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82 results for “Climate model simulation”
Model data created by MANDIFORE using ED2 simulating structure and biomass under harvest legacies and climate change as the Morton Arboretum, IL, USA, 2007-2099"
The data in this archive was used to investigate the long-term impacts of harvest-based management strategies on structure and resistance to climate-induced biomass loss using a process-based ecosystem model and an ensemble of climate change drivers in a midwestern USA hardwood forest. The model used was a modified version of the Ecosystem Demography model version 2.1 (ED2) where trees could be grouped based on plant functional type and size allowing for different management regimes to be simulated. The simulation site was an actively managed mid-continental North American temperate deciduous forest at The Morton Arboretum (TMA) near Chicago, USA. The initial conditions (composition and structure) of the forest simulated were initialized using field survey data from 487, 8.92-m radius plots collected in 2018. Different levels of tree removal based on traditional forestry management techniques for temperate deciduous forests were simulated and evaluated for their effects on forest structure and resistance to drought induced mortality for the period of 2006 to 2100. Model initialization and spin up occurred from 2006-2020 at which point management was simulated for the period of 2020-2024 before running without management for 2024 to 2100. Fourteen CMIP5 GCMs were used for meteorological drivers for 2006 to 2100 for both RCP4.5 and RCP 8.5 warming scenarios. CO2 concentration was held constant at 380 ppm over the simulation period. Four management regimes were simulated for each of these GCM-RCP pairings leading to a total of 112 distinct model runs. The four management regimes were group selection, shelterwood, understory thinning and an additional no management scenario served as a control. Data in this archived is separated into the 112 distinct model runs.
Data from: Climate-mediated hybrid zone movement revealed with genomics, museum collection and simulation modeling
Climate-mediated changes in hybridization will dramatically alter the genetic diversity, adaptive capacity and evolutionary trajectory of interbreeding species. Our ability to predict the consequences of such changes will be key to future conservation and management decisions. Here we tested through simulations how recent warming (over a 32-year period) is affecting the geographic extent of a climate-mediated developmental threshold implicated in maintaining a butterfly hybrid zone (Papilio glaucus and Papilio canadensis; Lepidoptera: Papilionidae). These simulations predict a 68 km shift of this hybrid zone. To empirically test this prediction, we assessed genetic and phenotypic changes using contemporary and museum collections and document a 40 km northward shift of this hybrid zone. Interactions between the two species appear relatively unchanged during hybrid zone movement. We found no change in the frequency of hybridization and regions of the genome that experience little to no introgression moved largely in concert with the shifting hybrid zone. Model predictions based on climate scenarios predict this hybrid zone will continue to move northward, but with substantial spatial heterogeneity in the velocity (55-144 km/1°C), shape, and contiguity of movement. Our findings suggest that the presence of non-climatic barriers (e.g., genetic incompatibilities) and/or non-linear responses to climatic gradients may preserve species boundaries as the species shift. Further, we show that variation in the "geography" of hybrid zone movement could result in evolutionary responses that differ for geographically distinct populations spanning hybrid zones and thus have implications for the conservation and management of genetic diversity.
