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33 results for “CMIP5”
Data from 'Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models'
<p><strong>Abstract from '<em>Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models</em>':</strong></p> <p>Despite the importance of interdecadal climate variability, we have a limited understanding of which geographic regions are associated with global temperature variability at these timescales. The instrumental record tends to be too short to develop sample statistics to study interdecadal climate variability, and Coupled Model Intercomparison Project, Phase 5 (CMIP5) climate models tend to disagree about which locations most strongly influence global mean interdecadal temperature variability. Here we use a new paleoclimate data assimilation product, the Last Millennium Reanalysis (LMR), to examine where local variability is associated with global mean temperature variability at interdecadal timescales. The LMR framework uses an ensemble Kalman filter data assimilation approach to combine the latest paleoclimate data and state-of-the-art model data to generate annually resolved field reconstructions of surface temperature, which allow us to explore the timing and dynamics of preinstrumental climate variability in new ways. The LMR consistently shows that the middle- to high-latitude north Pacific and the high-latitude North Atlantic tend to lead global temperature variability on interdecadal timescales. These findings have important implications for understanding the dynamics of low-frequency climate variability in the preindustrial era.</p>
Brazilian Earth System Model: CMIP5 Sea ice concentration and Air Temperature data
<p>The Brazilian Earth System Model, Version 2.5 (BESM-OAV2.5) used here is a global climate coupled ocean-atmosphere-sea ice model, and is part of CMIP5 project. The atmospheric component of BESM-OAV2.5 is BAM (Brazilian Atmospheric Model) and was described in detail by Figueroa et al., (2016). BAM, developed at Center for Weather Forecasting and Climate Studies of the National Institute for Space Research CPTEC-INPE has been constantly reformulated over the last years (Figueroa et al., 2016; Nobre et al., 2013). The lastest version, used here and described by Veiga et al., (2019), has spectral horizontal representation truncated at triangular wave number 62, grid resolution of approximately 1.875∘×1.875∘, and 28 sigma levels in the vertical, with unequal increments between the vertical levels (i.e., a T62L28). The oceanic component of BESM-OAV2.5 is the Modular Ocean Model, Version 4p1, from National Oceanic and Atmospheric Administration-Geophysical Fluid Dynamics Laboratory (MOM4p1/NOAA-GFDL), described in detail by Griffies, (2009). The MOM4p1 includes a Sea Ice Simulator (SIS) built-in ice model (Winton 2000). The SIS has five ice thickness categories and three vertical layers (one snow and two ice). To calculate ice internal stresses are used the elastic-viscous-plastic technique described by Hunke and Dukowicz, (1997). The thermodynamics is given by a modified Semtner’s three-layer scheme (Semtner, 1976). SIS is able to calculate sea ice concentration, snow cover, thickness, brine content and temperature. Furthermore, SIS calculates ice-ocean fluxes and transmits fluxes between atmosphere and ocean. The horizontal grid resolution of MOM4p1 in the longitudinal direction is a set to 1˚. The latitudinal direction varies uniformly, in both hemispheres, from 1∕4<sup>o </sup>between 10<sup>o</sup> S and 10<sup>o </sup>N to 1<sup>o </sup>of resolution at 45<sup>o</sup> and to 2<sup>o</sup> of resolution at 90<sup>o</sup>. The vertical axis has 50 levels (upper 220m, has 10 m resolution, increasing to about 360 at deeper levels. The MOM4p1 and BAM models were coupled using FMS coupler. FMS coupled was developed by NOAA-GFDL. The BAM model receives SST and ocean albedo from MOM4p1 and SIS (hour by hour). The MOM4p1 receives momentum fluxes, specific humidity, pressure, heat fluxes, vertical diffusion of velocity components and freshwater. </p> <p>This study used two numerical experiments from CMIP5: (i) piControl: it runs for 700 years, forced by invariant pre-industrial atmospheric CO<sub>2</sub> concentration level (280ppmv) and (ii) Abrupt 4xCO<sub>2</sub>: it runs for 460 years, comprising an abrupt instantaneous quadrupling of atmospheric CO<sub>2 </sub>level concentration from the piControl simulation. The design of both experiments follows the CMIP5 protocol (Taylor et al., 2012).</p> <p> </p>
