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47 results for “climate forcing”
Decadal BIOCLIM estimates based on ISIMIP3b climatic forcing data for the European continent
<p>This dataset contains BIOCLIM variables (plus huss, sfcwind, rsds) which have been prepared and calculated from the original ISIMIP3b bias-adjusted climate forcing data from 5 GCM models (obtained on 2023-08-07). <br><br>For more information on the original data and its properties, please see the ISIMIP3b modelling protocol and here specifically the climate forcing section <a href="https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data" target="_blank" rel="noopener">https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data</a> and <a href="https://doi.org/10.5194/gmd-17-1-2024">Frieler et al. (2024)</a>.</p> <p>The original climate forcing data (global extent, daily temporal grain) were cropped to the European extent and spatial-temporally aggregated. Here 10 year (decadal) steps were chosen as target climatology.<br><br>For each time slot (e.g. 10 years) and scenario (historical or ssps) the following 22 variables were calculated:</p> <p>bioclim01 = Annual Mean Temperature<br>bioclim02 = Mean Diurnal Range (Mean of monthly (max temp - min temp))<br>bioclim03 = Isothermality (BIO2/BIO7) (×100)<br>bioclim04 = Temperature Seasonality (standard deviation ×100)<br>bioclim05 = Max Temperature of Warmest Month<br>bioclim06 = Min Temperature of Coldest Month<br>bioclim07 = Temperature Annual Range (BIO5-BIO6)<br>bioclim08 = Mean Temperature of Wettest Quarter<br>bioclim09 = Mean Temperature of Driest Quarter<br>bioclim10 = Mean Temperature of Warmest Quarter<br>bioclim11 = Mean Temperature of Coldest Quarter<br>bioclim12 = Annual Precipitation<br>bioclim13 = Precipitation of Wettest Month<br>bioclim14 = Precipitation of Driest Month<br>bioclim15 = Precipitation Seasonality (Coefficient of Variation)<br>bioclim16 = Precipitation of Wettest Quarter<br>bioclim17 = Precipitation of Driest Quarter<br>bioclim18 = Precipitation of Warmest Quarter<br>bioclim19 = Precipitation of Coldest Quarter<br>huss = Average (arithmetric mean) specific humidity<br>rsds = Average (arithmetric mean) Surface downwelling shortwave radiation<br>sfcwind = Average near-surface wind speed (arithmetric mean)<br><br>---<br><strong>Data properties:</strong></p> <table> <tbody> <tr> <td>Shared Socioeconomic Pathways (SSP)</td> <td>SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5</td> </tr> <tr> <td>General circulation models (GCMs)</td> <td>GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL</td> </tr> <tr> <td>Spatial grain</td> <td>0.5 degree (~50km²)</td> </tr> <tr> <td>Geographic projection</td> <td>WGS 84</td> </tr> <tr> <td>Temporal grain</td> <td>10 year steps</td> </tr> <tr> <td>Spatial extent</td> <td>Continental Europe including Turkey (see screenshot)</td> </tr> <tr> <td>Temporal extent</td> <td>1850 to 2010 (Historical), 2010 - 2100 (Future)</td> </tr> <tr> <td>Number of variables</td> <td>22</td> </tr> </tbody> </table> <p><br>All files are provided in netCDF (nc) format. The preprocessed datasets are provided as it and the author takes no responsibility for errors or misuse. </p>
Paleoclimate Data-Model Comparison and the Role of Climate Forcings over the Past 1500 Years
<p>The past 1500 years provide a valuable opportunity to study the response of the climate system to external forcings. However, the integration of paleoclimate proxies with climate modeling is critical to improving the understanding of climate dynamics. In this paper, a climate system model and proxy records are therefore used to study the role of natural and anthropogenic forcings in driving the global climate. The inverse and forward approaches to paleoclimate data-model comparison are applied, and sources of uncertainty are identified and discussed. In the first of two case studies, the climate model simulations are compared with multiproxy temperature reconstructions. Robust solar and volcanic signals are detected in Southern Hemisphere temperatures, with a possible volcanic signal detected in the Northern Hemisphere. The anthropogenic signal dominates during the industrial period. It is also found that seasonal and geographical biases may cause multiproxy reconstructions to overestimate the magnitude of the long-term preindustrial cooling trend. In the second case study, the model simulations are compared with a coral d18O record from the central Pacific Ocean. It is found that greenhouse gases, solar irradiance, and volcanic eruptions all influence the mean state of the central Pacific, but there is no evidence that natural or anthropogenic forcings have any systematic impact on El Nino-Southern Oscillation. The proxy climate relationship is found to change over time, challenging the assumption of stationarity that underlies the interpretation of paleoclimate proxies. These case studies demonstrate the value of paleoclimate data-model comparison but also highlight the limitations of current techniques and demonstrate the need to develop alternative approaches.</p>
