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223 results for “model ice”
A 1D Model for Nucleation of Ice from Aerosol Particles: An Application to a Mixed-Phase Arctic Stratus Cloud Layer
<p>A 1D Model for Nucleation of Ice from Aerosol Particles:</p> <p>An Application to a Mixed-Phase Arctic Stratus Cloud Layer</p> <p>Daniel A. Knopf<sup>1</sup>*, Israel Silber<sup>2</sup>, Nicole Riemer<sup>3</sup>, Ann M. Fridlind<sup>4</sup>, Andrew S. Ackerman<sup>4</sup></p> <p><sup>1</sup>School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA</p> <p><sup>2</sup>Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA</p> <p><sup>3</sup>Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, IL, USA</p> <p><sup>4</sup>NASA Goddard Institute for Space Studies, New York, NY, USA</p> <p> </p> <p>Corresponding author: Daniel Knopf (daniel.knopf@stonybrook.edu)</p> <p> </p> <p><strong>This repository contains all model output data in netCDF format to reproduce simulation results and corresponding figures given in above listed publication.</strong></p> <p>File name description:</p> <p>Type of parameterization: <em>INN, INAS, ABIFM</em></p> <p>INP treatment, diagnostic or prognostic: <em>diag, prog</em></p> <p>Aerosol size distribution:</p> <p><em>05mu</em>: monodisperse 0.5 μm diameter</p> <p><em>15mu</em>: monodisperse 0.5 μm diameter</p> <p><em>polydisperse</em>: polydisperse particle size distribution</p> <p>Model initialization and thermodynamic data: <em>model_data</em></p> <p>Aerosol/INP data: <em>aerosol_data</em>, <em>polydisperse_data</em></p> <p>Change in ice nucleation efficiency: <em>10X, 100X</em></p>
Radiative transfer model and datasets for Li et al. (2023), 'Wintertime low-level clouds over sea ice cool the Arctic climate system'
<p>Source code for the radiative transfer model (RAPRAD) and cloud radiative flux data used in the study Li et al. (2022).</p>
Quantum mechanical modeling of the on-grain formation of acetaldehyde on H2O:CO dirty ice surfaces
<p>This Supporting Material contains:</p> <ul> <li>Cartesian coordinates of HF-3c optimized minima and transition state for the reaction in gas phase, in .xyz format, computed using <a href="https://gaussian.com/">Gaussian16</a> code;</li> <li>Fractional coordinates of HF-3c optimized minima and trasition state structures for crystalline periodic models in <a href="https://www.moldraw.unito.it/_sgg/m1m1s43_1.htm">.mol</a> format, editable with <a href="http://www.moldraw.unito.it/">MOLDRAW</a>, computed using <a href="http://www.crystal.unito.it/">CRYSTAL17</a> computer code.</li> </ul>
Model simulation data used in "A global climatology of ice-nucleating particles under cirrus conditions derived from model simulations with EMAC-MADE3" (Beer et al., Atmos. Chem. Phys., 2022)
<p>This dataset contains the output and the namelist setup of the EMAC-MADE3 global model simulations analysed and discussed in Beer et al. ("A global climatology of ice nucleating particles at cirrus conditions derived from model simulations with EMAC-MADE3", <em>Atmos. Chem. Phys.</em>, 2022).</p>
Data set for model validation in "Simulating ice segregation and thaw consolidation in permafrost environments with the CryoGrid community model"
<p>This upload contains the data set for model validation in the manuscript "Simulating ice segregation and thaw consolidation in permafrost environments with the CryoGrid community model".</p>
Data from: A model of near-sea ice phytoplankton blooms
Open the record for dataset details and reuse information.
Model code for reconstructing biomass burning emissions from ice core records of acetylene, ethane, and methane
Open the record for dataset details and reuse information.
