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45 results for “Weather Extremes”
Supply Chain Shocks due to extreme weather events
<p>Projected supply chain shocks due to extreme weather events measured in annual percentage change in a country-sector's export activity compared to the baseline period</p>
Extreme Weather Event database over Aotearoa New Zealand
<p><strong>The Aotearoa New Zealand (ANZ) Extreme Weather Events (EWE) database </strong>(EWE_database_V1.0.0.xlsx)<strong> is a comprehensive record of extreme weather events in ANZ. The events listed in this database have been carefully assessed and categorized based on their meteorological significance, considering their rarity and whether they broke records or triggered official weather warnings. Some of the metrics used to classify each event rely on subjective judgment and expert opinions. The database captures meteorologically significant events, including those that have caused substantial damage to properties or led to casualties, and, in some cases, includes supplementary information about their socioeconomic impacts. The information in the EWE database is primarily sourced from the Meteorological Service of New Zealand Ltd (MetService) and the National Institute of Water and Atmospheric Research (NIWA). Additional impact data have been added from various media sources, with insured loss data for some events sourced from the Insurance Council of New Zealand (ICNZ).</strong></p> <p>Note - For more information about the database and the other additional files, please look into the Metadata (Metadata_EWE_V.1.0.0.docx) and the supplementary document (Supplementary document on EWE_V.1.0.0.docx).</p>
Participant Notes from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>Compilation of electronic meeting notes made by attendees at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes.</p> <p>Files are provided for Days 1-3 of the meeting. Day 4 inputs are included in Discussion notes under a separate doi.</p> <p>The Chapman Conference was supported by NSF Award AGS 1848885 and NASA grants 936723.02.01.09.14 and 936723.02.01.11.21</p>
Extreme weather events threaten biodiversity and functions of river ecosystems: dataset for conducting the meta-analysis
<p>This repository contains the code and dataset to replicate the meta-analysis conducted by Sabater et al. entitled "Extreme weather events threaten biodiversity and functions of river ecosystems: evidence from a meta-analysis"</p> <p>Metadata:</p> <p>- metaanalysis_GlobalEvidenceRivers_Rscript.R - R Script to conduct the meta-analysis</p> <p>- structural_resp.csv - table with data to perform the species richness, density, and biomass meta-analysis. It includes the mean, SD (or SE), and sample number of the studies included in the meta-analysis, as well as information on the paper authors, year of publication, and type of study (experimental or observational). It also includes co-variates and the author who subtracts the information from the paper.</p> <p>- functional_resp.csv - table with data to perform the primary productivity, respiration, and decomposition meta-analysis. It includes the mean, SD (or SE), and sample number of the studies included in the meta-analysis, as well as information on the paper authors, year of publication, and type of study (experimental or observational). It also includes co-variates and the author who subtracts the information from the paper.</p> <p>-refMap.csv - Geographical information of the papers included in the meta-analysis.</p>
Data from public granaries as a source of proxy data on grain harvests and weather extremes (Sušice region, Czech Republic)
<p><span>This deposit contains eight .xlsx files related to the study of public granary data and their relation to grain harvests and weather extremes in the Sušice region (Czech Republic). In the study, annual values of grain borrowed by serfs, their grain depositions, the total grain storage and the total debt of serfs at the end of year were used to calculation of weighted grain indices considering the balance between borrowed and returned grain: a weighted bad harvest index (WBHI), a weighted good harvest index (WGHI), a weighted stored grain index (WSGI: WSGI-, more borrowed than returned; WSGI+, more returned than borrowed) and a weighted serf debt index (WSDI: WSDI+, more borrowed than returned grain; WSDI-, more returned than borrowed grain). WBHI, WSGI- and WSDI+ were used to select years of extreme bad harvest and WGHI, WSGI+ and WSDI- years of extreme good harvest. Selected extreme harvest years were tested against documentary weather data and reconstructed temperature, precipitation and drought series of the Czech Lands.