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382 results for “Climate impacts”
Time of emergence of climate change impacts
<p>Expected year in which climate impacts would exceed an extreme past economic shock value (95th percentile).</p> <p>Model used: CLIMRISK</p> <p>Scale: 0.5 degrees * 0.5 degrees</p> <p>Shock database consists of changes in annual GDP between 1950 - 2016.</p> <p>Citation: Ignjacevic, Predrag, Francisco Estrada Porrua, and Willem Jan Wouter Botzen. "Time of emergence of economic impacts of climate change." <em>Environmental Research Letters</em> (2021).</p>
Climate change impacts on energy demand
<p>Climate change impacts on energy demand by energy carrier (electricity, natural gas, and petroleum) and sector (agriculture, industry, residential, and commercial).</p>
Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"
<p>This dataset is associated with the following publication:</p> <p>Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2.</p> <p>The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error.</p> <p>The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts. Soil and airborne mineral fraction datasets.
<p>These datasets correspond to soil and airbone mass mineral fractions as described and generated for "Modeling dust mineralogical composition: sensitivity to soil mineralogy" by Gonçalves Ageitos, M., Obiso, V., Miller, R.L., Jorba, O., Klose, M., Dawson, M., Balkanski, Y., Perlwitz, J., Basart, S., Di Tomaso, E., Escribano, J., Macchia, F., Montané, G., Mahowald, M.M., Green, R.O., Thompson, D.R. and Pérez García-Pando, C., ACP, 2023. </p> <p>There are 4 netCDF files that include the soil mass mineralogical fractions (0-1) in the clay (0-2 <span class="math-tex">\(\mu\)</span>m in diameter) and silt (2-63 <span class="math-tex">\(\mu\)</span>m in diameter) size classes as derived from the works of Claquin et al., (1999), and updated by Nickovic et al. (2012): <strong>C1999-SMA</strong>, and Journet et al. (2014): <strong>J2014-SMA</strong>. The data is mapped in a regular global grid with a horizontal resolution of 0.083º. Additional information on the FAO soil units, and soil texture data from HWSDv1.2 is provided in the J2014-SMA files. </p> <p>File details: </p> <ul> <li>C1999-SMA_CLAY_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>C1999-SMA_SILT_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_CLAY_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_SILT_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> </ul> <p>There are 2 additional files that report the multiannual (2006-2010 period) monthly mean of the <strong>aerosol mass mineral fractions</strong> as obtained from the <strong>MONARCH model</strong> simulations described in Gonçalves Ageitos et al. (2023). The mass fractions are provided in each of the 8 size bins used in the model (ranging from 0.2 to 20 <span class="math-tex">\(\mu\)</span>m in diameter), and normalized so as to sum 1 (i.e., the sum of all minerals in all bins equals 1). Note that in order to reduce the size of these files, the variables have been compressed to short format and include an offset and scale factor as attributes. </p> <p>File details: </p> <ul> <li>20062010_monarch_minfrac_C1999.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH C1999 experiment. </li> <li>20062010_monarch_minfrac_J2014.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH J2014 experiment. </li> </ul> <p> </p> <p><em>Legend for the minerals:</em></p> <p>quar: quartz, feld: feldspars, calc: calcite, gyps: gypsum, illi: illite, mont: montmorillonite/smectite, kaol: kaolinite, verm:vermiculite, chlo: chlorite, mica: mica, hema: hematite, goet: goethite, irox:iron oxides (hematite and goethite). </p> <p>References:</p> <p>Claquin, T., Schulz, M., and Balkanski, Y. J.: Modeling the mineralogy of atmospheric dust sources, Journal of Geophysical Research<br> Atmospheres, https://doi.org/10.1029/1999JD900416, 1999.</p> <p>FAO-UNESCO: Soil Map of the World- Volume I Legend, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Paris, http://www.fao.org/3/as360e/as360e.pdf, 1974.</p> <p>FAO-UNESCO: Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization. Digital Soil Map of the World and Derived Soil Properties, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Rome, 1995.</p> <p>FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.2), Food and Agriculture Organization, FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012.</p> <p>Journet, E., Balkanski, Y., and Harrison, S. P.: A new data set of soil mineralogy for dust-cycle modeling, Atmospheric Chemistry and<br> Physics, 14, 3801–3816, https://doi.org/10.5194/acp-14-3801-2014, 2014.</p> <p>Nickovic, S., Vukovic, A., Vujadinovic, M., Djurdjevic, V., and Pejanovic, G.: Technical Note: High-resolution mineralogical database of dust-productive soils for atmospheric dust modeling, Atmospheric Chemistry and Physics, 12, 845–855, https://doi.org/10.5194/acp-12-845-2012, 2012.</p> <p> </p>
Climate Change Impacts for 14 Tree Species in Southwest Colorado
Forest management traditionally has been based on expectation of a steady climate. In the face of a changing climate, management requires projections of changes in the distribution of the climatic niche of the major species and strategies for applying the projections. We prepared climatic habitat models incorporating heatload as a topographic predictor for the 14 upland tree species of southwestern Colorado, USA, an area that has already seen substantial climate impacts. Models were trained with over 800,000 points of known presence and absence. Using 11 climate scenarios for the decade around 2060, we classified and mapped change for each species. Projected impacts are extensive. Except for the low-elevation woodland species, persistent habitat is rare. Most habitat is lost or threatened and is poorly compensated by emergent habitat. Three species may be locally extirpated. Nevertheless, strategies are described that can use the projections to apply management where it is likely to be most effective, to facilitate or assist migration, to favor species likely to be suited in the future, and to identify potential climate refugia.