Model code and output of CSIBv4 pan-Arctic sea ice-ocean DMS simulation
<p>here i deposit the model source code and output of the manuscript "Spatio-temporal variability in modelled bottom-ice and sea-surface dimethylsulfide concentrations and fluxes in the Arctic during 1979-2015" by Hayashida et al. (2020).</p> <p><strong>See "readme.txt" for information.</strong></p> <p>For question, contact Hakase Hayashida.</p>
Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)
<p>Datasets for the Geoscientific Model Development publication: "Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)"</p> <p>Abstract: This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea-ice simulations that are obtained following the OMIP-2 protocol (Griffies et al., 2016) and integrated for one cycle (1958-2018) of the JRA55-do atmospheric state and runoff dataset (Tsujino et al., 2018). Our goal is to assess the robustness of climate-relevant improvements in ocean simulations (mean and variability) associated with moving from coarse (~1º) to eddy-resolving (~0.1º) horizontal resolutions. The models are diverse in their numerics and parameterizations, but each low-resolution and high-resolution pair of models is matched so as to isolate, to the 20 extent possible, the effects of horizontal resolution. A variety of observational datasets are used to assess the fidelity of simulated temperature and salinity, sea surface height, kinetic energy, heat and volume transports, and sea ice distribution. This paper provides a crucial benchmark for future studies comparing and improving different schemes in any of the models used in this study or similar ones. The biases in the low-resolution simulations are familiar and their gross features – position, strength, and variability of western boundary currents, equatorial currents, and Antarctic Circumpolar Current – are 25 significantly improved in the high-resolution models. However, despite the fact that the high-resolution models “resolve’’ most of these features, the improvements in temperature or salinity are inconsistent among the different model families and some regions show increased bias over their low-resolution counterparts. Greatly enhanced horizontal resolution does not deliver unambiguous bias improvement in all regions for all models.</p>
Data and code for publication "The role of history and strength of the oceanic forcing in sea level projections from Antarctica with the Parallel Ice Sheet Model"
<p>Data and code underlying the publication <a href="https://tc.copernicus.org/preprints/tc-2019-330/">"The role of history and strength of the oceanic forcing in sea level projections from Antarctica with the Parallel Ice Sheet Model"</a>.</p> <p>Journal: The Cryosphere</p> <p>Authors: <em>Ronja Reese<sup>1*</sup></em><em>, Anders Levermann</em><sup><em>1,2,3</em></sup><em>, Torsten Albrecht</em><sup><em>1</em></sup><em>, Hélène Seroussi<sup>4</sup></em><em>, Ricarda Winkelmann<sup>1,2 </sup></em></p> <p>(1) Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany</p> <p>(2) Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany</p> <p>(3) LDEO, Columbia University, New York, USA</p> <p>(4) Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA</p> <p>(*) email ronja.reese@pik-potsdam.de</p> <p>Abstract:<br> Mass loss from the Antarctic Ice Sheet constitutes the largest uncertainty in projections of future sea level rise. Ocean-driven melting underneath the floating ice shelves and subsequent acceleration of the inland ice streams is the major reason for currently observed mass loss from Antarctica and is expected to become more important in the future. Here we show that for projections of future mass loss from the Antarctic Ice Sheet, it is essential (1) to better constrain the sensitivity of sub-shelf melt rates to ocean warming and (2) to include the historic trajectory of the ice sheet. In particular, we find that while the ice sheet response in simulations using the Parallel Ice Sheet Model is comparable