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1,826 results for “burn”
Data for the journal article "Brown Carbon from Biomass Burning Imposes Strong Circum-Arctic Warming"
<p>The data for the 3 figures in the journal article "Brown Carbon from Biomass Burning Imposes Strong Circum-Arctic Warming"</p>
County-level of particle and gases emission inventory for animal dung burning in the Qinghai–Tibetan Plateau, China
<p>County-level activity data and emission factors</p>
Reduced predation by arthropods and higher herbivory in burned Amazonian forests
<p>Biodiversity losses have increased in tropical forests due to fire-related disturbances. As landscape fragmentation and climate change increase, fires will become more frequent and widespread across tropical rain forests worldwide, with important implications for forest dynamics by altering plant-animal interactions. Here we tested the hypothesis that recurrent fires in tropical rain forests change bottom-up and top-down forces controlling the abundance of insect herbivores, which in turn increases herbivory. To quantify herbivory, we collected 50 leaves per tree of five species in burned and unburned experimental plots (N = 75) in southeastern Amazonian forests. We measured leaf nitrogen content and leaf thickness of tree leaves as bottom-up factors that could explain differences in herbivory; we measured predation pressure on model caterpillars and estimated the abundance of predatory ants as top-down factors. We found higher herbivory in burned than in unburned forests, as well as lower predator attacks in caterpillar models and lower abundance of predatory ants. Leaf nitrogen content did not vary across treatments. Birds attacked model caterpillars more frequently in burned than in unburned forests, and leaf thickness was higher in burned forests, but these factors together were not enough to offset the higher herbivory in burned plots. Fire degrades tropical forests not only by killing trees and altering their structure and community dynamics, but also by reducing predatory arthropods and disrupting predator-prey interactions, which triggers increased herbivory. These indirect impacts of recurrent fires probably contribute to further alter forest structure, functioning, and to decrease forest regeneration in Amazonian forests.</p>
Field data synthesis accompanying "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>Synthesis of fuel load and fuel consumption field measurements accompanying the publication:</p><p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p><p>Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton5</p><p>1Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br>2Department of Earth System Science, University of California, Irvine, CA 92697, USA<br>3Woodwell Climate Research Center, Falmouth, MA 02540, USA<br>4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br>5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p><p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p><p> </p><p>Units are g C / m2</p>
Savanna plant and soil carbon data from different burn seasons and histories across Mole National Park, Ghana
<p>Aboveground plant pool and belowground (soil plus root) carbon data for a space-for-time substitution survey of different burn seasons and histories across Mole National Park, Ghana. Carbon data was collected to determine the impact of unintentional late growing season wildfires on carbon storage in a protected area dominated by early growing season prescribed burning land management. The methodology and findings from the study are detailed in the below publication:</p> <p>Awuah J, Smith SW, Speed JDM, Graae BJ. 2022. Can seasonal fire management reduce the risk of carbon loss from wildfires in a protected Guinea savanna? Ecosphere, e4283. https://doi. 88 org/10.1002/ecs2.4283 </p> <p>This data repository contains the following data (and descriptive metadata): </p> <p>(1) Study_site_coordinates: locations for 28 sites surveyed in 2016 as part of an ecosystem carbon stock assessment</p> <p>(2) Aboveground_carbon: aboveground tree, shrub, herbaceous vegetation, deadwood and litter carbon stocks estimated from either destructive biomass sampling or