Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
105
datasets available to search
ShareScore release 0.7.1
Dataset results
105 results for “CMIP6”
A large ensemble of CMIP6-based transient climate scenarios for impact assessment in Great Britain.
<p>Climate change impact assessments often require a large ensemble of local-scale transient climate scenarios. Each ensemble member represents plausible long weather series at a local scale. The climate projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. Moreover, very few projections are usually available for each GCM due to a high computational cost. An alternative approach involves employing a stochastic weather generator to produce a large number of transient scenarios based on the climate projections from GCMs. In a current dataset, transient climate scenarios were generated using the LARS-WG weather generator, based on climate projections from GCMs from the CMIP6 ensemble across 26 representative sites throughout the UK. Each transient scenario spans the period from 2020 to 2090. At each site, 100 transient scenarios were generated for two emission scenarios (SSP2-4.5 and SSP5-8.5) and five selected GCMs from CMIP6 (ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR, and MRI-ESM2-0). The choice of GCMs were based on their performance over northern Europe and their climate sensitivity. The use of a subset of GCMs substantially reduces computational time required for impact assessment, while allowing to quantify uncertainties in impacts related to uncertain future climate. The dataset can be used with impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare, when undertaking impact assessment of climate change and decision support for mitigation and adaptation.</p>
CMIP6-based local-scale climate scenarios for impact assessment in Great Britain.
<p>Climate change impact assessments require local-scale climate scenarios. The climate change projections from <span>Global Climate Models (GCMs) </span>are difficult to use at local scale due to their <span>coarse spatial and temporal resolution. </span><span>It is important to have climate change scenarios based on GCMs climate projections GCMs ensembles, e.g. CMIP6, downscaled to local scale to account for their inherent uncertainty, and to generate a sufficient large number of </span>realisations <span>to account for inter-annual climate variability and low frequency but high impact extreme climatic events. A</span><span> <span>dataset of future climate change scenarios was therefore generated at </span></span><span>26 representative sites across the UK</span><span> based on the latest </span><span>CMIP6 multi-model ensemble </span><span>downscaled to local-scale by using a </span><span>stochastic weather generator LARS-WG 7.0. The data set provides </span><span>1,000 years of daily weather at each selected site for a baseline (1985-2015), and very near- (2030) and near-future (2050) climate change scenarios, based on five GCMs and two emission scenarios (</span><span>Shared Socioeconomic Pathways - SSPs <em>viz</em>. </span>SSP2-4.5 and <span>SSP5-8.5)</span><span>.</span><span> </span><span>A total of </span>15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 7.0. <span>LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. </span>Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, <em>viz</em>.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs.<span> <span>The 1000 years of </span></span>realisations <span>of daily weather for the baseline as well as future climate change scenarios are helpful for estimating </span>seasonality and<span> inter-annual variation, and for detecting short, </span>low frequency but high impact extreme climatic signals, such as heat waves, floods and drought events. The dataset <span>can be used as an input to climate change impact models in various fields, including, </span><span>land and water resources, agriculture and food production, </span>ecology and epidemiology, and <span>human health and welfare. Researchers, breeders, farm and programme managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessment of climate change and decision support for mitigation and adaptation.</span></p>