to the median response of models in three Antarctic Ice Sheet Intercomparison projects – initMIP, LARMIP-2 and ISMIP6 – conducted with a range of ice sheet models, the projected 21st century sea level contribution differs significantly depending on these two factors. For the highest emission scenario RCP8.5, this leads to projected ice loss ranging from 1.4 to 4.0 cm of sea level equivalent in the ISMIP6 simulations where the sub-shelf melt sensitivity is comparably low, opposed to a likely range of 9.2 to 35.9 cm using the exact same initial setup, but emulated from the LARMIP-2 experiments with a higher melt sensitivity based on oceanographic studies. Furthermore, using two initial states, one with and one without a previous historic simulation from 1850 to 2014, we show that while differences between the ice sheet configurations in 2015 are marginal, the historic simulation increases the susceptibility of the ice sheet to ocean warming, thereby increasing mass loss from 2015 to 2100 by about 50 %. Our results emphasize that the uncertainty that arises from the forcing is of the same order of magnitude as the ice dynamic response for future sea level projections.</p> <p>Large zip files contain data, small zip file python notebooks for data analysis and PISM code. Please contact ronja.reese@pik-potsdam.de if you have any further questions.</p>
Raw Date of Manuscript 《Quantifying the ice storage in the Upper Indus River basin with the ground-penetrating radar measurements and Glacier Bed Topography version 2 modeling》
<p>Raw date and materials of the manuscript 《Quantifying the ice storage in the Upper Indus River basin with the ground-penetrating radar measurements and Glacier Bed Topography Version 2 modelling》</p>
Modelling plastic concentrations in sea ice and the water column
<p>Results for the last year of a 50-year simulation of the dispersion of plastics in the global ocean using the NEMO modelling framework in configuration ORCA1-LIM2. These are NetCDF. Variable names are largely self-explanatory. PLASTIC_1 to PLASTIC_3 refer to buoyant, neutrally boyant and non-buoyant plastic concentrations in water; plt1 to plt3 are the corresponding concentrations in sea ice. Data for years 1-to-49 will be made available by the authors upon request.</p>
An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3
<p>Physical processes within geoscientific models are sometimes described by simplified schemes known as parameterisations. The values of the parameters within these schemes can be poorly constrained by theory or observation. Uncertainty in the parameter values translates into uncertainty in the outputs of the models. Proper quantification of the uncertainty in model predictions therefore requires a systematic approach for sampling parameter space. In this study, we develop a simple and efficient approach to identify regions of multi-dimensional parameter space that are consistent with observations. Using the Parallel Ice Sheet Model to simulate the present-day state of the Antarctic Ice Sheet, we find that co-dependencies between parameters preclude the identification of a single optimal set of parameter values. Approaches such as large ensemble modelling are therefore required in order to generate model predictions that incorporate proper quantification of the uncertainty arising from the parameterisation of physical processes.</p>
Dataset for "McSnow 2.0: Explicit habit-prediction in a Lagrangian super-particle ice microphysics model"
<p>Data and plot scripts to create figures included in the paper draft "McSnow 2.0: Explicit habit-prediction in a Lagrangian super-particle ice microphysics model" (submitted to JAMES).</p> <p>This work has been funded by the German Science Foundation (DFG) under grant SE 1784/3-1, project ID 408011764 as part of the DFG priority program SPP 2115 on radar polarimetry.</p>
Dataset associated with "Emulating subglacial hydrology in ice sheet models with deep learning methods" by Verjans and Robel.