</span></p> <p><span>First six files (01a_borrowed grain_plus, 01b_returned grain_plus, 01c_stored grain_minus, 01d_stored grain_plus, 01e_serfs debt_plus, 01f_serfs debt_minus) contain three sheets representing individual cereals (rye, oats and barley). For each cereal, there are series of multiples of standard deviation used for calculation of individual indices mentioned above. These multiples were obtained from detrended series (using high-pass filter) of every grain characteristic as arithmetic mean plus/minus corresponding multiple of standard deviation.</span></p> <p><span>The file „</span> <span>02_indices“ contains three sheets with weighted grain indices: WBHI, WGHI, WSGI-, WSGI+, WSDI+, WSDI-. Sheets represent individual cereals – rye, oats and barley.</span></p> <p><span>The file „</span> <span>03_clima factors“ contains mean seasonal (DJF, MAM, JJA, and SON) temperature, precipitation and scPDSI for the period 1789–1849, expressed in deviations relative to the 1961–1990 reference period. The series are reconstructed temperatures for central Europe (Dobrovolný et al., 2010), reconstructed precipitation for the Czech Lands (Dobrovolný et al., 2015) and both of them were used for creation of scPDSI series (Brázdil et al., 2016).</span></p>
Socio-Technical Approach for the Assessment of Critical Infrastructure Systems Resiliency in Extreme Weather Events
<p>Datasets generated during and/or analyzed during the Socio-Technical Approach for the Assessment of Critical Infrastructure Systems Resiliency in Extreme Weather Events.</p>
Morphed extreme weather data for Vantaa and Sodankylä under RCP climate change scenarios by 2030, 2050 and 2080
<p>Morphed extreme weather data for 2 Finnish locations: Vantaa and Sodankylä. Created for "Near-, medium- and long-term impacts of climate change on the thermal energy consumption of buildings in Finland under RCP climate scenarios" publication (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.energy.2024.131636" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.energy.2024.131636</span></a>). Used climate change scenarios are RPC2.6, RCP4.5 and RCP8.5. Data is created for 2030, 2050 and 2080 and includes 6 extreme weather scenarios: </p> <ul> <li>W1 - Winter with high heating demand</li> <li>W2 - Winter with low heating demand</li> <li>W3 - Winter with the coldest individual day by average temperature</li> <li>S1 - Summer with the lowest cooling demand</li> <li>S2 - Summer with the highest heating demand</li> <li>S3 - Summer with the warmest individual day by average temperature</li> </ul> <p>Selected years and the procedure for their selection are described in <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.energy.2024.131636" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.energy.2024.131636</span></a>.</p> <p>Original weather data is downloaded for the selected years from Finnish Meteorological Institute's Open data repository: https://www.ilmatieteenlaitos.fi/havaintojen-lataus under CC BY 4.0 licence.</p> <p>Future change in climate is based on Finnish Meteorological Institute's data used in creating Test Reference Year weather files (<a href="https://www.ilmatieteenlaitos.fi/energialaskenta-try2020">https://www.ilmatieteenlaitos.fi/energialaskenta-try2020</a>) for which the climate change data is presented by Ruosteenoja et al. (2016).</p> <p>The data is statistically downscaled through a method called morphing created by Belcher et al. (2005) with some parts using methods from Räisänen & Räty (2013) and Jylhä et al, (2015). Morphing was computationally conducted through created software <a href="https://github.com/japulk/Weather-Morphing-Tool">https://github.com/japulk/Weather-Morphing-Tool</a> For additional information please refer to <a href="https://doi.org/10.1016/j.energy.2024.131636">original article</a> or contact the authors.</p> <p> </p>
Data for "Extreme Weather Variability on Hot Rocky Exoplanet 55 Cancri e Explained by Magma Temperature-Cloud Feedback"
<p>Data supporting "Extreme Weather Variability on Hot Rocky Exoplanet 55 Cancri e Explained by Magma Temperature-Cloud Feedback" by Loftus*, Luo*, Fan, & Kite (2025). </p> <p>* Note, these authors contributed equally.</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Kortrijk Kennedy Park, 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 Kortrijk Kennedy Park (50°48'2"N 3°16'13" 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, Antwerp Berchem, 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 Antwerp Berchem (51°12'00"N 4°26'24" 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 future climate (2069-2098, RCP 8.5), Sint-Katelijne-Waver, 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 Sint-Katelijne-Waver (51°3'25"N 4°11'24" 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, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future 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 future climate (2069-2098, RCP 8.5), 