Data Release for Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2-CISM2 Coupled Climate–Ice Sheet Model
<p>CESM2 and CISM2 data files for figures in "Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2-CISM2 Coupled Climate–Ice Sheet Model" (Sommers et al., 2021, Paleoceanography and Paleoclimatology)</p>
Application of a Comprehensive Hygroscopicity Framework for Simulating Aerosol Water Uptake in a Large-scale Climate Model
<p>This dataset was generated from 18-month simulations of the NCAR Community Atmosphere Model (CAM); the atmospheric component of the Community Earth System Model version 2 (CESM2). The dataset can be used to reproduce the analysis and figures presented in the upcoming JGR publication.</p>
Supporting Data for "Climate Sensitivity and Relative Humidity Changes in Global Storm-Resolving Model Simulations of Climate Change"
<p>Code and netcdf files of processed X-SHiELD and CMIP6 simulations to reproduce the figures of Timothy M. Merlis, Kai-Yuan Cheng, Ilai Guendelman, Lucas Harris, Christopher S. Bretherton, Maximilien Bolot, Linjiong Zhou, Alex Kaltenbaugh, Spencer K. Clark, Gabriel A. Vecchi, and Stephan Fueglistaler (2024): "Climate Sensitivity and Relative Humidity Changes in Global Storm-Resolving Model Simulations of Climate Change".</p>
Data assimilation products by using multiple climate model simulations and different combinations of proxies
<p>This dataset of the climate reconstruction by data assimilation using isotope ratios provides annual surface air temperature, precipitation amount, and other climate variables during 850–2000.</p> <p>Two isotopes-incorporated atmospheric general circulation models and 129 isotopic proxy data (65 corals, 43 ice cores, and 21 tree-ring cellulose) were used in this study. There are nine experiments using three type of simulations and three combinations of proxies.</p> <p>The associated publication: Shoji, S., Okazaki, A., & Yoshimura, K. (2020). Impact of proxies and prior estimates on data assimilation using isotope ratios for the climate reconstruction of the last millennium. (submitted to Earth and Space Science)</p> <p>[Data structure]<br> X(lon) x Y(lat) x Z(2) x Variables(8) x Year(1151)<br> Z(1): analyses<br> Z(2): priors</p>
Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations
<p>Data for article "Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations"</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>
Supporting information for Hausfather et al 2022, Climate simulations: recognize the 'hot model' problem, comment in Nature
<p>This is the supporting information for the figure in Hausfather et al 2022, Climate simulations: recognize the ‘hot model’ problem, <em>Nature</em>. It includes CMIP6 ECS and TCR values, the screening used in our TCR screened assessment, as well annual global mean surface temperature anomalies relative to preindustrial (1850-1899) for the AR6 assessed warming, CMIP6 multimodel mean, and TCR screened subset shown in Figure 1 in our comment. </p>
High-resolution climate simulations using the Model for Prediction Across Scales - Atmosphere (MPAS-A; version 5.1)
<p>We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations.</p> <p>Due to storage limitations, the full dataset is much too large to be published (~50TB). Instead, a subset consisting of 6-hourly warm season (May-September) 2-meter temperature, precipitation, and 500hPa height is presented. If you wish to access the full dataset (as presented in Michaelis et al. 2019), please contact one of the authors.</p>
ModelE simulation output used in the study "Severe Global Cooling After Volcanic Supereruptions? The Answer Hinges on Unknown Aerosol Size" in Journal of Climate (2024)
<p>The included files are the GISS ModelE output needed to replicate the figures in McGraw et al 2023, "Severe Global Cooling After Volcanic Supereruptions? The Answer Hinges on Unknown Aerosol Size"</p> <p>Most of the data herein is output from GISS ModelE2.2 simulations that did not include interactive aerosol microphysics and chemistry. Instead, aerosol extinction and effective radius were input into the model from scaled Easy Volcanic Aerosol [Toohey et al, GMD 2016] output, as described in this study's Methods section. To calculate volcanic temperature impacts and forcings at combinations of injected sulfur mass and peak effective radius (Reff) that were not simulated, we used 2D linear interpolation with the scipy function 'Rbf'.