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617 results for “Climate models”
Potential distributional shifts in North America of allelopathic invasive plant species under climate change models
<p>Occurrence data for invaive species used in ecological niche modeling for predictive studies. These data are cleaned to removed data with duplicates, incomplete coordinates, unlikely coordinates (e.g., 0,0), or those lacking environmental data were removed using the scrubr v.0.1.1 package in R (Chamberlain, 2016). Points falling outside of the respective training region for each species were also removed. These data represent downloads from iDigBio and GBIF.</p>
Dataset for "Climate and ice sheet evolutions from the last glacial maximum to the pre-industrial period with an ice sheet -- climate coupled model"
<p>This archive contains the source data of the figures presented in the manuscript "Climate and ice sheet evolutions from the last glacial maximum to the pre-industrial period with an ice sheet -- climate coupled model".</p> <p>Contact: aurelien.quiquet@lsce.ipsl.fr</p>
Data for "Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"
<p>Data for "<strong>Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"</strong></p>
CESM 1.2 climate model simulation output for: The Essential Role of Westerly Wind Bursts in ENSO Dynamics and Extreme Events Quantified in Model 'Wind Stress Shaving' Experiments
<p>Westerly wind bursts (WWBs)—brief but strong westerly wind anomalies in the equatorial Pacific—are believed to play an important role in El Niño Southern Oscillation (ENSO) dynamics, but quantifying their effects is challenging. Here, we investigate the cumulative effects of WWBs on ENSO characteristics, including the occurrence of extreme El Niño events, via modified coupled model experiments within Community Earth System Model (CESM1) in which we progressively reduce the impacts of wind stress anomalies associated with model-generated WWBs. In these "wind stress shaving" experiments we limit momentum transfer from the atmosphere to the ocean above a preset threshold, thus "shaving off" wind bursts. To reduce the tropical Pacific mean state drift, both westerly and easterly wind bursts are removed, although the changes are dominated by WWB reduction. As we impose progressively stronger thresholds, both ENSO amplitude and the frequency of extreme El Niño decrease, and ENSO becomes less asymmetric. The warming center of El Niño shifts westward, indicating less frequent and weaker Eastern Pacific (EP) El Niño events. Removing most of wind bursts-related wind stress anomalies reduces ENSO amplitude by 22%. The essential role of WWBs in the development of extreme El Niño events is revealed in the suppressed eastward migration of the western Pacific warm pool and hence a weaker Bjerknes feedback under wind shaving. Overall, our results reaffirm the importance of WWBs in shaping the characteristics of ENSO and its extreme events and imply that WWB changes with global warming could influence future ENSO.</p>
Dataset for "Assessing the aerosols, clouds and their relationship over the northern Bay of Bengal using a global climate model"
<p><strong>The dataset is organized into two ZIP folders as described below.</strong></p> <p><strong>1. Model simulation dataset:</strong> This folder (<strong>merged_data_files</strong>) contains 2 folders.</p> <ol> <li>Netcdf files of CESM experiments output for the study. A total of 8 experiments have been performed which can be identified by the filenames (CAM5, CAM5G, Q6, Q48, Q96, UV6, UV6Q48). Each experiment has a total of 360 time steps corresponding to daily output December-January-February (DJF) months of 2006-2010. It is to be noted that all the simulated cloud variables are from the MODIS COSP simulator output and have been restricted to low clouds (grids with cloud top pressure less than 680 hPa have been excluded). Following are the variables in each of the netcdf file of experiments which are analysed in the research article: <ol> <li> <p>AODVISEXT - Aerosol optical depth visible range</p> </li> <li> <p>LCODEXTLOW - MODIS Liquid Cloud Optical Thickness</p> </li> <li> <p>LCFEXTLOW - MODIS Liquid Cloud Fraction</p> </li> <li> <p>LCEREXTLOW - MODIS Liquid Cloud Particle Size</p> </li> <li> <p>LWPEXTLOW - MODIS Cloud Liquid Water Path</p> </li> <li> <p>CTPEXTLOW - MODIS Cloud Top Pressure</p> </li> <li> <p>CDNC - Cloud droplet number concentration</p> </li> </ol> </li> <li>CSV files of CTP-COT joint histogram output for each experiment averaged over the study region during DJF season for 2006-2010.