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617 results for “Climate models”
Statistical characterization of Andalusian wave climate for several combinations of Global Climate Models and Regional Climate Models and periods 2026 - 2045 and 2081 - 2100.
<p>The following text is an extract of the extended abstract entitled "<strong>Parametric Characterization of Wave Climate along the Andalusian Coast for Non-Stationary Stochastic Simulation</strong>" whose authors are Manuel Cobos, Pedro Magaña, Pedro Otiñar and Asunción Baquerizo, and that was included in proceedings of <em>39th IAHR World Congress</em> where this dataset is included.</p> <p><em>Processed data comes from PIMA Adapta Costas project (Ramírez et al., 2019), in particular, from projections of maritime climate for 2026-2045 and 2081-2100. Sea climate contains, among other information, time series of the significant wave height (H<sub>s</sub>) obtained for several combinations of GCM-RCM projections of EUR-11 for the RCP 8.5. GCM-RCM combinations ACCE, CMCC, CNRM, GFDL, HADG, IPSL, MIRO with a 0.1 degrees grid were used for the Atlantic facade while CNRM, HADG, IPSL, MIRO, MEDC, MPIE, ESM2, EART models with 1/11 degrees were used for the Mediterranean one. A total of 210 locations were analyzed, 54 at the Atlantic facade and 156 at the Mediterranean one (Figure 1). The data was bias adjusted using the Empirical Quantile Mapping (Déqué et al., 2007; Michelangeli et al., 2009). Information of the significant wave height and the dependence between the values at a given time with previous values with a VAR(q) model is already available. </em></p> <p><em>At each location, the methodology of Lira-Loarca et al. (2021) was applied, using the software described in Cobos et al. (2022a). More precisely, for every GCM-RCM (hereinafter, model n for n = 1, .., N where N = 7 for Atlantic data and N = 8 for the Mediterranean data), a non-stationary marginal distribution of H<sub>s</sub>, , assuming that the year was the largest periodicity of the climate, was fitted to data using a lognormal model for the central part and two generalized Pareto distribution for the lower and upper tails, as in Solari and Losada (2011). The non- stationarity is considered by assuming a decomposition of the parameters of the distribution and of the percentiles of the common end points of the interval into a trigonometric truncated expansion.</em></p> <p><em>In addition, the coefficients of the matrix, C<sub>n</sub>, of a VAR(q) model with q up to 92 hours were estimated. The ensemble multi-model characteristics of the data were obtained from the compound distributions and the weighted averaged matrix coefficients. </em></p> <p><em>Soon, the results of the peak period (T<sub>p</sub>) and mean incoming wave direction (ϑ<sub>m</sub>) and the coefficients of the multivariate VAR model will also be included.</em></p> <p> </p> <p> </p>
Archived Model Output for "Simulating Observations of Southern Ocean Clouds and Implications for Climate"
<p>This is an archive of CAM6 simulation output used in the paper Southern Ocean Aerosol and Ice Nucleating Particles in the Community Earth System Model Version 2, submitted to the Journal of Geophysical Research Atmospheres. </p>
Intital simulation of Hunga-Tonga volcanic aerosol cloud with the UM-UKCA composition-climate model
