Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5789-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-5789-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
24 Sep 2021
Model description paper |
| 24 Sep 2021
| 24 Sep 2021
GCAP 2.0: a global 3-D chemical-transport model framework for past, present, and future climate scenarios
Dept. of Earth and Environmental Sciences, University of Rochester, Rochester, NY, USA
Dept. of Physics and Astronomy, University of Rochester, Rochester, NY, USA
Eric M. Leibensperger
Dept. of Physics and Astronomy, Ithaca College, Ithaca, NY, USA
Clara Orbe
NASA Goddard Institute for Space Studies, New York, NY, USA
Loretta J. Mickley
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Melissa Sulprizio
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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Lu Shen, Loretta J. Mickley, and Lee T. Murray
Atmos. Chem. Phys., 17, 4355–4367, https://doi.org/10.5194/acp-17-4355-2017, https://doi.org/10.5194/acp-17-4355-2017, 2017
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L. T. Murray, L. J. Mickley, J. O. Kaplan, E. D. Sofen, M. Pfeiffer, and B. Alexander
Atmos. Chem. Phys., 14, 3589–3622, https://doi.org/10.5194/acp-14-3589-2014, https://doi.org/10.5194/acp-14-3589-2014, 2014
Sarah E. Hancock, Daniel J. Jacob, Zichong Chen, Hannah Nesser, Aaron Davitt, Daniel J. Varon, Melissa P. Sulprizio, Nicholas Balasus, Lucas A. Estrada, María Cazorla, Laura Dawidowski, Sebastián Diez, James D. East, Elise Penn, Cynthia A. Randles, John Worden, Ilse Aben, Robert J. Parker, and Joannes D. Maasakkers
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William J. Collins, Fiona M. O'Connor, Connor R. Barker, Rachael E. Byrom, Sebastian D. Eastham, Øivind Hodnebrog, Patrick Jöckel, Eloise A. Marais, Mariano Mertens, Gunnar Myhre, Matthias Nützel, Dirk Olivié, Ragnhild Bieltvedt Skeie, Laura Stecher, Larry W. Horowitz, Vaishali Naik, Gregory Faluvegi, Ulas Im, Lee T. Murray, Drew Shindell, Kostas Tsigaridis, Nathan Luke Abraham, and James Keeble
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Yoshio Kawatani, Kevin Hamilton, Shingo Watanabe, James A. Anstey, Jadwiga H. Richter, Neal Butchart, Clara Orbe, Scott M. Osprey, Hiroaki Naoe, Dillon Elsbury, Chih-Chieh Chen, Javier García-Serrano, Anne Glanville, Tobias Kerzenmacher, François Lott, Froila M. Palmerio, Mijeong Park, Federico Serva, Masakazu Taguchi, Stefan Versick, and Kohei Yoshioda
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Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
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Paul T. Griffiths, Laura J. Wilcox, Robert J. Allen, Vaishali Naik, Fiona M. O'Connor, Michael J. Prather, Alexander T. Archibald, Florence Brown, Makoto Deushi, William Collins, Stephanie Fiedler, Naga Oshima, Lee T. Murray, Christopher J. Smith, Steven T. Turnock, Duncan Watson-Parris, and Paul J. Young
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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
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The main aim of this research is to improve understanding of carbon-14 that is produced by cosmic rays in ice sheets. Measurements of carbon-14 in ice cores can provide a range of useful information (age of ice, past atmospheric chemistry, past cosmic ray intensity). Our results show that almost all (>99 %) of carbon-14 that is produced in the upper layer of ice sheets is rapidly lost to the atmosphere. Our results also provide better estimates of carbon-14 production rates in deeper ice.
Cheng Zheng, Yutian Wu, Mingfang Ting, and Clara Orbe
Atmos. Chem. Phys., 24, 6965–6985, https://doi.org/10.5194/acp-24-6965-2024, https://doi.org/10.5194/acp-24-6965-2024, 2024
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Trace gases and aerosols in the Arctic, which typically originate from midlatitude and tropical emission regions, modulate the Arctic climate via their radiative and chemistry impacts. Thus, long-range transport of these substances is important for understanding the current and the future change of Arctic climate. By employing chemistry–climate models, we explore how year-to-year variations in the atmospheric circulation modulate atmospheric long-range transport into the Arctic.