</li> </ol> <p><strong>2. Study region shapefile:</strong> This folder (<strong>study_region_shapefile</strong>) contains the shapefile having the outline of the northern Bay of Bengal region which is our study region. The ACI sensitivities discussed in the article have been calculated using the spatial average of data points extracted for this region for the December-January-February months for the years 2006-2010 (360 time steps).</p>
Biasadjusted Regional Climate Model Data for Europe - Temperature
<p>This repository contains the bias-adjusted temperature data used in the production of numbers and figures contained in our research article entitled "Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change".</p> <p>Ferdini S., von Cossel M., Wulfmeyer V., Warrach-Sagi K. (2023) Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change. <em>Global Change Biology - Bioenergy.</em></p>
Biasadjusted Regional Climate Model Data for Europe - Precipitation
<p>This repository contains the bias-adjusted precipitation data used in the production of numbers and figures contained in our research article entitled "Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change".</p> <p>Ferdini S., von Cossel M., Wulfmeyer V., Warrach-Sagi K. (2023) Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change. <em>Global Change Biology - Bioenergy.</em></p>
Model outputs and species-level data for "Functional traits and climate drive interspecific differences in disturbance-induced tree mortality"
<p>This repository is divided in three sub-directories: </p> <ul> <li><em><strong>sensitivity </strong></em>contains the posterior of each parameter estimated by the bayesian mortality model in a rdata file. This file was generated by the script https://github.com/jbarrere3/SalvageModel/tree/withFinland</li> <li><em><strong>climate </strong></em>contains for each tree species the climatic variables (mean annual temperature, minimum annual temperature and annual precipitation) extracted from CHELSA and the disturbance-related climatic indices (Fire Weather Index, Snow Water Equivalent and Gust Wind Speed)</li> <li><em><strong>traits </strong></em>contains the traits calculated directly with NFI data (bark thickness, height to dbh ratio, maximum growth), and a text file with the Species and Trait ID to request to TRY database. </li> </ul> <p>The content of this repository can be used to reproduce the analyses of the paper, with the script stored in in https://github.com/jbarrere3/DisturbancePaper</p> <p><strong>Edit (19/09/2023):</strong> A minor coding error was found in the pre-formatted data of the paper, which did not affect the main results but led to minor change in the value of the posterior estimates. An updated version of the posterior estimates of this dataset was made available at https://zenodo.org/record/8358921. </p>
Model output data for 3D Climate modelling of LP 890-9 c with a modern Venus-like atmosphere
<p>We make available the output data from 3D climate modelling of LP 890-9 c with a modern Venus-like atmosphere. The data here has been produced for the publication submitted to Monthly Notices of the Royal Astronomical Society: Letters under the title: «3D Global Climate Model of an Exo-Venus: a modern Venus-like Atmosphere for the Nearby Super-Earth LP 890-9 c». The data includes the temperature profiles, emission (thermal) phase curves and transmission spectra files calculated for JWST/NIRSpec Prism. We also make available larger versions of the synthetic observable figures. Proper credit should be given to the authors. For further information, please get in touch with the corresponding author (Diogo Quirino) at: dfquirino@fc.ul.pt</p>