<p>This dataset is from a series of “forward projection” interactive stratospheric aerosol simulations of the Jan 2022 Hunga-Tonga volcanic aerosol cloud with the UM-UKCA composition-climate model. The model experiments predict how the cloud will disperse through 2022, and apply the UM-UKCA model at GA4 (Walters et al., 2014), with GLOMAP v8.2, as applied for the “MajorVolc” datasets for Agung, El Chichon and Pinatubo (Dhomse et al., 2020), those runs aligned with the Historical Eruption SO2 emissions Assessment experiment within ISA-MIP (Timmreck et al., 2018).</p> <p>The “standard” Hunga-Tonga GA4 UM-UKCA experiment emits 0.4Tg of SO2 at 29-31km, within a 24-hour period, matching the detrainment duration specified for the ISA-MIP HErSEA experiment protocol. Following the stronger than expected mid-visible backscatter ratios (BSR) measured by CALIOP satellite-borne lidar, and from ground-based lidar from Reunion Island (very high BSR values > 200), we also ran UM-UKCA simulations with “scaled-up Hunga-Tonga SO2 emission”, at 0.8, 1.2 and 1.6 Tg of SO2 emitted.</p> <p>Unexpectedly strong stratospheric AOD observed from the OMPS satellite months after the eruption further strengthens the motivation for these simulations.</p> <p>Several hypotheses for the high AOD from Hunga-Tonga have been suggested:<br> 1) an unusual amount of (or influence from) co-emitted ultra-fine ash particles<br> 2) “in-plume oxidised sulphate” already converted from SO2 at the time of detrainment<br> (e.g. via aqueous-phase oxidation within water droplets within the eruptive plume).<br> 3) co-emitted marine aerosol (e.g. sea-salt aerosol) from seawater vaporized in the plume<br> </p> <p>There are 4 types of netcdf files, Stratospheric AOD (saod), Effective Radius (reff), Extinction (ext) and sulphate aerosol surface area density (sad).</p> <p><br> <br> For e.g. <br> saod550_HT_0pt4Tg_T2Mz-20220101-20230831.nc contains<br> Stratospheric aerosol optical depth (sAOD) at 550nm (2D-monthly dataset vs latitude and time) with 0.4 Tg SO2 injection Jan2022 to August 2023<br> Whereas other files<br> reff_HT_0pt4Tg_T2Mz_20220101-20230831.nc,<br> sad_HT_0pt4Tg_T2Mz_20220101-20230831.nc<br> ext550_HT_0pt4Tg_T2Mz-20220101-20230831.nc</p> <p>contain particle effective radius (reff), aerosol surface area density, aerosol extinction as 3D-monthly fields (altitude, latitude , time) from the same simulation.<br> Other saod and extinction files are also available at 870 and 1020 nm.</p> <p> </p> <p>Note that these are preliminary simulations, hence we do not expect good match with the observations. We plan to perform additional UM-UKCA simulations, comparing to the satellite and ground-based lidar measurements, and to in-situ balloon observations from Reunion Island rapid response campaign & upcoming high-altitude balloon sampling flights in Brazil.</p> <p> </p> <p>References :<br> Dhomse SS, Mann GW, Antuña Marrero JC, Shallcross SE, Chipperfield MP, Carslaw KS, Marshall L, Abraham NL, Johnson CE. 2020. Evaluating the simulated radiative forcings, aerosol properties, and stratospheric warmings from the 1963 Mt Agung, 1982 El Chichón, and 1991 Mt Pinatubo volcanic aerosol clouds. Atmospheric Chemistry and Physics. 20(21), pp. 13627-13654</p> <p><br> Timmreck, C., Mann, G. W., Aquila, V., Hommel, R., Lee, L. A., Schmidt, A., Brühl, C., Carn, S., Chin, M., Dhomse, S. S., Diehl, T., English, J. M., Mills, M. J., Neely, R., Sheng, J., Toohey, M., and Weisenstein, D.: The Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP): motivation and experimental design, Geosci. Model Dev., 11, 25812608, https://doi.org/10.5194/gmd-11-2581-2018, 2018.</p> <p> </p>
Model output and analysis scripts for "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity"
<p>Here, we provide annually averaged model output from a 3000-year control simulation of PlaSim–LSG, a climate model of intermediate complexity. Processed variables and a Jupyter notebook to reproduce all figures of the manuscript (Mehling et al.: "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity") can also be found in this repository.</p> <p>In addition, a Python implementation of the three-box model proposed in the manuscript can be found in the notebook <em>boxmodel.ipynb</em>.</p>
Processed model output of the climate simulation in the study: The effects of diachronous surface uplift of the European Alps on regional climate and the isotopic composition of precipitation (δ18Op) [Boateng et al.]