Compound hot and dry and wet and windy events in CMIP6 models
<p>NetCDF files containing maps of return periods (in years) for the joint occurrence of</p> <ol> <li>strong surface winds (sfcWind) and heavy rain (pr) and</li> <li>heatwaves (EHF) and drought (SPI) </li> </ol> <p>as realised by models participating in the Coupled Model Intercomparison Project Round 6 (CMIP6). Included is output from models that provided daily data for sfcWind, pr, tmax and tmin (to calculate EHF) and experiments historical, SSP126, SSP245, and SSP585 for ensemble member r1i1p1f1. The base period for the determination of hazard thresholds was 1980 – 2014 for all experiments. Time periods over which return periods were calculated were 1980 – 2014 for the 'historical' experiment and 2066 – 2100 for experiments 'SSP126', 'SSP245', and 'SSP585'.</p> <p>Values for return periods are determined following the method in Ridder et al. (2020a) doi: 10.1038/s41467-020-19639-3; Ridder et al. (2020b) doi: 10.1029/2020GL091152 and Ridder et al. (2021) doi: 10.1038/s41612-021-00224-4. </p> <p>Name convention:</p> <ul> <li> historical experiments: <br> <em>map_RP_${hazardX}_${hazardY}_${CMIP6model}_historical_r1i1p1f1_${model_grid}_19800101_20141231.nc</em></li> <li>ScenarioMIPs:<br> map_RP_<em>${hazardX}_${hazardY}_${CMIP6model}</em>_historic_threshold_${experiment}_r1i1p1f1_2066-2100.nc</li> </ul>
UKESM1-forced BORIS-1 seafloor biomass under CMIP6 SSPs
<p>Change in total simulated seafloor biomass between the late Scenario period (2081-2100) and late Historical period (1995-2014) under the SSP scenarios 126 to 585. Simulations use the benthic BORIS model (Kelly-Gerreyn et al., Biogeosciences, 2014) forced using output from the UKESM1 model (Sellar et al., JAMES, 2019; Yool et al., GMD, 2021) in the same experimental design used in Yool et al. (GCB, 2017). Simulations use Matlab v2020a.</p> <p> </p> <p>Kelly-Gerreyn, B. A., Martin, A. P., Bett, B. J., Anderson, T. R., Kaariainen, J. I., Main, C. E., Marcinko, C. J., and Yool, A.: Benthic biomass size spectra in shelf and deep-sea sediments, Biogeosciences, 11, 6401–6416, https://doi.org/10.5194/bg-11-6401-2014, 2014.</p> <p>Sellar, A. A., Jones, C. G., Mulcahy, J., Tang, Y., Yool, A., Wiltshire, A. O’Connor, F. M., Stringer, M., Hill, R., Palmiéri, J.,<br> Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N.<br> L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards,<br> J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C.<br> D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R.,Woodhouse, M., Zeng, G., and Zerroukat, M.: UKESM1: Description and evaluation of the UK Earth System Model, J. Adv. Model. Earth Sy., U J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019.</p> <p>Yool, A., Palmiéri, J., Jones, C. G., de Mora, L., Kuhlbrodt, T., Popova, E. E., Nurser, A. J. G., Hirschi, J., Blaker, A. T., Coward, A. C., Blockley, E. W., and Sellar, A. A.: Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 historical simulations, Geosci. Model Dev., 14, 3437–3472, https://doi.org/10.5194/gmd-14-3437-2021, 2021.</p> <p>Yool, A., Martin, A.P., Anderson, T.R., Bett, B.J., Jones, D.O.B., Ruhl, H.A.: Big in the benthos: Future change of seafloor community biomass in a global, body size-resolved model. Glob Change Biol., 23: 3554– 3566, https://doi.org/10.1111/gcb.13680, 2017.</p> <p> </p>
UKESM1-forced BORIS-1 seafloor biomass under CMIP6 SSPs
<p>Annual mean seafloor biomass for periods 1980 to 2014 (Historical) and 2015 to 2100 (Future) for Shared Socioeconomic Pathways SSP126 to SSP585. Simulations use the benthic BORIS model (Kelly-Gerreyn et al., Biogeosciences, 2014) forced using output from the UKESM1 model (Sellar et al., JAMES, 2019; Yool et al., GMD, 2021) in the same experimental design used in Yool et al. (GCB, 2017). Simulations use Matlab v2020a. Each file contains seafloor detritus, total seafloor biomass and seafloor biomass for each of BORIS-1's 16 size classes.</p> <p>Kelly-Gerreyn, B. A., Martin, A. P., Bett, B. J., Anderson, T. R., Kaariainen, J. I., Main, C. E., Marcinko, C. J., and Yool, A.: Benthic biomass size spectra in shelf and deep-sea sediments, Biogeosciences, 11, 6401–6416, https://doi.org/10.5194/bg-11-6401-2014, 2014.