</p> <p>Separately included is output from GISS ModelE2.1 with MATRIX interactive aerosol microphysics and chemistry [Bauer et al, ACP 2008]. Note that the injections were scaled to match that a 6.5 Tg sulfur (S) injection in ModelE2.1/MATRIX best replicated the aerosol optical depth (AOD) and effective radius observations of the 1991 Pinatubo event despite this injection being most commonly considered an 9 Tg S injection. Hence, to produce the 1000 Tg S eruption, a 722 Tg S injected was simulated. Such a mismatch has been found in other GCMs (eg Mills et al, JGRA 2016) and may be due to aerosol quick-removal processes not represented in these models.</p> <p>Please note that simulated eruption masses are in this dataset listed in units of Tg S, but in the publication are in Tg SO2 (Tg S x 2).</p> <p>Data from other modeling studies included in Fig. 1 and tree ring estimates in Figs. S2 & S4 can be found within the cited studies.</p> <p>For additional information, please contact zachary.mcgraw@columbia.edu</p>
Datasets used for the paper "Enhanced Blocking Frequencies in Very-high Resolution Idealized Climate Model Simulations"
<p>Atmospheric model and processed data for reproducing the results of "Enhanced Blocking Frequencies in Very-high Resolution Idealized Climate Model Simulations" currently submitted to Geophysical Research Letters.</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 used in "Revealing dominant patterns of aerosols regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations" (Li et al., Atmos. Chem. Phys. 2024)
<p>This dataset contains the processed EMAC simulation used as input to the clustering algorithm and the resulting regimes discussed in Li et al. (<em>Atmos. Chem. Phys.</em>, 2024).</p>
Data supplement for: Agreement of analytical and simulation-based estimates of the required land depth in climate models
<p>Many current-generation climate models have land components that are too shallow. Under climate change conditions, the long-term warming trend at the surface propagates deeper into the ground than the commonly used 3-10m. Shallow models alter the terrestrial heat storage and distribution of temperatures in the subsurface, influencing the simulated land-atmosphere interactions. Previous studies focusing on annual timescales suggest that deeper models are required to match subsurface-temperature observations and the classic analytical heat conduction solution. However, for a systematic investigation of land-model deepening in the frame of anthropogenic climate change, the classic analytical solution is inaccurate because it does not mimic the timescale and amplitude of the simulated warming trend. This study intends to bridge the gap between analytical and simulation-based estimates of the subsurface thermodynamic state by adapting the classic analytical framework to mimic long-term anthropogenic warming. The analysis shows that a land-model depth of at least 170m is recommended for a proper simulation of the post-1850 ground climate, which differs up to 30% from the estimate of the classic approach. Compared to previous studies, this provides an accurate estimate of the required land model depth for long-term climate-change simulations and indicates the relative bias in insufficiently deep land models.</p>
Data for "Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"
<p>Data for "<strong>Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"</strong></p>
CESM 1.2 climate model simulation output for: The Essential Role of Westerly Wind Bursts in ENSO Dynamics and Extreme Events Quantified in Model 'Wind Stress Shaving' Experiments
<p>Westerly wind bursts (WWBs)—brief but strong westerly wind anomalies in the equatorial Pacific—are believed to play an important role in El Niño Southern Oscillation (ENSO) dynamics, but quantifying their effects is challenging. Here, we investigate the cumulative effects of WWBs on ENSO characteristics, including the occurrence of extreme El Niño events, via modified coupled model experiments within Community Earth System Model (CESM1) in which we progressively reduce the impacts of wind stress anomalies associated with model-generated WWBs. In these "wind stress shaving" experiments we limit momentum transfer from the atmosphere to the ocean above a preset threshold, thus "shaving off" wind bursts. To reduce the tropical Pacific mean state drift, both westerly and easterly wind bursts are removed, although the changes are dominated by WWB reduction. As we impose progressively stronger thresholds, both ENSO amplitude and the frequency of extreme El Niño decrease, and ENSO becomes less asymmetric. The warming center of El Niño shifts westward, indicating less frequent and weaker Eastern Pacific (EP) El Niño events. Removing most of wind bursts-related wind stress anomalies reduces ENSO amplitude by 22%. The essential role of WWBs in the development of extreme El Niño events is revealed in the suppressed eastward migration of the western Pacific warm pool and hence a weaker Bjerknes feedback under wind shaving. Overall, our results reaffirm the importance of WWBs in shaping the characteristics of ENSO and its extreme events and imply that WWB changes with global warming could influence future ENSO.</p>