Sensitivity of the global ocean carbon sink to the ocean skin in a climate model : IPSL-CM6 dataset
<p>Daily outputs of 2000-2014 historical run with IPSL-CM6 (Boucher et al. 2020) with Bellenger et al. (2017) parameterization of the ocean skin (Bellenger et al. 2023)</p> <p>CM62-OSCO2-hist-2000-2014-1D.nc contains air-sea CO2 fluxes:</p> <p>F_CTL : Prognostic classical bulk flux from the control (CTL) run</p> <p>F_MBL_CTL : Diagnostic flux using the interactive ocean skin and the equilibrium model (Woolf et al. 2016) from the CTL run</p> <p>F_TBL_CTL : Diagnostic flux using the interactive ocean skin and the rapid model (Woolf et al. 2016) from the CTL run</p> <p>F_Wat_CTL : Diagnostic flux using a uniform ocean skin (Watson et al. 2020) from the CTL run</p> <p>F_MBL_CPL: Prognostic flux using the interactive ocean skin and the equilibrium model from the coupled (CPL) run</p> <p>CM62-OSCO2-hist-2000-2014-1D_oceanskin.nc contains ocean skin related outputs:</p> <p>tos / sos : Temperature / salinity of the ocean model's first level</p> <p>t_int / s_int : Temperature /salinity at the interface</p> <p>t_mbl / s_mbl : Temperature /salinity at the base of the Mass Boundary Layer (MBL)</p> <p>t_tbl : Temperature at the base of the Thermal Boundary Layer (TBL)</p> <p><em>Bellenger H., K. Drushka, W. E. Asher, G. Reverdin, M. Katsumata, and M. Watanabe: Extension of the prognostic model of sea surface temperature to rain-induced cool and fresh lenses, J. Geophys. Res. Oceans, 122, 484–507</em></p> <p>Bellenger, H., Bopp, L., Ethé, C., Ho, D., Duvel, J. P., Flavoni, S., Guez L., T. Kataoka, X. Perrot, L. Parc, and M. Watanabe (2023). Sensitivity of the global ocean carbon sink to the ocean skin in a climate model. Journal of Geophysical Research : Oceans, 128, e2022JC019479. <a href="https://doi.org/10.1029/2022JC019479">https://doi.org/10.1029/2022JC019479</a></p> <p><em>Boucher, O., and coauthors, 2020: Presentation and evaluation of the IPSL-CM6A-LR climate model, Journal of Advances in Modeling Earth System, 12, e2019MS002010, doi:</em><a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019MS002010"><em>10.1029/2019MS002010</em></a></p> <p><em>Watson A. J., U. Schuster, J. D. Shutler, T. Holding, I. G. C. Ashton, P. Landschützer, D. K. Woolf, and L. Goddijn-Murphy, 2020: Revised estimates of ocean-atmosphere CO<sub>2</sub> flux are consistent with carbon inventory, Nature Comm., 11:4422, https://doi.org/10.1038/s41467-020-18203-3</em></p> <p><em>Woolf, D. K., P. E. Land, J. D. Shutler, L. M. Goddijn-Murphy, and C. J. Donlon, 2016: On the calculation of air-sea fluxes of CO2 in the presence of temperature and salinity gradients, J. Geophys. Res. Oceans, 121, 1229-1248.</em></p> <p> </p> <p> </p>
Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model
<p>Dataset accompanying the publication "Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model"</p> <p> </p>
'Projected Landscape-scale Repercussions of Global Action for Climate and Biodiversity Protection' - model outputs
<p>Archive of model outputs produced for the MAgPIE v4.3.5 paper 'Projected Landscape-scale Repercussions of Global Action for Climate and Biodiversity Protection'.</p> <p>The model code of the MAgPIE and SEALS models can be accessed via:</p> <p><strong>MAgPIE model code</strong>: <a href="https://doi.org/10.5281/zenodo.5394196">https://doi.org/10.5281/zenodo.5394196</a> and <a href="https://github.com/magpiemodel/magpie">https://github.com/magpiemodel/magpie</a></p> <p><strong>MAgPIE model documentation</strong>: <a href="https://rse.pik-potsdam.de/doc/magpie/4.3.5/">https://rse.pik-potsdam.de/doc/magpie/4.3.5/</a></p> <p><strong>SEALS model code</strong>: <a href="https://doi.org/10.5281/zenodo.7795957">https://doi.org/10.5281/zenodo.7795957</a></p> <p>Data descriptions:</p> <p><strong>glosem_input.zip </strong>contains the spatially-explicit RLSK and C-factor data for each of the modelled scenarios at 10 arcseconds and the R code to estimate C-factor values based on the MAgPIE-SEALS outputs.</p> <p><strong>glosem_output.zip</strong> contains the spatially-explicit soil loss estimates for all scenarioso and the R code used to process the input data. The data was used to create Fig. 7.</p> <p><strong>magpie_ouput.zip</strong> contains the MAgPIE model outputs of all scenarios. The data is shown in Figs. 2, 3, 4, & 5.</p> <p><strong>pollination_sufficiency.zip</strong> contains the spatially-explicit pollination sufficiency estimates for all modelled scenarios and the R code used to derive the pollination sufficiency scores. The data is displayed in Fig. 6.</p>