allometric equations. Aboveground carbon data are presented per site. </p> <p>(3) LOI_to_carbon_conversion: a subset of soil samples were analysed for both loss on ignition (LOI) and automated dry combustion using an elemental analyser, the latter more accurate for carbon determination and used to correct LOI values. </p> <p>(4) Belowground_carbon: combined soil and root carbon collected collected to a maximum depth of 17 cm, and split into four soil layers (0-2 cm, 2-7 cm, 7-12 cm and 12-17 cm). </p> <p>MCD14DL MODIS Active Fire Detections data used to defined different burn seasons and histories for sites has not been uploaded and is freely available from online sources detailed in the journal article. </p>
A field test of mechanisms underpinning animal diversity in recently burned landscapes
<p>1. Planned burning generates different types of pyrodiversity, however, experimental tests of how alternative spatial patterns of burning influence animal communities remain rare. Field tests are needed to understand the mechanisms through which spatial variation in planned fire affects fauna, and how fire can be applied to benefit biodiversity.</p> <p>2. We tested five hypotheses of how fire-driven variation in habitat composition and configuration affects fauna at fine scales. Small mammal, reptile and invasive predator activity was monitored at 12 burnt and eight unburnt sites through the year following a large, planned burn in semi-arid 'mallee' woodlands of southern Australia. We explored measures of burnt or unburnt habitat ("habitat status"); amount of unburnt vegetation ("habitat amount"); interspersion of burnt and unburnt patches ("habitat complementation"); distance to external or internal unburnt vegetation ("habitat connectivity"); and unburnt patch size and local vegetation cover ("habitat refuge"). Generalized linear models were used to test the influence of each variable on capture rate of three small mammal and 11 reptile species; activity of the introduced red fox (Vulpes vulpes); and species richness of native animals.</p> <p>3. We found strong support for the habitat status hypothesis and moderate support for four hypotheses relating to spatial patterns of fire. Reptile assemblages varied between burnt and unburnt sites, and relationships were identified between abundance of one or more reptile species and each measure of spatial variation. Reptile species richness was higher at unburnt sites and at sites with more unburnt vegetation in the surrounding area. Sites that were less connected to unburnt vegetation had fewer reptile species. Mammals did not have clear relationships with fine-scale fire patterns.</p> <p>4. Synthesis and applications: Application of planned fire to promote biodiversity is globally important. We show that retaining unburnt areas and well-connected habitat refuges is important for reptile diversity. We also found that several species of small mammals and reptiles appear resilient to the fine-scale patterns of planned fire experienced in this study, despite activity of introduced predators. The diversity of animals can remain relatively high in areas subject to planned fire, provided that internal and external habitat refuges are retained.</p>
Auto Music Room, from Burning Man
Created in RealityCapture by Capturing Reality from 48 images in 00h:07m:08s. Source: Objaverse 1.0 / Sketchfab
SeverusPT - A multi-source burn severity dataset for mainland Portugal
<h3>Abstract (EN)</h3> <p>The SeverusPT project aims to periodically and timely provide relevant and standardized information on burn severity supported by satellite and field observations. Key objectives include developing a spatially explicit framework for assessing, mapping, and predicting burn severity and delivering a co-designed product/service to enhance institutional and operational capacity for fire hazard management and post-fire ecosystem restoration.