</p> <p>Sellar, A. A., Jones, C. G., Mulcahy, J., Tang, Y., Yool, A., Wiltshire, A. O’Connor, F. M., Stringer, M., Hill, R., Palmiéri, J.,<br> Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N.<br> L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards,<br> J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C.<br> D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R.,Woodhouse, M., Zeng, G., and Zerroukat, M.: UKESM1: Description and evaluation of the UK Earth System Model, J. Adv. Model. Earth Sy., U J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019.</p> <p>Yool, A., Palmiéri, J., Jones, C. G., de Mora, L., Kuhlbrodt, T., Popova, E. E., Nurser, A. J. G., Hirschi, J., Blaker, A. T., Coward, A. C., Blockley, E. W., and Sellar, A. A.: Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 historical simulations, Geosci. Model Dev., 14, 3437–3472, https://doi.org/10.5194/gmd-14-3437-2021, 2021.</p> <p>Yool, A., Martin, A.P., Anderson, T.R., Bett, B.J., Jones, D.O.B., Ruhl, H.A.: Big in the benthos: Future change of seafloor community biomass in a global, body size-resolved model. Glob Change Biol., 23: 3554– 3566, https://doi.org/10.1111/gcb.13680, 2017.</p> <p> </p>
Evaluation of dynamically downscaled CMIP6-CCAM models over Australia
<p>Downscaled CCAM-CMIP6 model data used in the evaluation of CCAM-CMIP6 models against AGCD observations:</p><ol><li>Data required for daily evaluation of precipitation and temperature variables, and calculation of Perkins skill score</li><li>Data required for evaluation of bias for precipitation and temperature variables</li><li>Data required for KGE skill score</li></ol>
Future monthly discharge and water temperature simulations under global change (CMIP6)
<p>Monthly discharge (m3 s-1) and water temperature (K) simulated by a global hydrological model coupled to a surface water quality model (<i>PCR-GLOBWB2-DynQual)</i> for the time period 2005 - 2100, for an ensemble of 15 projections based on three combined climate and socio-economic scenarios (SSP1-RCP2.6; SSP3-RCP7.0 and SSP5-RCP8.5) and five general circulation models (GFDL-ESM4; UKESM1-0-LL; MPI-ESM1-2-hr; IPSL-CM6A-LR and MRI-ESM2-0)</p><p>Output data are provided at 10km resolution and are averaged at monthly temporal resolution.</p><p>These datasets were generated as part of the work presented in: Jones, E.R., Bierkens, M.F.P., van Puijenbroek, P.J.T.M. <i>et al.</i> Sub-Saharan Africa will increasingly become the dominant hotspot of surface water pollution. <i>Nat Water</i> <strong>1</strong>, 602–613 (2023). <a href="https://www.nature.com/articles/s44221-023-00105-5#citeas">https://doi.org/10.1038/s44221-023-00105-5</a></p><p>Relevant model description papers can be found at the following links:</p><ul><li><i>PCR-GLOBWB2</i>: Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P.: PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model, <i>Geoscientific Model Development</i>, 11, 2429–2453, <a href="https://gmd.copernicus.org/articles/11/2429/2018/gmd-11-2429-2018.html">https://doi.org/10.5194/gmd-11-2429-2018</a>, 2018.</li><li><i>DynQual</i>: Jones, E. R., Bierkens, M. F. P., Wanders, N., Sutanudjaja, E. H., van Beek, L. P. H., and van Vliet, M. T. H.: DynQual v1.0: a high-resolution global surface water quality model, <i>Geoscientific Model Development</i>, 16, 4481–4500, <a href="https://gmd.copernicus.org/articles/16/4481/2023/gmd-16-4481-2023.html">https://doi.org/10.5194/gmd-16-4481-2023</a>, 2023.</li></ul><p>Additional information on the water temperature modelling can also be found at:</p><ul><li>Wanders, N., van Vliet, M. T. H., Wada, Y., Bierkens, M. F. P., & van Beek, L. P. H. (Rens): High-resolution global water temperature modeling. <i>Water Resources Research</i>, 55, 2760–2778, <a href="https://doi.org/10.1029/2018WR023250">https://doi.org/10.1029/2018WR023250</a>, 2019</li><li>van Beek, L. P. H., Eikelboom, T., van Vliet, M. T. H., and Bierkens, M. F. P.: A physically based model of global freshwater surface temperature, <i>Water Resources. Research</i>, 48, W09530, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2012WR011819">https://doi.org/10.1029/2012WR011819</a> , 2012.</li></ul>