20% of US electricity from wind will have limited impacts on system efficiency and regional climate
<p>Simulations of wind turbine wakes conducted with WRF for current and possible future installed capacities upto 20% US electricity from wind.</p>
Climate impact_River flow_Sweden
<p>Climate-impact ensemble of River flow (m<sup>3</sup> yr<sup>-1</sup>) for 12 selected hydropower plants in Swedish rivers, as well as the total river discharge to the Swedish coast. The dataset include modelled time-series from 18 ensemble members with daily values from 1981-2100, calculated with the HYPE model code (HYPE_version_4_8_0) in the S-HYPE model set-up (s-hype2012_version_2_0_0). Model code can be downloaded from: http://hypecode.smhi.se/ and climate projections from ESGF at https://www.cordex.org/. The data is in Excel format with one sheet per climate projection.</p>
SWAT river water, TN & TP loads to Limfjorden under climate change scenarios (Delta change) + baseline SWAT loads 2009-2018. Paper ". Impacts of climate change on water quality, benthic mussels and suspended mussel culture in a shallow, eutrophic estuary by Maar et al. Heliyon,
<p>SWAT river water, TN & TP loads to Limfjorden under climate change scenarios (Delta change) + baseline SWAT loads 2009-2018 </p>
Datasets used for "Heat Pump - Heating Electrification and Climate Change - Grid Impact Studies"
<h2> Summary</h2> <p> </p> <p>In this work, we explore long term patterns in electricity demand driven by the dual effects of space heating electrification and climate change. We use an open source nodal power system model of the Electric Reliability Council of Texas (ERCOT) system to investigate a wide range of future climate and technology scenarios that evolve over time, and report results in terms of market prices, reliability and corresponding relative capacity requirements </p> <h2> About </h2> <p>The technical analysis aimed to:</p> <h3>1) Understand the Long-Term Patterns:</h3> <p>We aim to analyze patterns in peak load, total load, loss of load, and the seasonality of these phenomena, driven by widespread heat pump adoption alongside climate change.</p> <h3>2) Use Extensive Scenario Analysis:</h3> <p>Explore a wide range of future scenarios, including variations in climate pathways, to capture the uncertainty associated with these long-term changes. In total, 1280 simulation years.</p> <h3>3) Use a validated open source DC OPF model(reproducibility)</h3> <p>Use an open-source nodal power system model of the ERCOT system to simulate and understand the potential impacts on market prices, reliability, and relative capacity requirements. Similar models are available for all interconnections of the conterminous US.</p> <h3>4) Assess Grid Vulnerability:</h3> <p>Assess the vulnerability of the grid to these simultaneous changes, identify potential vulnerability.</p> <h3>5) Provide Insights for System Planners:</h3> <p>Offer results that can assist long-term system planners in anticipating and preparing for potential shifts in grid reliability.</p>
Updated Supplementary Figures for Can leafhoppers help us trace the impact of climate change on agriculture?
