Data for paper "Investigating the sign of stratocumulus adjustments to aerosols in the global storm-resolving model ICON"
<p>Data for paper "Investigating the sign of stratocumulus adjustments to aerosols in the global storm-resolving model ICON". The code used to generate, analyze and plot these data is provided separately on Zenodo. The zip files named 2.zip_file_name are used in the 2.make_comparison_plots notebooks in the companion Zenodo software repository. The zip files named 3.zip_file_name are generated using the code contained in 1.process_data in the software repository and used for the analyses in 3.calculate_causal_effects in the software repository. </p> <p><strong>References - Code ______________________________________________</strong></p> <p>J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. <a href="http://doi.org/10.1038/ncomms9502">http://doi.org/10.1038/ncomms9502</a></p> <p>J. Runge, Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables, Advances in Neural Information Processing Systems, 2021, 34. <a href="https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html">https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html</a></p> <p><strong>References - Data ______________________________________________</strong></p> <p><em>SEVIRI</em> <br>Benas, N., Solodovnik, I., Stengel, M., Hüser, I., Karlsson, K.-G., Håkansson, N., Johansson, E., Eliasson, S., Schröder, M., Hollmann, R., and Meirink, J. F.: CLAAS-3: The Third Edition of the CM SAF Cloud Data Record Based on SEVIRI Observations, Earth System , Science Data Discussions, pp. 1-38, <a href="https://doi.org/10.5194/essd-2023-79">https://doi.org/10.5194/essd-2023-79</a>, 2023.</p> <p><em>GOES</em><br>Walther, A. and Straka, W.: Algorithm Theoretical Basis Document For Daytime Cloud Optical and Microphysical Properties (DCOMP), 2020</p> <p><em>ERA5</em><br>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thébaut, J.-N.: ERA5 Hourly Data on Single Levels from 1959 to Present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), <a href="https://doi.org/10.24381/cds.adbb2d47">https://doi.org/10.24381/cds.adbb2d47</a>, 2018a.<br>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thébaut, J.-N.: ERA5 Hourly Data on Pressure Levels from 1959 to Present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), <a href="https://doi.org/10.24381/cds.bd0915c6">https://doi.org/10.24381/cds.bd0915c6</a>, 2018b.</p> <p><em>GPM</em><br>Huffman, G., Stocker, E., Bolvin, D., Nelkin, E., and Tan, J.: GPM IMERG Final Precipitation L3 Half Hourly 0.1 Degree x 0.1 Degree V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), <a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/07">https://doi.org/10.5067/GPM/IMERG/3B-</a><a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/07">HH/07</a>, 2023</p> <p><em>MODIS</em><br>Platnick, S. et al. MODIS atmosphere L3 daily product. NASA<a href="https://doi.org/10.5067/MODIS/MOD08_D3.006"> https://doi.org/10.5067/MODIS/MOD08_D3.006 </a>, 2015.</p> <p><em>MIDAS</em><br>Eastman, R., McCoy, I. L., Schulz, H., and Wood, R.: A Survey of Radiative and Physical Properties of North Atlantic Mesoscale Cloud Morphologies from Multiple Identification Methodologies, EGUsphere, pp. 1–33, <a href="https://doi.org/10.5194/egusphere-2023-2118">https://doi.org/10.5194/egusphere-2023-2118</a>, 2023. <br>McCoy, I. L., McCoy, D. T., Wood, R., Zuidema, P., and Bender, F. A.-M.: The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback, Geophysical Research Letters, 50, e2022GL101 042, <a href="https://doi.org/10.1029/2022GL101042">https://doi.org/10.1029/2022GL101042</a>, 2023.</p> <p><em>ICON</em><br>Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) Modelling Framework of DWD and MPI-M: Description of the Non-Hydrostatic Dynamical Core, Quarterly Journal of the Royal Meteorological Society, 141, 563–579, <a href="https://doi.org/10.1002/qj.2378">https://doi.org/10.1002/qj.2378</a>, 2015<br><a href="https://code.mpimet.mpg.de/projects/iconpublic/wiki/Instructions_to_obtain_the_ICON_model_code_with_a_personal_non-commercial_research_license">https://code.mpimet.mpg.de/projects/iconpublic/wiki/Instructions_to_obtain_the_ICON_model_code_with_a_personal_non-commercial_research_license </a></p>
ShareScore
36/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 4
- Engagement
- 8