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>
Global mean TAS and net TOA flux in CMIP5 piControl and abrupt4xCO2 experiments using EC-Earth model
<p>Near surface air temperature (tas) and net heat flux at top of the atmosphere (NetTOA) from CMIP5 piControl and abrupt4xCO2 experiments with EC-Earth2.3. Each file contains the annual global mean from one experiment for the respective variable, calculated using cdo.</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>
Ensemble mean of CMIP5 TOS, for the period 1971 to 2000
<p>Ensemble mean of the variable TOS (temperature of surface, i.e. Sea Surface Temperature), from CMIP5 control run.</p> <p>Data are independant of any RCP (before 2006), and can thus be used for computing an SST climatology.</p>
Monthly climatology of CMIP5 models historical run, for 1971-2000
<p>Monthly climatology (12 months, and the annual climatology), from CMIP5 historical run (1971 to 2000)</p>
Global and tropical band averages for a selection of CMIP5 and CMIP6 models: piControl and abrupt-4xCO2 experiments (compressed)
<p>This dataset provides post-processed spatial averages for a selection of 55 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> <p>*rnt and rns were calculated for the creation of this dataset using the model output rlut rsut, rsds, rsus, rlds, rlus.</p> <p>This version updates the previous version by providing the dataset in a compressed tarball. Use `tar -xzvf CMIP_data.tar.gz` to extract and decompress.</p>
Multi-model Hydropower Projections for the United States Federal Power Marketing Areas under CMIP5 Climate Change Conditions
<p>This dataset contains an ensemble of monthly hydropower generation projections for the United States Federal Hydropower plants for the periods of 1966-2005 (historical period) and 2011-2050 (future period). The dataset includes the monthly hydropower projections developed in (Kao et al. 2016) based on the Watershed Runoff-Energy Storage (WRES) model and is complemented with another ensemble based on the process-based Water Management Power (WMP) model.</p> <p>The hydrologic projections are estimated through a cascading modeling toolchain that include ten global climate change model projections (ACCESS1-0, BCC-CSM1-1, CCSM4, CMCC-CM, GFDL-ESM2M, MIROC5, MPI-ESM-MR, MRI-CGCM3, NorESM1-M and IPSL-CM5A-LR) under RCP8.5 scenario, which are dynamically downscaled with a regional climate model (RegCM4) ( Pal et al. 2007, Giorgi et al. 2012)), which then inform the Variable Infiltration Capacity (VIC) hydrology model (Liang et al. 1994). The ensemble of hydrologic projections is then informing two processes to translate runoff into hydropower projections. First, WRES models monthly river routing and employs a non-linear statistical approach relating monthly natural flow to hydropower generation, including processes such as spilling. Second, MOSART-WM (Voisin et al. 2013), a large-scale river routing and water management model, provides daily reservoir storage and regulated release at dam locations as well as regulated flow at run-of-the-river power plants. The WMP model then translates reservoir and regulated river dynamics into hydropower projections (Zhou et al. 2018). Those projections are further calibrated to monthly generation provided by the federal utilities. The US federal hydropower plants analyzed in this study include 132 facilities that were built and/or are operated by the US Army Corps of Engineers (USACE), the Bureau of Reclamation (Reclamation), and the International Boundary and Water Commission (IBWC). The electricity generation projected for these hydropower plants were aggregated by four Power Marketing Administrations (PMAs), including Bonneville Power Administration (BPA), Southeastern Power Administration (SEPA), Southwestern Power Administration (SWPA), and Western Area Power Administration (WAPA), and their associate subregions.</p> <p>The two files, <em>SWA9505V2_Gsim_PMA_WRES.mat</em> and <em>SWA9505V2_Gsim_PMA_WMP.mat</em>, represent model outputs from the two hydropower models, WRES and WMP respectively.