Climate Forcing due to Future Ozone Changes: An intercomparison of metrics and methods
<p>The data provided in this repository relates to a paper on ozone radiative forcing submitted for publication in Atmos. Chem. Phys., as part of the TOAR-II special issue (<a href="https://acp.copernicus.org/articles/special_issue1256.html">ACP – Special issue – Tropospheric Ozone Assessment Report Phase II (TOAR-II) Community Special Issue (ACP/AMT/BG/GMD inter-journal SI)</a>). The paper is entitled "<span>Climate Forcing due to Future Ozone Changes</span><span>: An intercomparison of metrics and methods" by authors <span><span>William J. Collins</span></span><span><span>,</span> <span>Fiona M. O’Connor</span></span><span><span>, </span><span>Connor R. Barker</span></span><span><span>, </span><span>Rachael E. Byrom</span></span><span><span>, </span><span>Sebastian D. Eastham</span></span><span><span>,</span> <span>Øivind Hodnebrog</span></span><span><span>, Patrick Jöckel</span></span><span><span>, </span><span>Eloise A. Marais</span></span><span><span>, </span><span>Mariano Mertens</span></span><span><span>, Gunnar Myhre</span></span><span><span>, Matthias Nützel</span></span><span><span>, Dirk Olivié</span></span><span><span>, Ragnhild </span><span>Bieltvedt</span><span> Skeie</span></span><span><span>5</span></span><span><span>, Laura Stecher</span></span><span><span>, Larry W. Horowitz</span></span><span><span>, Vaishali Naik</span></span><span><span>, Gregory Faluvegi</span></span><span><span>, Ulas Im</span></span><span><span>, Lee T. Murray</span></span><span><span>, Drew Shindell</span></span><span><span>, Kostas Tsigaridis</span></span><span><span>, Nathan Luke Abraham</span></span><span><span>, James Keeble.</span></span></span></p>
Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (<a href="https://doi.org/10.1034/j.1600-0870.2001.00241.x" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the article "<em>The evolution of a non-autonomouys chaotic system under non-periodic forcing: a climate change example</em>", published by Chaos (<a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a>.</p> <p>All files uploaded were generated from simulations run by the authors.</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p> <p><strong>Note:</strong> This version (v1.1) is the same version as v1.0 but with the correct README file.</p>
Supplementary data for "Effect of Uncertainty in Water Vapor Continuum Absorption on CO2 Forcing, Longwave Feedback, and Climate Sensitivity"
<h3>This dataset is supplementary to the article "Effect of Uncertainty in Water Vapor Continuum Absorption on CO2 Forcing, Longwave Feedback, and Climate Sensitivity".</h3> <h3>spectral_olr.nc</h3> <p>This file contains the spectral outgoing longwave radiation (OLR) calculated using the line-by-line radiative transfer model ARTS and the radiative-convective equilibrium model konrad. It contains spectral OLR for surface temperatures from 270K to 330K for different strengths of the water vapor continuum absorption.</p> <h3>opacity_emission_level.py</h3> <p>This file also contains the spectrally resolved optical depth and the emission level of outgoing longwave radiation for the considered absorption species (H2O lines, H2O continuum, H2O self continuum, H2O foreign continuum, CO2, N2, and O2).</p> <h3>continuum_reference_conditions.nc</h3> <p>This file contains the reference continuum absorption coefficients that were used to calculate the adjustment to the foreign continuum for the single-constraint experiment.</p> <h3>continuum_all_profiles.nc</h3> <p>This file contains the reference continuum absorption coefficients that were used to calculate the adjustment to the foreign continuum for the general-constraint experiment.</p> <h3>modified_continuum_input_files_single_constraint.zip and modified_continuum_input_files_general_constraint.zip</h3> <p>These files contain the modified continuum data files used for the implementation of the MT_CKD continuum model in the line-by-line model ARTS for the single-constraint and general-constraint experiments, respectively.</p> <h3>tau_column.nc and tau_profile.nc</h3> <p>These files contain separately for each absorption species the vertically integrated opacity spectra, and the opacity profiles at two selected wavenumbers.</p> <p> </p>
