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104 results for “Earth System Modeling”
Data from: Gene trees, species trees and Earth history combine to shed light on the evolution of migration in a model avian system
The evolution of migration in birds has fascinated biologists for centuries. In this study, we performed phylogenetic-based analyses of Catharus thrushes, a model genus in the study of avian migration, and their close relatives. For these analyses, we used both mitochondrial and nuclear genes, and the resulting phylogenies were used to trace migratory traits and biogeographic patterns. Our results provide the first robust assessment of relationships within Catharus and relatives and indicate that both mitochondrial and autosomal genes contribute to overall support of the phylogeny. Measures of phylogenetic informativeness indicated that mitochondrial genes provided more signal within Catharus than did nuclear genes, whereas nuclear loci provided more signal for relationships between Catharus and close relatives than did mitochondrial genes. Insertion and deletion events also contributed important support across the phylogeny. Across all taxa included in the study, and for Catharus, possession of long-distance migration is reconstructed as the ancestral condition, and a North American (north of Mexico) ancestral area is inferred. Within Catharus, sedentary behaviour evolved after the first speciation event in the genus and is geographically and temporally correlated with Central American distributions and the final closure of the Central American Seaway. Migratory behaviour subsequently evolved twice in Catharus and is geographically and temporally correlated with a recolonization of North America in the late Pleistocene. By temporally linking speciation events with changes in migratory condition and events in Earth history, we are able to show support for several competing hypotheses relating to the geographic origin of migration.
Model data for "The GERB Obs4MIPs Radiative Flux Dataset: A new tool for climate model evaluation", submitted to Earth System Science Data
<p>© Crown Copyright, Met Office</p><p>The E1hrClimMon files contain the monthly mean diurnal cycles of TOA radiative fluxes (all-sky and clear-sky) for amip experiment of two configurations of HadGEM3: GC3.1 and GC5.0. The monthly mean diurnal cycle is constructed by averaging each UTC hourly mean over the entire month. The HadGEM3 OLR diagnostics used in this study differ from those submitted to CFMIP3. The OLR diagnostics submitted to CFMIP3 contain a correction that accounts for the surface temperature adjustment by the boundary layer scheme in model time steps between radiation time steps. This OLR diagnostic adjustment is introduced to conserve energy, but it significantly distorts the diurnal cycle of OLR. For comparison with the GERB obs4MIPs products, the OLR without this correction is recommended.</p><p>The COSP file contains the average monthly climatologies for the variables cfadLidarsr532 and clisccp for the amip simulations of GC3.1 and GC5.0.</p>
The evaluation data and source codes of a new conceptual coupled Earth system model and the MOC box model.
<p>The dataset contains the results of a conceptual Atmosphere-Ocean-Ice-Land coupled Earth system model and a MOC box model and the evaluation data of their.</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2011_2015)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Data and Codes of A Deep Learning-Based Consistency Test for Earth System Models on Heterogeneous Many-Core Systems
<p>These are the supporting information to verify the results in the paper, including input data, model outputs, the postprocessing scripts and the source codes.</p>
Supporting Dataset for the study "A simple Approach to Represent Irrigation Water Withdrawals in Earth System Models"
<p><span><span>This archive contains the following information (8 directories):</span></span></p> <ol> <li> <p><span><span>surfex_v8.0climat : ISBA-CTRIP source code from CNRM-ESM-2 used in the study</span></span></p> </li> <li> <p><span><span>model_data : parameters used by the model and to plot the figures</span></span></p> </li> <li> <p><span><span>fig : ncl scripts to plot the figures & figures in eps</span></span></p> </li> <li> <p><span><span>discharges : simulated and observed river discharges data</span></span></p> </li> <li> <p><span><span>fluxes : simulated water fluxes plotted on the figures</span></span></p> </li> <li> <p><span><span>tws : estimated and simulated terrestrial water storage data</span></span></p> </li> <li> <p><span><span>withdrawals : imposed (impirrig) and simulated (irrig) irrigation water withdrawals</span></span></p> </li> <li> <p><span><span>wtd : simulated and estimated groundwater levels and trends</span></span></p> </li> </ol>
Simulation outputs associated with Maffre et al. "GEOCLIM7, an Earth System Model for multi-million years evolution of the geochemical cycles and climate." (submitted to GMD)
Open the record for dataset details and reuse information.
