_Station_Analysis: </strong>here a general analysis of the possible available rural background stations based on historical data was performed. Supplemental Figures 1 and 2 are produced in this script.<br><strong>02_Calibration: </strong>this script entails the analysis around the sensor calibration, producing Figures 3 and 4 of the manuscript. <br><strong>03_Rural_Background: </strong>here, the rural background temperatures for the investigation period are calculated. Other scripts (04_ and 05_) access this script to calculate UHI intensities. Figures 5 of the manuscript and supplemental Figure 3 are produced here.<br><strong>04_Sensor_Analysis:</strong> here, the data of the fixed sensors established in the investigated towns is compiled and analyzed. Figures 6, 7 and 8 of the manuscript and supplemental figures 4, 5 and 6 are produced here.<br><strong>05_Mobile_Transects:</strong> this entails all data and analyses around the mobile transects conducted, both the test run and the "actual" run in Forchheim. Here the GIS data underlying Figure 2 is processed and Figures 9, 11 and 12 are produced as well as supplemental figure 7. <br><br>Figures 1, 2 and 10 of the manuscript were produced in QGIS. </p>
Data from the article "Modulation of wintertime canopy Urban Heat Island (CUHI) intensity in Beijing by synoptic weather pattern in planetary boundary layer"
<p>The link includes four datasets, "pcttype" is weather typing data, "pblh" is PBLH data, "uhii-UV" is the mean value of CUHII and wind direction UV of all urban stations, and "uhii-sws" is the value of CUHII, wind speed and wind direction of all urban stations.</p>
Urban Heat: Forward-Looking Climate Modelling for West-Africa : Togolese Cities
<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 present-day and future conditions under selected climate scenarios. The study focuses on five cities in Togo: Tsevie, Dapaong, Kara, Sokode and Atakpame.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP2-4.5, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2031-2050, and 2051-2070</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>100 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 32631</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>. </li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. </li> <li>The indicators in NetCDF and GeoTiff format as well as the visualized PNG files can be found in the <strong>{city}_indicators.zip</strong>.</li> <li>Three representative locations within the study domain have been selected to retrieve the WBGT profile on a chosen date (a hot day in 2020). The results are stored in WBGT_data.xlsx and visualized as WBGT_{date}.png. The shapefile is named as selected_locations.shp. These data together with the visualization of the land use map is compressed in <strong>{city}_landuse_wbgt.zip</strong>. </li> <li>More information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the <strong>Technical_Annex_Togo.docx</strong></li> </ul>
Urban Heat: Forward-Looking Climate Modelling for West-Africa: Guinea cities
<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 present-day and future conditions under selected climate scenarios. The study focuses on two cities in Guinea: Conakry and Kankan.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP2-4.5, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2031-2050, and 2051-2070</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>100 m </strong>(Kankan) and <strong>200 m </strong>(Conakry), 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 32629 </strong>(Kankan) and <strong>EPSG 32628 </strong>(Conakry). 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>. </li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. </li> <li>The indicators in NetCDF and GeoTiff format as well as the visualized PNG files can be found in the <strong>{city}_indicators.zip</strong>.</li> <li>Three representative locations within the study domain have been selected to retrieve the WBGT profile on a chosen date (a hot day in 2020). The results are stored in WBGT_data.xlsx and visualized as WBGT_{date}.png. The shapefile is named as selected_locations.shp. These data together with the visualization of the land use map is compressed in <strong>{city}_landuse_wbgt.zip</strong>. </li> <li>More information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the <strong>Technical_description_Guinea.docx</strong></li> </ul>
Urban Heat: Forward-Looking Climate Modelling for West-Africa : Corridor Abidjan-Lagos
<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 present-day and future conditions under selected climate scenarios. The study focuses on five cities at Corridor Abidjan-Lagos: Abidjan, Cotonou, Lome, Accra and Lagos.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator for the reference period <strong>(present: 2001 to 2020</strong>). For city Lome two more future scenarios (<strong>SSP2-4.5, SSP3-7.0</strong>) and with two twenty-year periods (<strong>2031-2050, and 2051-2070</strong>) were applied.</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>100 m to 200 m</strong> (depending on the size of the city), 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. 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>. </li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. </li> <li>The indicators in NetCDF and GeoTiff format as well as the visualized PNG files can be found in the <strong>{city}_present_indicators.zip </strong>or <strong>{city}_indicators.zip </strong>(for Lome).</li> <li>Three representative locations within the study domain have been selected to retrieve the WBGT profile on a chosen date (a hot day in 2020). The results are stored in WBGT_data.xlsx and visualized as WBGT_{date}.png. The shapefile is named as selected_locations.shp. These data together with the visualization of the land use map is compressed in <strong>{city}_landuse_wbgt.zip</strong>. </li> <li>More information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the <strong>Technical_description_corridor.docx</strong></li> </ul>