Data for the publication "The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity"
<p>This repository contains the data for the paper:</p> <p>"Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Stier, P. Partridge, D. G., Tegen, I., Bey, I., Stanelle, T., Kokkola, H., and Lohmann, U.: The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity, Geosci. Mod. Dev., https://doi.org/10.5194/gmd-2018-307, 2019."</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.2553891)</p> <p> </p>
Climate Forced Hydropower Simulations Using NASA NEX-GDDP
<p>* This update includes the corrected values for all Peruvian Hydropower Plants included in the original dataset. </p> <p>This dataset includes the results of simulations of future hydropower usable capacity for power plants in Brazil, Colombia, and Peru. These simulations have been forced using NASA's Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, which includes maximum temperature, minimum temperature, and precipitation simulations from 21 Global Climate Models (GCM) and three scenarios. The scenarios include a retrospective run (1950-2005) and two projection runs for Representative Concentration Pathways (RCP) 4.5 and 8.5. There is a folder for each country that includes a power plant characteristics file, with a list of all the power plants and the characteristics used for the analysis (installed capacity, effective height, reservoir specifications, etc.). Additionally, there is a folder including the dates for the usable capacity files. Each file inside the usable capacity folder is labeled "power_", followed by the power plant name (e.g. "tres_irmaos"), and the scenario (e.g. "rcp45_2006_2099"). </p> <p>This work is based on the future publication: Caceres, A.L., Jaramillo, P., Matthews, H.S., Samaras, C. & Nijssen B. "Hydropower under climate uncertainty: characterizing the usable capacity of Brazilian, Colombian and Peruvian power plants under climate scenarios".</p>
PermaRisk Projekt: Soil Parameters and Climate Forcing
<p>This dataset contains soil parameters and climate forcing data required to simulate the ground thermal dynamics for regions in Alaska (Prudhoe Bay), Canada (Churchill), and Russia (Lena-River Delta) using the permafrost model CryoGrid (https://github.com/CryoGrid). The soil data were compiled in the frame of the Project: Simulating erosion processes in permafrost landscapes under a warming climate – a risk assessment for ecosystems and infrastructure within the Arctic - PermaRisk funded by the German Federal Ministry of Education and Research (BMBF).</p>
Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity
<p><strong>Citation:</strong> Zhu, J., & Poulsen, C. J. (2021). Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity. <em>Clim. Past</em>, <em>17</em>(1), 253–267. <a href="https://doi.org/10.5194/cp-17-253-2021">https://doi.org/10.5194/cp-17-253-2021</a></p> <p>Casename:</p> <ul> <li>FCM_PI: b.e12.B1850C5.f19_g16.iPI.01</li> <li>FCM_LGM: b.e12.B1850C5.f19_g16.i21ka.03</li> <li>SOM_PI: e.e12.E1850C5.f19_g16.PI.02</li> <li>SOM_GHG: e.e12.E1850C5.f19_g16.PI.21kaGHG.02</li> <li>SOM_ICE: e.e12.E1850C5.f19_g16.PI.21kaICE.02</li> <li>SOM_2CO2: e.e12.E1850C5.f19_g16.PIx2.02</li> <li>ATM_PI: f.e12.F1850C5.f19_g16.iPI.01</li> <li>ATM_GHG: f.e12.F1850C5.f19_g16.iPI.21kaGHG_ERF</li> <li>ATM_ICE: f.e12.F1850C5.f19_g16.iPI.21kaICE_ERF</li> <li>ATM_2CO2: f.e12.F1850C5.f19_g16.iPI.01.x2</li> </ul> <p><strong>Boundary condition files and the restart files are also provided as .zip files (bc.zip & rest.zip).</strong></p> <p><strong>Check out the Github repository for the setup of the LGM simulation</strong> (i.e., the entire CESM case folder): <a href="https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne">https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne</a></p> <p><strong>[NEW IN V3] More monthly data for PMIP4 (cmorized) are provided (files starting with `PMIP4.NCAR.CESM1.2-FV2`).</strong></p>
Subglacial hydrology modulates basal sliding response of the Antarctic ice sheet to climate forcing
<p><strong><em>Kazmierczak22_data.zip</em></strong><strong> contains the </strong><strong>dataset for the publication </strong><strong>« </strong>Subglacial hydrology modulates basal sliding response of the Antarctic ice sheet to climate forcing » <strong>and </strong><strong>the <em>MATLAB</em> codes used to create the figures appearing in the paper. For more details, please, open the <em>Read me.txt</em> file. </strong></p>
