Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5789-2021
https://doi.org/10.5194/gmd-14-5789-2021
Model description paper
 | 
24 Sep 2021
Model description paper |  | 24 Sep 2021

GCAP 2.0: a global 3-D chemical-transport model framework for past, present, and future climate scenarios

Lee T. Murray, Eric M. Leibensperger, Clara Orbe, Loretta J. Mickley, and Melissa Sulprizio

Related authors

Characterization of in situ cosmogenic 14CO production, retention and loss in firn and shallow ice at Summit, Greenland
Benjamin Hmiel, Vasilii V. Petrenko, Christo Buizert, Andrew M. Smith, Michael N. Dyonisius, Philip Place, Bin Yang, Quan Hua, Ross Beaudette, Jeffrey P. Severinghaus, Christina Harth, Ray F. Weiss, Lindsey Davidge, Melisa Diaz, Matthew Pacicco, James A. Menking, Michael Kalk, Xavier Faïn, Alden Adolph, Isaac Vimont, and Lee T. Murray
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-121,https://doi.org/10.5194/tc-2023-121, 2023
Preprint under review for TC
Short summary
Nitrate chemistry in the northeast US – Part 1: Nitrogen isotope seasonality tracks nitrate formation chemistry
Claire Bekker, Wendell W. Walters, Lee T. Murray, and Meredith G. Hastings
Atmos. Chem. Phys., 23, 4185–4201, https://doi.org/10.5194/acp-23-4185-2023,https://doi.org/10.5194/acp-23-4185-2023, 2023
Short summary
Nitrate chemistry in the northeast US – Part 2: Oxygen isotopes reveal differences in particulate and gas-phase formation
Heejeong Kim, Wendell W. Walters, Claire Bekker, Lee T. Murray, and Meredith G. Hastings
Atmos. Chem. Phys., 23, 4203–4219, https://doi.org/10.5194/acp-23-4203-2023,https://doi.org/10.5194/acp-23-4203-2023, 2023
Short summary
Intercomparison of commercial analyzers for atmospheric ethane and methane observations
Róisín Commane, Andrew Hallward-Driemeier, and Lee T. Murray
Atmos. Meas. Tech., 16, 1431–1441, https://doi.org/10.5194/amt-16-1431-2023,https://doi.org/10.5194/amt-16-1431-2023, 2023
Short summary
Heterogeneity and chemical reactivity of the remote troposphere defined by aircraft measurements – corrected
Hao Guo, Clare M. Flynn, Michael J. Prather, Sarah A. Strode, Stephen D. Steenrod, Louisa Emmons, Forrest Lacey, Jean-Francois Lamarque, Arlene M. Fiore, Gus Correa, Lee T. Murray, Glenn M. Wolfe, Jason M. St. Clair, Michelle Kim, John Crounse, Glenn Diskin, Joshua DiGangi, Bruce C. Daube, Roisin Commane, Kathryn McKain, Jeff Peischl, Thomas B. Ryerson, Chelsea Thompson, Thomas F. Hanisco, Donald Blake, Nicola J. Blake, Eric C. Apel, Rebecca S. Hornbrook, James W. Elkins, Eric J. Hintsa, Fred L. Moore, and Steven C. Wofsy
Atmos. Chem. Phys., 23, 99–117, https://doi.org/10.5194/acp-23-99-2023,https://doi.org/10.5194/acp-23-99-2023, 2023
Short summary

Related subject area

Atmospheric sciences
Advances and prospects of deep learning for medium-range extreme weather forecasting
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024,https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024,https://doi.org/10.5194/gmd-17-2265-2024, 2024
Short summary
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024,https://doi.org/10.5194/gmd-17-2247-2024, 2024
Short summary
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024,https://doi.org/10.5194/gmd-17-2053-2024, 2024
Short summary
Deep learning applied to CO2 power plant emissions quantification using simulated satellite images
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024,https://doi.org/10.5194/gmd-17-1995-2024, 2024
Short summary

Cited articles

Achakulwisut, P., Mickley, L. J., Murray, L. T., Tai, A. P. K., Kaplan, J. O., and Alexander, B.: Uncertainties in isoprene photochemistry and emissions: implications for the oxidative capacity of past and present atmospheres and for climate forcing agents, Atmos. Chem. Phys., 15, 7977–7998, https://doi.org/10.5194/acp-15-7977-2015, 2015. a
AIRS project: Aqua/AIRS L3 Daily Standard Physical Retrieval (AIRS+AMSU) 1 degree x 1 degree V7.0, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/8XB4RU470FJV, 2019. a
Allen, D. J., Rood, R., Thompson, A. M., and Hudson, R.: Three-dimensional radon 222 calculations using assimilated meteorological data and a convective mixing algorithm, J. Geophys. Res.-Atmos., 101, 6871–6881, https://doi.org/10.1029/95JD03408, 1996. a
Allen, D. J., Dibb, J., Ridley, B., Pickering, K., and Talbot, R.: An estimate of the stratospheric contribution to springtime tropospheric ozone maxima using TOPSE measurements and beryllium-7 simulations, J. Geophys. Res.-Atmos., 108, 8355, https://doi.org/10.1029/2001JD001428, 2003. a, b
Balkanski, Y., Jacob, D. J., Gardner, G., Graustein, W., and Turekian, K.: Transport and Residence Times of Tropospheric Aerosols Inferred from a Global 3-Dimensional Simulation of Pb-210, J. Geophys. Res.-Atmos., 98, 20573–20586, https://doi.org/10.1029/93jd02456, 1993. a
Download
Short summary
Chemical-transport models are tools used to study air pollution and inform public policy. However, they are limited by the availability of archived meteorology. Here, we describe how the GEOS-Chem chemical-transport model may now be driven by meteorology archived from a state-of-the-art general circulation model for past and future climates, allowing it to be used to explore the impact of climate change on air pollution and atmospheric composition.