Urban Heat: Forward-Looking Climate Modelling for West-Africa: extra indicator
<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 present-day and future conditions under selected climate scenarios. This dataset serves as a supplement to the previous two datasets: https://zenodo.org/doi/10.5281/zenodo.11085333 and https://zenodo.org/doi/10.5281/zenodo.11073297. It contains an extra indicator Heat Index (HI) based on the apparent temperature (AT) for 12 cities in west Africa. </p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for HI across three scenarios (<strong>present, SSP2-4.5, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2031-2050, and 2051-2070</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>The indicator is available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats. It is named as HIAT in the file name with extra information such as scenario, resolution, projection, etc. </li> <li>The indicator is calculated at a resolution from <strong>100 m </strong>to <strong>200 m </strong>(depending on the size of the city). Additionally, downscaled versions of the indicator is provided at a resolution of <strong>30 m</strong>.</li> <li>The Heat Index is calculated as the <strong>yearly average number of days when apparent temperature reaches 105 F</strong>. More information regarding the definition and calculation of HI can be found: Rohat, G., Flacke, J., Dosio, A., Dao, H., & Van Maarseveen, M. (2019). Projections of human exposure to dangerous heat in African cities under multiple socioeconomic and climate scenarios. <em>Earth's Future</em>, <em>7</em>(5), 528-546.</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format are available. </li> <li>The indicators in NetCDF and GeoTiff format as well as the visualized PNG files can be found in the <strong>{city}_HIAT.zip </strong>(for the cities with future projection) or <strong>{city}</strong><strong>_present_HIAT.zip </strong>(for those cities without future projection).</li> <li>More information about other indicators, including the simulation, methodology, all available data list, contact information, etc. can be found in the other two datasets.</li> </ul>
Montreal 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 Montreal 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 Montreal, 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>
Toronto 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 Toronto 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 Toronto, 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>
Code for [Urban food delivery services as extreme-heat adaptation]
<p>This repository contains the code to reproduce all figures in our paper "Urban food delivery services as extreme-heat adaptation".</p>
Effect of urban heat island mitigation strategies on precipitation and temperature in Montreal, Canada: case studies - Data from numerical experiments
<p>Surface data from the numerical experiments for the paper : "Effect of urban heat island mitigation strategies on precipitation and temperature in Montreal, Canada: case studies", submitted in PLOS Climate. </p> <p>The data is in NetCDF, structure as follow: 1 file per initialization date that contains the model outputs for the surface variables (air temperature, dew-point, relative humidity) and 1 file per initialization date that contains the accumulated precipitation. </p>
Urban Heat: Forward-Looking Climate Modeling for Gaza City
<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 present-day and future conditions under selected climate scenarios. The study domain focuses on Gaza City.</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>100 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 32636</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>Ten representative locations within the study domain have been selected to retrieve the WBGT profile on a chosen date (2017-07-11). The results and the locations are stored in wbgt_profile.xlsx.</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_Gaza.docx</li> </ul>
UrbAlytics - Remote Sensing tools for Urban Heat Island Assessment and Climate Change Adaptation through Nature-Based Solutions
<p>Urban Heat Island (UHI) is considered one of the significant problems posed to human beings due to the urbanization and industrialization of human civilization. The leading causes of UHI are the vast amounts of heat urban structures produce as they absorb and re-radiate solar radiation and anthropogenic heat sources. The issue mainly affects cities or metropolises with a vast population and a thriving economy. The problem will worsen significantly in the future due to the predicted three billion people living in urban areas worldwide. Due to the severity of the problem, accessing up-to-date information layers that can support city planners and decision-makers in the context of climate resilience is a demanding problem nowadays.</p> <p><strong>UrbAlytics</strong> is an experimental sub-project of the H2020-funded project <a href="https://ai4copernicus-project.eu/"><strong>AI4Copernicus</strong></a> that aims to bridge Artificial Intelligence with Earth Observations, producing information layers that can support city planners and decision-makers in the context of climate resilience and related challenges in urban areas. This research investigates, thanks to the joint expertise of the partners <a href="https://www.latitudo40.com/"><strong>Latitudo 40</strong></a> and <a href="https://www.landsrl.com/land-research-lab"><strong>LAND Research Lab®</strong></a>, the Urban Heat Island (UHI) effect, evaluating its impacts on cities, assessing Ecosystem Services provided by Blue and Green Infrastructures and proposing a set of Nature-Based Solutions (NBS) for climate adaptation and extreme heat mitigation. </p> <p><strong>The dataset</strong></p> <p>This dataset is the tool's output of a fully automated workflow realized during the project and tested for the cities of <strong>Milan</strong> and <strong>Naples</strong>, pilot users of the experiment. The choice of Milan and Naples allows for different readiness levels, data availability, and urban-climatic conditions.