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24 results for “Climate Attribution”
Temperature and Climate Attribution estimates supporting "Human Fingerprints on Daily Temperatures in 2022" (2x2 degrees, 2022)
<p>These data support the publication of "Human Fingerprints on Daily Temperatures in 2022" published in the <a href="https://www.ametsoc.org/index.cfm/ams/publications/bulletin-of-the-american-meteorological-society-bams/explaining-extreme-events-from-a-climate-perspective/">BAMS-EEE special issue</a> in 2024 (DOI: <a href="https://doi.org/10.1175/BAMS-D-23-0264.1">10.1175/BAMS-D-23-0264.1</a>). Included are:</p> <ul> <li>Temperatures: <strong>Gilfordetal2024_BAMS-EEE_T2022.nc</strong></li> <li>Attributions estimates (Climate Shift Index and Change in Information due to Perspective): <strong>Gilfordetal2024_BAMS-EEE_ChIP2022.nc</strong></li> </ul> <p>And an accompanying land-sea mask from ERA5 (<strong>Gilfordetal2024_BAMS-EEE_LandSeaMask.nc</strong>). All data values valid for the 2022 calendar year and interpolated to a 2x2 degrees spatial grid to support the study's analysis.</p> <p>For more information on this dataset or to follow up, please contact Daniel Gilford (<a href="mailto:dgilford@climatecentral.org" target="_blank" rel="noopener">dgilford@climatecentral.org</a>).<br><br><em>Funding for this work was provided by the Bezos Earth Fund, The Schmidt Family Foundation, High Meadows Foundation, and the William and Flora Hewlett Foundation.</em></p>
Attributing decadal climate variability in coastal sea-level trends
<p>The data produced from analysis to be published in Ocean Science Discussions, paper entitled "Attributing decadal climate variability in coastal sea-level trends". NetCDF contains the following sets of fields:</p> <p>1. Indexing: An <em>index</em> and location (<em>lat, lon</em>) of the coastal grid cells, a locator index attributing each cell to Atlantic, Pacific and Indian Ocean basin, a <em>time</em> (decimal year) index.</p> <p>2. NEMO model trends (<em>nemo_<component>_trend</em>): Rolling decadal trends at each coastal grid cell from the NEMO model run for steric, manometric (dynamic) and GRD. The sum of these components gives the equivalent to absolute sea level trend. </p> <p>3. Climate and oceanographic mode indices: The rolling decadal trends in climate indices and the AMOC index calculated from the AMOC model (<em>ci_trend</em>) and their names (<em>ci_index</em>).</p> <p>4. Empirical Orthogonal Function spatial pattern (<em>eof_<basin>_<component>_D</em>) and Principal Component time series (<em>eof_<basin>_<component>_PC</em>)<em> </em>of the NEMO model trends.</p> <p>5. Coefficient of linear regression between PC and climate indices (<em>recon_<basin>_<component>_beta</em>) and the rolling trend time series at each grid cell from the reconstruction, sum{ci_trend*beta} (<em>recon_<basin>_<component>_trend</em>).</p> <p>In 4 and 5, the indices are given by basin. The total coastline is a concatenation of the Atlantic, Pacific and Indian basin data in that order. The absolute SSH is given by the sum of components. i.e. the SSH for all coastal cells in order <em>index</em>:</p> <p>recon_sum_trend([index(Atlantic_index); index(Pacific_index); index(Indian_index)] = ...</p> <p> [recon_Atlantic_manometric_trend+recon_Atlantic_steric_trend+recon_Atlantic_grd_trend; ...</p> <p> recon_Pacific_manometric_trend+recon_Pacific_steric_trend+recon_Pacific_grd_trend; ...</p> <p> recon_Indian_manometric_trend+recon_Indian_steric_trend+recon_Indian_grd_trend]</p>
Paired Vegetation and Soil Burn Severity Metrics and Associated Climate, Weather, Topographical, and Land Cover Attributes
<p>This dataset pairs differenced Normalized Burn Ratio (dNBR) and soil burn severity (SBS) for 254 large (>400 ha in size) fires across the western US. Dataset also includes climate, weather, topography, physical and chemical soil characteristics, and land cover attributes of each burned pixel at the time of fire. This effort provided a table of 16.3 million burned pixels and their associated characteristics including dNBR, SBS, and 94 biological and physical covariates. After removing correlated features, the final data includes 18 fire covariates namely: dNBR, elevation, slope, aspect, land cover type, wind speed, energy release component, vapor pressure deficit, annual precipitation, and annual average daily max temperature, as well as the clay, sand and silt content of the soil and volumetric fraction of coarse fragments and soil organic carbon content. We also included spatial coherence metrices for dNBR, including DVAR, SHADE and SAVG. This data is provided as CSV files in Xtrain, Xvalidation, Xtest, as well as Ytrain, Yvalidation, and Ytest; in which X files (model input) provide all features except for SBS and Y files (model output) include SBS.</p><p>We also provided this data for an additional 16 large fires across the western US ("Extra Test" folder, including Dataset – X file – and Label – Y file).</p><p>Finally, the trained XGBoost model to translate dNBR to SBS using the associated features is also provided in this folder.</p>