Data accompanying "Diurnal variability of the upper ocean simulated by a climate model"
<p>Data used for creating figures in the draft article "Diurnal variability of the upper ocean simulated by a climate model". This includes:</p> <ul> <li>Multi-year, monthly mean diurnal cycle metrics at all model grid points.</li> <li>Monthly mean diurnal cycle data for individual years at selected locations.</li> </ul> <p>Code used to create these data files, and to create the plots, is in a Github repository (https://github.com/JackReevesEyre/cfs-analysis-gaea/). The repository is also archived on Zenodo (https://doi.org/10.5281/zenodo.7846095).</p>
Deglacial climate changes as forced by different ice sheet reconstructions - model ouputs
<p>This dataset contains the model output corresponding to the paper entitled "Deglacial climate changes as forced by different ice sheet reconstructions" submitted to Climate of the Past. For the description of the model and simulations we refer to this article.</p> <p> </p> <p><strong>Simulations:</strong><br> degla_P_bathy_500yr_is_SH_nobathy = with ICE_6G_C, fixed bathymetry<br> degla_P_bathy_500yr_is_SH = with ICE_6G_C, evolving bathymetry<br> degla_P_bathy_500yr_is_SH_bis = with ICE_6G_C, evolving bathymetry, mask modified<br> degla_T_bathyT_100yr_is_SH_nobathy = with GLAC-1D, fixed bathymetry<br> degla_T_bathyT_100yr_is_SH = with GLAC-1D, evolving bathymetry<br> degla_T_bathyT_100yr_is_SH_FWF = with GLAC-1D, evolving bathymetry, fresh water flux<br> degla_T_bathyT_100yr_is_SH_FWFtest3 = with GLAC-1D, evolving bathymetry, fresh water flux with intensity divided by 3<br> degla_T_bathyT_100yr_is_SH_FWFtest4 = with GLAC-1D, evolving bathymetry, fresh water flux with intensity divided by 4</p> <p> </p> <p><strong>Variables and corresponding files:</strong><br> <em>Evolution of ocean volume (m3):</em><br> volume_ocean_degla_P_bathy_500yr_is_SH.txt<br> volume_ocean_degla_T_bathyT_100yr_is_SH.txt</p> <p><em>Evolution of ocean surface area (1e6 km2):</em><br> surface_area_degla_P_bathy_500yr_is_SH.txt<br> surface_area_degla_T_bathyT_100yr_is_SH.txt</p> <p><em>Sea land masks for time slices:</em><br> tmask_bathy_P_0yr_SH_CC_PI.nc<br> tmask_degla_P_bathy_500yr_is_SH_21ka.nc<br> tmask_degla_T_bathyT_100yr_is_SH_21ka.nc<br> tmask_degla_P_bathy_500yr_is_SH_12ka.nc<br> tmask_degla_T_bathyT_100yr_is_SH_12ka.nc<br> tmask_degla_P_bathy_500yr_is_SH_9ka.nc<br> tmask_degla_T_bathyT_100yr_is_SH_9ka.nc</p> <p><em>Evolution of global mean temperature (degree C):</em><br> Temperature_evolution_degla_P_bathy_500yr_is_SH_nobathy.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_nobathy.txt<br> Temperature_evolution_degla_P_bathy_500yr_is_SH.txt<br> Temperature_evolution_degla_P_bathy_500yr_is_SH_bis.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_nobathy.txt<br> Temperature_evolution_degla_T_bathyT_500yr_is_SH.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWF.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWFtest3.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWFtest4.txt</p> <p><em>Temperature maps for time slices:</em><br> temp_degla_P_bathy_500yr_is_SH_nobathy_21ka.nc<br> temp_degla_T_bathyT_100yr_is_SH_nobathy_21ka.nc<br> temp_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc<br> temp_degla_T_bathyT_100yr_is_SH_nobathy_10ka.nc</p> <p><em>Evolution of salinity:</em><br> iLOVECLIM_salinity_ICE-6G_C.nc<br> iLOVECLIM_salinity_GLAC-1D.nc</p> <p><em>Temperature evolution at NGRIP location:</em><br> iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWF.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWFtest3.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWFtest4.nc</p> <p><em>Temperature evolution at EDC location:</em><br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWF.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWFtest3.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWFtest4.nc</p> <p><em>Evolution of surface albedo (all globe):</em><br> iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_alb_all_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_all_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Evolution of surface albedo (Northern Hemisphere):</em><br> iLOVECLIM_alb_NH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_NH_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH_bis.nc</p> <p><em>Evolution of surface albedo (Southern Hemisphere):</em><br> iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_alb_SH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_SH_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Evolution of sea ice area in the Northern Hemisphere (1e12 km2):</em><br> iLOVECLIM_sea_ice_NH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_NH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_sea_ice_NH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_NH_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Evolution of sea ice area in the Southern Hemisphere (1e12 km2):</em><br> iLOVECLIM_sea_ice_SH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_SH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_sea_ice_SH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_SH_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Winter sea ice fraction and mixed layer depth (m) at time slices:</em><br> iLOVECLIM_sea_ice_mld_bathy_P_21000yr_SH_21ka.nc<br> iLOVECLIM_sea_ice_mld_bathy_T_21000yr_SH_21ka.nc<br> iLOVECLIM_sea_ice_mld_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc<br> iLOVECLIM_sea_ice_mld_degla_T_bathyT_100yr_is_SH_nobathy_10ka.nc<br> iLOVECLIM_sea_ice_mld_degla_P_bathy_500yr_is_SH_10ka.nc<br> iLOVECLIM_sea_ice_mld_degla_T_bathyT_100yr_is_SH_10ka.nc</p> <p><em>Evolution of the maximum strength of AMOC:</em><br> iLOVECLIM_AMOC_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_AMOC_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWF.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWFtest3.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWFtest4.nc</p> <p><em>Meridional overtunring circulation at time slices:</em><br> MOC_degla_P_bathy_500yr_is_SH_21ka.nc<br> MOC_degla_P_bathy_500yr_is_SH_10ka.nc<br> MOC_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc<br> MOC_degla_T_bathyT_100yr_is_SH_21ka.nc<br> MOC_degla_T_bathyT_100yr_is_SH_10ka.nc</p>
Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks [datasets]
<p>Outputs used in:</p> <p><em>van der Meer, M., de Roda Husman, S., Lhermitte, S.: </em>Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks</p> <ul> <li>MAR(ACCESS1-3)_monthly_SMB.nc: MAR outputs with monthly values of SMB and components over the Antarctic ice sheet (1980--2100)</li> <li>MAR(ACCESS1-3)-stereographic_monthly_GCM_like.nc: MAR outputs upscaled to GCM resolution (1980--2100)</li> <li>ACCESS1-3-stereographic_monthly_cleaned.nc: GCM monthly outputs over the Antarctic ice sheet (1980--2100)</li> </ul> <p>The up-to-date working versions of our experiments and source code can be found and are available on our GitHub: <a href="https://github.com/marvande/RCM-Emulator">https://github.com/marvande/RCM-Emulator</a> and at this link: <a href="https://doi.org/10.5281/zenodo.7875967">https://doi.org/10.5281/zenodo.7875967</a></p> <p>Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. You should also refer to and cite the following paper:</p> <p><strong>Cite as: </strong>Marijn van der Meer, Sophie de Roda Husman, S Lhermitte. Deep Learning Regional Climate Model Emulators: a comparison of two downscaling training frameworks. <em>Authorea.</em> December 27, 2022 <br> DOI: <a href="https://doi.org/10.22541/essoar.167214210.02213149/v1">10.22541/essoar.167214210.02213149/v1</a> </p>
Climate model results for H2-H2O atmospheres
<p>This dataset is a realization of a greenhouse climate model for H2-H2O atmospheres</p>
Code and Data to support "Atmospheric circulation-constrained model sensitivity recalibrates Arctic climate projections"
