Articles | Volume 14, issue 10
Geosci. Model Dev., 14, 5977–5997, 2021
https://doi.org/10.5194/gmd-14-5977-2021
Geosci. Model Dev., 14, 5977–5997, 2021
https://doi.org/10.5194/gmd-14-5977-2021
Development and technical paper
06 Oct 2021
Development and technical paper | 06 Oct 2021

Grid-stretching capability for the GEOS-Chem 13.0.0 atmospheric chemistry model

Liam Bindle et al.

Related authors

Improved Advection, Resolution, Performance, and Community Access in the New Generation (Version 13) of the High Performance GEOS-Chem Global Atmospheric Chemistry Model (GCHP)
Randall V. Martin, Sebastian D. Eastham, Liam Bindle, Elizabeth W. Lundgren, Thomas L. Clune, Christoph A. Keller, William Downs, Dandan Zhang, Robert A. Lucchesi, Melissa P. Sulprizio, Robert M. Yantosca, Yanshun Li, Lucas Estrada, William M. Putman, Benjamin M. Auer, Atanas L. Trayanov, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-42,https://doi.org/10.5194/gmd-2022-42, 2022
Revised manuscript under review for GMD
Short summary

Related subject area

Climate and Earth system modeling
Transport parameterization of the Polar SWIFT model (version 2)
Ingo Wohltmann, Daniel Kreyling, and Ralph Lehmann
Geosci. Model Dev., 15, 7243–7255, https://doi.org/10.5194/gmd-15-7243-2022,https://doi.org/10.5194/gmd-15-7243-2022, 2022
Short summary
Analog data assimilation for the selection of suitable general circulation models
Juan Ruiz, Pierre Ailliot, Thi Tuyet Trang Chau, Pierre Le Bras, Valérie Monbet, Florian Sévellec, and Pierre Tandeo
Geosci. Model Dev., 15, 7203–7220, https://doi.org/10.5194/gmd-15-7203-2022,https://doi.org/10.5194/gmd-15-7203-2022, 2022
Short summary
Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0
Chahan M. Kropf, Alessio Ciullo, Laura Otth, Simona Meiler, Arun Rana, Emanuel Schmid, Jamie W. McCaughey, and David N. Bresch
Geosci. Model Dev., 15, 7177–7201, https://doi.org/10.5194/gmd-15-7177-2022,https://doi.org/10.5194/gmd-15-7177-2022, 2022
Short summary
Grid refinement in ICON v2.6.4
Günther Zängl, Daniel Reinert, and Florian Prill
Geosci. Model Dev., 15, 7153–7176, https://doi.org/10.5194/gmd-15-7153-2022,https://doi.org/10.5194/gmd-15-7153-2022, 2022
Short summary
Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset
Sébastien Gardoll and Olivier Boucher
Geosci. Model Dev., 15, 7051–7073, https://doi.org/10.5194/gmd-15-7051-2022,https://doi.org/10.5194/gmd-15-7051-2022, 2022
Short summary

Cited articles

Allen, D., Pickering, K., Stenchikov, G., Thompson, A., and Kondo, Y.: A three-dimensional total odd nitrogen (NOy) simulation during SONEX using a stretched-grid chemical transport model, J. Geophys. Res.-Atmos., 105, 3851–3876, 2000. a
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys. Res.-Atmos., 106, 23073–23095, https://doi.org/10.1029/2001JD000807, 2001. a
Boersma, K., Braak, R., and van der A, R. J.: Dutch OMI NO2 (DOMINO) data product v2. 0, Tropospheric Emissions Monitoring Internet Service on-line documentation, available at: http://www.temis.nl/docs/OMI_NO2_HE5_2.0_2011.pdf (last access: 5 July 2020), 2011. a
Boersma, K. F., Eskes, H. J., Richter, A., De Smedt, I., Lorente, A., Beirle, S., van Geffen, J. H. G. M., Zara, M., Peters, E., Van Roozendael, M., Wagner, T., Maasakkers, J. D., van der A, R. J., Nightingale, J., De Rudder, A., Irie, H., Pinardi, G., Lambert, J.-C., and Compernolle, S. C.: Improving algorithms and uncertainty estimates for satellite NO2 retrievals: results from the quality assurance for the essential climate variables (QA4ECV) project, Atmos. Meas. Tech., 11, 6651–6678, https://doi.org/10.5194/amt-11-6651-2018, 2018. a, b
Cooper, M. J., Martin, R. V., McLinden, C. A., and Brook, J. R.: Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument, Environ. Res. Lett., 15, 104013, https://doi.org/10.1088/1748-9326/aba3a5, 2020. a
Download
Short summary
Atmospheric chemistry models like GEOS-Chem are versatile tools widely used in air pollution and climate studies. The simulations used in such studies can be very computationally demanding, and thus it is useful if the model can simulate a specific geographic region at a higher resolution than the rest of the globe. Here, we implement, test, and demonstrate a new variable-resolution capability in GEOS-Chem that is suitable for simulations conducted on supercomputers.