</p> <p>Each file contains 6 variables:</p> <p>1) “Models”: the 10 global climate models (GCMs).</p> <p>2) “PMA_areas”: the 18 subregions of PMAs as defined in (Kao et al. 2015).</p> <p>3) “PMA_G_mn_6605”: 1966-2005 projected monthly hydropower generation for each PMA sub-regions. Dimension: (12 [months], 40 [years], 18 [subregions], 10 [GCMs]). Unit: MWH.</p> <p>4) “PMA_G_mn_1150”: Same as “PMA_G_mn_6605”, but for 2011-2050 projected hydropower generation.</p> <p>5) “PMA_G_yr_6605”: 1966-2005 projected annual hydropower generation. Dimension: (40 [years], 18 [subregions], 10 [GCMs]) . Unit: MWH.</p> <p>6) “PMA_G_yr_1150”: Same as “PMA_G_yr_6605”, but for 2011-2050 projected hydropower generation.</p> <p>The following journal paper details the method in creating the dataset:</p> <p><strong>Impacts of Climate Change on Subannual Hydropower Generation: A Multi-model Assessment of the United States Federal Hydropower Plants</strong></p> <p><strong>Zhou et al. (2022) Preparing for submission to Environmental Research Letters.</strong></p>
Manipulation of netCDF data with R for climate change research: Multi-model analysis for CMIP5 models.
<p>Geoscientists now live in a world with an exponential growth in digital data and methods.<br> Climate change studies usually describe computational methods informally. Climate scientists seek to<br> share their information, the justification of reproducible research has received increasing attention in<br> geosciences. To have it in an open-source format makes it easier to interchange not only with fellow<br> scientists but also a variety of sources including funders, publishers, and journalists. R is a open-source<br> computer language powerful and highly extensible that can promotes reproductive science techniques in a<br> easier way. R is highly accessible for non-computational scientists when coupled with packages like<br> ‘raster', ‘netcdf', ´rgdal`and ‘rasterVis', R enables scientists to make sense of their data and to carry out<br> complex data analysis. In this paper we have assessed the power of R language for manipulating climate<br> data from a huge dataset: the Coupled Model Intercomparison Project Phase 5 (CMIP5). Moreover we<br> have proposed an example of best practices to handle model ensembles. This is the first study to our<br> knowledge to promote best practices for CMIP5 ensemble. The NetCDF data accessible to R via raster<br> package capabilities provides efficient access to the multi-model, with crucial applications in climate<br> change research. In recent years more than 100 peer-reviewed scientific publications have used the<br> CMIP5 data sets. We envision that in the near future (5-10 years), scientists will use radically new tools<br> to author papers and disseminate information about the process and products of their research.</p>
Cloud feedback maps from CMIP5 and CMIP6 models
<p>This repository contains maps of cloud feedbacks computed using up to three methodologies from two types of experiments conducted across CMIP5 and CMIP6 models.</p> <p>Methods:</p> <ul> <li>The approximate partial radiative perturbation technique (shortwave cloud feedbacks only), denoted with 'APRP' in filename. </li> <li>The adjusted change in cloud radiative effect, denoted with 'adjCRE' in filename. </li> <li>The cloud radiative kernel method, in files without 'APRP' or 'adjCRE' in the name. </li> </ul> <p>Experiments:</p> <ul> <li>amip+4K experiments, known as amip4K and amip-p4K in CMIP5 and CMIP6, respectively</li> <li>abrupt CO2 quadrupling experiments, known as abrupt4xCO2 and abrupt-4xCO2 in CMIP5 and CMIP6, respectively</li> </ul> <p>Data formats:</p> <ul> <li>For cloud feedbacks derived using APRP and adjCRE methods, all models' maps are contained within single netcdf files.</li> <li>For cloud feedbacks derived using cloud radiative kernels, Individual netcdf files are provided for each model, which are compressed into a zip file.</li> </ul>
AC&C/SPARC Ozone Database for CMIP5 - Hitop Version