<p><a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/sic.sep.5member.dat">sic.sep.5member.dat</a> contains direct binary data of spatial monthly averaged sea ice concentrations for 1979 January to 2020 December from the CESM2 wind-nudging runs.</p> <p><a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/cism2.exp.smb.01.nc">cism2.exp.smb.01.nc</a> to <a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/cism2.exp.smb.01.nc">cism2.exp.smb.05.nc</a> contain netcdf files of annual averaged surface mass balance output from the CESM2-CISM2 wind-nudging runs between 1979 and 2020.</p> <p>topal&ding_code1.py - data preparation Python code</p> <p>topal&ding_code2.py - creating the main text and supplementary figures.</p>
Development of a Machine Learning-Based Model to Determine the Optimum and Safe Restriping Timing of Thermoplastic Pavement Markings in Hot and Humid Climates
<p>Due to limited budget, most transportation agencies restripe their thermoplastic pavement markings based on a fixed schedule or based on visual inspection instead of monitoring the retroreflectivity and restriping when the retroreflectivity drops below a pre-determined threshold. These strategies are questionable in terms of efficiency and economy. Therefore, previous studies proposed degradation models to predict the retroreflectivity of thermoplastic markings based on key variables. Yet, most of these studies reported low R<sup>2</sup> (as low as 0.1), which placed little confidence in these models. Therefore, the objective of this study was to evaluate and predict the field performance of thermoplastics and to propose cost-effective restriping strategies for thermoplastics used in hot and humid climate service conditions. To achieve this objective, National Transportation Product Evaluation Program (NTPEP) data were mined and analyzed. Results indicated that the service life (SL) of thermoplastics ranged between 0.4 and 12.1 years (according to the initial retroreflectivity, traffic, and surface type) with an average value of 3.4 ± 0.2 years. Four regression models with relatively high accuracy were developed to predict the SL of thermoplastics based on key variables. In addition, the genetic algorithm was used to develop a model that predicts the future retroreflectivty of these pavement markings. The predicted values were compared against actual retroreflectivity measurements collected from a field experiment at Louisiana State University. The results of this study could be used to make effective decisions related to restriping scheduling. Using the proposed models in restriping scheduling can result in considerable cost savings (up to $8,212 per lane-mile), as compared to the conventional restriping strategy.</p>
Equilibrium climate sensitivity experiments using EC-Earth3-LR model — Surface Air Temperature data
<p>Three experiments was conducted using a EC-Earth model with the EC-Earth3-LR configuration (REF), which couples atmosphere, land, ocean and sea-ice components. First, we performed a pre-industrial (PI) control simulation (E280) using pre-industrial forcing, holding atmospheric constituents constant at 1850 levels (e.g., CO<sub>2</sub> concentration at 280 ppm). This simulation was initialized by a pre-run steady restart file (from a 500-year pre-industrial control simulation) and ran for 2000 years. We also conducted two sensitivity experiments (E400 and E560) by adjusting the CO<sub>2</sub> concentration to 400 ppm and 560 ppm, respectively, at the start year of the E280 experiment, and continued for over 3000 years (3069 years for E400, and 3013 years for E560). For our statistical analysis, we only considered the integration periods after the spin-up, using the last 2000-year outputs from the three simulations.</p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Cao et al. (2023).</p> <p>Cao, N., Zhang, Q., Wang, Z., Power, K.E., & Liu, C. (2023). The non-negligible impact of internal multi-centennial climate variability on estimating equilibrium climate change. Submitted to <em>Geophysical Research Letters</em>.</p> <p> </p> <p><strong>Model configuration</strong><br> Time periods: 2000-year time slice for all three experiments<br> ESM configuration: EC-Earth3-LR<br> Horizontal resolution: ~1.125° (~125 km)</p> <p><strong>Available data</strong><br> Annual mean data for Surface Air Temperature data.</p>