</p> <p>The project currently provides standardized satellite-based datasets on mainland Portugal’s observed/historical burn severity, leveraging multiple satellite missions (Sentinel-2, Landsat, MODIS), spectral indices (e.g., Normalized Burn Ratio – NBR, Tasseled Cap Transformation – TCT), and burn severity indicators. The datasets are derived from pre-calculated severity products through algorithms that integrate satellite image time series (SITS) in two main approaches: (i) a delta-based pipeline, employing “classical” severity measurements (e.g., delta NBR) and focused primarily on high spatial resolution satellites; and (ii) a trajectory-based pipeline supported by SITS and the analysis of post-fire trajectories for multiple dimensions of ecosystem functioning and primarily focusing on high-temporal/moderate spatial resolution satellites.</p> <p>Field assessments, critical for validating satellite products and obtaining nuanced results regarding post-fire effects, were used to provide information on burn severity across different structural components of vegetation. The project used a purposive stratified approach for field surveys, focusing on the 2022 fire season across mainland Portugal. Selection criteria based on fire size, location, main vegetation type, and other ancillary layers enabled comprehensive coverage and diversity in post-fire conditions. Approximately 111 sites in 28 burned areas were surveyed in north and centre Portugal (the wildfire foci in the country) using the Geometrically Structured Composite Burn Index (GeoCBI) protocol. Two methods were used to validate the delta-based products by comparing in situ GeoCBI and satellite burn severity estimates: (i) non-parametric linear correlation (Spearman method) and nonlinear correlation; and (ii) a nonlinear exponential model adapted from pre-existing studies.</p> <p>Delta-based SeverusPT products agreed well with GeoCBI field measures of burn severity. The best linear correlation results were bounded between 0.64 and 0.71. Sentinel-2 and the NBR spectral index with RBR generally ranked higher when compared to Landsat-8. For nonlinear correlation, results were between 0.65 and 0.76, with the best results for Landsat-8 TCTG, closely followed by Sentinel-2 NBR spectral index with RDT or RBR indicators. The results for the nonlinear model validation were similar, with the best marks attained by the RdNBR, RBR, and dNBR indicators (R2= 0.64, 0.62, and 0.60, respectively).</p> <p>The project’s data portal is a centralized gateway for accessing and downloading project data and metadata. It offers two primary levels of data access: Level 1 includes the products, and Level 2 comprises image data files along with metadata. The trajectory-based pipeline and products are still under active development and will be added to the SeverusPT Data Portal.</p> <p>SeverusPT builds on a comprehensive approach combining satellite data with rigorous field validation, yielding significant insights into wildfire severity. The project’s innovative methodologies and the data portal’s accessibility contribute to the field of wildfire severity assessment, offering valuable data and tools for fire management and prioritizing post-fire mitigation and recovery strategies.</p> <p><strong>SeverusPT Data Products Manual</strong>: <a href="https://doi.org/10.5281/zenodo.10640961" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10640961</a></p> <p>___</p> <h3>Resumo (PT)</h3> <p>O projecto SeverusPT tem como objetivo disponibilizar, de forma regular e atempada, informação relevante e pradonizada da severidade da área ardida, baseada em dados de satélite e observações no terreno. Os principais objetivos incluem o desenvolvimento de uma moldura de análise espacialmente explícita para avaliar, mapear e prever a severidade da área ardida, fornecendo um produto/serviço resultante de co-desenho para melhorar a capacidade institucional e operacional para a gestão do risco de incêndio e o restauro dos ecossistemas pós-incêndio.