Data from ATTRICI 1.1 - counterfactual climate for impact attribution
<p>Data as produced and presented in the publication</p> <pre><strong>ATTRICI v1.1 - counterfactual climate for impact attribution</strong></pre> <p>in Geoscientific Model Development.</p> <p>Abstract:</p> <p>Attribution in its general definition aims to quantify drivers of change in a system. According to IPCC WGII a change in a natural, human or managed system is attributed to climate change by quantifying the difference between the observed state of the system and a counterfactual baseline that characterizes the system’s behavior in the absence of climate change, where “climate change refers to any long-term trend in climate, irrespective of its cause". Impact attribution following this definition remains a challenge because the counterfactual baseline cannot be observed. Process-based and empirical impact models can fill this gap as they allow to simulate the counterfactual climate impact baseline. In those simulations, the models are forced by observed direct (human) drivers such as land use changes, changes in water or agricultural management but a counterfactual climate without long-term changes. We here present ATTRICI (ATTRIbuting Climate Impacts), an approach to construct the required counterfactual stationary climate data from observational (factual) climate data. Our method identifies the long-term shifts in the considered daily climate variables that are correlated to global mean temperature change assuming a smooth annual cycle of the associated scaling coefficients for each day of the year. The produced counterfactual climate datasets are used as forcing data within the impact attribution set-up of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). Our method preserves the internal variability of the observed data in the sense that factual and counterfactual data for a given day have the same rank in their respective statistical distributions. The associated impact model simulations allow for quantifying the contribution of climate change to observed long-term changes in impact indicators and for quantifying the contribution of the observed trend in climate to the magnitude of individual impact events. Attribution of climate impacts to anthropogenic forcing would need an additional step separating anthropogenic climate forcing from other sources of climate trends, which is not covered by our method.</p>
Masting is shaped by tree-level attributes and stand structure, more than climate, in a Rocky Mountain conifer species
<p>Many tree species mast, meaning seed production is highly variable from year to year and synchronous within a stand, but this phenomenon remains poorly understood. To better understand how a changing climate, altered disturbance regimes, or novel management strategies might affect future seed production, we quantified the joint influence of both biotic (tree size, age, and neighborhood competition) and abiotic factors (climate and weather) on seed production in a widespread conifer species, Rocky Mountain ponderosa pine (<em>Pinus ponderosa</em> var. <em>scopulorum</em>). We reconstructed individual-level annual cone production across a large portion of this species' range using the cone abscission scar method, and mixed models were used to test hypotheses related to the causes and drivers of masting in this species. Our results suggest that masting in ponderosa pine is a process shaped at the individual-level, and this leads to high, local-scale variation in annual cone production. The effects of weather were strongest at climatically marginal sites, but overall, the joint effects of weather and climate only weakly described individual-level patterns of annual cone production in ponderosa pine (R<sup>2</sup><sub>m</sub> = 1.6%, R<sup>2</sup><sub>c</sub> = 30.1%). Rather, we found that masting was strongly influenced by tree- and stand-level factors such as diameter, age, and local neighborhood density, all of which were associated with the mean, interannual variability, and between-tree synchrony of cone production at the individual-level. Larger and older trees produced more cones, more frequently, and with less synchrony than smaller and younger trees. Open-grown trees experiencing lower levels of neighborhood competition also produced more cones with less interannual variability, but with higher between-tree synchrony. Because tree- and stand-level traits appear to regulate seed production more strongly than climate or weather in this species, management interventions targeting these factors could be powerful tools to manage future tree recruitment. Thus, current efforts to reduce stand density and conserve large trees in some ponderosa pine forests may enhance tree-level seed production and reduce variability in seed crops among years.</p>