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
<p>This dataset contains the EMAC model output and the ESMValTool recipes used to created the figures of</p> <p>Lauer, A., L. Bock, B. Hassler, P. Jöckel, L. Ruhe, and M. Schlund: Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0, Geosci. Model Dev. (accepted).</p> <p>The files in <strong>emac_3hr.tar.gz</strong> contain 3-hourly data from the EMAC simulation used for figure 4 (diurnal cycles).</p> <p>The files in <strong>emac_Amon.tar.gz</strong> contain monthly mean data from the EMAC simulation used for figures 1, 2, 3, 5, 6, 7, 8 (time series, seasonal cycles, map plots, zonal mean plots, box plots, portrait diagram).</p> <p>The ESMValTool recipes used to produces these figures are contained in<strong> esmvaltool_recipes_v2.tar.gz</strong> and can be used with Earth System Model Evaluation Tool v2.12.0 available on GitHub at <a href="https://github.com/ESMValGroup/ESMValTool">https://github.com/ESMValGroup/ESMValTool</a> or on Zenodo at <a href="https://doi.org/10.5281/zenodo.3401363">https://doi.org/10.5281/zenodo.3401363</a>.</p>
Data and Codes of Characterizing Uncertainties of Earth System Modeling with Heterogeneous Many-core Architecture Computing
<p>These are the supporting information to verify the results in the paper, including input data, model outputs, the postprocessing scripts and the source codes.</p>
Diurnal rainfall response to the physiological and radiative effects of CO2 in tropical forests in the Energy Exascale Earth System Model v1
<p>Necessary outputs and scripts for recreating the figures for the journal article with the same title.</p>
Code and data for results and figures of the manuscript "Multi-million year cycles in modelled δ13C as a response to astronomical forcing of organic matter fluxes." submitted to Earth System Dynamics
<p>This dataset contains the code of the model used in the manuscript submitted to Earth System Dynamics "Multi-million year cycles in modelled δ13C as a response to astronomical forcing of organic matter fluxes.". It also contains some model outputs and code to draw the figures.</p>
Model configuration and input files for: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea"
<p>This collection hosts the configuration and input files to reproduce the regional CESM/MOM6 simulation in: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea". <br><br>The initial, open boundary conditions and monthly means for the sponge layers were generated from the GLORYS12V1 reanalysis: <a href="https://doi.org/10.3389/feart.2021.698876">https://doi.org/10.3389/feart.2021.698876</a></p> <p>The tidal amplitudes and phases were generated from the TPXO model: <a href="https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2">https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2 </a></p> <p>The monthly Chlorophyll-a climatology was generated from the SeaWifs mission dataset: <a href="10.5067/ORBVIEW-2/SEAWIFS/L3M/CHL/2018">10.5067/ORBVIEW-2/SEAWIFS/L3M/CHL/2018</a></p> <p>The river runoff to ocean was generated from the GloFAS dataset: <a href="https://doi.org/10.24381/cds.a4fdd6b9">https://doi.org/10.24381/cds.a4fdd6b9 </a></p> <p>The topography was generated from the Shuttle Radar Topography Mission: <a href="https://doi.org/10.1029/2019EA000658">https://doi.org/10.1029/2019EA000658</a> and smoothed with a Cressman weighted interpolation scheme.</p> <p>This collection does not include the JRA55-do atmospheric forcing dataset: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ocemod.2018.07.002" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ocemod.2018.07.002</a> </p> <p>Steps to setup a regional configuration of CESM/MOM6 can be found here: <a href="https://github.com/NCAR/regional_cesm_mom6.git">https://github.com/NCAR/regional_cesm_mom6.git</a> </p> <p> </p>
Model output for: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea"
<p>This collection hosts the model output fields used in : "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea". <br><br>daily_surface_fields.tar: contains daily output of sea surface salinity, surface u and v velocity components, sea-surface height and mixed layer depths. Files are split in two: 2000-01-01:2009-12-31 and 2010-01-01:2019-12-31 as specified by each filename.</p> <p>monthly_3D_tracers.tar: contains monthly mean output of temperature and salinity for the full 3D field (lat,lon,depth). Files are split in two: 2000-01-01:2009-12-31 and 2010-01-01:2019-12-31 as specified by each filename.</p> <p> </p> <p> </p>