Output files from SPEEDY v.42 ensembles described in the paper: "Multi-decadal pacemaker simulations with an intermediate-complexity climate model" by F. Molteni, F. Kucharski and R. Farneti (part 1 of 2)
<p>The monthly-mean output from SPEEDY v.42 ensembles (either driven by prescribed sea-surface temperature (SST) or coupled to the TOM3 model) consists of a series of IEEE little-endian binary files and metadata files in text format.<br> For each year of integration (indicated by a 4-digit number YYYY) and ensemble member (indicated by a 3-digit number NNN), two binary files are present, named:<br> • attmNNN_YYYY.grd, including data on the 120x60 grid-point atmospheric grid;<br> • sftmNNN_YYYY.grd, including data on the 360x180 grid-point surface grid.<br> The metadata for these files are contained in the text files <strong>attmEEE.ctl</strong> and <strong>sftmEEE.ctl</strong> respectively, where EEE is a 3-digit ensemble identifier (usually, but not necessarily, equal to one of the ensemble-member number NNN).</p> <p><br> This repository contains data from:</p> <ul> <li>(part 1) a 41-year 5-member ensemble (653) run with prescribed SST</li> <li>(part 2) a 70-year 5-member ensemble (104) run with the coupled SPEEDY-TOM3 model.</li> </ul> <p>Integration years are 1980 to 2020 for ensemble 653 and 1951 to 2020 for ensemble 104.</p> <p><br> The structure of the binary data and metadata files follows the conventions for gridded datasets set by the GrADS diagnostic and plotting package (developed by the Center for Ocean-Land-Atmosphere Studies of George Mason University), as described here:<br> http://cola.gmu.edu/grads/gadoc/aboutgriddeddata.html</p> <p><br> In addition to the COLA-GMU web site, free version of the GrADS package for different platforms can be downloaded from the OpenGrADS web site:<br> http://opengrads.org/</p> <p><br> Specifically, the SPEEDY v.42 output consists of sequential-access files where each record contains a two-dimensional field. Three-dimensional fields are stored as a sequence of consecutive records, one for each of the 8 pressure levels where model-level data are interpolated by the post-processing routines. For each month of the year:</p> <p><br> the <strong>attmNNN_YYYY.grd</strong> files contain a sequence of <strong>9 3-D variables and 26 2-D variables</strong>;<br> the <strong>sftmNNN_YYYY.grd</strong> files contain a sequence of <strong>21 2-D variables</strong>.</p> <p>Within each record, grid-point data are stored as a NLONxNLAT array with longitude varying from west to east and latitude varying from south to north. The list of variables and levels is specified in the <strong>attmEEE.ctl</strong> and s<strong>ftmEEE.ctl</strong> files. These files contain descriptors which allow the data of each ensemble to be accessed as a single dataset by the GrADS package.</p> <p>Although the metadata files are specific to the GrADS package, the binary data can be read by different types of code. As example of fortran90 instructions to read the content of the <strong>attmNNN_YYY.grd</strong> and <strong>sftmNNN_YYY.grd</strong> files for one year/ens.member is as follows:</p> <p>integer, parameter :: nlon=120<br> integer, parameter :: nlat=60<br> integer, parameter :: nlev=8<br> integer, parameter :: nlon0=360<br> integer, parameter :: nlat0=180</p> <p>integer :: jmonth, jvar3d, jvar2d, jlev<br> real :: fld3d(nlon,nlat,nlev)<br> real :: fld2d(nlon,nlat), fld0(nlon0,nlat0)</p> <p>open (unit=1, file=”attmNNN_YYY.grd”, form=”formatted”, access=”sequential”)<br> open (unit=2, file=”sftmNNN_YYY.grd”, form=”formatted”, access=”sequential”)</p> <p>do jmonth=1,12</p> <p> do jvar3d=1,9<br> do jlev=1,nlev<br> read (1) fld3d(:,:,jlev)<br> …………<br> enddo<br> enddo</p> <p> do jvar2d=1,26<br> read (1) fld2d(:,:)<br> ………<br> enddo</p> <p> do jvar2d=1,21<br> read (2) fld0(:,:)<br> ………<br> enddo</p> <p>enddo</p> <p>close (1)<br> close (2)</p> <p> </p> <p> </p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.