<p>See Readme file for descriptions.</p>
Data and code for manuscript ``Insights on the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty''
<p>Supporting data and code for manuscript:</p><p>Seroussi, H., Verjans, V., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., Van Katwyk, P., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: Insights into the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty, The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, 2023.</p><p> </p><p>It contains the code to prepare the datasets, to create the figures and the data for the analysis, and the scalar values computed for the 198 Antarctic glaciers stored by ice flow models.</p><p>The files Glacier_XX contain the data to emulate the results for individual glaciers.</p><p>The files Antarctica and AntarcticaWithCtrl contain the data to emulate the results for the Antarctic runs without and with the ctrl_proj experiment.</p><p>The files GROUP_ICEFLOW contain the ice flow model data for all the experiments recomputed for the 198 glaciers in Antarctica.</p>
Dataset of 'Development of a total variation diminishing (TVD) Sea ice transport scheme and its application in in an ocean (SCHISM v5.11) and sea ice (Icepack v1.3.4) coupled model on unstructured grids'
<p>As a dataset of the paper "Development of a total variation diminishing (TVD) Sea ice transport scheme and its application in in an ocean (SCHISM v5.11) and sea ice (Icepack v1.3.4) coupled model on unstructured grids" , this dataset includes all configuration files of the idealized case and realistic test on the Arctic Ocean and the source code.</p>
Databases generated for Manuscript titled "Quantifying downward radiative fluxes from nighttime Martian water ice clouds: Applications to thermal modeling of surface temperatures"
<p>Databases generated for manuscript "<strong>Quantifying downward radiative fluxes from nighttime Martian water ice clouds: Applications to thermal modeling of surface temperatures</strong>"</p> <p>There are two zip files containing generated databases:</p> <p>The zip file titled "database.zip" contains generated database for calculated fluxes using the methodology mentioned in the manuscript. The database spans calculated fluxes in one degree bins for latitudes spanning 30° to -10° N and longitudes spanning 0° to 360°. There are 14760 separate .csv files that are for each one by one degree bin. The title of each file contains its coordinates in the format XXXNXXXEtb.csv (e.g. 000N000Etb.csv for 0°N, 0°E). Each .csv file contains four separate columns and variable rows. The columns have headers corresponding to specific values. "ls" corresponds to solar longitude or date based on Mars' orbit around the Sun. "Flux" corresponds to calculated flux based on the methodology presented on the manuscript. "Delta-T" is the difference in temperature comparing modeled temperature compared to Thermal Emission Spectrometer (TES) measured temperature. "Tau" corresponds to calculated Dust visible opacities using the methodology presented in this work. The rows in each file vary based on the temporal observations from TES at each location. </p> <p>The zip file titled "fitdatabase.zip" contains generated database for fitted fluxes using the methodology mentioned in the manuscript. The database spans calculated fluxes in one degree bins for latitudes spanning 30° to -10° N and longitudes spanning 0° to 360°. There are 14760 separate .csv files that are for each one by one degree bin. The title of each file contains its coordinates in the format XXXNXXXEtbf.csv (e.g. 000N000Etbf.csv for 0°N, 0°E). Each .csv file contains six separate columns and three hundred and sixty rows. The columns have headers corresponding to specific values. "ls" corresponds to solar longitude or date based on Mars' orbit around the Sun. "Flux" corresponds to calculated flux based on the methodology presented on the manuscript. "Delta-T" is the difference in temperature comparing modeled temperature compared to measured temperature. The fitting algorithm interpolates points between values in the calculated flux database and applies a rolling mean fit with a window spanning ten degrees in solar longitude centered at each calculated flux point. "FLAG" indicates the amount of points of calculated flux points that exist within the ten degree window centered at each flux point to demonstrate to the user how much data had to be fitted. "From Ls" shows the leftmost edge of the rolling mean fit window. "To Ls" shows the rightmost edge of the rolling mean fit window. The rows in each file correspond to one degree of solar longitude the fitting algorithm was designed to cover each solar longitude bin. </p> <p> </p>