NUIST-Earth System Model and inputdata
<p>Fortran code and required input data of Nanjing University of Information Science and Technology (NUIST) earth system model (ocean biogeochemical version)</p>
Data and analysis for "Fldgen v1.0: An Emulator with Internal Variability and Space-Time Correlation for Earth System Models"
<p>This is an archive of the raw data and analysis source code for the paper "Fldgen v1.0: An Emulator with Internal Variability and Space-Time Correlation for Earth System Models". The archive contains:</p> <ul> <li><strong>devel.Rmd : </strong>Source code for the worksheet that contains the early development and figures for the paper.</li> <li><strong>devel.html</strong> : HTML rendering of devel.Rmd</li> <li><strong>lg-ensemble-stats.Rmd </strong>: Source code for the worksheet that contains the statistical analysis described in the paper.</li> <li><strong>lg-ensemble-stats.html</strong> : HTML rendering of lg-ensemble-stats.Rmd</li> <li><strong>cc-analysis.Rmd </strong>: Analysis of the compromise conjecture raised by some readers of the paper</li> <li><strong>cc-analysis.nb.html</strong> : HTML rendering of cc-analysis.Rmd</li> <li><strong>data.tar.bz2 </strong>: Input data for the analyses above.</li> </ul> <p>The source code in this archive is written in R and requires the R runtime environment. It also uses the fldgen package, version 1.0.0, which is available at <a href="https://github.com/JGCRI/fldgen">https://github.com/JGCRI/fldgen</a></p> <p> </p>
CESM1.2 simulation data for "Quantifying the cloud particle-size feedback in an Earth system model"
<p>CESM1.2-CAM5 simulation data for "Quantifying the cloud particle-size feedback in an Earth system model"</p> <p><strong>Citation: </strong>Zhu, J., & Poulsen, C. J. (2019). Quantifying the cloud particle-size feedback in an Earth system model. <em>Geophysical Research Letters</em>, <em>46</em>, 10910–10917. <a href="https://doi.org/10.1029/2019GL083829">https://doi.org/10.1029/2019GL083829</a></p> <p>Data include:</p> <p>(1) cloud liquid particle size for liquid (AREL) and ice (AREI), grid box averaged cloud liquid (CLDLIQ) and ice (CLDICE), fractional occurrence of liquid (FREQL) and ice (FREQI), and surface temperature (TS) in the preindustrial and 2xCO2 experiments; and<br> (2) the cloud feedback (lam_CLDTOT) and cloud particle-size feedback (lam_CLDEFR3L) from our PRP-based method.</p>
iCESM1.2 restart file from "The Connected Isotopic Water Cycle in the Community Earth System Model Version 1"
<p>iCESM1.2 restart file at year 1850</p> <p><strong>Citation: </strong>Brady, E. C., Stevenson, S., Bailey, D., Liu, Z., Noone, D., Nusbaumer, J., … Zhu, J. (2019). The Connected Isotopic Water Cycle in the Community Earth System Model Version 1. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>, 2547–2566. https://doi.org/10.1029/2019MS001663</p> <p><strong>iCESM1.2 GitHub:</strong> https://github.com/NCAR/iCESM1.2</p> <p>A CLM land surface data set is included: surfdata_1.9x2.5_simyr1850_c140303.nc.</p>
Trained Surface Layer Models and Metrics for "Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications"
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Earth system model simulation results (1980-2014) for snow analysis
<p>This dataset contains monthly output in 1984-2014 from simulations using E3SM. To extract them, you should first collect the files together and run <code>zip -F sd_simulation_output_sliced.zip --out sd_simulation_output.zip, then unzip sd_simulation_output.zip</code>.</p>
Supporting data to reproduce figures and anaylisis presented in Bruciaferri et al. 2023 - submitted to Journal of Advances in Modeling Earth Systems (JAMES)
<p>Data for reproducing figures and the analysis of</p> <p>Diego Bruciaferri, Catherine Guiavarc’h, Helene T. Hewitt, James Harle, Mattia Almansi and Pierre Mathiot. Localised general vertical coordinates for quasi-Eulerian ocean models: the Nordic overflows test-case, submitted to JAMES.</p> <p>Data includes (© Crown copyright Met Office):</p> <p>1) models_geometry: bathymetry, horizontal grid and domain files needed to run the models and analyse their results.</p> <p>2) hpge: output data from HPG error idealised test.</p> <p>3) ideal_ovf: output data from the idealised overflow experiment</p> <p>4) realistic: output data from the realistic simulations</p>
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