Climate Forced Hydropower Simulations for the African Continent Using NASA NEX GDDP
<p>This dataset includes the results of simulations of future hydropower usable capacity for power plants across the five African power pools. These include 87 power plants in 27 different countries. These simulations have been forced using NASA's Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, which includes maximum temperature, minimum temperature, and precipitation simulations from 21 Global Climate Models (GCM) and three scenarios. The scenarios include a retrospective run (1950-2005) and two projection runs for Representative Concentration Pathways (RCP) 4.5 and 8.5. We include a PDF file "Description of Data.pdf" that describes all the information included in the dataset. </p> <p>This work is based on the future publication: Caceres, A.L., Jaramillo, P., Matthews, H.S., Samaras, C, & Nijssen, Bart. "Power pools for the win: Assessing climate resilience of hydropower resources in African power pools".</p>
Data supporting: "Bayesian diagnosis of climate feedback evolution and forced temperature response"
<p><strong>This data set supports the paper:<br> Calafat, F. M., & Cael, B. B. (2023). Bayesian diagnosis of climate feedback evolution and forced temperature response, Geophysical Research Letters, submitted.</strong></p> <p>Please cite this paper when using this data set.</p> <p><em>Data description:</em></p> <ul> <li><strong>EBM_estimates.nc:</strong> this file contains Bayesian estimates from the energy balance model, including estimates of the climate feedback parameter and forced global average temperature change.</li> <li><strong>EBM_input_data.nc:</strong> this file contains all of the data used as input to the Bayesian energy balance model.</li> </ul>
Data and code for: Nature-based climate solutions can help mitigate the radiative forcing that follows deforestation
Open the record for dataset details and reuse information.
Data from: Climate warming prolongs the time interval between leaf-out and flowering in temperate trees: effects of chilling, forcing and photoperiod
<p><span>1. Leaf-out and flowering are two key phenological events of plants, denoting the respective onsets of visible vegetative growth and reproduction during the year. For each species, the schedule of vegetative growth and reproduction is crucial to the maximization of its fitness. Warming-induced advances of leaf-out and flowering have been reported frequently, however, it is unclear whether the responses of the two events are equal for any given species. </span></p> <p><span>2. Using long-term phenological records in Europe, we examined simultaneously the responses of both leaf-out and flowering of four common temperate tree species to climate warming and further examined the effects of winter chilling, spring forcing and photoperiod on the responses of the two events. </span></p> <p><span>3. We found that regardless whether flowering or leaf-out occurred first, the first event advanced more than the second during 1950 – 2013, resulting in a prolonged time interval between the two events. The temporal changes were also supported by a similar geographical trend that the time interval between the two events increased from cold to warm sites. Due to the warming-induced reduction in chilling, the spring forcing accumulated until the second event was increased more than the forcing accumulated until the first event, and that reduced the temperature sensitivity of the second event. In addition to the effect of chilling, the shorter photoperiod, associated with the advanced spring phenology, was also likely to substantially increase the spring forcing accumulated until the second event, which thus slowed down its advance, compared to the advance of the first event. The relative contributions of chilling and photoperiod to the increased forcing varied between species and events, with chilling mostly outweighing photoperiod. </span></p> <p><span>4. Synthesis. This study provides the large-scale empirical evidence of prolonged time interval between leaf-out and flowering with climate warming. The unequal advances of the two events may alter the partition of resources between vegetative growth and reproduction and cause different changes of spring frost damage to vegetative and reproductive tissues, which may alter species fitness and further affect ecosystem structure and function.</span></p>
Impact of host climate model on contrail cirrus effective radiative forcing estimates