Brian Nathan, Joannes D. Maasakkers, Stijn Naus, Ritesh Gautam, Mark Omara, Daniel J. Varon, Melissa P. Sulprizio, Lucas A. Estrada, Alba Lorente, Tobias Borsdorff, Robert J. Parker, and Ilse Aben
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Tiehan Zhou, Kevin J. DallaSanta, Clara Orbe, David H. Rind, Jeffrey A. Jonas, Larissa Nazarenko, Gavin A. Schmidt, and Gary Russell
Atmos. Chem. Phys., 24, 509–532, https://doi.org/10.5194/acp-24-509-2024, https://doi.org/10.5194/acp-24-509-2024, 2024
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The El Niño–Southern Oscillation (ENSO) tends to speed up and slow down the phase speed of the Quasi-Biennial Oscillation (QBO) during El Niño and La Niña, respectively. The ENSO modulation of the QBO does not show up in the climate models with parameterized but temporally constant gravity wave sources. We show that the GISS E2.2 models can capture the observed ENSO modulation of the QBO period with a horizontal resolution of 2° by 2.5° and its gravity wave sources parameterized interactively.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
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We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Daniel J. Varon, Daniel J. Jacob, Benjamin Hmiel, Ritesh Gautam, David R. Lyon, Mark Omara, Melissa Sulprizio, Lu Shen, Drew Pendergrass, Hannah Nesser, Zhen Qu, Zachary R. Barkley, Natasha L. Miles, Scott J. Richardson, Kenneth J. Davis, Sudhanshu Pandey, Xiao Lu, Alba Lorente, Tobias Borsdorff, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 7503–7520, https://doi.org/10.5194/acp-23-7503-2023, https://doi.org/10.5194/acp-23-7503-2023, 2023
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We use TROPOMI satellite observations to quantify weekly methane emissions from the US Permian oil and gas basin from May 2018 to October 2020. We find that Permian emissions are highly variable, with diverse economic and activity drivers. The most important drivers during our study period were new well development and natural gas price. Permian methane intensity averaged 4.6 % and decreased by 1 % per year.
Ruijun Dang, Daniel J. Jacob, Viral Shah, Sebastian D. Eastham, Thibaud M. Fritz, Loretta J. Mickley, Tianjia Liu, Yi Wang, and Jun Wang
Atmos. Chem. Phys., 23, 6271–6284, https://doi.org/10.5194/acp-23-6271-2023, https://doi.org/10.5194/acp-23-6271-2023, 2023
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We use the GEOS-Chem model to better understand the magnitude and trend in free tropospheric NO2 over the contiguous US. Model underestimate of background NO2 is largely corrected by considering aerosol nitrate photolysis. Increase in aircraft emissions affects satellite retrievals by altering the NO2 shape factor, and this effect is expected to increase in future. We show the importance of properly accounting for the free tropospheric background in interpreting NO2 observations from space.
Zichong Chen, Daniel J. Jacob, Ritesh Gautam, Mark Omara, Robert N. Stavins, Robert C. Stowe, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Drew C. Pendergrass, and Sarah Hancock
Atmos. Chem. Phys., 23, 5945–5967, https://doi.org/10.5194/acp-23-5945-2023, https://doi.org/10.5194/acp-23-5945-2023, 2023
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We quantify methane emissions from individual countries in the Middle East and North Africa by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We show that the ability to simply relate oil/gas emissions to activity metrics is compromised by stochastic nature of local infrastructure and management practices. We find that the industry target for oil/gas methane intensity is achievable through associated gas capture, modern infrastructure, and centralized operations.
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
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Nitrate is a critical component of the atmosphere that degrades air quality and ecosystem health. We have investigated the nitrogen isotope compositions of nitrate from deposition samples collected across the northeastern United States. Spatiotemporal variability in the nitrogen isotope compositions was found to track with nitrate formation chemistry. Our results highlight that nitrogen isotope compositions may be a robust tool for improving model representation of nitrate chemistry.
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
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Atmospheric nitrate has an important impact on human and ecosystem health. We evaluated atmospheric nitrate formation pathways in the northeastern US utilizing oxygen isotope compositions, which indicated a significant difference between the phases of nitrate (i.e., gas vs. particle). Comparing the observations with model simulations indicated that N2O5 hydrolysis chemistry was overpredicted. Our study has important implications for improving atmospheric chemistry model representation.