<p><strong>The geodynamic evolution of the Alps suggests that the Alps did not rise monotonically due to the different post-collisional processes such as slab break-off. However, understanding such subsurface dynamics would require adequate knowledge about its surface uplift history. Stable isotope paleoaltimetry methods are widely used to infer past surface elevation using geologic archives. However, its accurate interpretation relies on attributing the extracted isotopic signal from proxies to surface uplift despite other influences such as climate. To resolve this issue, topographic sensitivity experiments across the Alps are used to investigate the impacts of the diachronous surface uplift on regional climate and δ18Op. The Atmospheric General Circulation Model ECHAM5 with water isotope tracking capabilities (ECHAM5-wiso) is used to simulate the climate with varied topographic scenarios. We present the processed (long-term means) model output of the relevant climate variables (i.e δ18Op, near-surface temperature, precipitation amount, near-surface meridional and zonal winds, mean sea level pressure, and elevation) in response to the changes in topography. The file names are representative of the topographic scenarios used for the simulations. For example, the file “W2E1.nc” is the model output produced by a topographic scenario in which the topography across the west-central Alps was set to 200% of its modern height, and the Eastern Alps were kept at 100%. The “CTL.nc” file contains model output from the control simulation that uses present-day topography. The datasets for instance can be used to select far-field sampling points for the δ-δ paleoaltimetry method that are not significantly affected by the topographic changes.</strong></p>
Climatological data from mechanistic model experiments of Boljka and Birner (2022/3; npj Climate and Atmospheric Science)
<p>Some climatological output data from mechanistic dry dynamical core model experiments used for the paper of Boljka and Birner (2022/3): "Potential impact of tropopause sharpness on the structure and strength of the general circulation", npj Climate and Atmospheric Science. For more details see the manuscript. </p>
Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models (Ocean and Climate variables from Simple Climate Models)
<p>This dataset provide global ocean and climate variables from 8 Simple Climate Models used in the study "Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models"</p>
Modelled urban climate island during the record-breaking 2022 heatwave in London
<p>This record is created as a data supplement for the manuscript "Estimated mortality attributable to the urban heat island during the record-breaking 2022 heatwave in London".</p> <p>These data were produced using the Weather Research Forecasting model with BEP-BEM. The model setup is described in Brousse et al (2023) <a href="doi.org/10.1175/JAMC-D-22-0142.1">10.1175/JAMC-D-22-0142.1</a>. These data cover the period 2022-07-10 to 2022-07-25, during which temperatures exceding<strong> </strong>40 °C were recorded in London for the first time.</p> <p>The data comprise two NetCDF files. One is labelled "Urb" one "Nourb". In the "Nourb" file, the urban tile is removed from the model and the land surface replaced by the nearest natural tile. This can be used to estimate the influence of the urban tile on the local climate.</p> <p>Variables included in the file are T2 (temperature at 2 m elevation in Kelvin), V10 and U10 (winds at 10 m elevation in metres per second), PSFC (surface level pressure in Pascal), RAINNC (rain in mm), TH2 (potential temperature at 2m elevation in Kelvin), and Q2 (specific humidity at 2 m elevation, which is dimensionless). All variables are provided at hourly timestep.</p> <p>Queries about this dataset can be directed to o.brousse@ucl.ac.uk</p>
Early Cretaceous climate model output from the Kiel Climate Model (from Steinig et al. 2024)