<p>Data to reproduce the figures in the article 'Impact of host climate model on contrail cirrus effective radiative forcing estimates'.</p>
Glacier model simulations of moraine building forced by interannual variability in climate
<p>A set of 2,000-year simulations of moraine building by a glacier flowing through a synthetic alpine landscape forced by interannual variability in weather imposed on an otherwise stable climate. Moraine relief is shown for a standard deviation in mean annual air temperature (dT) of 0.5°C, 1.5°C, and 3.0°C around a long-term mean of 7.0°C. Simulations were made using the ice-flow model iSOSIA (Egholm et al., 2011, <em>Geomorphology</em>).</p>
Changes Monitoring in Hongjiannao Lake from 1987-2023 using Google Earth Engine and Analysis of Climatic and Anthropogenic Forces (Climatic Data)
<p>This dataset presents temporal (1987 to 2023) climatic data for the weather station near Hongjiannao Lake.</p>
Relationship between Channel Island Melospiza melodia (Song Sparrow) bill size and vegetation, seed extraction time, bite force, and climate (2014-2016)
<p>Inferring the environmental selection pressures responsible for phenotypic variation is a challenge in adaptation studies as traits often have multiple functions and are shaped by complex selection regimes. We provide indirect evidence that morphology of the multifunctional avian bill is primarily shaped by climate and thermoregulatory ability in <em>Melospiza melodia</em> (Song Sparrows) on the California Channel Islands. Our research builds on a study in Song Sparrow museum specimens that demonstrated a positive correlation between bill surface area and maximum temperature, suggesting a greater demand for dry heat dissipation in hotter, xeric environments. We sampled contemporary sparrow populations across three climatically distinct islands to test the hypotheses that bill morphology is influenced by habitat differences with functional consequences for foraging efficiency and is related to maximum temperature and, consequently, important for thermoregulation. Measurements of >500 live individuals indicated a significant, positive relationship between maximum temperature and bill surface area when correcting for body size. In contrast, maximum bite force, seed extraction time, and vegetation on breeding territories (a proxy for food resources) were not significantly associated with bill dimensions. While we cannot exclude the influence of foraging ability and diet on bill morphology, our results are consistent with the hypothesis that variation in Song Sparrows' need for thermoregulatory capacity across the northern Channel Islands selects for divergence in bill surface area.</p>
Data from: Connections between the Southern Ocean and the Eastern tropical Pacific in unforced and forced climate model simulations
<p>The sea surface temperature (SST) over the eastern tropical Pacific significantly influences global-mean climate feedback and may be driven in part by the SST over the Southern Ocean. Previous studies demonstrated a teleconnection from the Southern Ocean to the eastern tropical Pacific by perturbing the Southern Ocean climate. We investigate if this teleconnection holds in a fully coupled, freely running climate system using CMIP6 models. We assess the relationship between the Southern Ocean (SO) and the eastern tropical Pacific (SEP) by calculating correlations between SO and SEP SST timeseries within each model and regressions between mean SO and SEP SSTs across models. We show robust, positive SO-SEP relationships in an unforced climate using pre-industrial SSTs, in a forced climate using SST anomalies between pre-industrial and quadrupled CO<sub>2</sub> simulations, and in the SST pattern of the forced response relative to the global-mean SST anomaly. The strength of SO-SEP correlations is positively related to the stratocumulus cloud feedback off the west coast of South America, and negatively related to ocean heat uptake in the same region. As both shortwave cloud feedback and ocean heat uptake are underestimated in climate models, understanding their effects on SO-SEP teleconnections and their interactions is crucial for determining the strength of SO-SEP teleconnection in the real world and its trustworthiness in climate model projection.</p>
Data for manuscript "Evidence for large-scale climate forcing of dense shelf water variability in the Ross Sea"
<p>Relevant data shown in figures of the manuscript "Evidence for large-scale climate forcing of dense shelf water variability in the Ross Sea"</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.