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, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future 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 future climate (2069-2098, RCP 8.5), 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 Leuven City Centre (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, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future 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 future climate (2069-2098, RCP 8.5), Leuven Casa Blanca, 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 Casa Blanca neighbourhood Leuven (50°52'48"N, 4°43'48"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, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future 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 Casa Blanca, 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 the Casa Blanca Neighbourhood Leuven (50°52'48"N 4°43'48"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, 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>
Algal growth, bumblebee colony and individual development, bee behavior and yield of oilseed rape under a trophic cascade and extreme weather
<p><span>Trophic cascades in the aquatic environment constitute important mechanisms for improving water quality. However, how the presence or non-presence of these trophic cascades may affect interactions across the aquatic-terrestrial interface remains poorly investigated. Pollinators such as bees may be especially vulnerable to changes in water resource quality induced by trophic cascades. Understanding how aquatic trophic cascades affect bees and pollination becomes even more pressing under ongoing climate change due to increased physiological demands for water under extreme weather events.</span><span>In a novel field experiment combining terrestrial and aquatic mesocosms, we aimed to test how changes in water quality induced by an aquatic trophic cascade </span><span>affected foraging and growth of bumblebee colonies as well as foraging of solitary bees. While we expected fish predation to reduce top-down control of zooplankton on phytoplankton and thereby, indirectly, induce increased growth of toxic cyanobacteria</span><span>, we instead found the trophic cascade to induce the formation of algal surface mats that bumblebees used to access water under a severe heat wave and drought. This access to water was associated with higher bumblebee colony reproductive success, growth and weight compared to control colonies with no trophic cascade induced (and hence no algal surface mats). We also found marginal </span><span>but non-significant</span><span> effects on oilseed rape yield, but surprisingly with higher yields in the control treatment where bumblebees could not access water.</span><span>Our results provide new insights on how aquatic trophic cascades can lead to unpredicted ecological interactions across the aquatic-terrestrial interface facilitated by climate change. Our study highlights the importance of water for the fitness of terrestrial ecosystem service providers under altered environmental conditions.</span></p>
Radiation Effects on Satellites during Extreme Space Weather Events (pre-publication dataset)
<p>Data for submitted paper entitled "Radiation Effects on Satellites during Extreme Space Weather Events".</p> <p>Submitted to AGU Space Weather.</p> <p> </p>
Ripple resonance amplifies economic welfare loss from weather extremes
<p>Using the loss-propagation model Acclimate (doi: 10.5281/zenodo.853345) we computed the regional and sectoral economic repercussion due to single disasters categories (heat stress, floods and tropical cyclones) as well as their consecutive disaster scenarios. The name component "en-20xx" refers to the economic network baseline. Our main analysis was based on the economic network of 2015.</p> <p>These data sets contain the annual aggregated economic quantities: production, production value, production (zero), direct loss, direct loss value, total loss, total value loss, consumption (zero), consumption value, consumption, GDP, GDP value, GDP (zero). Consumption and GDP (and corresponding variables) are regional quantities and therefore have only one non-NaN sectoral dimension (dimension zero). Quantities with a “(zero)” correspond to the annual baseline of the quantity (leap years taken into account).</p> <p>The quantities of the disaster scenarios are divided as follows:<br> Heat stress: hs_observable<br> Floods: fl_observable<br> Tropical cyclones: tc_observable<br> Consecutive disasters: cp_observable</p> <p>These variables have - next to “year”, “region”, “sector”, and “quantities” - the dimensions of the corresponding representative concentration pathway, global climate model, tropical cyclone season realization and hydrological model.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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