Global and tropical band averages for a selection of CMIP5 and CMIP6 models: piControl and abrupt-4xCO2 experiments
<p>This dataset provides post-processed spatial averages for a selection of CMIP5 and CMIP6 models. The experiments contained in this dataset are only the pre-industrial controls (piControl) and the experiments with a four-fold increase in the atmospheric CO$_{2}$ concentration in relation to the pre-industrial level (abrupt-4xCO2). The spatial averages are global and tropical bands from x°S to x°N, where the x value is between 5 and 40 in increments of 5°. This dataset was created to study climate sensitivity in general and the effect of stratospheric circulation changes on the tropical equilibrium climate sensitivity. It contains the following variables:</p> <ul> <li>incoming (d) short-wave (SW, s) radiative flux (RF, r) at the top of the atmosphere (TOA, t): rsdt</li> <li>outgoing (u) SW RF at TOA: rsut</li> <li>outgoing long-wave (LW, l) RF at TOA: rlut</li> <li>net (n) RF at TOA: rnt</li> <li>incoming SW RF at the surface (s): rsds</li> <li>outgoing SW RF at the surface: rsus</li> <li>incoming LW RF at the surface: rlds</li> <li>outgoing LW RF at the surface: rlus</li> <li>net RF at the surface: rns</li> <li>sensible heat flux (hfs) at the surface: hfss</li> <li>latent heat flux (hfl) at the surface: hfls</li> <li>surface temperature (t): ts</li> <li>atmospheric temperature: ta</li> <li>specific humidity: hus</li> <li>zonal component of wind: ua</li> <li>meridional component of wind: va</li> <li>lagrangian tendency of pressure (vertical component of wind in pressure per time dimensions): wap</li> <li>surface pressure: ps</li> <li>geopotential height: zg</li> </ul>
EPTGODD-WHU: Ensemble Precipitation and Temperature from CMIP6 GCMs optimized by OLS-DT-DNN methods integration (1850-2100)
<p>This monthly global climate dataset EPTGODD-WHU (precipitation and mean temperature variables with grid size of 0.5°×0.5°) was ensembled from 16 selected CMIP6 GCMs. The published dataset was optimized by OLS (Ordinary Linear Square)-DT (Decision Tree)-DNN (Deep Neural Network) methods integration. The CF (Climate and Forecast) v1.6 was employed as the guideline for NetCDF4 format. The periods of temperature files can be divided into historical (1850-1900) and future (2015-2100) periods. For precipitation, this product provides future (2015-2100) period. Three future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) were selected for both variables. The units of this dataset are degrees Celsius and mm/month for temperature and precipitation, respectively. Each NetCDF4 file in this dataset includes three dimensions (time, latitude (-89.75°N to 89.75°N) and longitude (-179.75°E to 179.75°E)).</p>
Near-surface Temperature from CMIP6 NCAR CESM2 historical monthly dataset for CLIVAR CMIP6 Bootcamp
<p>This dataset has been created from CMIP6 data through CMIP6 online catalog. It is meant to be used for training purposes only.</p> <p> </p> <p>Data is from CESM2 (NCAR) and is a monthly dataset from 1850 to 2014 containing near-surface temperature (TAS).</p>
CMIP6 model vertically-integrated net primary production data
<p>Vertically-integrated net primary production (NPP) data from 12 models that participated in phase six of the Coupled Model Intercomparison Project (CMIP6). All data pulled from the Earth System Grid Federation.</p> <p>All model output was regridded onto a common, regular horizontal grid of 1x1 degrees (360 x 180) in longitude by latitude.</p> <p>Units are mol C per metre squared per second.</p> <p>Models are:</p> <ol> <li>ACCESS-ESM1-5</li> <li>CanESM5</li> <li>CESM2</li> <li>CNRM-ESM2-1</li> <li>GFDL-CM4</li> <li>GFDL-ESM4</li> <li>IPSL-CM6A-LR</li> <li>MIROC-ES2L</li> <li>MPI-ESM1-2-HR</li> <li>MRI-ESM2-0</li> <li>NorESM2</li> <li>UKESM1-0-LL</li> </ol>
Reliquary of contacts for: A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations
<p>Photos and metadadata pannels of a "Reliquary of contacts for: A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations" produced to support the "A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations" presentation given at EGU 2024.</p> <p>------</p> <p>With ever growing abilities to process greater volumes of data the abiiity to sustain the citability and tracability of the underluing source data within outputs such as publications is becoming increasingly challenging. With a range of use-cases, work on how to handle complex citations from the perspective of those producing outputs, journals and those handling the knowledge graph and associated services, is exmaning a how to handle these situations in a sustainable and manageable fashion.<br><br>At the European Geophysical Union (EGU) General Assembly in Vienna, 2024, a pragmatic solution using Zenodo to store 'reliquary' objects was presented. The poster presentation demonstrated the use of existing strucutres within a Zenodo object to address the complex citation use-case around figure, the related data and the source datasets related to the IPCC's AR5 figure data. I.e. how to utulise the existing constructs of a Zenodo item and the range of available metadata fields to give an off-the-shelf solution to allow tracability to the specific datasets used (via their Handle identifiers) and citability of the higher level, DOI-ed dataset collections within which the specific Handle-ed datasets were selected from. Additionally, the connectivity between these two levels of PID objects was also captured within the stored files around which the rich metata was captured.<br><br>The concept of a complex citation 'reliquary' as a metadtata rich object, acting as a referencable nexus in the knowledge graph has been put forth as a solution to the complex citation challenge. It borrows the concept from its historical use, denoting a container or shrine, often richly embellished, for sacred relics (e.g. saints bones, artefacts etc). In the same way here we have both the rich metadata 'container' around the specific details (the 'bones in the box', with their preserved connectivity).<br><br>However, the term 'reliquary' is often a hard one to convey, being somewhat of an obscure term (likewise the term 'nexus' may also be one lacking wider recogniton). Thus, to aid the discussions around the presentation by Pascoe et al. (2024) at the EGU 2023 General Assembly, a physical representation of a metadata reliquary object was produced.<br><br>The purpose of this object was two fold:<br><br> - The first was to show how the reliquary container itself is metadata rich, detailing through the use of ORCIDS, RORs and a DOI, references to external items, complemented by further metadata concerning the specifics of the reliquary's own metadata (its title and the credit for the artist that created it). Futher more, the relationship between the reliquary and those referenced parties/objects was also captured. The contents were also used to demonstrate the importance of making the contents useful for onward users (in this case contact details on business cards). <br> - The second, and for the funder of this piece, arguably the most important aspect was a degree of outreach this provided, both to engage the audience of Pascoe et al (2024), and directly to the artist to demonstrate the importance of this work to the international research data management community and overall to aid engagemeng with the funder's work.<br><br>This resource is provided here as a repository of images of the reliquary itself and in context at the EGU 2024 event as a potential resource others may use to aid further discussions around the use of reliquaries with regards to complex citations. The slides provided of the reliquary box labels are also provided with some annotation to further expand on the metadata aspects of their content.</p>
GLM2_modified and Results as used in Ma et al: Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2, Geosci. Model Dev., 2020
<p>Code modified GLM2, scripts and result as used in Ma et al 2019, Ma et al 2019, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-146</p>
CRCM5-CMIP6 : A dynamically-downscaled ensemble of CMIP6 simulations.