Data accompanying "Diurnal variability of the upper ocean simulated by a climate model"
<p>Data used for creating figures in the draft article "Diurnal variability of the upper ocean simulated by a climate model". This includes:</p> <ul> <li>Multi-year, monthly mean diurnal cycle metrics at all model grid points.</li> <li>Monthly mean diurnal cycle data for individual years at selected locations.</li> </ul> <p>Code used to create these data files, and to create the plots, is in a Github repository (https://github.com/JackReevesEyre/cfs-analysis-gaea/). The repository is also archived on Zenodo (https://doi.org/10.5281/zenodo.7846095).</p>
Output files from SPEEDY v.42 ensembles described in the paper: "Multi-decadal pacemaker simulations with an intermediate-complexity climate model" by F. Molteni, F. Kucharski and R. Farneti (part 1 of 2)
<p>The monthly-mean output from SPEEDY v.42 ensembles (either driven by prescribed sea-surface temperature (SST) or coupled to the TOM3 model) consists of a series of IEEE little-endian binary files and metadata files in text format.<br> For each year of integration (indicated by a 4-digit number YYYY) and ensemble member (indicated by a 3-digit number NNN), two binary files are present, named:<br> • attmNNN_YYYY.grd, including data on the 120x60 grid-point atmospheric grid;<br> • sftmNNN_YYYY.grd, including data on the 360x180 grid-point surface grid.<br> The metadata for these files are contained in the text files <strong>attmEEE.ctl</strong> and <strong>sftmEEE.ctl</strong> respectively, where EEE is a 3-digit ensemble identifier (usually, but not necessarily, equal to one of the ensemble-member number NNN).</p> <p><br> This repository contains data from:</p> <ul> <li>(part 1) a 41-year 5-member ensemble (653) run with prescribed SST</li> <li>(part 2) a 70-year 5-member ensemble (104) run with the coupled SPEEDY-TOM3 model.</li> </ul> <p>Integration years are 1980 to 2020 for ensemble 653 and 1951 to 2020 for ensemble 104.</p> <p><br> The structure of the binary data and metadata files follows the conventions for gridded datasets set by the GrADS diagnostic and plotting package (developed by the Center for Ocean-Land-Atmosphere Studies of George Mason University), as described here:<br> http://cola.gmu.edu/grads/gadoc/aboutgriddeddata.html</p> <p><br> In addition to the COLA-GMU web site, free version of the GrADS package for different platforms can be downloaded from the OpenGrADS web site:<br> http://opengrads.org/</p> <p><br> Specifically, the SPEEDY v.42 output consists of sequential-access files where each record contains a two-dimensional field. Three-dimensional fields are stored as a sequence of consecutive records, one for each of the 8 pressure levels where model-level data are interpolated by the post-processing routines. For each month of the year:</p> <p><br> the <strong>attmNNN_YYYY.grd</strong> files contain a sequence of <strong>9 3-D variables and 26 2-D variables</strong>;<br> the <strong>sftmNNN_YYYY.grd</strong> files contain a sequence of <strong>21 2-D variables</strong>.</p> <p>Within each record, grid-point data are stored as a NLONxNLAT array with longitude varying from west to east and latitude varying from south to north. The list of variables and levels is specified in the <strong>attmEEE.ctl</strong> and s<strong>ftmEEE.ctl</strong> files. These files contain descriptors which allow the data of each ensemble to be accessed as a single dataset by the GrADS package.</p> <p>Although the metadata files are specific to the GrADS package, the binary data can be read by different types of code. As example of fortran90 instructions to read the content of the <strong>attmNNN_YYY.grd</strong> and <strong>sftmNNN_YYY.grd</strong> files for one year/ens.member is as follows:</p> <p>integer, parameter :: nlon=120<br> integer, parameter :: nlat=60<br> integer, parameter :: nlev=8<br> integer, parameter :: nlon0=360<br> integer, parameter :: nlat0=180</p> <p>integer :: jmonth, jvar3d, jvar2d, jlev<br> real :: fld3d(nlon,nlat,nlev)<br> real :: fld2d(nlon,nlat), fld0(nlon0,nlat0)</p> <p>open (unit=1, file=”attmNNN_YYY.grd”, form=”formatted”, access=”sequential”)<br> open (unit=2, file=”sftmNNN_YYY.grd”, form=”formatted”, access=”sequential”)</p> <p>do jmonth=1,12</p> <p> do jvar3d=1,9<br> do jlev=1,nlev<br> read (1) fld3d(:,:,jlev)<br> …………<br> enddo<br> enddo</p> <p> do jvar2d=1,26<br> read (1) fld2d(:,:)<br> ………<br> enddo</p> <p> do jvar2d=1,21<br> read (2) fld0(:,:)<br> ………<br> enddo</p> <p>enddo</p> <p>close (1)<br> close (2)</p> <p> </p> <p> </p>