<p>The AC&C/SPARC ozone is provided on pressure levels between 1000-1hPa. For use in high-top models, an extrapolation of the dataset up to ~0.01 hPa or higher, may be required. The UK National Centre for Atmospheric Science (NCAS) has produced an updated/extended version of the SPARC ozone dataset. The following were done: (1) A multiple-linear regression is performed on the historical raw pressure-level data between 1000-1 hPa consistent with the Randal and Wu method used to construct the timeseries. The ozone is then represented as: O3(t) = a*SOL + b*EESC + seasonal_cycle + residuals. For consistency, the 11-yr solar cycle and EESC indices are identical to those used to prepare the original dataset. NOTE: the SOLAR index is a 180.5nm timeseries provided by Fei Wu at NCAR. (2) The seasonal cycle and residual fields are extrapolated and reduced above 1 hPa using O3(z)=O3(1hPa)*exp(-(z-z(1hPa))/H). Where H = 7 km. The seasonal cycle field is then used to provide a smooth climatology of ozone up to the model top. It has also been smoothed in latitude to the poles with a cosine function. (3) To minimize the 11-yr solar cycle and trend components in the mesosphere the a and b regression coefficients are extrapolated and reduced above 1 hPa twice as rapidly as the seasonal cycle, i.e. using the same method but with H = 3.5 km. (4) The full ozone timeseries is then reconstructed using the equation from (1). The ozone climatologies are not affected. (5) The standard SPARC ozone dataset which extends into the future does not include solar cycle variability post-2009. For production of a dataset extending into the future including an 11-yr ozone solar cycle, the solar regression index is used to build a future time series consistent with a repeating solar irradiance compiled by the UK Met Office and is modelled as a sinusoid with a period of 11 years, with mean and max-min values corresponding to solar cycle 23 (Gareth Jones, pers. comm.).</p>
cb-oura-1.0 : Generic climate scenarios from bias-adjusted CMIP5 global models
<p><strong>Context </strong></p> <p>The need to adapt to climate change is present in a growing number of fields, leading to an increase in the demand for climate scenarios for often interrelated sectors of activity. In order to meet this growing demand and to ensure the availability of climate scenarios responding to numerous vulnerability, impact, and adaptation (VIA) studies, <a href="https://www.ouranos.ca/">Ouranos</a> is working to create a set of operational multipurpose climate scenarios. The initial version of “Scénarios Génériques” (generic scenarios, acronym cb-oura-1.0) is used mainly in Ouranos’ work to provide a consistent image of the changing climate over the North East of North America, principally the province of Québec. Cb-oura-1.0 was produced in 2016 by downscaling and bias-adjusting a selection of global climate model simulations available through the CMIP5 program. </p> <p><strong>Climate simulations </strong></p> <table> <caption>Climate simulations in the ensemble</caption> <thead> <tr> <th scope="col">Modeling center</th> <th scope="col">Acronym</th> <th scope="col">Model</th> <th scope="col">RCP</th> <th scope="col">Status*</th> </tr> </thead> <tbody> <tr> <td><strong>College of Global Change and Earth System Science, Beijing Normal University</strong></td> <td>GCESS</td> <td>BNU-ESM</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Canadian Centre for Climate Modelling and Analysis</strong></td> <td>CCCMA</td> <td>CanESM2</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Centro Euro-Mediterraneo per I Cambiamenti Climatici</strong></td> <td>CMCC</td> <td>CMCC-CMS</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia</strong></td> <td>CSIRO-BOM</td> <td>ACCESS1.3</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>a</td> </tr> <tr> <td><strong>Institute for Numerical Mathematics</strong></td> <td>INM</td> <td>INM-CM4</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>a</td> </tr> <tr> <td><strong>Institut Pierre-Simon Laplace</strong></td> <td>IPSL</td> <td>IPSL-CM5A-LR</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td>IPSL-CM5B-LR</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Met Office Hadley Centre</strong></td> <td>MOHC</td> <td>HadGem2</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology)</strong></td> <td>MPI-M</td> <td>MPI-ESM</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>Norwegian Climate Centre</strong></td> <td>NCC</td> <td>NorESM</td> <td>4.5</td> <td>a</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> <tr> <td><strong>NOAA