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
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Methane / ethane ratios can be used to identify and partition the different sources of methane, especially in areas with natural gas mixed with biogenic methane emissions, such as cities. We tested three commercially available laser-based analyzers for sensitivity, precision, size, power requirement, ease of use on mobile platforms, and expertise needed to operate the instrument, and we make recommendations for use in various situations.
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
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We have prepared a unique and unusual result from the recent ATom aircraft mission: a measurement-based derivation of the production and loss rates of ozone and methane over the ocean basins. These are the key products of chemistry models used in assessments but have thus far lacked observational metrics. It also shows the scales of variability of atmospheric chemical rates and provides a major challenge to the atmospheric models.
Amy Christiansen, Loretta J. Mickley, Junhua Liu, Luke D. Oman, and Lu Hu
Atmos. Chem. Phys., 22, 14751–14782, https://doi.org/10.5194/acp-22-14751-2022, https://doi.org/10.5194/acp-22-14751-2022, 2022
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Understanding tropospheric ozone trends is crucial for accurate predictions of future air quality and climate, but drivers of trends are not well understood. We analyze global tropospheric ozone trends since 1980 using ozonesonde and surface measurements, and we evaluate two models for their ability to reproduce trends. We find observational evidence of increasing tropospheric ozone, but models underestimate these increases. This hinders our ability to estimate ozone radiative forcing.
Lu Shen, Ritesh Gautam, Mark Omara, Daniel Zavala-Araiza, Joannes D. Maasakkers, Tia R. Scarpelli, Alba Lorente, David Lyon, Jianxiong Sheng, Daniel J. Varon, Hannah Nesser, Zhen Qu, Xiao Lu, Melissa P. Sulprizio, Steven P. Hamburg, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 11203–11215, https://doi.org/10.5194/acp-22-11203-2022, https://doi.org/10.5194/acp-22-11203-2022, 2022
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We use 22 months of TROPOMI satellite observations to quantity methane emissions from the oil (O) and natural gas (G) sector in the US and Canada at the scale of both individual basins as well as country-wide aggregates. We find that O/G-related methane emissions are underestimated in these inventories by 80 % for the US and 40 % for Canada, and 70 % of the underestimate in the US is from five O/G basins, including Permian, Haynesville, Anadarko, Eagle Ford, and Barnett.
Zichong Chen, Daniel J. Jacob, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Elise Penn, and Xueying Yu
Atmos. Chem. Phys., 22, 10809–10826, https://doi.org/10.5194/acp-22-10809-2022, https://doi.org/10.5194/acp-22-10809-2022, 2022
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We quantify methane emissions in China and contributions from different sectors by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We find that anthropogenic methane emissions for China are underestimated in the national inventory. Our estimate of emissions indicates a small life-cycle loss rate, implying net climate benefits from the current
coal-to-gasenergy transition in China. However, this small loss rate can be misleading given China's high gas imports.
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles
Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, https://doi.org/10.5194/gmd-15-5787-2022, 2022
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Reducing atmospheric methane emissions is critical to slow near-term climate change. Globally surveying satellite instruments like the TROPOspheric Monitoring Instrument (TROPOMI) have unique capabilities for monitoring atmospheric methane around the world. Here we present a user-friendly cloud-computing tool that enables researchers and stakeholders to quantify methane emissions across user-selected regions of interest using TROPOMI satellite observations.
Tia R. Scarpelli, Daniel J. Jacob, Shayna Grossman, Xiao Lu, Zhen Qu, Melissa P. Sulprizio, Yuzhong Zhang, Frances Reuland, Deborah Gordon, and John R. Worden
Atmos. Chem. Phys., 22, 3235–3249, https://doi.org/10.5194/acp-22-3235-2022, https://doi.org/10.5194/acp-22-3235-2022, 2022
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We present a spatially explicit version of the national inventories of oil, gas, and coal methane emissions as submitted by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. We then use atmospheric modeling to compare our inventory emissions to atmospheric methane observations with the goal of identifying potential under- and overestimates of oil–gas methane emissions in the national inventories.
