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45 results for “Weather Extremes”
Past and future weather extremes across Europe
<pre><strong>Past and future weather extremes across Europe </strong> This repository contains the annual exceedance index data for past and future weather extremes across Europe on NUTS1 scale. The code and an accompanying paper analyzing the impact of this weather extremes on the European agricultural sector on subnational scale will be published during 2023. We use a percentile-based approach to assess the annual exceedance index of the four weather extremes heat waves, cold waves, fire-risk and droughts for the past (1981–2020) and future (2006–2100) [<em><strong>Zhang</strong></em> et al., 2005]. For the past, we used daily weather records on a grid level (around 11 km at the equator) from the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> reanalysis dataset, and for future projections, we use modelled daily weather records from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> [<em><strong>Christensen</strong></em> et al., 2020, <em><strong>Muñoz</strong></em>, 2019]. For past and future fire-risk we use precalculated fire weathernindex data from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview">ERA5</a> and <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-tourism-fire-danger-indicators?tab=overview">EURO-CORDEX</a>, respectively [<em><strong>Giannakopoulos</strong></em> et al., 2020]. We used the model average of the following driving GCMs and RCMs for future projections: ICHECs Earth System Model (EC-Earth), MPI-Ms Earth System Model (MPI-ESM-LR), SMHIs Regional Climate Model (RCA4). The baseline period for the historical scenario is 1981–2010, and for future projections 1981–2005. Daily thresholds for heat waves, cold waves, and flash droughts are estimated from the 90th percentile of the daily minimum and maximum temperature, 10th percentile of the daily minimum and maximum temperature, and 30th percentile of the soil volumetric water content (0–28cm), respectively [**Sutanto** et al., 2020]. We use a five days centre data window for all three extreme events to estimate the thresholds from the previously listed baseline periods. The annual exceedance index for heat waves is calculated as the sum of days, at least for three consecutive days; the daily temperature values exceed the thresholds for June, July, and August. For cold waves, the annual exceedance index is the sum of days, at least for three consecutive days; the daily temperature values are below the thresholds for January, February, October, November, and December. In-base, exceedance is calculated using bootstrapping (1000x repetitions) for both extreme events. Heat and cold wave exceedance indices are rescaled to NUTS1 regions using a maximum resampling. We use sequent peak analysis to detect annual flash droughts, remove minor droughts, and pool interdependent droughts for the season from June to October [**Biggs** et al., 2004]. The annual exceedance index of droughts is rescaled to NUTS1 regions by using a mean resampling. Parameters for fire-risk are listed in the table below while. </pre> <table> <caption>Parameters of the analysis of the percentile-based extreme.</caption> <thead> <tr> <th scope="col">Type</th> <th scope="col">Variable</th> <th scope="col">Percentile</th> <th scope="col">Window</th> <th scope="col">Min duration</th> <th scope="col">Rescaling</th> <th scope="col">Months</th> <th scope="col">Bootstrapping</th> </tr> </thead> <tbody> <tr> <td>Heat wave</td> <td>tmin and tmax</td> <td>90</td> <td>5</td> <td>3</td> <td>max</td> <td>6, 7, 8</td> <td>yes</td> </tr> <tr> <td>Cold wave</td> <td>tmin and tmax</td> <td>10</td> <td>5</td> <td>3</td> <td>max</td> <td>1, 2, 10, 11, 12</td> <td>yes</td> </tr> <tr> <td>Flash drought</td> <td>swvl 0-28cm</td> <td>30</td> <td>5</td> <td>5</td> <td>mean</td> <td>6, 7, 8, 9, 10</td> <td>no</td> </tr> <tr> <td>Fire risk</td> <td>FWI</td> <td>90</td> <td>5</td> <td>1</td> <td>mean</td> <td>3, 4, 5, 6, 7, 8, 9</td> <td>yes</td> </tr> </tbody> </table> <pre>Xuebin <em><strong>Zhang</strong></em>, Gabriele Hegerl, Francis W. Zwiers, and Jesse Kenyon. Avoiding inhomogeneity in percentile-based indices of temperature extremes. Journal of Climate, 18 (11):1641–1651, 2005. ISSN 08948755. doi: 10.1175/JCLI3366.1. Samuel Jonson <em><strong>Sutanto</strong></em>, Claudia Vitolo, Claudia Di Napoli, Mirko D’Andrea, and Henny A.J. Van Lanen. Heatwaves, droughts, and fires: Exploring compound and cascading dry hazards at the pan-European scale. Environment International, 134 (March 2019):105276, jan 2020. ISSN 01604120. doi: 10.1016/j.envint.2019.105276. J. Sabater <em><strong>Muñoz</strong></em>. ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2019. O. B. <em><strong>Christensen</strong></em>, W. J. Gutowski, G. Nikulin, and S. Legutke. CORDEX Archive Design, 2020. URL https://is-enes-data.github.io/cordex_archive_specifications.pdf Barry J. F. <em><strong>Biggs</strong></em>, Bente Clausen, Siegfried Demuth, Miriam Fendeková, Lars Gottschalk, Alan Gustard, Hege Hisdal, Matthew G. R. Holmes, Ian G. Jowett, Ladislav Kašpárek, Artur Kasprzyk, Elzbieta Kupczyk, Henny A.J. Van Lanen, Henrik Madsen, Terry J. Marsh, Bjarne Moeslund, Oldřich Novický, Elisabeth Peters, Wojciech Pokojski, Erik P. Querner, Gwyn Rees, Lars Roald, Kerstin Stahl, Lena M. Tallaksen, and Andrew R. Young. Hydrological Drought: Processes and Estimation Methods for Stream- flow and Groundwater. Elsevier, 1 edition, 2004. ISBN 0444517677. <em><strong>Giannakopoulos</strong></em>, C., Karali, A., Cauchy, A. (2020): Fire danger indicators for Europe from 1970 to 2098 derived from climate projections, version 1.0, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.ca755de7 <strong>Funding</strong> Tobias Seydewitz acknowledges funding from the German Federal Ministry of Education and Research for the [BIOCLIMAPATHS](https://www.pik-potsdam.de/en/output/projects/all/647) project (grant agreement No 01LS1906A) under the Axis-ERANET call. The funders had no role in study design, data collection, analysis, decision to publish, or manuscript preparation. </pre>
Supplementary figures for 'Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall.'
<p>Supplementary dynamics and skill plots for the paper 'Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall', submitted to QJRMS.</p>
Algal growth, bumblebee colony and individual development, bee behavior and yield of oilseed rape under a trophic cascade and extreme weather
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Data from: Weather extremes in the Mediterranean winter are associated with reduced apparent survival and delayed initiation of egg-laying in a migratory raptor
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Discussion Survey from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>These files include summaries of pre-meeting survey of important topics to discuss at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes</p> <p>The Chapman Conference was supported by</p> <p>NASA Grants: 936723.02.01.09.14 and 936723.02.01.11.21 and by NSF Award AGS 1848885</p> <p> </p>
Questions for Presenters at the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>This file lists the questions asked of Chapman Conference presenters by the Chapman Conference attendees.</p> <p>-Questions, along with presenter names and presentation titles are included</p> <p>-The document contains</p> <p> 1) an explanation sheet </p> <p> 2) a sheet with all questions sorted by day and presenter</p> <p> 3) a sheet with questions that were asked in a general setting.</p> <p>The materials have been lightly edited to spell out acronyms, correct spelling and clarify non specific references when possible.</p> <p>The Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes was supported by NASA Grants 936723.02.01.09.14 and 936723.02.01.11.21 and by NSF Award AGS 1848885</p> <p> </p>
Priorities Survey from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>Mid-meeting survey results on Priorities from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes</p> <p>2 Files are provided: An Excel Spreadsheet and a summary pdf of highest priorities.</p> <p>Statistics compiled by Tomoko Matsuo</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>
Discussion Notes from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>Discussion notes from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes</p> <p>Notes from Days 1, 3 and 4 are provided. Conference Day 2 was a 'Poster Day'</p> <p>These notes were aggregated from meeting scribes and conveners</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>
Table of Contents for Meeting Artifacts from Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes
<p>Table of Contents of Meeting Artifacts and Output from the Chapman Conference on Scientific Challenges Pertaining to Space Weather Forecasting Including Extremes, 11-15 February 2019, Pasadena, CA, USA</p> <p>Each entry in the table of contents provides a short description and/or artifact title, along with the number of associated files and a weblink showing the associated DOI or permanent URL.</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>
Data from: Transgenerational effects of extreme weather: perennial plant offspring show modified germination, growth and stoichiometry