<h1>README</h1> <p>This directory contains climate model output data for 36 different Early Cretaceous (Aptian/Albian) simulations performed with the Kiel Climate Model (KCM; ECHAM5/NEMO). Each zip file contains climatological mean values (monthly mean for the atmosphere; annual mean for the ocean) averaged over the last 100 model years of each simulation in netCDF format. A detailed description of the model setup, boundary conditions and integration strategy is given in the associated publication ("Controls on Early Cretaceous South Atlantic Ocean circulation and carbon burial – a climate model-proxy synthesis" in Climate of the Past; <a href="https://doi.org/10.5194/egusphere-2023-2732">https://doi.org/10.5194/egusphere-2023-2732</a>). Differences in the paleogeographies are limited to the South Atlantic and Southern Ocean regions, but the output is available globally.</p> <h2>Available Models</h2> <p>Table 1: Overview of available model simulations sorted by the four main boundary condition differences discussed in the associated paper:</p> <table> <tbody> <tr> <th>Model ID</th> <th>Opening Stage</th> <th>CO2</th> <th>Drake Passage Depth</th> <th>Walvis Ridge Depth</th> </tr> </tbody> <tbody> <tr> <td>DC1</td> <td>stage 1</td> <td>1200 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC2</td> <td>stage 2</td> <td>1200 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC3</td> <td>stage 3</td> <td>1200 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC4</td> <td>stage 1</td> <td>600 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC5</td> <td>stage 2</td> <td>600 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DC6</td> <td>stage 3</td> <td>600 ppm</td> <td>0 m</td> <td>1200 m</td> </tr> <tr> <td>DS1</td> <td>stage 1</td> <td>1200 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS2</td> <td>stage 2</td> <td>1200 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS3</td> <td>stage 3</td> <td>1200 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS4</td> <td>stage 1</td> <td>600 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS5</td> <td>stage 2</td> <td>600 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>DS6</td> <td>stage 3</td> <td>600 ppm</td> <td>200 m</td> <td>1200 m</td> </tr> <tr> <td>C51</td> <td>stage 1</td> <td>1200 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C53</td> <td>stage 2</td> <td>1200 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C54</td> <td>stage 3</td> <td>1200 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C61</td> <td>stage 1</td> <td>600 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C63</td> <td>stage 2</td> <td>600 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>C64</td> <td>stage 3</td> <td>600 ppm</td> <td>1400 m</td> <td>1200 m</td> </tr> <tr> <td>WC1</td> <td>stage 1</td> <td>1200 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC2</td> <td>stage 2</td> <td>1200 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC3</td> <td>stage 3</td> <td>1200 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC4</td> <td>stage 1</td> <td>600 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC5</td> <td>stage 2</td> <td>600 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WC6</td> <td>stage 3</td> <td>600 ppm</td> <td>0 m</td> <td>200 m</td> </tr> <tr> <td>WS1</td> <td>stage 1</td> <td>1200 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS2</td> <td>stage 2</td> <td>1200 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS3</td> <td>stage 3</td> <td>1200 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS4</td> <td>stage 1</td> <td>600 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS5</td> <td>stage 2</td> <td>600 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WS6</td> <td>stage 3</td> <td>600 ppm</td> <td>200 m</td> <td>200 m</td> </tr> <tr> <td>WI1</td> <td>stage 1</td> <td>1200 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI2</td> <td>stage 2</td> <td>1200 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI3</td> <td>stage 3</td> <td>1200 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI4</td> <td>stage 1</td> <td>600 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI5</td> <td>stage 2</td> <td>600 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> <tr> <td>WI6</td> <td>stage 3</td> <td>600 ppm</td> <td>1400 m</td> <td>200 m</td> </tr> </tbody> </table> <h2>Available Variables</h2> <p>Each model zip file contains a <code>means</code> directory with the climatological mean output files for the atmosphere and ocean.</p> <h3>Atmosphere</h3> <p>Files starting with <code><Model ID>_mm_*</code> contain the monthly mean climatologies of 2D atmosphere variables. Each file represent a single variable for the following list of variables:</p> <p>Table 2: Overview of available atmospheric variables. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>aclcov</td> <td>total cloud cover</td> </tr> <tr> <td>evap</td> <td>evaporation</td> </tr> <tr> <td>precip</td> <td>total precipitation</td> </tr> <tr> <td>slp</td> <td>mean sea level pressure</td> </tr> <tr> <td>tau_x</td> <td>zonal wind stress</td> </tr> <tr> <td>tau_y</td> <td>meridional wind stress</td> </tr> <tr> <td>temp2</td> <td>2m air temperature</td> </tr> <tr> <td>tsw</td> <td>surface temperature of water</td> </tr> <tr> <td>uwnd</td> <td>10m u-velocity (winds)</td> </tr> <tr> <td>vwnd</td> <td>10m v-velocity (winds)</td> </tr> </tbody> </table> <h3>Ocean</h3> <p>Ocean variables are split across 4 individual files following the staggered grid of the NEMO ocean model. Naming structure follows <code><Model ID>.mean_grid_{T|U|V|W}.nc</code></p> <p>Table 3: Overview of available ocean variables on the T-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>iowaflup</td> <td>Ice=>ocean net freshwater</td> </tr> <tr> <td>sobowlin</td> <td>Bowl Index</td> </tr> <tr> <td>sohefldo</td> <td>Net Downward Heat Flux</td> </tr> <tr> <td>soicealb</td> <td>Ice Albedo</td> </tr> <tr> <td>soicecov</td> <td>Ice Cover</td> </tr> <tr> <td>soicetem</td> <td>Ice Surface Temperature</td> </tr> <tr> <td>somixhgt</td> <td>Turbocline Depth</td> </tr> <tr> <td>somxl010</td> <td>Mixed Layer Depth 0.01</td> </tr> <tr> <td>sorunoff</td> <td>Runoffs</td> </tr> <tr> <td>sosalflx</td> <td>Surface Salt Flux</td> </tr> <tr> <td>sosaline</td> <td>Sea Surface Salinity</td> </tr> <tr> <td>soshfldo</td> <td>Shortwave Radiation</td> </tr> <tr> <td>sosheig</td> <td>Sea Surface Height</td> </tr> <tr> <td>sosstsst</td> <td>Sea Surface temperature</td> </tr> <tr> <td>sowaflcd</td> <td>concentration/dilution water flux</td> </tr> <tr> <td>sowaflep</td> <td>atmos=>ocean net freshwater</td> </tr> <tr> <td>sowaflsp</td> <td>solid precipitation from atm</td> </tr> <tr> <td>sowafltp</td> <td>total PE flux from atm</td> </tr> <tr> <td>sowaflup</td> <td>Net Upward Water Flux</td> </tr> <tr> <td>vosaline</td> <td>Salinity</td> </tr> <tr> <td>votemper</td> <td>Temperature</td> </tr> </tbody> </table> <p> </p> <p>Table 4: Overview of available ocean variables on the U-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>sozotaux</td> <td>Wind Stress along i-axis</td> </tr> <tr> <td>vozocrtx</td> <td>Zonal Current</td> </tr> <tr> <td>vozoeivu</td> <td>Zonal EIV Current</td> </tr> </tbody> </table> <p> </p> <p>Table 5: Overview of available ocean variables on the V-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>sometauy</td> <td>Wind Stress along j-axis</td> </tr> <tr> <td>vomecrty</td> <td>Meridional Current</td> </tr> <tr> <td>vomeeivv</td> <td>Meridional EIV Current</td> </tr> </tbody> </table> <p> </p> <p>Table 6: Overview of available ocean variables on the W-grid. See the individual file metadata for further information.</p> <table> <tbody> <tr> <th>Variable</th> <th>Long Name</th> </tr> </tbody> <tbody> <tr> <td>soleaeiw</td> <td>eddy induced vel. coeff. at w-point</td> </tr> <tr> <td>soleahtw</td> <td>lateral eddy diffusivity</td> </tr> <tr> <td>voddmavs</td> <td>Salt Vertical Eddy Diffusivity</td> </tr> <tr> <td>votkeavm</td> <td>Vertical Eddy Viscosity</td> </tr> <tr> <td>votkeavt</td> <td>Vertical Eddy Diffusivity</td> </tr> <tr> <td>votkeevd</td> <td>Enhanced Vertical Diffusivity</td> </tr> <tr> <td>votkeevm</td> <td>Enhanced Vertical Viscosity</td> </tr> <tr> <td>voverctz</td> <td>Vertical Velocity</td> </tr> <tr> <td>voveeviw</td> <td>Vertical EIV Velocity</td> </tr> </tbody> </table> <p> </p> <p>Publication: Steinig, S., Dummann, W., Hofmann, P., Frank, M., Park, W., Wagner, T., and Flögel, S.: Controls on Early Cretaceous South Atlantic Ocean circulation and carbon burial – a climate model-proxy synthesis, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2732, 2023.</p> <p>Source: This dataset is available via <a href="https://doi.org/10.5281/zenodo.11386835">Zenodo DOI: 10.5281/zenodo.11386835</a>.</p> <p>Authors: Steinig, S., Dummann, W., Hofmann, P., Frank, M., Park, W., Wagner, T., and Flögel, S.</p> <p>License: This work is licensed under <a href="https://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0 </a>.</p>
RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models
<p>This Zenodo repository contains the runs data for the paper <strong>RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models </strong>[<a href="https://arxiv.org/abs/2408.16118" target="_blank" rel="noopener">https://arxiv.org/abs/2408.16118</a>] presented at the NeurIPS 2024 workshop on Tackling Climate Change with Machine Learning, as well as the Master of Research (MRes) report <strong>Towards improving weather and climate models using reinforcement learning</strong> at the University of Cambridge<strong>.</strong> For questions, please contact Pritthijit Nath, <a href="mailto:pn341@cam.ac.uk" target="_blank" rel="noopener">pn341@cam.ac.uk</a>. Full documentation is available in the README.md file of the associated <a href="https://github.com/nathzi1505/climate-rl" target="_blank" rel="noopener">GitHub repo</a>.</p>