Xiao Lu, Daniel J. Jacob, Haolin Wang, Joannes D. Maasakkers, Yuzhong Zhang, Tia R. Scarpelli, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Hannah Nesser, A. Anthony Bloom, Shuang Ma, John R. Worden, Shaojia Fan, Robert J. Parker, Hartmut Boesch, Ritesh Gautam, Deborah Gordon, Michael D. Moran, Frances Reuland, Claudia A. Octaviano Villasana, and Arlyn Andrews
Atmos. Chem. Phys., 22, 395–418, https://doi.org/10.5194/acp-22-395-2022, https://doi.org/10.5194/acp-22-395-2022, 2022
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We evaluate methane emissions and trends for 2010–2017 in the gridded national emission inventories for the United States, Canada, and Mexico by inversion of in situ and satellite methane observations. We find that anthropogenic methane emissions for all three countries are underestimated in the national inventories, largely driven by oil emissions. Anthropogenic methane emissions in the US peak in 2014, in contrast to the report of a steadily decreasing trend over 2010–2017 from the US EPA.
Sabour Baray, Daniel J. Jacob, Joannes D. Maasakkers, Jian-Xiong Sheng, Melissa P. Sulprizio, Dylan B. A. Jones, A. Anthony Bloom, and Robert McLaren
Atmos. Chem. Phys., 21, 18101–18121, https://doi.org/10.5194/acp-21-18101-2021, https://doi.org/10.5194/acp-21-18101-2021, 2021
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We use 2010–2015 surface and satellite observations to disentangle methane from anthropogenic and natural sources in Canada. Using a chemical transport model (GEOS-Chem), the mismatch between modelled and observed methane concentrations can be used to infer emissions according to Bayesian statistics. Compared to prior knowledge, we show higher anthropogenic emissions attributed to energy and/or agriculture in Western Canada and lower natural emissions from Boreal wetlands.
Liam Bindle, Randall V. Martin, Matthew J. Cooper, Elizabeth W. Lundgren, Sebastian D. Eastham, Benjamin M. Auer, Thomas L. Clune, Hongjian Weng, Jintai Lin, Lee T. Murray, Jun Meng, Christoph A. Keller, William M. Putman, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 14, 5977–5997, https://doi.org/10.5194/gmd-14-5977-2021, https://doi.org/10.5194/gmd-14-5977-2021, 2021
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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.
Zhen Qu, Daniel J. Jacob, Lu Shen, Xiao Lu, Yuzhong Zhang, Tia R. Scarpelli, Hannah Nesser, Melissa P. Sulprizio, Joannes D. Maasakkers, A. Anthony Bloom, John R. Worden, Robert J. Parker, and Alba L. Delgado
Atmos. Chem. Phys., 21, 14159–14175, https://doi.org/10.5194/acp-21-14159-2021, https://doi.org/10.5194/acp-21-14159-2021, 2021
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The recent launch of TROPOMI offers an unprecedented opportunity to quantify the methane budget from a top-down perspective. We use TROPOMI and the more mature GOSAT methane observations to estimate methane emissions and get consistent global budgets. However, TROPOMI shows biases over regions where surface albedo is small and provides less information for the coarse-resolution inversion due to the larger error correlations and spatial variations in the number of observations.
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 Wofsy
Atmos. Chem. Phys., 21, 13729–13746, https://doi.org/10.5194/acp-21-13729-2021, https://doi.org/10.5194/acp-21-13729-2021, 2021
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The NASA Atmospheric Tomography (ATom) mission built a climatology of the chemical composition of tropospheric air parcels throughout the middle of the Pacific and Atlantic oceans. The level of detail allows us to reconstruct the photochemical budgets of O3 and CH4 over these vast, remote regions. We find that most of the chemical heterogeneity is captured at the resolution used in current global chemistry models and that the majority of reactivity occurs in the
hottest20 % of parcels.
Marta Abalos, Natalia Calvo, Samuel Benito-Barca, Hella Garny, Steven C. Hardiman, Pu Lin, Martin B. Andrews, Neal Butchart, Rolando Garcia, Clara Orbe, David Saint-Martin, Shingo Watanabe, and Kohei Yoshida
Atmos. Chem. Phys., 21, 13571–13591, https://doi.org/10.5194/acp-21-13571-2021, https://doi.org/10.5194/acp-21-13571-2021, 2021
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The stratospheric Brewer–Dobson circulation (BDC), responsible for transporting mass, tracers and heat globally in the stratosphere, is evaluated in a set of state-of-the-art climate models. The acceleration of the BDC in response to increasing greenhouse gases is most robust in the lower stratosphere. At higher levels, the well-known inconsistency between model and observational BDC trends can be partly reconciled by accounting for limited sampling and large uncertainties in the observations.