Geophysical Fluid Dynamics Laboratory</strong></td> <td>NOAA-GFDL</td> <td>GFDL-ESM2M</td> <td>4.5</td> <td>s</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>8.5</td> <td>s</td> </tr> </tbody> </table> <p>From the complete ensemble of RCP 4.5 and 8.5 driven CMIP5 climate simulations, a selection of 22 simulations (11 per RCP) was made using a clustering ensemble reduction methodology (Casajus et al. 2016). This objective selection method identifies a reduced number of simulations that best represent the overall ensemble. Input criteria for the reduction were the monthly changes between the present (1981-2010) and two future horizons (2041-2070 and 2071-2100), at 15 regions distributed across Canada, for three variables (mean daily maximum temperature, mean daily minimum temperature and total precipitation). An initial selection of 16 simulations shows a distribution projected changes for the 12 (months) x 2 (horizons) x 15 (regions) x 3 (variables) indices that is not statistically different from the complete ensemble. A small number of simulations were subsequently added to have a complete set with both RCPs represented equally (11 members for each emission scenario). </p> <p><strong>Reference dataset </strong></p> <p>The bias-adjustment reference (or target) is a gridded observation dataset produced by Natural Resources Canada (McKenney et al., 2011; et Hutchinson et al. ,2009). It uses the ANUSPLIN interpolation method over station observations to derive daily grids of minimum and maximum temperature, as well as total precipitation for the Canadian landmass. The grid has a resolution of 10 km x 10 km and cover the time period from 1950 to 2013. </p> <p>As this dataset is not available over the United States, it was merged with another observation interpolation dataset produced by Livneh et al. (2015) in order to enable the production of bias-corrected climate scenarios covering a portion of the northern United States. </p> <p><strong>Coverage </strong></p> <p>The final version of this dataset covers a region covering the Atlantic provinces, Québec, Ontario, Manitoba and Saskatchewan and part of the northern United States: From 120°W to 54°W and from 40°N to 62°N. </p> <p>It contains the daily minimum temperature, daily maximum temperature and daily precipitation flux, covering the period 1950 to 2100. </p> <p><strong>Bias-adjustment </strong></p> <p>The global simulations where downscaled to the reference grid using bilinear interpolation and then bias-adjusted with a 1-D quantile mapping method, as described by Gennaretti et al. (2015). A moving window of 31 days was used to adjust each day of the year, using 50 quantiles to define the statistical distributions to match. The long-term linear trends of the temperature variables were preserved explicitly. </p> <p><strong>Climate indicators </strong></p> <p>This dataset is used to in the first versions (up to 1.3) of Ouranos’ <a href="https://www.ouranos.ca/en/climate-portraits">Climate Portraits </a>website. A selection of 26 seasonal and annual climate indicators were computed from the daily scenarios, using the xclim software package (Logan et al. 2022). The "virtual indicator module" used for the computation is made available here in the "indicators.yml" file.</p> <p>On the Climate Portraits website, the information is presented from three aspects: spatial, temporal and summary. This repository stores the reduced ensemble data as shown on the website. Filenames are constructed as "{aspect}_{indicator}_{season}.nc".</p> <ul> <li> <p>Maps (files "spatial_*") : Climate indicators for each bias-adjusted climate simulation and for a given RCP emission scenario are averaged over 30-year horizons. Ensemble percentiles are computed in order to summarize climate model uncertainty. In particular the 10, 25, 50, 75 and 90th percentiles over the 11 members are calculated for each RCP. </p> </li> <li> <p>Timeseries (files "temporal_*") : Climate indicators for each bias-adjusted simulation are averaged spatially over each region for every time step (annual or seasonal). The ensemble statistics are computed by first pooling all regional average values for the 11 members using within a centred 30-year window and then calculating percentile values (same as above) on the pooled data. </p> </li> </ul> <ul> <li> <p>Summary (files "summary_*") : The indicators are averaged over each region and then over 30-year horizons. The ensemble statistics (same as above) are then computed. </p> </li> </ul> <p>In versions 2.x of the app, this data will be presented as "CMIP5".