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12 results for “compound events”
Compound hot and dry and wet and windy events in CMIP6 models
<p>NetCDF files containing maps of return periods (in years) for the joint occurrence of</p> <ol> <li>strong surface winds (sfcWind) and heavy rain (pr) and</li> <li>heatwaves (EHF) and drought (SPI) </li> </ol> <p>as realised by models participating in the Coupled Model Intercomparison Project Round 6 (CMIP6). Included is output from models that provided daily data for sfcWind, pr, tmax and tmin (to calculate EHF) and experiments historical, SSP126, SSP245, and SSP585 for ensemble member r1i1p1f1. The base period for the determination of hazard thresholds was 1980 – 2014 for all experiments. Time periods over which return periods were calculated were 1980 – 2014 for the 'historical' experiment and 2066 – 2100 for experiments 'SSP126', 'SSP245', and 'SSP585'.</p> <p>Values for return periods are determined following the method in Ridder et al. (2020a) doi: 10.1038/s41467-020-19639-3; Ridder et al. (2020b) doi: 10.1029/2020GL091152 and Ridder et al. (2021) doi: 10.1038/s41612-021-00224-4. </p> <p>Name convention:</p> <ul> <li> historical experiments: <br> <em>map_RP_${hazardX}_${hazardY}_${CMIP6model}_historical_r1i1p1f1_${model_grid}_19800101_20141231.nc</em></li> <li>ScenarioMIPs:<br> map_RP_<em>${hazardX}_${hazardY}_${CMIP6model}</em>_historic_threshold_${experiment}_r1i1p1f1_2066-2100.nc</li> </ul>
Elevated increase in compound extreme heat-precipitation events over China
<p>This file contains the fractions (in percentage) of the compound extreme precipitation events that are preceded by an extreme heat event in China during 1961-2017. The compound events are identified based on the CN05.1 dataset at 0.5x0.5 resolution. Please contact us with any questions or concerns (email: luo.ming@hotmail.com).</p>
Data and code for "Extreme and compound ocean events are key drivers of projected low pelagic fish biomass"
<p>This repository provides the data and code for the paper "Extreme and compound ocean events are key drivers of projected low pelagic fish biomass". Almost all data required to produce the figures in this study are provided. However, not all raw data are provided, because of too large file sizes. For more information, please contact natacha.legrix@unibe.ch</p> <p>In Version 2, an error has been corrected in the computation of the grid cell area, which significantly affected values in Fig. A1.</p>
Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events
<p>Data, code and supplementary Figures for paper "Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events".</p>
Drought-heatwave compound events are stronger in drylands
<p>This data is supplementary data or code for the research article "Weather and Climate Extremes". The file name Global_CDHWs is the specific data of the six indicators of the global composite event calculated from 1961 to 2020, the granger_test file is the result data of the Granger causality test, and the lag_granger file is the lag result of the Granger causality test.</p> <p>Note: (Tsum, Tmax, Toccr, Tmean, AH, TAH,) correspond sequentially to (HDF, DHD, DHO, DHM, DHC, DHA). Inside variables correspond one to one.</p> <p>The Global CDHWs folder includes dry-heatwave compound events (state3) and wet heatwave compound events (state4), and each of the two folders contains six variables with annual and monthly scales, where the next level folder contains monthly scale data.</p>
Datasets and R code associated with: Acute Health Effects of Wildfire Smoke Exposure During a Compound Event: A Case-Crossover Study of the 2016 Great Smoky Mountain Wildfires
<p>The attached code and csv files accompany the manuscript titled: Acute Health Effects of Wildfire Smoke Exposure During a Compound Event: A Case-Crossover Study of the 2016 Great Smoky Mountain Wildfires by Duncan et al. accepted for publication in the journal GeoHealth in September 2023. </p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Project%20wildfire%20data.csv">Project wildfire data.csv</a> contains a subset wildfire data obtained from:</p> <p>Short, Karen C. 2022. Spatial wildfire occurrence data for the United States, 1992-2020 [FPA_FOD_20221014]. 