Haipeng Lin, Daniel J. Jacob, Elizabeth W. Lundgren, Melissa P. Sulprizio, Christoph A. Keller, Thibaud M. Fritz, Sebastian D. Eastham, Louisa K. Emmons, Patrick C. Campbell, Barry Baker, Rick D. Saylor, and Raffaele Montuoro
Geosci. Model Dev., 14, 5487–5506, https://doi.org/10.5194/gmd-14-5487-2021, https://doi.org/10.5194/gmd-14-5487-2021, 2021
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Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. It allows users to select, add, and scale emissions from different sources through a configuration file with no change to the model source code. We demonstrate the implementation of HEMCO in several models, all sharing the same HEMCO core code and database library.
Hannah Nesser, Daniel J. Jacob, Joannes D. Maasakkers, Tia R. Scarpelli, Melissa P. Sulprizio, Yuzhong Zhang, and Chris H. Rycroft
Atmos. Meas. Tech., 14, 5521–5534, https://doi.org/10.5194/amt-14-5521-2021, https://doi.org/10.5194/amt-14-5521-2021, 2021
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Analytical inversions of satellite observations of atmospheric composition can improve emissions estimates and quantify errors but are computationally expensive at high resolutions. We propose two methods to decrease this cost. The methods reproduce a high-resolution inversion at a quarter of the cost. The reduced-dimension method creates a multiscale grid. The reduced-rank method solves the inversion where information content is highest.
Jun Meng, Randall V. Martin, Paul Ginoux, Melanie Hammer, Melissa P. Sulprizio, David A. Ridley, and Aaron van Donkelaar
Geosci. Model Dev., 14, 4249–4260, https://doi.org/10.5194/gmd-14-4249-2021, https://doi.org/10.5194/gmd-14-4249-2021, 2021
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Dust emissions in models, for example, GEOS-Chem, have a strong nonlinear dependence on meteorology, which means dust emission strengths calculated from different resolution meteorological fields are different. Offline high-resolution dust emissions with an optimized global dust strength, presented in this work, can be implemented into GEOS-Chem as offline emission inventory so that it could promote model development by harmonizing dust emissions across simulations of different resolutions.
Xu Feng, Haipeng Lin, Tzung-May Fu, Melissa P. Sulprizio, Jiawei Zhuang, Daniel J. Jacob, Heng Tian, Yaping Ma, Lijuan Zhang, Xiaolin Wang, Qi Chen, and Zhiwei Han
Geosci. Model Dev., 14, 3741–3768, https://doi.org/10.5194/gmd-14-3741-2021, https://doi.org/10.5194/gmd-14-3741-2021, 2021
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WRF-GC is an online coupling of the WRF meteorological model and GEOS-Chem chemical transport model for regional atmospheric chemistry and air quality modeling. In WRF-GC v2.0, we implemented the aerosol–radiation interactions and aerosol–cloud interactions, as well as the capability to nest multiple domains for high-resolution simulations based on the modular framework of WRF-GC v1.0. This allows the GEOS-Chem users to investigate the meteorology–atmospheric chemistry interactions.
Xiao Lu, Daniel J. Jacob, Yuzhong Zhang, Joannes D. Maasakkers, Melissa P. Sulprizio, Lu Shen, Zhen Qu, Tia R. Scarpelli, Hannah Nesser, Robert M. Yantosca, Jianxiong Sheng, Arlyn Andrews, Robert J. Parker, Hartmut Boesch, A. Anthony Bloom, and Shuang Ma
Atmos. Chem. Phys., 21, 4637–4657, https://doi.org/10.5194/acp-21-4637-2021, https://doi.org/10.5194/acp-21-4637-2021, 2021
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We use an analytical solution to the Bayesian inverse problem to quantitatively compare and combine the information from satellite and in situ observations, and to estimate global methane budget and their trends over the 2010–2017 period. We find that satellite and in situ observations are to a large extent complementary in the inversion for estimating global methane budget, and reveal consistent corrections of regional anthropogenic and wetland methane emissions relative to the prior inventory.