<br> For each city, the dataset contains the following layers for the analysis period 2018-2022.</p> <p><strong> HEATWAVE POTENTIAL RISK (HPR)</strong></p> <p>Risk Assessment mapping concerning extreme heat, considering the severity of the heat island phenomenons, the exposure of sensitive age groups and the vulnerability due to city morphology and surface materials. The risk assessment is the first step in defining a methodology that aims to assess the effectiveness of mitigation and adaptation strategies to climate extremes. It's a value in [0,1], where the higher the value higher the risk.</p> <p><strong> MICROCLIMATIC PERFORMANCE INDEX (MPI)</strong></p> <p>The role of vegetation in the city in abating the Heat Island effect has been widely demonstrated. In this context, deploying Urban Green Infrastructure is recognized as one of the most important strategies to mitigate UHI and promote a resilient city environment. The significance of the mitigation role of the Heat Island phenomenon that vegetation assumes makes it necessary to map Urban Green Infrastructure to estimate a cooling potential. Estimating the microclimatic performance of urban vegetation is crucial to plan adaptation and mitigation actions for the UHI effect. In this work, up-to-date Tree Cover Density and Land Cover maps have been produced using machine learning applied to Sentinel-2 satellite imagery. Those maps have been interpolated and combined, creating 20 Blue and Green Infrastructures classes. Each category's microclimatic performance score was attributed based on evapotranspiration potential, shading and albedo. The output is a map with integer values in [1, 20], where the lower the value higher the microclimatic performance. </p> <p><strong> PARK COOL ISLANDS (PCI)</strong></p> <p>Park Cool Islands layer identifies the most performing areas during extreme summer heatwaves, according to their size and relevant characteristics, providing reliable information to citizens and urban planners about the safest and coolest areas during extreme heatwaves. Since the green areas' type and composition can influence their cooling effects, we considered both the size and composition of urban parks to identify the most performing green areas in terms of the Park Cool Island effect. <strong> </strong>The layer distinguishes between major and minor Park Cool Islands. <em>Major PCI</em> includes areas covered by at least 50% of tree canopy coverage and bigger than 2 hectares with an estimated cooling distance of 300 m buffer<strong>. </strong><em>Minor PCI</em> includes green areas whose surface is between 1 and 2 hectares as well as those green areas bigger than 2 hectares but covered by less than 50% of tree canopy coverage, with an estimated cooling distance of 100 m buffer.</p> <p> </p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset or if you experience any issues downloading files, please contact us <a href="mailto:giovanni.giacco@latitudo40.com">giovanni.giacco@latitudo40.com</a>, <a href="mailto:giulia.castellazzi@landsrl.com">giulia.castellazzi@landsrl.com</a></p>
Prioritizing equity in urban heat reduction: Beyond cooling as a metric
<p>Archived simulations for five heat wave events happened over Houston from 2017 - 2019 with different urban overheating mitigation strategies. </p>
Data from: Evolution of thermal tolerance and its fitness consequences: parallel and non-parallel responses to urban heat islands across three cities
Open the record for dataset details and reuse information.
Data from: Snail shell colour evolution in urban heat islands detected via citizen science
Open the record for dataset details and reuse information.
Data from: The Urban Heat Island and its spatial scale dependent impact on survival and development in butterflies of different thermal sensitivity
Climate alteration is one of the most cited ecological consequences of urbanization. However, the magnitude of this impact is likely to vary with spatial scale. We investigated how this alteration affects the biological fitness of insects, which are especially sensitive to ambient conditions and well-suited organisms to study urbanization-related changes in phenotypic traits. We monitored temperature and relative air humidity in wooded sites characterized by different levels of urbanization in the surroundings. Using a split-brood design experiment, we investigated the effect of urbanization at the local (i.e., 200 × 200 m) and landscape (i.e., 3 × 3 km) scale on two key traits of biological fitness in two closely related butterfly species that differ in thermal sensitivity. In line with the Urban Heat Island concept, urbanization led to a 1°C increase in daytime temperature and an 8% decrease in daytime relative humidity at the local scale. The thermophilous species Lasiommata megera responded at the local scale: larval survival increased twofold in urban compared to rural sites. Urbanized sites tended to produce bigger adults, although this was the case for males only. In the woodland species Pararge aegeria, which has recently expanded its ecological niche, we did not observe such a response, neither at the local, nor at the landscape scale. These results demonstrate interspecific differences in urbanization-related phenotypic plasticity and larval survival. We discuss larval pre-adaptations in species of different ecological profiles to urban conditions. Our results also highlight the significance of considering fine-grained spatial scales in urban ecology.
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.