6th Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2013-0009.6</p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Modeled%20PM2.5%20data_clean.csv">Modeled PM2.5 data_clean.csv</a> includes modeled PM<sub>2.5</sub> concentrations used to make Figure 2. Model results can be obtained from <a href="https://www.epa.gov/hesc/rsig-related-downloadable-data-files">EPA's Fused Air Quality Surfaces Using Downscaling Tool</a>. <a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/plot_Modeled.R">plot_Modeled.R</a> contains the R code to generate this figure. </p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/ORs%20all%20Counties.csv">ORs all Counties.csv</a> contains the odds ratios, confidence intervals, and p-values used to make Figures 3, 4, and 5.<a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/PM2.5%2035ug-m3%20ORs.csv">PM2.5 35ug-m3 ORs.csv</a> contains the odds ratios, confidence intervals, and p-values used to make Figure S1. <a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Wildfire_Forest.R">Wildfire_Forest.R</a> contains the R code to generate these figures. </p>
Data from : "The massive 2016 marine heatwave in the Southwest Pacific: an "El Niño - Madden-Julian Oscillation" compound event"
<p>File : PD-JRA-v1-c6_TMLtrend_1993-2022.nc</p> <p>Daily mixed layer temperature budget between 1993 and 2022 from a NEMO simulation.</p> <p>Data were extracted over the Southwest Pacific region [142.5°E-222.5°E;45°S;1°N].</p> <p>These data were used in Dutheil et al., (2024) The massive 2016 marine heatwave in the Southwest Pacific: an “El Niño - Madden-Julian Oscillation” compound event</p>
Data for Organosulfur Compounds: An Non-negligible Component Affecting the Light Absorption of Brown Carbon during North China Haze Events
<p>This is the data used in manuscript of "Organosulfur Compounds: An Non-negligible Component Affecting the Light Absorption of Brown Carbon during North China Haze Events"</p>
Compound coastal, fluvial, and pluvial flooding during historical hurricane events in the Sabine-Neches Estuary, Texas
<p>This dataset contains boundary forcing (offshore water level and river discharge), topobathy, and Manning's n roughness data used to simulate compound flooding due to historical hurricanes (Harvey, Ike, and Rita) in the Sabine-Neches Estuary in Texas. It also includes water level outputs used for the analysis presented in the manuscript entitled: <em>Compound coastal, fluvial, and pluvial flooding during historical hurricane events in the Sabine-Neches Estuary, Texas </em>(<a href="https://doi.org/10.1029/2022WR033144">https://doi.org/10.1029/2022WR033144</a>).</p>
Identifying non-synergestic effect of temporal variations of margin and dependence between extremes on the projected risk of compound dry-hot events in Yellow River, China
<p>Site-based daily precipitation and temperature data in China</p>
Compound Drought and Heatwave (CDHW) Event Indicators – Adige River Catchment, 1950–2023
<table> <tbody> <tr> <td> <p><strong>Science Case Name </strong></p> </td> <td> <p>Hot and dry compound events in the Adige River Catchment (Eastern Italian Alps)</p> </td> </tr> <tr> <td> <p><strong>Dataset Title </strong></p> </td> <td> <p>Compound Drought and Heatwave (CDHW) event indicators for the Adige River Catchment (1950-2023)</p> </td> </tr> <tr> <td> <p><strong>Dataset Description </strong></p> </td> <td> <p>Occurrence and severity gridded fields of CDHW events over the Adige River Catchment from 1950 to 2023. The dataset includes the event list with duration, extent, total severity, magnitude and ranking of each identified event.</p> </td> </tr> <tr> <td> <p><strong>Key Methodologies</strong></p> </td> <td> <p>A CDHW event is determined by the co-occurrence of drought and heatwave conditions over at least 60% of the Adige River Catchment. Drought and heatwave indicators were derived using daily temperature and precipitation data from the E-OBS gridded dataset.