Joannes D. Maasakkers, Daniel J. Jacob, Melissa P. Sulprizio, Tia R. Scarpelli, Hannah Nesser, Jianxiong Sheng, Yuzhong Zhang, Xiao Lu, A. Anthony Bloom, Kevin W. Bowman, John R. Worden, and Robert J. Parker
Atmos. Chem. Phys., 21, 4339–4356, https://doi.org/10.5194/acp-21-4339-2021, https://doi.org/10.5194/acp-21-4339-2021, 2021
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We use 2010–2015 GOSAT satellite observations of atmospheric methane over North America in a high-resolution inversion to estimate methane emissions. We find general consistency with the gridded EPA inventory but higher oil and gas production emissions, with oil production emissions twice as large as in the latest EPA Greenhouse Gas Inventory. We find lower wetland emissions than predicted by WetCHARTs and a small increasing trend in the eastern US, apparently related to unconventional oil/gas.
Paul T. Griffiths, Lee T. Murray, Guang Zeng, Youngsub Matthew Shin, N. Luke Abraham, Alexander T. Archibald, Makoto Deushi, Louisa K. Emmons, Ian E. Galbally, Birgit Hassler, Larry W. Horowitz, James Keeble, Jane Liu, Omid Moeini, Vaishali Naik, Fiona M. O'Connor, Naga Oshima, David Tarasick, Simone Tilmes, Steven T. Turnock, Oliver Wild, Paul J. Young, and Prodromos Zanis
Atmos. Chem. Phys., 21, 4187–4218, https://doi.org/10.5194/acp-21-4187-2021, https://doi.org/10.5194/acp-21-4187-2021, 2021
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We analyse the CMIP6 Historical and future simulations for tropospheric ozone, a species which is important for many aspects of atmospheric chemistry. We show that the current generation of models agrees well with observations, being particularly successful in capturing trends in surface ozone and its vertical distribution in the troposphere. We analyse the factors that control ozone and show that they evolve over the period of the CMIP6 experiments.
Vasilii V. Petrenko, Andrew M. Smith, Edward M. Crosier, Roxana Kazemi, Philip Place, Aidan Colton, Bin Yang, Quan Hua, and Lee T. Murray
Atmos. Meas. Tech., 14, 2055–2063, https://doi.org/10.5194/amt-14-2055-2021, https://doi.org/10.5194/amt-14-2055-2021, 2021
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This paper presents an improved methodology for measurements of atmospheric concentration of carbon-14-containing carbon monoxide (14CO), as well as a 1-year dataset that demonstrates the methodology. Atmospheric 14CO concentration measurements are useful for improving the understanding of spatial and temporal variability of hydroxyl radical concentrations. Key improvements over prior methods include a greatly reduced air sample size and accurate procedural blank characterization.
Yuzhong Zhang, Daniel J. Jacob, Xiao Lu, Joannes D. Maasakkers, Tia R. Scarpelli, Jian-Xiong Sheng, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Jinfeng Chang, A. Anthony Bloom, Shuang Ma, John Worden, Robert J. Parker, and Hartmut Boesch
Atmos. Chem. Phys., 21, 3643–3666, https://doi.org/10.5194/acp-21-3643-2021, https://doi.org/10.5194/acp-21-3643-2021, 2021
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We use 2010–2018 satellite observations of atmospheric methane to interpret the factors controlling atmospheric methane and its accelerating increase during the period. The 2010–2018 increase in global methane emissions is driven by tropical and boreal wetlands and tropical livestock (South Asia, Africa, Brazil), with an insignificant positive trend in emissions from the fossil fuel sector. The peak methane growth rates in 2014–2015 are also contributed by low OH and high fire emissions.
Bo Zhang, Hongyu Liu, James H. Crawford, Gao Chen, T. Duncan Fairlie, Scott Chambers, Chang-Hee Kang, Alastair G. Williams, Kai Zhang, David B. Considine, Melissa P. Sulprizio, and Robert M. Yantosca
Atmos. Chem. Phys., 21, 1861–1887, https://doi.org/10.5194/acp-21-1861-2021, https://doi.org/10.5194/acp-21-1861-2021, 2021
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We simulate atmospheric 222Rn using the GEOS-Chem model to improve understanding of 222Rn emissions and characterize convective transport in the model. We demonstrate the potential of a customized global 222Rn emission scenario to improve simulated surface 222Rn concentrations and seasonality. We assess convective transport using observed 222Rn vertical profiles. Results have important implications for using chemical transport models to interpret the transport of trace gases and aerosols.