<h1>CRCM-CMIP</h1> <h2>Data reference</h2> <p>Paquin, D., C. McCray, C. B. Gauthier, M. Giguère, O. Asselin, P .Bourgault, M.-P. Labonté and D. Matte. The CRCM5-CMIP6 Ouranos’ ensemble : A dynamically-downscaled ensemble of CMIP6 simulations over North America. Accepted in Scientific Data.</p> <p><a href="https://www.ouranos.ca/en">Ouranos</a> : Canadian Regional Climate Model – version 5</p> <p><strong>Martynov et al. 2013, Separovic et al. 2013</strong></p> <p>Based on GEM 3.3.3.1</p> <h3>Configuration</h3> <p>NAM-11 CORDEX North American domain at 0.11° 695x668 grid points including a 20-point sponge (and halo) zone surrounding the domain, 5-minute time steps, xlat1=28.525 xlon2=145.955. 56 vertical levels and a top at 10 hPa. 17 surface levels and a bottom at 15 m.</p> <h3>Spectral Nudging</h3> <p>A spectral nudging is applied to the horizontal wind component with a half-response wavelength of 1177km and a relaxation time of 13.34 h. The nudging strength is set to zero from the surface to a height of 500 hPa and increases linearly onward to the top of the model’s simulated atmosphere (10 hPa).</p> <h2>Parameterization</h2> <h3>Atmosphere</h3> <p>Precipitation: modified Sundqvist (1998); precipitation partition Bourgouin (2000) ; Implicit vertical diffusion. <br>Shallow convection: Kuo (1965) transient shallow, Non‐cloudy boundary layer formulation. <br>Deep convection: Kain-Fritsch (1990); <br>Radiation: Li & Barker (2005)</p> <h3>Surface</h3> <p>CLASS3.5c (Verseghy, 1993)</p> <p>Lake model: FLake</p> <h3>Ocean</h3> <p>Prescribed SST & sea ice fraction</p> <h3>Aerosol</h3> <p>Prescribed</p> <h2>Data Access</h2> <p>Due to its large size, the full dataset can't yet be shared publicly.</p> <p>A subset of the variables are stored on Ouranos' THREDDS server.</p> <p>- Annual files : <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/disk2/ouranos/CORDEX/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/disk2/ouranos/CORDEX/catalog.html</a><br>- Aggregated datasets : <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/RCM-CMIP6/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/RCM-CMIP6/catalog.html</a></p> <p>Other variables can be provided upon request by writing to simulations_ouranos@ouranos.ca.</p> <p>All data are available through a <a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY 4.0</a> license.</p> <h2>Acknowlegments</h2> <p>Developed by the <a href="https://escer.uqam.ca/">ESCER Centre</a> at UQAM (Université du Québec à Montréal) with the collaboration of Environment and Climate Change Canada (ECCC). <strong>CRCM5; Martynov et al. 2013, Separovic et al. 2013</strong></p> <p>The CRCM5 data has been generated and supplied by Ouranos.</p> <p>CRCM5 computations were made on the supercomputers beluga and narval managed by Calcul Québec and the <a href="https://alliancecan.ca/en">Digital Research Alliance of Canada</a>. The operation of this supercomputer received financial support from Innovation, Science and Economic Development Canada and the Ministère de l’Économie et de l’Innovation du Québec.</p> <h2>Some references for CRCM5</h2> <p>Asselin, M. Leduc, D. Paquin, K. Winger, A. Di Luca, M. Bukovsky, B. Music, and M. Giguère (2022). On the Intercontinental Transferability of Regional Climate Model Response to Severe Forestation. MDPI's Climate <br><a href="https://doi.org/10.3390/cli10100138">https://doi.org/10.3390/cli10100138</a> </p> <p>Bresson, E., R. Laprise, D. Paquin, J. M. Thériault, R. de Elia, 2017: Evaluating CRCM5 ability to simulate mixed precipitation. Atmosphere-Ocean. 55(2); 79-93. <a href="http://dx.doi.org/10.1080/07055900.2017.1310084">http://dx.doi.org/10.1080/07055900.2017.1310084</a> </p> <p>Leduc, M., A. Mailhot, A. Frigon, J.