1) Climate change is predicted to increase the frequency and magnitude of extreme climatic events. These changes will directly affect plant individuals and populations and thus modify plant community composition. Little is known, however, about transgenerational effects (i.e. the influence of the parental environment on offspring phenotype and performance beyond the effects of transmitted genes) of climate extremes and community composition. Perennial plants have been particularly neglected. This impedes projections on species adaptations and population dynamics under climate change. 2) Maternal plants of two widespread dwarf-shrub species (Genista tinctoria and Calluna vulgaris) recurrently experienced extreme weather event manipulations each year (drought and heavy rain). To test for transgenerational effects of community composition, C. vulgaris maternal plants were grown in communities differing in the number of neighbouring species. After six years, seeds of maternal plants were collected at least 2 month after the final weather treatments. We assessed transgenerational effects of the extreme events and of altered community composition on germination and monitored the development of offspring over two years. 3) We show that extreme events experienced by maternal plants influence offspring germination and growth beyond the seedling stage. Seeds produced by maternal plants experiencing stress, indicated by increased tissue die-back, germinated earlier in both observed species. We observed differences in leaf stoichiometry and growth rates for G. tinctoria offspring throughout the first year: Offspring from heavy rain-treated mothers showed reduced leaf C:N ratio and higher growth rates. Results further indicate that not only community density, as investigated in prior studies, but also community composition trigger transgenerational effects. 4) Synthesis: Our findings show that variation in the maternal environment not only affects number, but also performance of offspring. Extreme climatic events, terminated before seed set, induce transgenerational effects. Species richness of mother communities can affect the stress level of target species and thereby germination regardless of community density. In contrast to prior studies, which revealed direct effects of chronic stress on plant individuals, this study emphasizes the importance of addressing transgenerational effects of extreme weather events when projecting future ecological responses and adaptation to climate change.
Extreme Weather Event Real-time Attribution Machine (EWERAM) forecasts for Cyclone Gabrielle
<p>These files contain the hourly precipitation, wind, humidity, and pressure data as well as the land-sea mask, orography, and regional council data supporting the investigation into the human role in Cyclone Gabrielle performed by the EWERAM (Extreme Weather Event Real-time Attribution Machine) consortium. The EWERAM experiment design is outline by Tradowsky and co-authors (2023, 10.1088/2752-5295/acf4b4).</p> <p>Directory and data format follows the conventions of the Climate of the 20th Century Plus Detection and Attribution (C20C+ D&A) Project. Details are provided at https://portal.nersc.gov/c20c/experiment.html and in Stone and co-authors (2019, 10.1016/j.wace.2019.100206).</p>
Code and data for: Ladder fuels, not canopy volumes, consistently associated with forest wildfire severity even in extreme topographic-weather conditions
<p>Code and associated metrics for Hakkenberg et.al. 2024. Ladder fuels rather than canopy volumes consistently predict wildfire severity even in extreme topographic-weather conditions</p> <p>Contact chrishakkenberg@gmail.com for all questions and requests</p>
Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction
<p>This dataset contains the spatially-explicit gridded database based on social media data for over 150 different tourism-related classes that depicts tourism density (supply and demand) and perceived satisfaction in Europe, and the related exposure to selected climate extreme events. Information on tourism density (supply and demand) and perceived satisfaction are categorised for Attractions, Culinary, and Hospitality, while the exposure analysis of those clases are provided in separate, specific files. The provided dataset is made accessible to support large-scale and regional tourism research and extends its relevance to other fields that are part of tourism as a complex system, such as risk assessment and vulnerability studies. For citing this work, please refer to the research article "Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction", DOI 10.1088/1748-9326/ad3e91. Suggested citation: "Camatti, N., Hrast Essenfelder, A., & Giove, S. (2024). Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction. Environmental Research Letters."</p>