Yang Li, Loretta J. Mickley, and Jed O. Kaplan
Atmos. Chem. Phys., 21, 57–68, https://doi.org/10.5194/acp-21-57-2021, https://doi.org/10.5194/acp-21-57-2021, 2021
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Climate models predict a shift toward warmer, drier environments in southwestern North America. Under future climate, the two main drivers of dust trends play opposing roles: (1) CO2 fertilization enhances vegetation and, in turn, decreases dust, and (2) increasing land use enhances dust emissions from northern Mexico. In the worst-case scenario, elevated dust concentrations spread widely over the domain by 2100 in spring, suggesting a large climate penalty on air quality and human health.
David S. Stevenson, Alcide Zhao, Vaishali Naik, Fiona M. O'Connor, Simone Tilmes, Guang Zeng, Lee T. Murray, William J. Collins, Paul T. Griffiths, Sungbo Shim, Larry W. Horowitz, Lori T. Sentman, and Louisa Emmons
Atmos. Chem. Phys., 20, 12905–12920, https://doi.org/10.5194/acp-20-12905-2020, https://doi.org/10.5194/acp-20-12905-2020, 2020
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We present historical trends in atmospheric oxidizing capacity (OC) since 1850 from the latest generation of global climate models and compare these with estimates from measurements. OC controls levels of many key reactive gases, including methane (CH4). We find small model trends up to 1980, then increases of about 9 % up to 2014, disagreeing with (uncertain) measurement-based trends. Major drivers of OC trends are emissions of CH4, NOx, and CO; these will be important for future CH4 trends.
Yang Li, Loretta J. Mickley, Pengfei Liu, and Jed O. Kaplan
Atmos. Chem. Phys., 20, 8827–8838, https://doi.org/10.5194/acp-20-8827-2020, https://doi.org/10.5194/acp-20-8827-2020, 2020
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Using a coupled vegetation–fire–climate modeling framework, we show a northward shift in forests and increased lightning fire activity in northern US states, including Idaho, Montana, and Wyoming. Our findings suggest a large climate penalty on ecosystem, air quality, visibility, and human health in a region valued for its national forests and parks. The fine-scale smoke PM predictions provided in this study should prove useful to human health and environmental assessments.
Haipeng Lin, Xu Feng, Tzung-May Fu, Heng Tian, Yaping Ma, Lijuan Zhang, Daniel J. Jacob, Robert M. Yantosca, Melissa P. Sulprizio, Elizabeth W. Lundgren, Jiawei Zhuang, Qiang Zhang, Xiao Lu, Lin Zhang, Lu Shen, Jianping Guo, Sebastian D. Eastham, and Christoph A. Keller
Geosci. Model Dev., 13, 3241–3265, https://doi.org/10.5194/gmd-13-3241-2020, https://doi.org/10.5194/gmd-13-3241-2020, 2020
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Online coupling of meteorology and chemistry models often presents maintenance issues with hard-wired coding. We present WRF-GC, an one-way online coupling of the WRF meteorological model and GEOS-Chem atmospheric chemistry model for regional atmospheric chemistry and air quality modeling. Our coupling structure allows future versions of either parent model to be immediately integrated into WRF-GC. The WRF-GC model was able to well reproduce regional PM2.5 with greater computational efficiency.
Marta Abalos, Clara Orbe, Douglas E. Kinnison, David Plummer, Luke D. Oman, Patrick Jöckel, Olaf Morgenstern, Rolando R. Garcia, Guang Zeng, Kane A. Stone, and Martin Dameris
Atmos. Chem. Phys., 20, 6883–6901, https://doi.org/10.5194/acp-20-6883-2020, https://doi.org/10.5194/acp-20-6883-2020, 2020
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A set of state-of-the art chemistry–climate models is used to examine future changes in downward transport from the stratosphere, a key contributor to tropospheric ozone. The acceleration of the stratospheric circulation results in increased stratosphere-to-troposphere transport. In the subtropics, downward advection into the troposphere is enhanced due to climate change. At higher latitudes, the ozone reservoir above the tropopause is enlarged due to the stronger circulation and ozone recovery.