</p> <p><strong>Data availability </strong></p> <p>This repository stores the climate indicator ensemble statistics as shown on the Climate Portraits website and described above. The complete daily dataset is too large for this platform.</p> <p>The complete daily dataset is available through the public THREDDS server of the PAVICS platform maintained by Ouranos. This data might be removed in the future. When this is the case, please contact us for data requests.<br> <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip5/ouranos/cb-oura-1.0/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip5/ouranos/cb-oura-1.0/catalog.html</a> </p> <p>The annual and season indicators of the Climate Portraits website are available on the same server, along with a few more indicators not shown on the app. <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/ouranos/portraits-clim-1.3/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/ouranos/portraits-clim-1.3/catalog.html</a></p> <p><em>Terms of use</em>: Use of this dataset should be acknowledged as 'Data produced and provided by the Ouranos Consortium on Regional Climatology and Adaptation to Climate Change'. Furthermore, the modeling groups from which the bias-adjusted climate scenarios were constructed must also be acknowledged, please refer to: The Coupled Model Intercomparison Project <a href="https://pcmdi.llnl.gov/mips/cmip5/citation.html.">https://pcmdi.llnl.gov/mips/cmip5/citation.html.</a></p>
Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅱ
<p>A bias-corrected dataset containing daily meteorological data of the Qinghai-Tibet Plateau has been generated, by using a trend-preserving bias-correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach together with a high-quality gridded meteorological dataset based on ground observation (CN05.1). The data set contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and their downscaled high-resolution dataset (NEX-GDDP) in the Qinghai-Tibet Plateau (QTP) during 1986-2095. This dataset can provide important reference for the study on future climate change and its impacts in the Qinghai-Tibet Plateau region.</p> <p><strong>Note: For Tmin in historical periods, the values larger than 2606 refer to no data. Set them to NaN before using, for example (Matlab): Tmin(Tmin>2606)=nan;</strong></p> <p>More details about this dataset can be found in the article: S. Chen, T. Ye, W. Liu, A. Wang and P. Shi. Evaluation and bias correction of the historical and future near-surface climate forcing in NEX-GDDP and CMIP5 over the Qinghai-Tibet plateau[J], Plateau Meteorology (in Chinese), 2020, DOI: 10.7522/j.issn.1000-0534. 2020. 00019.</p>
Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅰ
<p>A bias-corrected dataset containing daily meteorological data of the Qinghai-Tibet Plateau has been generated, by using a trend-preserving bias-correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach together with a high-quality gridded meteorological dataset based on ground observation (CN05.1). The data set contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and their downscaled high-resolution dataset (NEX-GDDP) in the Qinghai-Tibet Plateau (QTP) during 1986-2095. This dataset can provide important reference for the study on future climate change and its impacts in the Qinghai-Tibet Plateau region.</p> <p><strong>Note: For Tmax in historical periods, the values larger than 2606 refer to no data. Set them to NaN before using, for example (Matlab): Tmax(Tmax>2606)=nan;</strong></p> <p>More details about this dataset can be found in the article: S. Chen, T. Ye, W. Liu, A. Wang and P. Shi. Evaluation and bias correction of the historical and future near-surface climate forcing in NEX-GDDP and CMIP5 over the Qinghai-Tibet plateau[J], Plateau Meteorology (in Chinese), 2020, DOI: 10.7522/j.issn.1000-0534. 2020. 00019.</p>
An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology.