</p> <p>Atualmente este projeto disponibiliza conjuntos de dados derivados de imagens de satélite para Portugal Continental acerca da severidade histórica/observada da área ardida. Estes conjuntos de dados fazem uso de informação proveniente de múltiplas missões espaciais (Sentinel-2, Landsat, MODIS), índices espetrais (p.ex. “Normalized Burn Ratio” – NBR, “Tasseled Cap Transformation” – TCT), e indicadores da severidade da área ardida. Os conjuntos de dados derivam de produtos pré-calculados de severidade através de algoritmos que integram séries temporais de imagens de satélite (SITS) em duas abordagens principais: (i) a cadeia de processamento (“pipeline”) baseado em deltas, que implementa medidas “clássicas” de severidade (p.ex. delta-NBR) e que se foca principalmente em satélites de alta resolução espacial; e (ii) a cadeia de processamento baseado em trajetórias, que se baseia na análise de trajetórias pós-incêndio para múltiplas dimensões do funcionamento dos ecossistemas e que se foca em dados de satélite de alta resolução temporal e resolução espacial moderada.</p> <p>No sentido de fornecer informação acerca da severidade da área ardida em vários componentes estruturais da vegetação, foram utilizados dados recolhidos no terreno, os quais são cruciais para validar produtos derivados de imagens de satélite e obter resultados pormenorizados relativamente a efeitos pós-incêndio. No âmbito do projeto foi usada uma abordagem estratificada para efetuar os levantamentos no terreno, focada na época de incêndios de 2022 em Portugal Continental. Critérios de seleção baseados no tamanho da área ardida, localização, tipo de vegetação principal e outras camadas auxiliares de informação permitiram cobrir uma maior diversidade de condições pós-incêndio. Foram visitados aproximadamente 111 locais pertencentes a um total de 28 áreas ardidas no Norte e Centro de Portugal Continental (as zonas do país mais afetadas por incêndios), usando o protocolo “Geometrically Structured Composite Burn Index” (GeoCBI). Foram utilizados dois métodos para validar os produtos baseados em deltas, através da comparação do GeoCBI in situ com estimativas obtidas por satélite: (i) correlações lineares não paramétricas (método de Spearman) e correlações não-lineares; e (ii) um modelo exponencial não-linear adaptado de estudo pré-existentes.</p> <p>Os produtos SeverusPT baseados em deltas apresentaram elevada concordância com as medidas de severidade da área ardida recolhidas no terreno através do GeoCBI. Os valores resultantes mais elevados para a correlação linear situaram-se entre 0,64 e 0,71. Foram obtidos valores geralmente mais elevados para Sentinel-2 e para o índice espetral NBR e o indicador de severidade RBR, em comparação com os resultados obtidos para Landsat-8. Relativamente às correlações não-lineares, foram obtidos valores entre 0,65 e 0,76, tendo os valores mais elevados sido obtidos para o índice espetral TCTG derivado de Landsat-8, seguido do índice espetral NBR derivado de Sentinel-2 com os indicadores de severidade RDT ou RBR. Foram obtidos resultados semelhantes para a validação através de modelo não-linear, com os valores mais elevados correspondendo aos indicadores de severidade RdNBR, RBR e dNBR (R2 = 0,64, 0,62 e 0,60, respetivamente).</p> <p>O portal de dados do projeto constitui uma via de acesso centralizada para a visualização e descarregamento de dados e metadados do projeto, oferecendo dois níveis primários de acesso: o Nível 1 inclui os produtos e o Nível 2 é constituído por ficheiros de imagens e metadados. Os produtos provenientes da cadeia de processamento baseada em trajetórias estão ainda em desenvolvimento activo e serão adicionados posteriormente no Portal de Dados do SeverusPT.</p> <p>O SeverusPT assenta numa abordagem abrangente que combina dados provenientes de satélites com uma rigorosa validação baseada em dados recolhidos no terreno, oferecendo uma melhor compreensão da severidade dos incêndios. As suas metodologias inovadoras e a acessibilidade do seu portal de dados contribuem para o campo da avaliação da severidade dos incêndios, providenciando dados e ferramentas valiosos para a gestão do fogo e para a priorização de estratégias de mitigação e recuperação pós-incêndio.</p> <p> </p>
Burned areas dataset for the enclaves of grasslands and savannah of the Mapinguari National Park (Amazonas, Brazil)