</p> <p>A drought period is identified as a sequence of consecutive months with a negative Standardized Precipitation Index (SPI), starting with the first month where the SPI-6 (6-month timescale) falls below -1. Heatwaves are defined as periods of at least three consecutive days where the daily maximum temperature (TX) exceeds the 90th percentile for that specific calendar day, determined using a 31-day running mean centred on the day under evaluation and considering all values from 1950 to 2023. When two or more periods of consecutive exceedances are separated by one day with TX below the threshold, they are considered as a single heatwave occurrence and the day below the threshold is included in the event duration. </p> <p>In the occurrence grids, for each day in a CDHW event, grid cells where both drought and heatwave conditions are detected are flagged as "1". If the compound condition is not met, the cell is flagged as "0". In the severity fields, the severity (dimensionless) for each day in a CDHW event is calculated as the product of the standardized daily TX over the days of the event and the absolute value of SPI-6 in the corresponding month. The calculation of the severity is similar to the one proposed by Mukherjee and Mishra (2021), but with the percentiles used in the standardization of TX varying with the day of the year. </p> <p>The list of CDHW events affecting the Adige River Catchment over 1950-2023 includes the start and end dates, the percentage of the area affected, the total severity (dimensionless), magnitude (dimensionless) and ranking of the event. The total severity of the event is defined as the average of the CDHW severities of all grid cells in the catchment experiencing the CDHW conditions. The total severity of the CDHW event at each grid cell is calculated as the sum of the daily severity values over days flagged as "1". The magnitude is the product of the total severity and the fraction of area affected. CDHW events are ranked based on their magnitude.</p> </td> </tr> <tr> <td> <p><strong>Temporal Domain</strong></p> </td> <td> <p>1950-2023</p> </td> </tr> <tr> <td> <p><strong>Spatial Domain</strong></p> </td> <td> <p>Extended region centred on the Adige River Catchment (7.10°-15.30°E, 44.10°-49.10°N); Spatial resolution: 0.1°x0.1° (EPGS:4326)</p> </td> </tr> <tr> <td> <p><strong>Key Indicators </strong></p> </td> <td> <p>CDHW occurrence and severity (grid) and CDHW duration, extent, total severity and magnitude (list)</p> </td> </tr> <tr> <td> <p><strong>Data Format</strong></p> </td> <td> <p>netCDF and CSV</p> </td> </tr> <tr> <td> <p><strong>Source Data</strong></p> </td> <td> <p>E-OBS dataset (v29.0e)</p> </td> </tr> <tr> <td> <p><strong>Accessibility</strong></p> </td> <td> <p>Zenodo, https://doi.org/10.5281/zenodo.13839122 </p> </td> </tr> <tr> <td> <p><strong>Stakeholder Relevance</strong></p> </td> <td> <p>The list of hot and dry events for the Adige River Catchment can support regional water managers to identify the most critical meteorological conditions over the past decades and link them to the observed local impacts. The details about event severity, magnitude and spatial and temporal extent provided in the list can be used directly to characterize the impactful hot and dry episodes. Moreover, the gridded fields offer a consistent description of the phenomena throughout the Adige River Catchment and can be used to localize the affected areas and identify the main spatial patterns of hot and dry conditions. </p> </td> </tr> <tr> <td> <p><strong>Limitations/Assumptions</strong></p> </td> <td> <p>Results accuracy can be influenced by potential biases in the original data source over the study area. The 0.1-grid of E-OBS limits a detailed representation of local conditions and spatial patterns, while the continuous temporal coverage enables historical event detection and analysis. Thresholds used in the proposed definitions are based on expert knowledge and other choices are also possible.</p> </td> </tr> <tr> <td> <p><strong>Contact Information</strong></p> </td> <td> <p>Elena Maines (Center for Climate Change and Transformation - Eurac Research) (data curator)</p> <p>Alice Crespi (Center for Climate Change and Transformation - Eurac Research) (data curator)</p> <p>Marc Lemus-Canovas (Center for Climate Change and Transformation - Eurac Research; Universidade de Santiago de Compostela) (data curator)</p> </td> </tr> </tbody> </table>
Data to: Downscaling and uncertainty analysis of future compound long-duration dry and hot events in China
<p>This dataset contains the data to generate the results in the manuscript "Downscaling and uncertainty analysis of future compound long-duration dry and hot events in China". The downscaled results (BCSD, BCCI, BCCAQ, and CDF-t) are created by R.</p>
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