<p>The pattern library is available on GitHub through the Joint Global Change Research Institution repository (https://github.com/JGCRI/CMIP5_patterns). GitHub is a web-based version control repository and Internet hosting service which uses git concepts and commands. The purpose of creating this pattern library was to allow for researchers across various fields to be able to efficiently use the statistical patterns generated by the described regression method to examine model response to change in global mean temperature for all the available CMIP5 models (41 models, at present). We also further intend for those patterns to be easy to scale using a scaler generated from a SCM of ones choosing.</p> <p>The pattern library contains patterns, generated by the least squared regression methodology, for the first realization of the 41 CMIP5 climate models. Annual, seasonal, and monthly patterns are provided for surface temperature and precipitation. For temperature patterns, units in Celsius were used as it is the standard temperature units for impact analysis.</p> <p>Included in each netCDF file for each model is:<br> 1. The individual model pattern (2-D);<br> 2. The adjusted R2 between the predictor and dependent terms (2-D);<br> 3. The standard error of the estimated regression coefficient (2-D);<br> 4. A historical climatology based on the 1961-1990 average from each model (2-D), which can be used to construct absolute values at time X;</p> <p>5. The 95th percentile confidence level pattern for model parameters.</p> <p>The patterns from all 41 CMIP5 models range in size (165 kB to 1 MB) due to spatial resolution, but all patterns were kept at the native resolution of the dependent variable and no regridding of input/output variables were done. This was done to retain model specific information, which may have been lost if regridded to a common spatial resolution.</p> <p>All source code used to produce patterns is available in the aforementioned repository. Source code is written in NCAR Command Language (Version 6.3.0; http://dx.doi.org/10.5065/D6WD3XH5).</p>
CMIP5 Quality Control Level 2 Exception Codes with Categories
<p>This document contains a list of exception codes for the interpretation of the quality control level 2 (QC L2) results of CMIP5 including exception categories. It was originally available on a web page under the url: http://cera-www.dkrz.de/CMIP5/QC/2/qc2list.html. The QC L2 results are available at: http://cera-www.dkrz.de/WDCC/CMIP5/QCResult.jsp .</p>
Dataset for ISMIP6 CMIP5 model selection
<p>Dataset associated with the manuscript entitled "CMIP5 model selection for ISMIP6 ice sheet model forcing: Greenland and Antarctica" for publication in The Cryosphere. This dataset was used to select CMIP5 models as forcing for the ISMIP6 stand-alone Greenland and Antarctica projections.</p>
Time series of annual TAS 40-year trend from historical to future in CMIP5 model simulations
<p>Time series of 40-year linear trend for the annual near surface air temperature (tas) for the period 1979-2100 as simulated by the CMIP5 models on a 2 x 2 deg grid. The data for the period 1979-2005 are taken from the CMIP5 historical simulations, while data for 2006-2100 are from the CMIP5 future scenario rcp2.6, rcp4.5 and rcp8.5, respectively. The 40 year trend of 1979-2018 from the ERA-Interim reanalysis is also provided in a separate file. </p> <p>Each file contains the time series of the 40-year linear trend for tas at each grid point from historical to one of the three scenarios simulated by one model. The linear trend is calculated using cdo, and the year associated with each data point in a file corresponding to the last year in the 40-year time period, e.g, the associated year 2018 corresponds to the linear trend calculated for the period 1979-2018. Unit: C/year.</p>
Time series of Area mean TAS 40-year trend from historical to future in CMIP5 model simulations
<p>Time series of 40-year linear trend for the Arctic (ARC) and the Eastern Arctic (eARC) mean annual near surface air temperature (tas) for the period 1979-2100 as simulated by the CMIP5 models. The data for the period 1979-2005 are taken from the CMIP5 historical simulations, while data for 2006-2100 are from the future scenario rcp2.6, rcp4.5 and rcp8.5, respectively. The 40 year trend of 1979-2018 from the ERA-Interim reanalysis is also provided in separate files. The Arctic is defined as the area north of 70 N, and the eastern Arctic is defined as 0 - 180 E and north of 70 N. Each file contains the time series of the 40-year linear trend for the respective area mean tas from historical to one of the three scenarios simulated by one model. The linear trend is calculated using cdo, and the year associated with each data point in a file corresponding to the last year in the 40-year time period, e.g, the associated year 2018 corresponds to the linear trend calculated for the period 1979-2018. Unit: C/year.</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.