Exploring the Relationship Between Upper Ocean States and the Falling Ice Radiative Effects using ECCO Product and Global Climate Models
<p><strong><span>Sensitivity test using CESM1-CAM5 following CMIP5 protocool from 1980-2005</span></strong></p> <p><strong><span>NOS: no falling ice radiative effects (FIREs), four data sets</span></strong></p> <p><strong><span>SON: with FIREs, for data sets</span></strong></p> <p><strong><span> Xsize = 362 Ysize = 182 Zsize = 18</span></strong></p> <p><strong><span>Format: netcdf</span></strong></p> <p><strong><span>Upper 200 meter ocean variables</span></strong></p> <p><strong><span>Annual mean (ANN)</span></strong></p> <p><strong><span>CESM2-var-NOS (or SON)-ANN.nc, var = (UO, VO, WO, TO) = (zonal velocity, meridional velocity, ascending velocity, potential temperature) : (cm/s, cm/s, cm/s, K)</span></strong></p>
Data for the publication "The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity"
<p>This repository contains the data for the paper:</p> <p>"Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Stier, P. Partridge, D. G., Tegen, I., Bey, I., Stanelle, T., Kokkola, H., and Lohmann, U.: The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity, Geosci. Mod. Dev., https://doi.org/10.5194/gmd-2018-307, 2019."</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.2553891)</p> <p> </p>
Model projection of the effect of climate change and fishing pressure on key species of the South East Asia Seas
<p>The dataset contain Projection from the Size-Spectra Bioclimatic Envelop Model (SS-DBEM), this work was part of the GCRF Blue communities Programme (www.blue-communities.org). The model provides distribution and abundance and/or biomass of fish and other species of commercial interest under climate change and fishing pressure. The model outputs are yearly abundance/biomass on a 0.5-by-0.5 degree grid, covering the period from 2000 to 2098. Further description of the model and relevant references are listed in the following file: Guide-fish-model-output-use.docx</p> <p>The model was run under two climate scenario: RCP4.5 and RCP8.5, with different combinations of fishing pressure expressed as the Maximum Sustainable Yield (MSY) for the following values: 0 (no fishing, climate change alone will cause variation in fish biomass), 1 (sustainable fishing), 2, 3 (overfishing), and, 4 (overfishing with destructive practice). The intent is not to reproduce current fishing level but to provide a range of scenarios with which the future of fisheries can be explored.</p> <p>We projected fish species that were identified as key in the South East Asia seas region by our regional partners.The full list is provided in document: Fish-list-modelguide.xlsx</p> <p>There are 4 zip files that contain the model outputs of in either abundance (number of fish) or biomass grams of fish) for the two climate scenario. For example Biomass-RCP45.zip will contain model outputs in biomass for projections under RCP4.5 and all MSY. within the zip files are .csv files of the outputs for each species under the 5 MSY (0 to 4), the individual file names identify the species (identified by a 6digit code), the output provided (abundance or biomass), the RCP (8.5 or 4.5), and the MSY (0, 1, 2, 3, or 4). For example the file labelled 600107-Abundance-rcp85-msy4.csv contains the outputs for species 600107 (Skipjack tuna, <em>Katsuwonnus pelamis</em>), as abundance, under RCP8.5 with MSY4. Headers indicate what is in each column (latitude, longitude and year).</p> <p> </p> <p>Note: some knowledge of Python, R, or a similar software is recommended to ensure easy of use.</p>
MARv3.10 outputs: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates
<p>MARv3.10 outputs used in:</p> <p><em>Mottram, R., Hansen, N., Kittel, C., van Wessem, M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-333, accepted, 2020.</em></p> <ul> <li>MARv3.10 forced by ERA-Interim outputs with monthly values of SMB and components (kg m<sup>-2</sup> month<sup>-1</sup>), and (near-) surface temperature (°C) over the Antarctic ice sheet (1981--2018)</li> <li>Grid file used in MAR simulation</li> </ul> <p>Be carreful that the unit metadata in the netcdf files from SMB and its components are uncorrect. <strong>Values are in kg m<sup>-2</sup> month<sup>-1</sup></strong> instead of kg m<sup>-2</sup> day<sup>-1</sup>.