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14 results for “Ice-Nucleating Particle”
Data for the publication "Retrieving ice-nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations"
<p>This repository contains the data for the paper:</p> <p>Wieder, J., Ihn, N., Mignani, C., Haarig, M., Bühl, J., Seifert, P., Engelmann, R., Ramelli, F., Kanji, Z. A., Lohmann, U., and Henneberger, J.: Retrieving ice nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-67, in review, 2022.</p> <p>More information can be found in the README files.</p> <p>Note that the scripts to reproduce the figures of the publication are available on request.</p>
Dataset to: Terrestrial runoff is an important source of biological ice-nucleating particles in Arctic marine systems
<p>The dataset contains supplementary information to the manuscript "Terrestrial runoff is an important source of biological ice-nucleating particles in Arctic marine systems"</p> <p>The file <a href="https://zenodo.org/api/records/14988900/draft/files/INP_data_all_samples.csv/content" target="_blank" rel="noopener noreferrer">INP_data_all_samples.csv</a> contains information on the ice nucleation measurements for all samples presented.</p> <p>The file "<a href="https://zenodo.org/api/records/14044414/draft/files/Significant_taxa_list_16S.xlsx/content" target="_blank" rel="noopener noreferrer">Significant_taxa_list_16S.xlsx</a>" contains a list of the bacterial taxa that significantly correlated with the concentration of INPs observed in the samples, while the file <a href="https://zenodo.org/api/records/14044414/draft/files/Significant_taxa_list_18S.xlsx/content" target="_blank" rel="noopener noreferrer">Significant_taxa_list_18S.xlsx</a> contains the same information for the microalgae. </p> <p>The relative abundance of the taxa in each sample is indicated in the columns "F" to "T". </p>
Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations"
<p>Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations", accepted for publication in Atmospheric Chemistry and Physics, 2023, same authors.<br> The simulations were done using MIMICA version4 (Savre at el., 2014) and the data includes all model output presented in the publication.</p>
Ice-Nucleating Particle Concentrations from the MC2/ISLAS 2021 campaign in Andenes, and NorESM2 simulations with observationally constrained INPs
<p>This dataset containts the data for the article <strong>Using a region-specific ice-nucleating particle parameterization improves the representation of Arctic clouds in a global climate model </strong>(https://doi.org/10.5194/acp-25-1617-2025), published in Atmospheric Chemistry and Physics (ACP). It consists of ice-nucleating particle (INP) measurements collected as part of the MC2/ISLAS campaign in Andenes, Norway (69<em>° </em>18’ N, 16<em>°</em> 07’ E) in 2021. Additionally, it consists of simultaneous aerosol measurements, back trajectories for the INP measurement times, and model data from the Norwegian Earth System model (Seland et al., 2020) where INP concentrations where constrained in the Arctic using these INP measurements. </p> <p><strong>Abstract:</strong></p> <p><em>Projections of global climate change and Arctic amplification are sensitive to the representation of low-level cloud phase in climate models. Ice-nucleating particles (INPs) are necessary for primary cloud ice formation at temperatures above approximately -38 °C, and thus significantly affect cloud phase and cloud radiative effect. Due to their complex and insufficiently understood variability, INPs constitute an important modelling challenge, especially in remote regions with few observations, such as the Arctic. In this study, INP observations were carried out at Andenes, Norway in March 2021. These observations were used as a basis for an Arctic-specific and purely temperature-dependent INP parameterization, and implemented into the Norwegian Earth System Model. This implementation results in an annual average increase in cloud liquid water path (CLWP) of 70 % for the Arctic, and improves the representation of cloud phase compared to satellite observations. The change in CLWP in boreal autumn and winter is found to likely be the dominant contributor to the annual average increase in net surface cloud radiative effect of 2 W m<sup>-2</sup>. This large surface flux increase brings the simulation into better agreement with Arctic ground-based measurements. Despite that the model cannot respond fully to the INP parameterization change due to fixed sea surface temperatures, Arctic surface air temperature increases with 0.7 °C in boreal autumn. These findings indicate that INPs could have a significant impact on Arctic climate, and that a region-specific INP parameterization can be a useful tool to improve cloud representation in the Arctic region.</em></p> <p>The dataset contains three subsets:</p> <p>i) <strong>aerosol_data:</strong> Observations of Ice-Nucleating Particles (INPs) in Andenes, Norway, March 2021, as well as simultaneous aerosol measurements</p> <p>ii) <strong>INP_trajectories: </strong>Back trajectories at the time of INP measurements</p> <p>iii) <strong>model_data: </strong>Simulations with NorESM2 using the Andenes 2021 INP observations to constrain INPs in the Arctic</p> <p>Additionally, scripts for visualizing the data and for reproducing the NorESM2 model setup can be found in the folder <strong>scripts. </strong>The data folders and scripts folder should be in the same repository when running the scripts.