Output files from SPEEDY v.42 ensembles described in the paper: "Multi-decadal pacemaker simulations with an intermediate-complexity climate model" by F. Molteni, F. Kucharski and R. Farneti (part 2 of 2)
<p>The monthly-mean output from SPEEDY v.42 ensembles (either driven by prescribed sea-surface temperature (SST) or coupled to the TOM3 model) consists of a series of IEEE little-endian binary files and metadata files in text format.<br> For each year of integration (indicated by a 4-digit number YYYY) and ensemble member (indicated by a 3-digit number NNN), two binary files are present, named:<br> • attmNNN_YYYY.grd, including data on the 120x60 grid-point atmospheric grid;<br> • sftmNNN_YYYY.grd, including data on the 360x180 grid-point surface grid.<br> The metadata for these files are contained in the text files <strong>attmEEE.ctl</strong> and <strong>sftmEEE.ctl</strong> respectively, where EEE is a 3-digit ensemble identifier (usually, but not necessarily, equal to one of the ensemble-member number NNN).</p> <p><br> This repository contains data from:</p> <ul> <li>(part 1) a 41-year 5-member ensemble (653) run with prescribed SST</li> <li>(part 2) a 70-year 5-member ensemble (104) run with the coupled SPEEDY-TOM3 model.</li> </ul> <p>Integration years are 1980 to 2020 for ensemble 653 and 1951 to 2020 for ensemble 104.</p> <p><br> The structure of the binary data and metadata files follows the conventions for gridded datasets set by the GrADS diagnostic and plotting package (developed by the Center for Ocean-Land-Atmosphere Studies of George Mason University), as described here:<br> http://cola.gmu.edu/grads/gadoc/aboutgriddeddata.html</p> <p><br> In addition to the COLA-GMU web site, free version of the GrADS package for different platforms can be downloaded from the OpenGrADS web site:<br> http://opengrads.org/</p> <p><br> Specifically, the SPEEDY v.42 output consists of sequential-access files where each record contains a two-dimensional field. Three-dimensional fields are stored as a sequence of consecutive records, one for each of the 8 pressure levels where model-level data are interpolated by the post-processing routines. For each month of the year:</p> <p><br> the <strong>attmNNN_YYYY.grd</strong> files contain a sequence of <strong>9 3-D variables and 26 2-D variables</strong>;<br> the <strong>sftmNNN_YYYY.grd</strong> files contain a sequence of <strong>21 2-D variables</strong>.</p> <p>Within each record, grid-point data are stored as a NLONxNLAT array with longitude varying from west to east and latitude varying from south to north. The list of variables and levels is specified in the <strong>attmEEE.ctl</strong> and s<strong>ftmEEE.ctl</strong> files. These files contain descriptors which allow the data of each ensemble to be accessed as a single dataset by the GrADS package.</p> <p>Although the metadata files are specific to the GrADS package, the binary data can be read by different types of code. As example of fortran90 instructions to read the content of the <strong>attmNNN_YYY.grd</strong> and <strong>sftmNNN_YYY.grd</strong> files for one year/ens.member is as follows:</p> <p>integer, parameter :: nlon=120<br> integer, parameter :: nlat=60<br> integer, parameter :: nlev=8<br> integer, parameter :: nlon0=360<br> integer, parameter :: nlat0=180</p> <p>integer :: jmonth, jvar3d, jvar2d, jlev<br> real :: fld3d(nlon,nlat,nlev)<br> real :: fld2d(nlon,nlat), fld0(nlon0,nlat0)</p> <p>open (unit=1, file=”attmNNN_YYY.grd”, form=”formatted”, access=”sequential”)<br> open (unit=2, file=”sftmNNN_YYY.grd”, form=”formatted”, access=”sequential”)</p> <p>do jmonth=1,12</p> <p> do jvar3d=1,9<br> do jlev=1,nlev<br> read (1) fld3d(:,:,jlev)<br> …………<br> enddo<br> enddo</p> <p> do jvar2d=1,26<br> read (1) fld2d(:,:)<br> ………<br> enddo</p> <p> do jvar2d=1,21<br> read (2) fld0(:,:)<br> ………<br> enddo</p> <p>enddo</p> <p>close (1)<br> close (2)</p>
ScienceDex guides
Understand access before you commit
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.