Laurentide Ice Sheet evolution towards the Last Glacial Maximum using AWIESM model with interactive ice sheets
<p>This archive contains model data and figures associated with the study titiled "Rapid Laurentide Ice Sheet growth preceding the Last Glacial Maximum due to summer snowfall" (Niu et al., 2024). The comprehensive Earth system model AWI-ESM with interactive ice sheets is used for the model simulations. The notation of the individual files in the archive corresponds to the respective figure numbers in the paper. The respective file content is described by the corresponding figure caption in the paper.</p> <p>Niu, L., Knorr, G., Krebs-Kanzow, U. et al. Rapid Laurentide Ice Sheet growth preceding the Last Glacial Maximum due to summer snowfall. Nat. Geosci. (2024). https://doi.org/10.1038/s41561-024-01419-z</p> <p> </p>
CMIP6 models rarely simulate Antarctic winter sea-ice anomalies as large as observed in 2023
<p><span>Contains processed data to create all figures and tables from the publication Diamond et al. (2024) </span><span>CMIP6 models rarely simulate Antarctic winter sea-ice anomalies as large as observed in 2023; Geophysical Research Letters </span><span>[Full reference available on publication]</span></p> <p><span> </span></p> <p><span>Directory structure and associated contents listed below. Folders contain all data required to make corresponding figure, apart from where indicated. Full description of analysis in Diamond et al. (2024). Unless otherwise indicated, 'SIE' refers to August Antarctic SIE.</span></p> <p><span> </span></p> <p><span>F1/</span></p> <p><span>f1a_obs_ts.csv: Timeseries of 1979-2023 SIE </span></p> <p><span>f1b_all_instances.csv: All instances (labelled by model, simulation, and simulation year) where an anomaly of magnitude SIE_2023 was detected. SIE timeseries from years -20 to +20 about each anomaly.</span></p> <p><span>f1b_quartiles.csv: Upper and lower quartiles, mean, and median, computed over all instances.</span></p> <p><span> </span></p> <p><span>F2/</span></p> <p><span>F2_a.csv: 5--95% range of SIE variability dataset, for all model simulations, and observations.</span></p> <p><span>F2_b.csv: T_aug23, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F2_c.csv: T_aug23, computed using GEVs, for all model simulations, and multi-model ensemble.</span></p> <p><span> </span></p> <p><span>F3/</span></p> <p><span>Data for all figures is already provided elsewhere:</span></p> <p><span>F3a: for observations in SF3/SF3_obs_dataset.csv, and for simulations in SF3/SF3_simulations.csv.</span></p> <p><span>F3b: for multi-model ensemble T_aug23 with associated errors in SF6/SF6_all.csv</span></p> <p><span>F3c: for GEV_estimated T_aug23 for all simulations and models in SF6/SF6_all.csv; and<span> </span>5--95% ranges of SIE variability datasets in F2/F2_a.csv</span></p> <p><span> </span></p> <p><span>~</span></p> <p><span>SF1/</span></p> <p><span>SF1_all.csv: SIE timeseries for all model simulations, and associated moving averages.</span></p> <p><span>SF2/</span></p> <p><span>sf2_all.csv: SIE decadal means and ranges for all model simulations.</span></p> <p><span>sf2_quartiles.csv: taken over all simulations above - mean decadal mean in each bin; and lower quartile, median and upper quartile of the decadal ranges in each bin.</span></p> <p><span>SF3/</span></p> <p><span>SF3_obs_dataset.csv: SIE variability dataset for 1979-2023 observations.</span></p> <p><span>SF3_simulations.csv: SIE variability datasets for all model simulations</span></p> <p><span>SF4/</span></p> <p><span>F4_a.csv: p_aug23, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_b.csv: p_aug22, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_c.csv: T_aug23, computed using GEVs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_d.csv: for all model simulations: p-value that simulation SIE variability dataset is drawn from same probability distribution as observations. Computed using Kolmogorov-Smirnoff 2-sample test.</span></p> <p><span>SF5/</span></p> <p><span>Data for both SF5a and b are already provided in F1/f1b_all_instances.csv.</span></p> <p><span>SF6/</span></p> <p><span>SF6_all.csv: for all model simulations, and multi-model ensemble, p_aug23 and T_aug23, with lower and upper errors (computed from 5--95% confidence interval using bootstrapping)</span></p> <p><span>SF7/</span></p> <p><span>Data for SF7a is already provided elsewhere: for observations in SF3/SF3_obs_dataset.csv, and for simulations in SF3/SF3_simulations.csv.</span></p> <p><span>SF7_all.csv: as for SF6_all.csv, but instead using subset of models.</span></p>
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