</p> <p>Some of the scripts use other openly available datasets. The availability of these are listed below. All the specific datasets can also be provided to the user upon request. </p> <p>The CALIOP L2 data used to derive SLF metrics (used in Fig07.py) and the CERES EBAF data (used in Fig12.py) can be downloaded freely at <a href="https://search.earthdata.nasa.gov/" rel="nofollow">https://search.earthdata.nasa.gov/</a>. The derived SLF metrics can also be found at Bruno (2022), and are also described in Hofer et al. (2024) and Shaw et al. (2022). The CALIPSO-GOCCP data product (used in Fig08.py) can be downloaded from https://climserv.ipsl.polytechnique.fr/cfmip-obs/Calipso_goccp.html. The surface radiation flux (used in Fig13.py) can be downloaded freely at <a href="https://www.pangaea.de/" rel="nofollow">https://www.pangaea.de/</a>. The ERA5 data used to produce the back trajectories can be found at https://doi.org/10.24381/cds.bd0915c6. The colormap from Crameri et al. (2020) was used when preparing the figures. </p> <p>References:</p> <ul> <li>Bruno, O. (2022). Distributions of supercooled liquid fraction from CALIOP V4 [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8289058" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8289058</a></li> <li>Crameri, F., Shephard, G.E. & Heron, P.J. The misuse of colour in science communication. <em>Nat Commun</em> <strong>11</strong>, 5444 (2020). https://doi.org/10.1038/s41467-020-19160-7</li> <li>Hofer, S., Hahn, L.C., Shaw, J.K. et al. Realistic representation of mixed-phase clouds increases projected climate warming. Commun Earth Environ 5, 390 (2024). <a href="https://doi.org/10.1038/s43247-024-01524-2" rel="nofollow">https://doi.org/10.1038/s43247-024-01524-2</a></li> <li>Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geoscientific Model Development, 13, 6165–6200, <a href="https://doi.org/10.5194/gmd-13-6165-2020" rel="nofollow">https://doi.org/10.5194/gmd-13-6165-2020</a>, 2020.</li> <li>Shaw, J., McGraw, Z., Bruno, O., Storelvmo, T., & Hofer, S. (2022). Using satellite observations to evaluate model microphysical representation of Arctic mixed-phase clouds. Geophysical Research Letters, 49, e2021GL096191. <a href="https://doi.org/10.1029/2021GL096191" rel="nofollow">https://doi.org/10.1029/2021GL096191</a></li> </ul>
Importance of high-latitude sources for ice-nucleating particles in cold-air outbreaks and the implications for cloud-phase feedback
<p>This dataset contains FLEXPART results and codes for data analysis and visualization used in the MRes research project. </p> <p>Numpy arrays for FLEXPART data are in "data_flexpart.zip" with longitude and latitude coordination files.</p> <p>Codes for data analysis and visualization are in "ipynb.zip". Codes were written in Python language. Codes are categorized by figures in the article. </p>
Data and metadata associated with ice-nucleating particles during the 2022 Arctic Cold Air Outbreak Campaign
<p>Data and metadata associated with analysis of ice-nucleating particle concentrations and aerosol-size distributions measured during the March 2022 Arctic Cold Air Outbreak campaign.</p> <p>Contains INP concentrations and aerosol-size normalisations of these, parametrisations of these, SEM data and metadata and backtrajectory modelling inputs and results.</p>
Dataset for manuscript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model"
<p>Datasets and Jupyterlab python script for plotting all figures relevant to the mansucript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model" by Herbert et al.</p> <p>https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1538/</p> <p>Data needs to be unzipped and paths (input and output) updated in the jupyterlab python script.</p> <p> </p>
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>
datasets for "Atmospheric Humic-Like Substances (HULIS) Act As Ice-Nucleating Particles "
<p>These are datasets for manuscript titled "Atmospheric Humic-Like Substances (HULIS) Act As Ice-Nucleating Particles "</p>
dataset for "Anthropogenic dust as a significant source of ice-nucleating particles in the urban environment"
<p>These are data that correspond to the publication of "anthropogenic dust ice nucleating particles"</p>
Model simulation data used in "Impacts of ice-nucleating particles on cirrus clouds and radiation derived from global model simulations with MADE3 in EMAC" (Beer et al., Atmos. Chem. Phys., 2024)
<p>This dataset contains the namelist setup and the output of the EMAC global model simulations analysed and discussed in Beer et al. (<i>Atmos. Chem. Phys.</i>, 2024). For details see the README.md file and Table 2 in the paper.</p>
Dataset for "Roles of marine biota in the formation of atmospheric bioaerosols, cloud condensation nuclei, and ice-nucleating particles over the North Pacific Ocean, Bering Sea, and Arctic Ocean"
<p>Atmospheric and oceanic observations were conducted over the North Pacific Ocean, Bering Sea, and Arctic Ocean during a cruise (MR19-03C) in early autumn of 2019. The dataset includes trace gases, chemical composition, fluorescent particles in the ambient and bioindicators in the surface seawater, observed along the ship track (time stamp and coordinates are included). This dataset is for Kawana et al., "Roles of marine biota in the formation of atmospheric bioaerosols, cloud condensation nuclei, and ice-nucleating particles over the North Pacific Ocean, Bering Sea, and Arctic Ocean", Atmospheric Chemistry and Physics, 2024 (in press).</p>
Physicochemical characterization of free troposphere and marine boundary layer ice-nucleating particles collected by aircraft in the eastern North Atlantic
<p>This repository contains the MATLAB data files to generate the figures pertaining to the STXM/NEXAFS analysis given in the publication:</p> <p>Title: Physicochemical characterization of free troposphere and marine boundary layer ice-nucleating particles collected by aircraft in the eastern North Atlantic<br> Authors: Daniel A. Knopf, Peiwen Wang, Benny Wong, Jay M. Tomlin, Daniel P. Veghte, Nurun N. Lata, Swarup China, Alexander Laskin, Ryan C. Moffet, Josephine Y. Aller, Matthew A. Marcus, and Jian Wang</p> <p>Journal: Atmospheric Chemistry and Physics, accepted 2023</p>
EAMv1 outputs Macquarie Island- Long-term variability in immersion-mode marine ice-nucleating particles from climate model simulations and observations
<p>EAMv1 outputs for the ACP publication </p> <p>https://acp.copernicus.org/articles/23/5735/2023/acp-23-5735-2023.pdf</p> <p>We have archived the outputs from the EAMv1 control simulations. </p>
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