-L. Martel, R. Ludwig, G.B. Brietzke, M. Giguère, F. Brissette, R. Turcotte, M. Braun, (2019) ClimEx project: a 50-member ensemble of climate change projections at 12-km resolution over Europe and northeastern North America with the Canadian Regional Climate Model (CRCM5). Journal of Applied Meteorology and Climatology. <a href="https://doi.org/10.1175/JAMC-D-18-0021.1" target="_blank" rel="noopener">https://doi.org/10.1175/JAMC-D-18-0021.1</a></p> <p>Martynov A, R Laprise, L Sushama, K Winger, L Separovic, B Dugas. 2013. Reanalysis-driven climate simulation over CORDEX North America domain using the Canadian Regional Climate Model, version 5: model performance evaluation. Clim Dyn 41:2973-3005. <a href="https://doi.org/10.1007/s00382-013-1778-9">https://doi.org/10.1007/s00382-013-1778-9</a></p> <p>Martynov A, L Sushama, R Laprise, K Winger, B Dugas. 2012. Interactive lakes in the Canadian regional climate model version 5: the role of lakes in the regional climate of North America. Tellus A 64, 016226. <a href="https://doi.org/10.3402/tellusa.v64i0.16226">https://doi.org/10.3402/tellusa.v64i0.16226</a>.</p> <p>Martynov A, L Sushama, R Laprise. 2010. Simulation of temperate freezing lakes by one-dimensional lake models: performance assessment for interactive coupling with regional climate models. Boreal Env Res 15:143-164.</p> <p>Matte, D., Thériault, J. M., & Laprise, R. (2019). Mixed precipitation occurrences over southern Québec, Canada, under warmer climate conditions using a regional climate model. Climate Dynamics, 53(1), 1125–1141. <a href="https://doi.org/10.1007/s00382-018-4231-2">https://doi.org/10.1007/s00382-018-4231-2</a></p> <p>McCray, C. D., D. Paquin, J. M. Thériault, É. Bresson (2022). A multi-algorithm analysis of projected changes to freezing rain over North America in an ensemble of regional climate model simulations. Journal of Geophysical Research -Atmospheres <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD036935">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD036935</a></p> <p>McCray, D. C., J. M. Thériault, D. Paquin, É. Bresson, 2022. Quantifying the impact of precipitation-type algorithm selection on the representation of freezing rain in an ensemble of regional climate model simulations. Journal of Applied Meteorology and Climatology. <a href="https://journals.ametsoc.org/view/journals/apme/aop/JAMC-D-21-0202.1/JAMC-D-21-0202.1.xml">https://journals.ametsoc.org/view/journals/apme/aop/JAMC-D-21-0202.1/JAMC-D-21-0202.1.xml</a> </p> <p>McCray, C.D., G. Schmidt, D. Paquin, M. Leduc, Z. Bi, M. Radiyat, C. Silverman, M. Spitz, B. Brettschneider (2023). Changing Nature of High-Impact Snowfall Events in Eastern North America. Journal of Geophysical Research: Atmospheres. <a href="https://doi.org/10.1029/2023JD038804">https://doi.org/10.1029/2023JD038804</a></p> <p>Mironov D, E Heise, E Kourzeneva, B Ritter, N Schneider, A Terzhevik. 2010. Implementation of the lake parameterisation scheme FLake into the numerical weather prediction model COSMO. Boreal Env Res 15:218-230.</p> <p>Mittermeier, M., E. Bresson, D. Paquin, R. Ludwig, 2021 A deep learning approach for the identification of long-duration mixed precipitation in Montréal (Canada). Atmosphere-Ocean. <a href="https://doi.org/10.1080/07055900.2021.1992341">https://doi.org/10.1080/07055900.2021.1992341</a></p> <p>Riette S, D Caya. 2002. Sensitivity of short simulations to the various parameters in the new CRCM spectral nudging. – In: RITCHIE, H. (Ed.): Research activities in Atmospheric and Oceanic Modeling, WMO/TD No. 1105, Report No. 32: 7.39–7.40.</p> <p>Pérez Bello, A., A. Mailhot and D. Paquin, 2021 The response of daily and sub-daily extreme precipitations to changes in surface and dew point temperatures. Journal of Geophysical Research – Atmospheres <a href="http://dx.doi.org/10.1029/2021JD034972">http://dx.doi.org/10.1029/2021JD034972</a></p> <p>Pérez Bello, A., A. Mailhot, D. Paquin and D. Paquin-Ricard (2022). Temperature-precipitation scaling rates: a rainfall event-based perspective. Journal of Geophysical Research – Atmospheres. <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD037873">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD037873</a></p> <p>Separovic L, A Alexandru, R Laprise, A Martynov, L Sushama, K Winger, K Tete, M Valin. 