<p>Supplementary Figures for: <strong>Can</strong> <strong>leafhoppers help us trace the impact of climate change on agriculture? </strong>to be posted in bioRxiv.</p><p><strong>Figure S1. </strong>Diversity indexes calculated in this study to compare leafhopper diversity each growing season investigated in this study and the geographic regions where the strawberry fields were located. Statistical analyses were performed for Shannon and Simpson finding that in both cases there is no interaction between years and regions with <i>p</i> = 0.0889 and <i>p</i> = 0.7139, respectively.</p><p><strong>Figure S2.</strong> Distinctive RFLP patterns obtained with <i>Cpn</i>ClassiPhyR from <i>in silico</i> digestion of <i>cpn60</i>UT from SbGPQ clones and AY-Col. Lanes labelled MW in <i>in silico</i> RFLP represent <i>Hae</i>III-digested phage <i>ϕ</i>X174 DNA.</p><p><strong>Figure S3.</strong> Phylogenetic tree using neighbour-joining method of the <i>16S, secY, nusA, rp, secA, cpn60 </i>and<i> tuf</i> sequences obtained in this study for the SbGP phytoplasma and sequences retrieved from Genbank. <i>Acholeplasma laidlawii</i> PG8 was used as an outgroup. The phylogenetic tree was bootstrapped 1000 times to achieve reliability. Bar, 1 substitution in 100 or 500 positions. </p><p><strong>Fig. S3 Panel 1: </strong>cpn60UT, tuf, and secY trees.</p><p><strong>Fig. S3 Panel 2:</strong> nusA, rp, and secA trees.</p><p><strong>Fig. S3 Panel 3:</strong> 16S tree with subtree showing heterogeneity of SbGPQ and 'Ca. P. tritici'.</p><p><strong>Figure S4.</strong> Leafhopper feeding-associated damages observed in strawberry plants. <strong>A</strong>, in the field. <strong>B</strong>, in the greenhouse after incubation with leafhoppers.</p><p><strong>Figure S5.</strong> Alpha diversity indexes were calculated to study <i>Macrosteles quadrilineatus</i> microbiome observed for each growing season. No statistical difference was observed among the sites for any of the indexes calculated.</p><p><strong>Figure S6.</strong> Effect of insecticides leafhopper population control. Only those with a number of applications higher or equal to five are presented. We did not find statistical differences among the treatments before and after the application of the insecticides with <i>p</i> = 0.8488.</p><p><strong>Figure S7.</strong> Effect of insecticides on <i>Macrosteles quadrilineatus</i> and <i>Empoasca fabae</i> population control. All insecticides (n = 12) are represented but the statistical analysis was only performed with those that the number of applications was higher than 5. We did not find statistical differences among the treatments before and after the application of the insecticides with <i>p</i> = 0.1781 for the aster leafhopper <i>M.</i> <i>quadrilineatus </i>and <i>p</i> = 0.6540 for the potato leafhopper <i>E. fabae</i>.</p><p><strong>Figure S8.</strong> Comparison among the Shannon index obtained for leafhopper populations in vineyards in 2007 and 2008 and for leafhopper populations in strawberry fields in 2021 and 2022 in Quebec.</p>
High quality figures of "Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China"
<p>This repository provides the figures for the publication "Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China" in their original resolution, ensuring clarity and high-quality visual representations for readers.</p>
Data for the submitted paper by Yasunari et al., "Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments"
<p>The dataset contains some of the outputs from the global climate model experiments by MIROC5 on changing Siberian wildfire severities, the other data used in the paper (see READ_ME files on the data sources), the analyzed data, and the scripts for analyses, which were used in the following submitted paper. Note that this dataset also includes unused data for the paper:</p> <p><br>Yasunari, T. J., D. Narita, T. Takemura, S. Wakabayashi, and A. Takeshima, Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments, submitted.</p> <p>Please read the READ_ME files for detailed information in each directory (especially see the "about_figures_and_tables/" directory first). Because of their large sizes, the data were separated into three zipped files.</p>
Data and code in support of "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower"
<p>This dataset contains all the data and processing needed to produce results and figures reported in the manuscript "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower".</p> <p> </p> <p>The README file guides through the material available to support replication of the results and figures.</p>
Impacts of Quaternary climatic changes on the diversification of riverine cichlids in the lower Congo River
<p>Climatic and geomorphological changes during the Quaternary period impacted global patterns of speciation and diversification across a wide range of taxa, but few studies have examined these effects on African riverine fishes. The lower Congo River is an excellent natural laboratory for understanding complex speciation and population diversification processes as it is hydrologically extremely dynamic and recognized as a continental hotspot of diversity harboring many narrowly endemic species. A previous study using genome-wide SNP data highlighted the importance of dynamic hydrological regimes to the diversification and speciation in lower Congo River cichlids. However, historical climate and hydrological changes (e.g., reduced river discharge during extended dry periods) have likely also influenced ichthyofaunal diversification processes in this system. The lower Congo River offers a unique opportunity to study climate-driven changes in river discharge, given the massive volume of water from the entire Congo basin flowing through this short stretch of the river. Here, we, for the first time, investigate the impacts of paleoclimatic factors on ichthyofaunal diversification in this system by inferring divergence times and modeling patterns of gene flow in four endemic lamprologine cichlids, including the blind cichlid, <em>Lamprologus lethops</em>.</p>
Future water level, discharge, and flood maps under climate change and infrastructure impacts along the Cambodian Mekong.