Outputs of the Jupyter Notebook - Deep learning and variational inversion to quantify and attribute climate change (CIRC23)
<p>The dataset contains the outputs of the notebook "Deep learning and variational inversion to quantify and attribute climate change (CIRC23)" published in The Environmental Data Science Book.</p>
Masting is shaped by tree-level attributes and stand structure, more than climate, in a Rocky Mountain conifer species
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Model output for: Attributing causes of future climate change in the California Current System with multi-model downscaling
<p>Regional Ocean Modeling System outputs from dynamic downscaling of Coupled Model Intercomparison Project climate forcings in the California Current system, including projections with full climate forcings, as well as attribution experiments with only changes in wind, heat fluxes and other properties changing stratification, and boundary biogeochemical forcings. Output variables include euphotic zone integrated net primary productivity, and incident photosytnehtically available radiation, and ocean temperature, salinity, vertical velocity, and dissolved oxygen and nitrate concentrations at select depths.</p>
Scripts for "Evaluation and Attribution of a Warm Winter Bias Over Arctic Sea Ice in a Climate Model"
<p>The scripts used to generate the main figures and results of the work entitled ''Evaluation and Attribution of a Warm Winter Bias Over Arctic Sea Ice in a Climate Model'' by Michalezyk et al., submitted for publication in JAMES - AGU in 2024.</p> <p>If you have any questions, please contact Nicolas MICHALEZYK : nicolas.michalezyk@locean.ipsl.fr</p>
Attributing human mortality from fire PM2.5 to climate change
<p>The dataset and scripts are for reanalysis the paper: Attributing human mortality from fire PM2.5 to climate change. </p> <p>The script is written in MATLAB. see Readme.txt and FireMort_SI_re.mlx for detail. </p> <p>Unzip files and make the folder (/Input, /Output, /Major_input, /Minor_input). </p> <p>Some original dataset (which can be directly driven from ISIMIP repository) were not included. </p> <p>If you need more information, please contact Chaeyeon Park (email- park.chaeyeon@aist.go.jp, or chaeyeon528@gmail.com)</p>
Machine learning-based evidence and attribution mapping of 100,000 climate impact studies - Data
<p>Data for the paper Machine learning-based evidence and attribution mapping of 100,000 climate impact studies</p> <p><strong>Document Metadata</strong></p> <p>0c_doc_info.csv contains basic document metadata for each document considered in our study</p> <p><strong>Predictions</strong></p> <p>In each predictions file, 1 refers to a document hand-labelled as belonging to a category, and 0 refers to a document hand-labelled as not belonging to a category. All values in between are predicted values, where for values greater than 0.5, a document is considered likely to belong to the given category.</p> <p>1_document_relevance.csv contains the predicted relevance of a document to the study.</p> <p>1_driver_predictions.csv contains the predicted climate driver of each document.</p> <p>1_impact_predictions.csv contains the predicted impact type of each document</p> <p><strong>Geographical data</strong></p> <p>Place_df.csv contains a row for each geographical entity automatically extracted from each study</p> <p>Study_gridcell_2.5.csv contains a row matching each study with each grid cell covered by the study’s smallest mentioned geographical entity</p> <p><strong>Merged data</strong></p> <p>2_study_da.csv contains a row for each study describing the aggregated detection and attribution characteristics of the grid cells the study refers to</p> <p>2_merged_da_data.csv contains a row for each grid cell describing the attribution categories and the number of weighted grid cells for each climate driver.</p>
Data from: A scoping review of mosquito vector range shifts: widespread expansions and evidence gaps in climate attribution
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Model output for: Attributing causes of future climate change in the California Current System with multi-model downscaling
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Tracks for "Attribution of 2020 hurricane season extreme rainfall to human-induced climate change"
<p>TempestExtremes track files for CAM5 analysis presented in "Attribution of 2020 hurricane season extreme rainfall to human-induced climate change"</p>
Fig. 2 in Flowering phenology of co-occurring Asteraceae: a matter of climate, ecological interactions, plant attributes or of evolutionary relationships among species?