<p><span>The present dataset includes annual mapping of fire scars for the enclaves of grasslands and savannah of the Mapinguari National Park (Amazonas, Brazil), at 30-meter spatial resolution, for the period 2000-2023. The enclave areas occupy a total of 241,000 hectares.</span></p> <p><span>The detection of burned areas was carried out using Burned Area Mapping (BAMS) algorithm (Bastarrika et al, 2014), followed by the performance of visual supervision processes and the use of active fire products to optimize the detection date of each fire event. The algorithm is applied to the Surface Reflectance series of Landsat (TM, ETM+, OLI and OLI-2) – Collection II, accessed using Google Earth Engine (Gorelick et al, 2017). Data from active fire products MCD14DL V006, VIIRS-NPP, VIIRS-NOA20 and GOES16, as well as burned area data from product MCD64A1 v006, were used to optimize the detection date of each fire scar.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p><span>July 08, 2023 – The version 1.0 includes the period 2000-2023, with a total of 356,688.5 hectares of fire affected areas, distributed across 453 fire scars.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p><span>The files available include: </span></p> <p><span>i) “fire_scars_dataset.rar”: annual burned areas vector files, in shapefile format (*.shp), projected at WGS84 UTM 20S. Each observation is an individual fire scar, with his attribute table indicating:</span></p> <p><span>- ID: Identification number for each;</span></p> <p><span>- area_ha - area of each fire scar, calculated in hectares.</span></p> <p><span>- date - detection date of the fire scar</span></p> <p><span>- date_preci - precision flag of the fire detection date:</span></p> <p><span>0 - fire date detected using only Landsat data</span></p> <p><span>1 - fire date optimized based on active fires dataset (MCD14DL V006; VIIRS-NPP; VIIRS-NOA20; or GOES16)</span></p> <p><span>ii) “enclaves_Mapinguari_National_Park.rar”: vector file of study area location, in shapefile format (*.shp), projected at WGS84 UTM 20S. Includes 8 enclaves of grasslands and savannah situated inside Mapinguari National Park.</span></p> <p><span>iii) “dataset_description.pdf”: description of the dataset.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p><span>We thank the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq) and the Instituto Chico Mendes de Conservação da Biodiversidade (ICMBIO) (process number 126772/2022-3) for the grant conceded to the second and third authors.</span></p> <p><span>_________________________________________________________________________________________________________</span></p> <p> </p> <p><span>References</span></p> <p> </p> <p><span>Bastarrika, Aitor, Maite Alvarado, Karmele Artano, Maria Pilar Martinez, Amaia Mesanza, Leyre Torre, Rubén Ramo, and Emilio Chuvieco. 2014. “BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data.” Remote Sensing 6: 12360–80. https://doi.org/10.3390/rs61212360.</span></p> <p><span>Gorelick, Noel, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore. 2017. “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote Sensing of Environment 202: 18–27. https://doi.org/10.1016/j.rse.2017.06.031.</span></p>
Figure 1. Pantanal burned area per year until 9 in The Pantanal is on fire and only a sustainable agenda can save the largest wetland in the world
Figure 1. Pantanal burned area per year until 9/25/2020.
Hydrogen Burning on Accreting White Dwarfs: Stability, Recurrent Novae, and the Post-nova Supersoft Phase
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2013ApJ...777..136W">Hydrogen Burning on Accreting White Dwarfs: Stability, Recurrent Novae, and the Post-nova Supersoft Phase</a></p>
Code dependencies of pre-supernova evolution and nucleosynthesis in massive stars: evolution to the end of core helium burning
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2015MNRAS.447.3115J">Jones et al. (2015)</a>. MESA version 3709.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stu2657">10.1093/mnras/stu2657</a></p> <p>Files are also available in a gihub repository <a href="https://github.com/swjones/mesa-Teile/tree/master/Jones.etal.2015.MNRAS.447.4.3115">here</a></p>
Prescribed Burn Related Increases of Population Exposure to PM2.5 and O3 Pollution in the Southeastern US over 2013–2020
<p>Daily prescribed burn PM2.5 and MDA8-O3</p>
Updated 30 m resolution global annual burned area map