2013. Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model. Clim Dyn 41:3167-3201. <a href="https://doi.org/10.1007/s00382-013-1737-5">DOI 10.1007/s00382-013-1737-5</a>.</p> <p>St-Pierre, M., J. Thériault and D. Paquin, 2019. Influence of the model spatial resolution on atmospheric conditions leading to freezing rain in regional climate simulations. Atmosphere-Ocean, <a href="https://doi.org/10.1007/s00382-013-1737-5">https://doi.org/10.1080/07055900.2019.1583088</a>.</p>
CMIP5 and CMIP6 post-processed AMOC and MLD data supporting Jesse et al. 2023
<p>These are the datasets supporting the paper "Why is CMIP6 projecting larger ocean dynamic sea level in the North Sea than CMIP5?" from Jesse et al. submitted to ERL.</p> <p>See the paper for more information about the data and GitHub for the code that generated the data: https://github.com/dlebars/CMIP_SeaLevel</p> <p>The Atlantic meridional overturning circulation (AMOC) is computed at two latitudes 26N and 35N from different CMIP variables:</p> <p>cmip5_amoc is computed from the variable "msftmyz".</p> <p>cmip6_amoc is computed from the variable "msftmz" or "msftyz" as indicated in the name of the file.</p> <p>cmip5_amoc_vo and cmip6_amoc_vo are computed from the meridional ocean velocity ("vo").</p> <p>For mixed layer depth the variable "mlotst" is used.</p> <p> </p>
CMIP6 variable counts per model
<p>The number of variables (y-axis) published for the historical simulation by each model (as represented in the DKRZ Earth System Grid Federation (ESGF) index node March 2022) is shown in blue columns against the model rank, where models are ranked in order of decreasing variable count. Also shown, in orange, is the number of variables which are included by all models up to the given rank.</p> <p>Data provided by Martin Juckes, image created by Beth Dingley</p>
CMIP6 Data Request infographic
<p>Infographic describing the CMIP6 Data Request. The four sections cover:</p> <ul> <li>What is the data request?</li> <li>Why was the data request created?</li> <li>How was the data request created?</li> <li>Perceived issues with the CMIP6 data request.</li> </ul> <p>Image created by Beth Dingley, text provided by Martin Juckes and edited by Beth Dingley.</p>
Data supporting manuscript "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble"
<p>Data supporting the results presented in the article Milovac et al: "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble".</p> <p>1. data_raw.tar contains annual and seasonal, global and regional (i.e. over ocean IPCC regions and ocean biomes), mean sea surface and near surface temperatures, calculated for the selected 26 CMIP6 global climate models (GCMs) at low resolution (listed in the file models_low_res.txt) and 1 GCM at high resolution (listed in the file models_high_res.txt). The original files, downloaded from one of the ESGF data centers, were all interpolated onto a common grid with the 1-degree resolution for low-resolution output and the 0.25-degree resolution for high-resolution output. The output was generated using the cdo tool (<a href="https://zenodo.org/record/7112925">https://zenodo.org/record/7112925</a>).</p> <p>2. data_txt.tar contains the results used to obtain all the figures given in the article.</p>
CMIP6 Data Request, Version 1.00.12
<p>Technical specification of the data requirements for the CMIP6 climate model intercomparison project,</p>
CMIP6 Climate Change indicators
<p>Paneuropean maps of climate change indicators (e.g. heating degree days) for different climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (reference, short time-horizon, medium time-horizon, long time-horizon) derived from CMIP6 climate data. This v2 includes the metadata.</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.