<p>Baseline and future (2036-2065) river water levels and discharges at 4 gauging stations along the Cambodian Mekong (Kratie, Kampong Cham, Chrouy Changva, and Neak Loeung) under different scenarios of climate change (RCP 4.5 and 8.5) and infrastructural developments. Average depth and duration flood maps are also included for each scenario.</p> <p> </p> <p>A full description of the methods and results can be found in the article: </p> <p>Alexander J. Horton, Nguyen V. K. Triet, Long P. Hoang, Sokchhay Heng, Panha Hok, Sarit Chung, Jorma Koponen, and Matti Kummu. (2022). The Cambodian Mekong floodplain under future development plans and climate change. <em>Nat. Hazards Earth Syst. Sci.</em></p>
Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon (Open)
<p>This work was carried out in the scope and with the support of the project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events (CLIMAX).</p> <p>The project consortium includes the following institutions: Centre National de la Recherche Scientifique CNRS/Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-IFAECI) (Argentina-France); General Coordination of Earth Sciences /National Institute for Space Research (INPE) (Brazil); Institut de Recherche pour le Développement (IRD)/ Unité Mixte de Recherche (UMR 245) (France); Le Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (France); Potsdam Institute for Climate Impact Research (PIK) (Germany); Technical University of Munich (TUM) (Germany) and Wageningen University and Research (WUR) Netherlands). The project is sponsored by the Collaborative Research Action (CRA) on “Climate Predictability and Inter-Regional Linkages” of the Belmont Forum, launched in 2015.</p> <p>Climate variability patterns linking the South American Monsoon region, including Amazonia, with southeastern South America influence climate extremes and impact several societal sectors. More than 200 million people live in the study region, which is also one of the largest agricultural production regions of the world and home to the world’s second largest hydroelectric power plant.</p> <p>The objectives of CLIMAX include better understanding the combined role of remote and local drivers on South American climate variability from sub-seasonal to decadal timescales, and its impact on the occurrence and intensity of extreme events. Special focus is given to an improved understanding of the effects of land use changes from the Amazon to the subtropics and their impact on climate.</p> <ol> <li> <p><strong>EXPERIMENT DESIGN</strong></p> </li> </ol> <p>We used four models that are classified as Dynamic Global Vegetation Models (DGVMs) (Prentice et al., 2007; Rezende et al., 2015): Integrated Model of Land Surface Processes (INLAND) (Tourigny, 2014); Lund-Potsdam-Jena managed Land model version 4 (LPJmL4) (Schaphoff et al., 2018), Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) (Smith et al. 2001, Hickler et al., 2012), and Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE) (Krinner et al., 2005).</p> <p>We used three forcings with climate data (GLDAS, GSWP3, and WATCH+WFDEI), Land Use Change (LUC) data and validation data (FLUXCOM (Remote sensor+meteorological data+artificial neural network approach), FLUXCOM (eddy covariance), MODIS (Light Use Efficiency), GLEAM, and TerraClimate (Rezende et al., 2022).</p> <p>We conducted two sets of simulation experiments with different values of CO2: 1) increasing CO2 from the pre-industrial period to 2010 named <strong>historical CO</strong><strong>2</strong> (<strong>hist CO</strong><strong>2</strong>); 2) constant concentration of 278 ppm of (pre-industrial) atmospheric CO2 named <strong>constant CO</strong><strong>2</strong><strong> (const CO</strong><strong>2</strong><strong>)</strong>. We ran both CO2 experiments under <strong>Land Use Change</strong> (<strong>LUC</strong>) and <strong>Potential Natural Vegetation </strong>(<strong>PNV</strong>) conditions. All combinations of CO2 and land use change resulted in four sets of simulation experiments per climate input: 1. <strong>LUC historical CO</strong><strong>2</strong>; 2. <strong>LUC constant CO</strong><strong>2</strong>; 3. <strong>PNV historical CO</strong><strong>2</strong>; 4. <strong>PNV constant CO</strong><strong>2 </strong> (Rezende et al., 2022).