<p>Updated 30 m resolution global annual burned area maps (GABAM) of 2014-2021 are released for free download.The annual burned area map is defined as <strong>spatial extent of fires that occurs within a whole year and not of fires that occurred in previous years.</strong>GABAM was generated via an automated pipeline based on Google Earth Engine (GEE), using all the available Landsat images on GEE platform. The product was projected in a Geographic (Lat/Long) projection at 0.00025° (approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10°×10° tiles spanning the range 180°W–180°E and 80°N–60°S.<br>contacts: zhangzhaoming@aircas.ac.cn/zhangzm@radi.ac.cn</p>
Figure source data for Riddell-Young et al., 2024 "Abrupt changes in biomass burning during the last glacial period"
<p>These are the source data files for all of the data presented in main text figures 1 through 4 in Riddell-Young et al., 2024: "Abrupt changes in biomass burning during the last glacial period". Each file contains several sheets that correspond to the data presented in each subplot of the figure. Please refer to the manuscript for a detailed description of the data that was collected and analysis that was performed as part of the study. The Data and Code Availability statements discuss where the official dataset and code publications can be found.</p>
Mr Burns: a post-electric play written by Anne Washburn / A tertiary performance staged by Te Kura o Toi Whakaari: NZ Drama School
<p>DIRECTOR</p> <p>Stella Reid</p> <p>MUSICAL DIRECTOR</p> <p>Hayden Taylor</p> <p>PRODUCER</p> <p>Natasha James</p> <p>CHOREOGRAPHER</p> <p>Stella Reid</p> <p>FIGHT CHOREOGRAPHY</p> <p>Shane Rangi</p> <p>LIGHTING DESIGNER</p> <p>Grace Newton</p> <p>SOUND DESIGNER</p> <p>Oliver Devlin</p> <p>DIALECT COACH</p> <p>Jacque Drew</p> <p>SET DESIGNER</p> <p>Eden Winters</p> <p>COSTUME DESIGNER</p> <p>Tessa Saunders</p> <p>ASSISTANT SOUND DESIGNER</p> <p>Jade Alborn</p> <div> <p>PRODUCTION TEAM</p> <p>PRODUCTION MANAGER</p> <p>Olivia Cowley</p> <p>STAGE MANAGER</p> <p>Michael Lyell-O'Reilly</p> <p>HEAD MECHANIST</p> <p>Caleb Grainger</p> <p>PUTY STAGE MANAGER/SHOW CALLER</p> <p>Sarah Davitt</p> <p>DEPUTY STAGE MANAGER</p> <p>Joseph McAllum</p> <p>PRODUCTION ASSISTANT</p> <p>Isla McLarin</p> <p>MECHANIST</p> <p>Eilidh Hamilton</p> <p>MECHANIST</p> <p>Jack Tankersley</p> <p>SOUND OPERATOR/ENGINEER</p> <p>Paige Johns</p> <p>LIGHTING OPERATOR</p> <p>Luci McDougall</p> <p>COSTUME SUPERVISORS</p> <p>Sally Huges-Allen, Maysie Pyatt, Evan Stone, Kaitlyn Jacobs</p> <p> </p> <p>PRODUCTION TEAM</p> <p>COSTUME CONSTRUCTION</p> <p>Maxence Benoist, Lillian Denness,</p> <p>Renske Gordon, Evie Howard, Maria McCarthy, Dottie Olsen, Mikkel Rivett,</p> <p>Hannah van den Eikhof, Jordyn Williams</p> <p>SET CONSTRUCTION & SCENIC PAINTING</p> <p>Emile Commarieu, Molly Friis, Azalea Lewis-Milne, Lora Nankivell, Grace O'Brien, Weston Symes</p> <p> </p> <p> </p> <p>CAST</p> <p>ZACHARY BELL - МАТТ/ACT II, HOMER/ACT III CHORUS</p> <p>BRAYDEN CRESWELL- GIBSON/SIDESHOW BOB, FIRST FBI AGENT / TROY MCCLURE</p> <p>FIPE FOAI - ACT III BART</p> <p>EMILIO FUENTES MANCILLA - ACT III LISA/APU</p> <p>KIMIORA HONEYCOMBE - JENNY ACT II, MARGE/ACT III CHORUS</p> <p>JUSTICE KALOLO - SAM ACT II, BART/ACT III, CHORUS /ACT III SCRATCHY</p> <p>SARAH LAWRENCE - COLLEEN, ACT III CHORUS</p> <p>MAIZY METEKINGI - ACT III MARGE/FLANDERS</p> <p>IZZIE NEWTON-CROSS - ITCHY/NELSON</p> <p>SUGAR REA-BRUCE MARIA/ACT II, LISA/2ND FBI AGENT/EDNA KRABAPPEL</p> <p>TAMAHOU TEHEI - ACT III HOMER / WILLY</p> <p>PARIS TUIMASEVE-FOX - QUINCY/ACT II, MAGGIE/ACT III CHORUS</p> <p>CASSIDY KEMP-WOFFENDEN - MR BURNS</p> <p>IAN BLACKBURN - MR BURNS SWING</p> </div>
Climate change risks illustrated by the IPCC "burning embers": dataset
