Articles | Volume 15, issue 2
Geosci. Model Dev., 15, 509–533, 2022
https://doi.org/10.5194/gmd-15-509-2022
Geosci. Model Dev., 15, 509–533, 2022
https://doi.org/10.5194/gmd-15-509-2022
Methods for assessment of models
25 Jan 2022
Methods for assessment of models | 25 Jan 2022

An aerosol classification scheme for global simulations using the K-means machine learning method

Jingmin Li et al.

Related authors

An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing
Robin N. Thor, Mariano Mertens, Sigrun Matthes, Mattia Righi, Johannes Hendricks, Sabine Brinkop, Phoebe Graf, Volker Grewe, Patrick Jöckel, and Steven Smith
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-250,https://doi.org/10.5194/gmd-2022-250, 2022
Preprint under review for GMD
Short summary
A global climatology of ice nucleating particles at cirrus conditions derived from model simulations with EMAC-MADE3
Christof Gerhard Beer, Johannes Hendricks, and Mattia Righi
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-529,https://doi.org/10.5194/acp-2022-529, 2022
Revised manuscript accepted for ACP
Short summary
Seasonal updraft speeds change cloud droplet number concentrations in low-level clouds over the western North Atlantic
Simon Kirschler, Christiane Voigt, Bruce Anderson, Ramon Campos Braga, Gao Chen, Andrea F. Corral, Ewan Crosbie, Hossein Dadashazar, Richard A. Ferrare, Valerian Hahn, Johannes Hendricks, Stefan Kaufmann, Richard Moore, Mira L. Pöhlker, Claire Robinson, Amy J. Scarino, Dominik Schollmayer, Michael A. Shook, K. Lee Thornhill, Edward Winstead, Luke D. Ziemba, and Armin Sorooshian
Atmos. Chem. Phys., 22, 8299–8319, https://doi.org/10.5194/acp-22-8299-2022,https://doi.org/10.5194/acp-22-8299-2022, 2022
Short summary
Exploring the uncertainties in the aviation soot–cirrus effect
Mattia Righi, Johannes Hendricks, and Christof Gerhard Beer
Atmos. Chem. Phys., 21, 17267–17289, https://doi.org/10.5194/acp-21-17267-2021,https://doi.org/10.5194/acp-21-17267-2021, 2021
Short summary
Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for extreme events, regional and impact evaluation, and analysis of Earth system models in CMIP
Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyring
Geosci. Model Dev., 14, 3159–3184, https://doi.org/10.5194/gmd-14-3159-2021,https://doi.org/10.5194/gmd-14-3159-2021, 2021
Short summary

Related subject area

Atmospheric sciences
A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF
Shunji Kotsuki, Takemasa Miyoshi, Keiichi Kondo, and Roland Potthast
Geosci. Model Dev., 15, 8325–8348, https://doi.org/10.5194/gmd-15-8325-2022,https://doi.org/10.5194/gmd-15-8325-2022, 2022
Short summary
A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions
Martin Vojta, Andreas Plach, Rona L. Thompson, and Andreas Stohl
Geosci. Model Dev., 15, 8295–8323, https://doi.org/10.5194/gmd-15-8295-2022,https://doi.org/10.5194/gmd-15-8295-2022, 2022
Short summary
Importance of different parameterization changes for the updated dust cycle modeling in the Community Atmosphere Model (version 6.1)
Longlei Li, Natalie M. Mahowald, Jasper F. Kok, Xiaohong Liu, Mingxuan Wu, Danny M. Leung, Douglas S. Hamilton, Louisa K. Emmons, Yue Huang, Neil Sexton, Jun Meng, and Jessica Wan
Geosci. Model Dev., 15, 8181–8219, https://doi.org/10.5194/gmd-15-8181-2022,https://doi.org/10.5194/gmd-15-8181-2022, 2022
Short summary
Evaluation of the NAQFC driven by the NOAA Global Forecast System (version 16): comparison with the WRF-CMAQ during the summer 2019 FIREX-AQ campaign
Youhua Tang, Patrick C. Campbell, Pius Lee, Rick Saylor, Fanglin Yang, Barry Baker, Daniel Tong, Ariel Stein, Jianping Huang, Ho-Chun Huang, Li Pan, Jeff McQueen, Ivanka Stajner, Jose Tirado-Delgado, Youngsun Jung, Melissa Yang, Ilann Bourgeois, Jeff Peischl, Tom Ryerson, Donald Blake, Joshua Schwarz, Jose-Luis Jimenez, James Crawford, Glenn Diskin, Richard Moore, Johnathan Hair, Greg Huey, Andrew Rollins, Jack Dibb, and Xiaoyang Zhang
Geosci. Model Dev., 15, 7977–7999, https://doi.org/10.5194/gmd-15-7977-2022,https://doi.org/10.5194/gmd-15-7977-2022, 2022
Short summary
Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation
Zhiquan Liu, Chris Snyder, Jonathan J. Guerrette, Byoung-Joo Jung, Junmei Ban, Steven Vahl, Yali Wu, Yannick Trémolet, Thomas Auligné, Benjamin Ménétrier, Anna Shlyaeva, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 15, 7859–7878, https://doi.org/10.5194/gmd-15-7859-2022,https://doi.org/10.5194/gmd-15-7859-2022, 2022
Short summary

Cited articles

Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. 
Amorim, R. C. D. and Hennig, C: Recovering the number of clusters in data sets with noise features using feature rescaling factors, Inform. Sciences, 324, 126–145, https://doi.org/10.1016/j.ins.2015.06.039, 2015. 
Aquila, V., Hendricks, J., Lauer, A., Riemer, N., Vogel, H., Baumgardner, D., Minikin, A., Petzold, A., Schwarz, J. P., Spackman, J. R., Weinzierl, B., Righi, M., and Dall'Amico, M.: MADE-in: a new aerosol microphysics submodel for global simulation of insoluble particles and their mixing state, Geosci. Model Dev., 4, 325–355, https://doi.org/10.5194/gmd-4-325-2011, 2011. 
Bauer, S. E., Wright, D. L., Koch, D., Lewis, E. R., McGraw, R., Chang, L.-S., Schwartz, S. E., and Ruedy, R.: MATRIX (Multiconfiguration Aerosol TRacker of mIXing state): an aerosol microphysical module for global atmospheric models, Atmos. Chem. Phys., 8, 6003–6035, https://doi.org/10.5194/acp-8-6003-2008, 2008. 
Beer, C. G.: Model simulation data used in “Modelling mineral dust emissions and atmospheric dispersion with MADE3 in EMAC v2.54” (Beer et al., Geosci. Model Dev., 2020), Zenodo [data set], https://doi.org/10.5281/zenodo.3941462, 2020. 
Download
Short summary
The growing complexity of global aerosol models results in a large number of parameters that describe the aerosol number, size, and composition. This makes the analysis, evaluation, and interpretation of the model results a challenge. To overcome this difficulty, we apply a machine learning classification method to identify clusters of specific aerosol types in global aerosol simulations. Our results demonstrate the spatial distributions and characteristics of these identified aerosol clusters.