</p> <p><strong>2. DATA DESCRIPTION</strong></p> <p>The complete description of the data, including the climate forcing, LUC, the validation datasets, methodology, simulations, discussion and conclusion is in Rezende et al. (2022). This archive contains only the data description from the simulations (outputs) by the DGVMs.</p> <p><strong>2.1 SOFTWARE </strong></p> <p><br> The data were manipulated, worked, standardized, converted using the software: <strong>Climate Data Operators (cdo) version 1.7.0</strong>, <strong>Grid Analysis and Display System (Grads) (</strong><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a><strong>) version 2.0.2, and RStudio Desktop version</strong> 1.3.1093 <strong>(R Core Team, 2020)</strong>, through command lines and several scripts developed for this purpose. The figures were generated with <strong>Grads,</strong> and <strong>RStudio</strong>, and some images were enhanced with <strong>Gimp version 2.8.22</strong>. All the software used is freeware.</p> <p><strong> 2.2 PRIMARY DATA FROM SIMULATIONS</strong></p> <p>Data originating from the simulations are in monthly resolution, covering South America, with all the forcings. Despite data spanning over 1948-2010 or 1950-2010 our study focuses on the period 1981-2010.</p> <p><strong>Variables</strong>: Gross Primary Productivity (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p>The naming of the files is according to the following rules:</p> <p><strong>DGVM_forcing_vegetation cover_CO2 concentration_attribute</strong></p> <p><strong>DGVMs</strong>;</p> <p> InLand (INLAND)</p> <p> LPJ-G (LPJ-GUESS)</p> <p> LPJmL (LPJmL4)</p> <p> ORCHI (ORCHIDEE)</p> <p>forcings: </p> <p> gld - GLDAS</p> <p> gsw – GSWP3</p> <p> wat – WATCH+WFDEI</p> <p> </p> <p>vegetation cover:</p> <p> LU – Land Use Change</p> <p> PNV – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration:</p> <p> CO2 – historical CO2</p> <p> noCO2 – constant CO2 = 278 ppm</p> <p> </p> <p>variables:</p> <p> E – evaporation</p> <p> Et – transpiration</p> <p> gpp – Gross Primary Productivity</p> <p> npp - Net Primary Productivity (not used in the experiment)</p> <p> </p> <p><strong>Example</strong>:</p> <p> inLand_gld_LU_noCO2_E.nc</p> <p> </p> <p><strong> 2.3 SUPPLEMENTARY DATA </strong></p> <p>These interception loss data (mm month-1) were requested by a reviewer to complement the analysis and are available only for the LUC CO2 scenario and for the study region: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).</p> <p>Files are named according to the following rules:</p> <p>variable_season_forcing_DGVM_vegetation cover CO2 concentration_region</p> <p>variable:</p> <p> inter – loss by interception</p> <p> </p> <p>season:</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>forcings: </p> <p> gl - GLDAS</p> <p> gs – GSWP3</p> <p> wa – WATCH+WFDEI</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or – ORCHIDEE</p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p> inter_D_gl_in_LC_SA.nc</p> <p><strong>2.4 PROCESSED DATA</strong></p> <p>Processed data cover all scenarios and input data sets and are restricted to the study area: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).They are in seasonal resolution with averages for January-February-March-April (JFMA) (rainy season) and averages for June-July-August-September (JJAS). Each of the files contains the Gross Primary Productivity variables (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p> </p> <p>Files are named according to the following rules:</p> <p> </p> <p><strong>season_DGVM_forcing_vegetation cover CO2 concentration_region</strong></p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>Example:</p> <p>D_in_Gs_LN.nc</p> <p><strong>2.5 DERIVED DATA</strong></p> <p> </p> <p>The variable Water Use Efficiency (WUE) (kg m-2 mm-1 month-1) results from rate: GPP / Tr (transpiration) (Eq. 1). </p> <p> </p> <table> <tbody> <tr> <td> <p> WUE = GPP / Tr</p> </td> <td> <p>(Eq. 1)</p> </td> </tr> </tbody> </table> <p> </p> <p>These data refer to the study region: southern Amazon and apply to only one scenario: Land Use Change and historic CO2. Files are named naming of according to the following rules:</p> <p> </p> <p>Variable:</p> <p> WUE – Water Use Efficiency</p> <p> </p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p>wue_D_lm_Gl_LC_SA.nc</p> <p> </p> <p><strong>How to cite this work</strong>:</p> <p>Rezende, Luiz F. C., Aline Castro, Celso Von Randow, Romina Ruscica, Boris Sakschewski, Phillip Papastefanou, Nicolas Viovy, Kirsten Thonicke, Anna Sörensson, Anja Rammig, Iracema F. A. Cavalcanti. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p><strong>References</strong></p> <p><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>. Center for Ocean-Land-Atmosphere Studies, Institute of Global Environment and Society,<a href="https://en.wikipedia.org/wiki/George_Mason_University"> George Mason University</a>. Archived from<a href="http://www.iges.org/grads/gadoc/"> the original</a> on 7 April 2015. Retrieved 14 March 2015.</p> <p>Hickler T. et al., 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species based dynamic vegetation model. Glob Ecol Biogeograp 21:50–63, <a href="https://doi.org/10.1111/j.1466-8238.2010.00613.x">https://doi.org/10.1111/j.1466-8238.2010.00613.x</a></p> <p>Krinner, G.et al., 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19, GB1015, doi:10.1029/2003GB002199.</p> <p>R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. </p> <p>Prentice IC (2007) Dynamic global vegetation modeling: quantifying terrestrial ecosystem responses to large-scale environmental change. In: Canadell J, Pataki D, Pitelka L (eds) Terrestrial ecosystems in a changing world. Springer, Berlin Heidelberg.</p> <p>Rezende, Luiz F. C. et al., 2015. Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2. Int J Biometeorol., 2015, doi: 10.1007/s00484-015-1087-6.</p> <p>Rezende, Luiz F. C. et al., 2022. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research - Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p>Schaphoff, S. et al., 2018. LPJmL4 – a dynamic global vegetation model with managed land –Part 1: Model description. Geosci. Model Dev., 11, 1343–1375, 2018, <a href="https://doi.org/10.5194/gmd-11-1343-2018">https://doi.org/10.5194/gmd-11-1343-2018</a>.</p> <p>Smith B. et al (2001) Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob Ecol Biogeograp 10:621–637</p> <p>Tourigny, E. (2014). Multi-scale fire modeling in the neotropics: coupling a land surface model to a high resolution fire spread model, considering land cover heterogeneity. Phd dissertation, Meteorology. INPE. Retrieved from http://urlib.net/sid.inpe.br/mtc-m21b/2014/05.30.00.36</p> <p><br> </p> <p> </p>
High resolution maps of climatological parameters for analyzing the impacts of climatic changes on Swiss forests
<p>Assessing the impacts of climatic changes on forests requires the analysis of actual climatology within the forested area. In mountainous areas, climatological indices vary markedly with the micro-relief, i.e. with altitude, slope, and aspect. Consequently, when modelling potential shifts of altitudinal belts in mountainous areas due to climatic changes, maps with a high spatial resolution of the underlying climatological indices are fundamental. Here we present a set of maps of climatological indices with a spatial resolution of 25 by 25 m. The presented dataset consists of maps of the following parameters: average daily temperature high and low in January, April, July, and October as well as of the year; seasonal and annual thermal continentality; first and last freezing day; frost-free vegetation period; relative air humidity; solar radiation; and foehn conditions. The parameters represented in the maps have been selected in a knowledge engineering approach. The maps show the climatology of the periods 1961-1990 and 1981-2010. The data can be used for statistical analyses of forest climatology, for developing tree distribution models, and for assessing the impacts of climatic changes on Swiss forests.</p>
Code and data from: Experiential legacies of early-life dietary polyunsaturated fatty acid (PUFA) content on juvenile Walleye: Potential impacts from climate change