<br> <br> If you need other variables or output frequencies from MAR, write me (c2kittel@gmail.com) and I will be glad to help you. I will also be happy to share the scripts I have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR outputs. However, note that these outputs are now considered as deprecated since new outputs using a more recent model version (MARv3.11) and forcing (ERA5) have been published (see Kittel et al., 2021: https://tc.copernicus.org/articles/15/1215/2021/).<br> <br> Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below that contained informations related to MAR. In order to document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact C. Kittel and C. Agosta to add their works in the list of MAR-related publications. </p> <p>"We thank the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations."</p> <p>You should also refer to and cite the following paper:</p> <p><em>Mottram, R., Hansen, N., Kittel, C., van Wessem, M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-333, accepted, 2020.</em></p>
Climate Policy Modelling Protocol
<p>This protocol includes current energy and climate policies for major economies, and details the instruments, targets and sectors for each policy. It provides a detailed list of climate policies as well as their quantification (following the Integrated Assessment Modelling Community (IAMC) conventions where possible). The final goal is to translate climate policies into energy and climate model input, and facilitate policy impact projections on greenhouse gas emissions.</p> <p>Climate policy on the national level, is defined as the result of climate policy formulation and climate policy implementation that encompasses aspirational goals not secured by legislation, national targets that are secured by legislation, and policy instruments designed to implement these targets. Only implemented policies are included in this protocol, and are defined as policies adopted by the government through legislation or executive orders, and non-binding targets backed by effective policy instruments.</p> <p>To compose the protocol, first a selection of climate policies with potentially high impact in terms of emission reductions is performed by the policy teams of PBL and NewClimate (<a href="https://www.climatepolicydatabase.org/">Climate Policy Database</a>), and translated into model input indicators. Then, with the help of (inter)national experts and partners, an evaluation round of the selected policies is performed, before finalizing the complete policy list. Policy instruments are represented in the integrated assessment models as explicit as possible, but simplification is sometimes necessary; replicating the impact on greenhouse gas emissions and the energy system transformation is considered as the most important factor. </p> <p>It should be noted that the policy environment is constantly changing, thus policy changes with a possibly high impact may occur between protocol updates that are not included in certain versions. Under ELEVATE, the protocol received major updates in terms of standardization of policy and target variable names and units - according to IAMC conventions, to facilitate use from all Integrated Assessment Models in the community.</p>
Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program
<p>This repository contains several data products associated with the New York Sea Grant project R/CHD-15 entitled <em>Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program.</em><strong><em> </em></strong>These products include:</p> <p>1. Estimates of the 25-year, 50-year, and 100-year flood across the New York coastline of Lake Ontario. These design events (reported in feet) are for still water levels that take into account both average water levels across the lake as well as local variations in water level due to storm surge. Wave setup and wave run-up are not considered in these design events. The design events incorporate the effects of water level regulation and the potential impacts of climate change on water supplies to Lake Ontario, and they are tailored for 79 unique locations along the shoreline (identified based on longitude and latitude). These flood levels are presented in an online flood risk assessment tool at: https://kts48.users.earthengine.app/view/lake-ontario-water-level-scenarios</p> <p>2. Protocols and summary of results for a series of focus groups and structured telephone interviews with local officials from communities along the Lake Ontario shoreline to assess barriers to participation in the New York State Climate Smart Communities Program.</p> <p>3. A Crosswalk between activities and administrative requirements of the New York State Climate Smart Communities Program and other federal and state flood resiliency programs. </p> <p>4. A final report summarizing the products above. </p>