Data for "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: A case study on the Italian grid"
<p>Data employed for the paper "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: A case study on the Italian grid"<br><br>Abstract<br>This study explores compounding impacts of climate change on power system's load and generation, emphasising the need to integrate adaptation and mitigation strategies into investment planning. We combine existing and novel empirical evidence to model impacts on: i) air-conditioning demand; ii) thermal power outages; iii) hydro-power generation shortages. Using a power dispatch and capacity expansion model, we analyse the Italian power system's response to these climate impacts in 2030, integrating mitigation targets and optimising for cost-efficiency at an hourly resolution. We outline different meteorological scenarios to explore the impacts of both average climatic changes and the intensification of extreme weather events. We find that addressing extreme weather in power system planning will require an extra 5-8 GW of photovoltaic (PV) capacity, on top of the 50 GW of the additional solar PV capacity required by the mitigation target alone. Despite the higher initial investments, we find that the adoption of renewable technologies, especially PV, alleviates the power system's vulnerability to climate change and extreme weather events. In fact, renewable energy sources are generally less vulnerable to the impacts of climate change, such as rising temperatures and shifting precipitation patterns, compared to thermal power and hydropower generation. Furthermore, enhancing short-term storage with lithium-ion batteries is crucial to counterbalance the reduced availability of dispatchable hydro generation.</p>
Ecological resilience and resistance to extreme weather events - review data
<p>Extreme weather events (EWEs) are expected to increase in stochasticity, frequency, and intensity due to climate change. Documented effects of EWEs, such as droughts, hurricanes, and temperature extremes, range from shifting community stable states to species extirpations. To date, little attention has been paid to how populations resist and/or recover from EWEs through compensatory (behavioural, demographic or physiological) mechanisms; limiting the capacity to predict species responses to future changes in EWEs. Here, we systematically reviewed the global variation in species' demographic responses, resistance to, and recovery from EWEs across weather types, species, and biogeographic regions. Through a literature review and meta-analysis, we <span>tested the prediction that population abundance and probability of persistence will decrease in populations after an EWE and how compensation affects that probability. Across 524 species population responses to EWEs reviewed (27 articles), we noted large variation in responses, such that, on average, the effect of EWEs on population demographics was not negative as predicted. The majority of species populations (80.4%) demonstrated compensatory mechanisms during events to reduce their deleterious effects. However, for populations that were negatively impacted, the demographic consequences were severe. Nearly 20% of the populations monitored experienced declines of over 50% after an EWE</span>, and 6.8% of populations were extirpated. Population declines were reflected in a <span>reduction in survival. Further, resilience was not common, as 80.0% of populations that declined did not recover to before EWE levels while monitored. </span>However, average monitoring time was only two years with over a quarter of studies tracking recovery for less than the study species generation time. We conclude that EWEs have positive and negative impacts on species demography, and this varies by taxa. Species population recovery over short time intervals is rare, but long-term studies are required to accurately assess species resilience to current and future events.</p>
Data from: Transgenerational effects of extreme weather: perennial plant offspring show modified germination, growth and stoichiometry
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Data from: Interaction between extreme weather events and mega‐dams increases tree mortality and alters functional status of Amazonian forests
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Ecological resilience and resistance to extreme weather events - review data
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Data from: Effects of extreme weather on two sympatric Australian passerine bird species
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Extreme weather affects colonization-extinction dynamics and the persistence of a threatened butterfly
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