Fig. 2 Number of Asteraceae species growing in Chaco Serrano forests of La Serranita-Los Aromos that bear flowers during each month of the year; for calculation, see text in Material and methods Section
Fig. 1 in Flowering phenology of co-occurring Asteraceae: a matter of climate, ecological interactions, plant attributes or of evolutionary relationships among species?
Fig. 1 Diagram of phylogenetic relationships between Asteraceae taxa studied in this work (adapted from Panero and Crozier 2008; Panero and Funk 2008)
Fig. 4 in Flowering phenology of co-occurring Asteraceae: a matter of climate, ecological interactions, plant attributes or of evolutionary relationships among species?
Fig. 4 Plot of PCA scores for 43 co-occurring Asteraceae species in Chaco Serrano forests of La Serranita-Los Aromos, showing first two principal component axes from analysis of flowering phenology considering plant traits and taxonomic membership. Vectors corre-
Data for: Climate and forest attributes influence aboveground biomass of deciduous broadleaf forests in China
<p>Forests provide a huge carbon pool, a substantial portion of which is stored in aboveground biomass (AGB). Deciduous broadleaf forests in China are an essential component of global deciduous broadleaf forests, yet the impacts of climate and forest attributes on their AGB are not well understood.</p> <p>Using a comprehensive forest inventory database available from 772 plots distributed across the temperate and subtropical deciduous broadleaf forests in China (23.51°-42.53° N and 104.24°-128.27° E), we applied variance partitioning analysis, model selection analysis and structural equation models to explore how AGB was associated with climate and forest attributes (species diversity, community-level functional traits, and stand structures) in different climatic regions (semi-arid forests, semi-humid forests and humid forests).</p> <p>Community-level functional traits and stand structures together explained a great portion of the variance in AGB. The effect of community-level functional traits was greater than that of stand structures in semi-arid forests and semi-humid forests, but smaller in humid forests. Further analyses showed that community-level maximum tree height, stem density and tree size inequality were the best explanatory variables. Although climate and species diversity had only minor effects, the direct positive effect of mean annual precipitation (MAP) was still important, especially in semi-arid forests.</p> <p><em><strong>Synthesis.</strong></em> Community-level functional traits but not species diversity were key drivers of AGB, indicating that tree species diversity loss may not impair AGB substantially in deciduous broadleaf forests in China. Moreover, stand structures had also strong effects on AGB in both semi-arid forests and humid forests, highlighting the importance of structural complexity. In addition, MAP had a direct positive effect on AGB in semi-arid forests and semi-humid forests, and a future predicted increase in drought might potentially reduce carbon storage in these forests.</p>
Victorian Water and Climate dataset: long-term streamflow, climate, and vegetation observation records and catchment attributes
<p>This dataset contains streamflow, climate, and vegetation data for 155 minimally impaired catchments in Victoria, Australia. </p> <p>What's included:</p> <p>- Streamflow long-term observation records in daily, monthly, and hydroannual (based on March to February water year) resolution</p> <p>- Catchment climate characteristics in daily, monthly, and hydroannual resolution</p> <p>- Catchment vegetation characteristics in monthly resolution and actual evapotranspiration estimate in monthly and hydroannual resolution</p> <p>- Catchment boundaries</p> <p>- Catchment attributes (topographic, geological, soil, groundwater, vegetation, and human impacts characteristics)</p> <p>- Hydroclimatic characteristics for 3 different periods.</p> <p>Precipitation (p), streamflow (q), and PET (pet) data and their derivatives are in mm (i.e. normalised by catchment area). </p> <p>A paper containing a detailed description of the data including units, data sources, and processing details is submitted, please contact Margarita Saft for a private copy of the draft.</p>
Data from: Climate severity and land-cover transformation determine plant community attributes in Colombian dry forests
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