<p>This dataset contains numerical data and descriptive information on all 'burning ember' diagrams presented in the reports of the Intergovernmental Panel on Climate Change (IPCC), from the first appearance of these diagrams in 2001 to the 6th Assessment Report, published in 2022. The aim of this dataset is to bring together the data and metadata needed to reconstruct the burning embers diagrams and acquire essential information on the risks assessed and their evolution, within a single, homogeneous framework. The file presented here has been extracted from the database at the indicated date: it is a versioned archive of the database (excluding internal development fields, which are not publicly available). Analyses and figures based on this dataset are presented in Marbaix et al., 2024 [1], which provides information about the data. The data are provided in a text file in JSON format, the structure of which is described in the file itself and in the Supplement to Marbaix et al. 2024 [1].</p> <p>The IPCC secretariat has confirmed that these data can be distributed under the CC-BY licence as indicated here. When using this dataset, we ask you to provide the reference to each IPCC report which is the source of the data (and additional sources listed in the references to this dataset when relevant), as well as to the dataset, adding the related paper [1] as soon as it is available.</p> <div> <div>[1] Marbaix, P., Magnan, A. K., Muccione, V, Thorne, P. W., and Zommers, Z: Climate change risks illustrated by the IPCC "burning embers", submitted.</div> </div>
Impacts of biomass burning in peninsular Southeast Asia on PM2.5 concentration and ozone formation in southern China during springtime – A case study
<p>Abstract: Biomass burning (BB) affects fine particulate matter (PM<sub>2.5</sub>) and ozone (O<sub>3</sub>) formations by emitting their gaseous precursors and primary aerosols. Impacts of BB in peninsular Southeast Asia (BB-PSEA) are evaluated on the PM<sub>2.5</sub> and O<sub>3</sub> formations in southern China, using a source-oriented WRF-Chem model to simulate an air pollution episode from 21 to 25 March 2015. The source-oriented model separates the emission from the BB-PSEA and other sources and is able to evaluate the effect of aerosol-radiation interactions (ARI) and aerosol-photolysis interactions (API) from the BB-PSEA. Comparisons with observations reveal that the model performs well in simulating the air pollution episode. Sensitivity experiments show that BB-PSEA increases PM<sub>2.5</sub> concentrations by 39.3 μg m<sup>-3</sup> (68.0%) in Yunnan Province (YNP) and 8.4 μg m<sup>-3</sup> (24.1%) in other downwind areas (ODA) in southern China (including the provinces of Guizhou, Guangxi, Hunan, Guangdong, Jiangxi, Fujian, and Zhejiang) on the regional average. The PM<sub>2.5</sub> enhancement is mainly contributed by primary aerosols in YNP but by secondary aerosols in the ODA. The BB-PSEA increases O<sub>3</sub> concentrations of 18.1 μg m<sup>-3 </sup>(19.4%) in YNP and decreases O<sub>3 </sub>concentrations in the ODA by 3.7 μg m<sup>-3</sup> (5.3%). The O<sub>3</sub> increase in YNP is contributed by the gaseous emissions of the BB-PSEA, and the O<sub>3</sub> decrease in the ODA is caused by the effects of ARI and API of the BB-PSEA. The NH<sub>3</sub> emissions from the BB-PSEA plays a key role in enhancing secondary inorganic aerosols in southern China, and also determine the PM<sub>2.5</sub> increase in the ODA.</p>
Twenty-five years of tree demography in a frequently burned oak woodland
<p>Due to decades of fire suppression, much of the Upper Midwest savanna habitat has converted to oak woodland. In efforts to restore oak savanna habitat, fire has been re-introduced in many of these woodlands. A primary purpose of these burns is to kill the fire-sensitive mesophytic tree species, which had established themselves during the decades of fire suppression, reduce the number of understory trees, and preserve the larger more widely spaced oaks. It is clear from ongoing efforts that restoring oak savannas will require frequent fires over decades. But frequent fires over the long term can also threaten the desirable oaks. Long-term demographic studies at savanna restoration sites experiencing frequent fires are necessary to determine the extent to the frequent burns are supporting and/or confounding restoration goals. Results presented here are from a twenty-five-year demographic study of an Upper Midwest bur oak (<i>Quercus macrocarpa</i>)<i> </i>savanna/woodland experiencing frequent fire, during which both the survival and growth of more than 9,000 trees were documented. Survival was assessed annually and growth every five years. In the face of frequent fires, stem survival was found