Data for the publication "Addressing complexity in global aerosol climate model cloud microphysics"
<p>This repository contains the data for the paper:</p> <p>Authors: Ulrike Proske, Sylvaine Ferrachat, and Ulrike Lohmann<br> Titel: Addressing complexity in global climate model cloud microphysics<br> Date: 2022</p> <p>Note that the scripts can be found in the accompanying package (https://doi.org/10.5281/zenodo.7375978).</p>
Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"
<p>This dataset is associated with the following publication:</p> <p>Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, <em>Astronomy and Astrophysics</em>, vol. 665, 2022. doi:10.1051/0004-6361/202142493.</p> <p>In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with:</p> <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> <p>Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations:<br> - 'ds_sel': contains monthly timeseries of selected intersecting data<br> - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10)<br> - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10)<br> - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10) </p> <p>The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2.</p> <p>The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error.</p> <p>The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures.</p> <p>Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a> </p> <p> </p>
Data: Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models
<p>These data accompany the publication "Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models". The data are organized as follows:</p> <p>- two files with time series (csv) of hourly near-surface climate and surface energy balance values for Neumayer station (ice shelf, East Antarctic ice sheet) and automatic weather station S5 (southwest Greenland ice sheet)</p> <p>- two files (nc) with monthly melt fields from the regional climate model RACMO2.3p2 forced by ERA5 over Greenland (0.05-degree resolution) and Antarctica (0.25-degree resolution)</p> <p>- two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) or the historical period (1950-2014)</p> <p>- two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) for the future emission scenario SSP5-8.5 (2015-2099)</p>
A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR: accompanying dataset
<p>Dataset containing surface temperature and surface mass balance datasets generated from the MAR regional climate model over Greenland over two test areas using statistical downscaling tools from 6 km to 100m. The abstract of the accompanying submitted paper follows: </p> <p> </p> <p>The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise and this contribution is projected to accelerate over next decades. A crucial tool for studying the evolution surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs) which can provide current estimates and future projections of sea level rise associated with such losses. However, one of the main limitations of RCMs is the relatively coarse horizontal spatial resolution at which outputs are currently generated. Here, we report results concerning the statistical downscaling of the SMB modeled by the Modèle Atmosphérique Régional (MAR) RCM from the original spatial resolution of 6 km to 100 m building on the relationship between elevation and mass losses in Greenland. To this goal, we developed a geospatial framework that allows the parallelization of the downscaling process, a crucial aspect to increase the computational efficiency of the algorithm. The results obtained in the case of the SMB, assessed through the comparison of the modeled outputs with in-situ SMB measurements, show a considerable improvement in the case of the downscaled product with respect to the original, coarse output. In the case of the downscaled MAR product, the coefficient of determination (R<sup>2</sup>) increases from 0.868 for the original MAR output to 0.935 for the downscaled product. Moreover, the value of the slope and intercept of the linear regression fitting modeled and measured SMB values shifts from 0.865 for the original MAR to 1.015 for the downscaled product in the case of the intercept and from the value -235mm (original) to -57 mm (downscaled) in the case of the slope, considerably improving upon results previously published in the literature.</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.