<p>Climate-induced shifts in plankton blooms may alter fish recruitment by affecting the fatty acid composition of early-life diets and corresponding performance. Early-life nutrition may immediately affect survival but may also have a lingering influence on size and growth via experiential legacies. We explored the short- and longer-term performance consequences of different concentrations of polyunsaturated fatty acids (PUFA) for juvenile Walleye (<em>Sander vitreus</em>, Mitchill 1818). For the first 10 d of feeding, juveniles were provided <em>Artemia </em>enriched with: oleic acid (low PUFA), high docosahexaenoic acid and high eicosapentaenoic acid (high PUFA), or high PUFA and a form of vitamin E (high PUFA + E). After 10 d, all fish were fed a high-quality diet and reared for an additional 27 d. Juveniles fed either high PUFA diet were 1.15-fold larger (PUFA mean ± SD = 20.0 ± 3.3 mg; PUFA + E = 19.8 ± 3.3 mg) than those fed the low PUFA (17.3 ± 2.8 mg) diet after 10 d of feeding. After 27 days, juveniles initially fed the high PUFA diet were still 1.10-1.20-fold larger (PUFA = 407.0 ± 61.6 mg; PUFA + E = 422.7 ± 58.7 mg) than those initially fed the low PUFA diet (356.5.0 ± 39.5 mg). Our findings demonstrate that fatty acid composition of juvenile Walleye diets has immediate and lingering size effects. As changes in climate continue to alter lower trophic levels, fish management and conservation may need to consider short- and long-term effects of temporal or spatial differences in early-life diet quality.</p>
Dataset for Surrogate Model Benchmarking for Dynamic Climate Impact Models
<p>The data represents time series of seasonal weather forecasts for rainfall and temperature. The dataset contains 10 forecasts of 6-month horizon from, two per year, from 2017 to 2021; start dates January 1 and July 1, respectively. Each forecast comprises 50 ensemble members. In total, this sums up to 91300 data points, each containing daily average rainfall, temperature.</p> <p>Each sample (row) comprises following features (columns):</p> <ul> <li><strong>datetime</strong>: Date of the forecast sample.</li> <li><strong>forecast</strong>: Identifier of the ensemble member, i.e. integer between 1 and total number of ensemblemembers.</li> <li><strong>precip</strong>: Averaged daily rainfall forecast in millimeters.</li> <li><strong>temp</strong>: Averaged daily temperature forecast in degree Celsius.</li> </ul> <p>Dataset created by The Weather Company, an IBM business. This service is based on data and products of the European Center for Medium-range Weather Forecasts (ECMWF-Archive and ECMWF-RT). Generated using Copernicus Climate Change Service information [2019 and ongoing]. ECMWF Archive data published under a Creative Commons Attribution 4.0 International (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/<br> Disclaimer: Neither the European Commission nor ECMWF is responsible for any use that may be made of the information it contains.</p>
Cloudiness delays projected impact of climate change on coral reefs
<p>The increasing frequency of mass coral bleaching and associated coral mortality threaten the future of warmwater coral reefs. Although thermal stress is widely recognized as the main driver of coral bleaching, exposure to light also plays a central role. Future projections of the impacts of climate change on coral reefs have to date focused on temperature change and not considered the role of clouds in attenuating the bleaching response of corals. In this study, we develop temperature- and light-based bleaching prediction algorithms using historical sea surface temperature, cloud cover fraction and downwelling shortwave radiation data together with a global-scale observational bleaching dataset observations. The model is applied to CMIP6 output from the GFDL-ESM4 Earth System Model under four different future scenarios to estimate the effect of incorporating cloudiness on future bleaching frequency, with and without thermal adaptation or acclimation by corals. The results show that in the low emission scenario SSP1-2.6 incorporating clouds delays the bleaching frequency conditions by multiple decades in some regions, yet the majority (>70%) of coral reef cells still experience dangerously frequent bleaching conditions by the end of the century. In the moderate scenario SSP2-4.5, however, thermal stress would overwhelm the mitigating effect of clouds by mid-century. Thermal adaptation or acclimation by corals could further shift the bleaching projections by up to 40 years, yet coral reefs would still experience dangerously frequent bleaching conditions by the end of century in SPP2-4.5. The findings show that multivariate models incorporating factors like light may improve the near-term outlook for coral reefs and help identify future climate refugia, but the long-term future of coral reefs remains questionable in moderate to higher emissions scenario.</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.