to be strongly associated with tree species, stem size, and stem growth. In turn, stem growth was found to be related to tree species and stem size. Decades of frequent burning in this oak woodland have substantially reduced the abundance of unwanted trees, specifically mesophytic species and <i>Q. ellipsoidalis</i>, the latter which outcompetes <i>Q. macrocarpa </i>in the absence of fire. While <i>Q. macrocarpa</i> mid-sized (10-25 cm dbh) and large (<u>></u> 25 cm dbh) trees are quite resistant to fire and now dominate the savanna landscape, they are not immune from fire-induced mortality. It is recommended that the number and density of these trees should be re-evaluated every few years to ensure that desirable numbers remain. If necessary, fires should be suspended for a period of time. This will give smaller <i>Q. macrocarpa </i>trees time to grow larger and become more fire-resistant, thereby ensuring successive generations of <i>Q. macrocarpa</i>. </p>
Model data for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions and burned area as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br><sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br><sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br><sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br><sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p><strong>UPDATE OF DATASET TO 2023:</strong></p> <p>This dataset has now been extended to 2023. Since the first release of this dataset, multiple updates to the model input data have been made:</p> <p>- Update from MODIS C6 to MODIS C6.1 for all MODIS input data, including MCD12Q1 land cover types, MCD14ML active fires, MCD15A2H fPAR, MOD44B VCF, MOD44W land-water mask, and MCD64A1 burned area.<br>- Update of Hansen forest loss data from v1.9 to v1.11.<br>- Update of GLEAM evaporative stress data from v3.6b to v3.7b.<br>- Extension of ERA5-land data to 2023.<br>- Addition of land cover type layers to the 500-m resolution data files.</p> <p> </p> <p>Files contain 500-m (per MODIS tile) and 0.25 degree aggregated (global grid) carbon emissions and burned area from biomass burning for 2002-2022, as part of the paper "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-15-8411-2022). 500-m resolution files include land cover type grids. 0.25 degree global grid files also include biome partitioning and accompanying biome fractional cover grids.</p> <p>Zip archives with filenames "500m_YYYY.zip" contain annual files named "Model500m_2002-2023yr_h##v##_YYYY.nc", which are the 500-meter resolution model results per MODIS tile using the MODIS sinusoidal projection. Carbon emission data layers are:</p> <p>- Total biomass burning carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_TOT)</p> <p>- Total biomass burning carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_TOT)</p> <p>- Fire-related forest loss carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_FL)</p> <p>- Fire-related forest loss carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_FL)</p> <p>Total emissions are calculated as: C_AG_TOT + C_BG_TOT. Total fire-related forest loss emissions are calculated as: C_AG_FL + C_BG_FL.</p> <p>Burned area data layers are:</p> <p>- Total burned area; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_TOT)</p> <p>- Burned area from fire-related forest loss; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_FL)</p> <p>The Zip archive with filename "025d_2002_2023.zip" contains annual files named "Model500m_2002-2023yr_025d_YYYY.nc", which are the 500-m model results aggregated to a 0.25 degree global lat-lon grid. These files contain the same variables as the 500-m files, but aggregated to 0.25 degree resolution (MOD_CMG025). Furthermore, these files include biome partitioning of emissions and burned area (MOD_CMG025BIOME) and provide accompanying biome fractional cover grids for all 20 biomes (variable 'biomes'). Biomes are listed in detail in Table S1 of the van Wees et al. (2022) paper. The biomes 'water', 'snow/ice' and 'barren' were excluded from Table S1 because of their negligible share, but are included in the files provided here for completeness.</p>
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.