Clara Orbe, David A. Plummer, Darryn W. Waugh, Huang Yang, Patrick Jöckel, Douglas E. Kinnison, Beatrice Josse, Virginie Marecal, Makoto Deushi, Nathan Luke Abraham, Alexander T. Archibald, Martyn P. Chipperfield, Sandip Dhomse, Wuhu Feng, and Slimane Bekki
Atmos. Chem. Phys., 20, 3809–3840, https://doi.org/10.5194/acp-20-3809-2020, https://doi.org/10.5194/acp-20-3809-2020, 2020
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Atmospheric composition is strongly influenced by global-scale winds that are not always properly simulated in computer models. A common approach to correct for this bias is to relax or
nudgeto the observed winds. Here we systematically evaluate how well this technique performs across a large suite of chemistry–climate models in terms of its ability to reproduce key aspects of both the tropospheric and stratospheric circulations.
Tia R. Scarpelli, Daniel J. Jacob, Joannes D. Maasakkers, Melissa P. Sulprizio, Jian-Xiong Sheng, Kelly Rose, Lucy Romeo, John R. Worden, and Greet Janssens-Maenhout
Earth Syst. Sci. Data, 12, 563–575, https://doi.org/10.5194/essd-12-563-2020, https://doi.org/10.5194/essd-12-563-2020, 2020
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Methane, a potent greenhouse gas, is emitted through the exploitation of oil, gas, and coal resources, and many efforts to reduce emissions have targeted these sources. We have created a global inventory of oil, gas, and coal methane emissions based on country reporting to the United Nations. The inventory can be used along with satellite observations of methane to better understand the contribution of these sources to global emissions and to identify potential biases in emissions reporting.
Julie M. Nicely, Bryan N. Duncan, Thomas F. Hanisco, Glenn M. Wolfe, Ross J. Salawitch, Makoto Deushi, Amund S. Haslerud, Patrick Jöckel, Béatrice Josse, Douglas E. Kinnison, Andrew Klekociuk, Michael E. Manyin, Virginie Marécal, Olaf Morgenstern, Lee T. Murray, Gunnar Myhre, Luke D. Oman, Giovanni Pitari, Andrea Pozzer, Ilaria Quaglia, Laura E. Revell, Eugene Rozanov, Andrea Stenke, Kane Stone, Susan Strahan, Simone Tilmes, Holger Tost, Daniel M. Westervelt, and Guang Zeng
Atmos. Chem. Phys., 20, 1341–1361, https://doi.org/10.5194/acp-20-1341-2020, https://doi.org/10.5194/acp-20-1341-2020, 2020
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Differences in methane lifetime among global models are large and poorly understood. We use a neural network method and simulations from the Chemistry Climate Model Initiative to quantify the factors influencing methane lifetime spread among models and variations over time. UV photolysis, tropospheric ozone, and nitrogen oxides drive large model differences, while the same factors plus specific humidity contribute to a decreasing trend in methane lifetime between 1980 and 2015.
Rachel F. Silvern, Daniel J. Jacob, Loretta J. Mickley, Melissa P. Sulprizio, Katherine R. Travis, Eloise A. Marais, Ronald C. Cohen, Joshua L. Laughner, Sungyeon Choi, Joanna Joiner, and Lok N. Lamsal
Atmos. Chem. Phys., 19, 8863–8878, https://doi.org/10.5194/acp-19-8863-2019, https://doi.org/10.5194/acp-19-8863-2019, 2019
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The US EPA reports a steady decrease in nitrogen oxide (NOx) emissions from fuel combustion over the 2005–2017 period, while satellite observations show a leveling off after 2009, suggesting emission reductions and related air quality gains have halted. We show the sustained decrease in NOx emissions is in fact consistent with observed trends in surface NO2 and ozone concentrations and that the flattening of the satellite trend reflects a growing influence from the non-anthropogenic background.
Joannes D. Maasakkers, Daniel J. Jacob, Melissa P. Sulprizio, Tia R. Scarpelli, Hannah Nesser, Jian-Xiong Sheng, Yuzhong Zhang, Monica Hersher, A. Anthony Bloom, Kevin W. Bowman, John R. Worden, Greet Janssens-Maenhout, and Robert J. Parker
Atmos. Chem. Phys., 19, 7859–7881, https://doi.org/10.5194/acp-19-7859-2019, https://doi.org/10.5194/acp-19-7859-2019, 2019
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We use 2010–2015 satellite observations of atmospheric methane to improve estimates of methane emissions and their trends, as well as the concentration and trend of tropospheric OH (hydroxyl radical, methane's main sink). We find overestimates of Chinese coal and Middle East oil/gas emissions in the prior estimate. The 2010–2015 growth in methane is attributed to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH is small in comparison.
Lei Zhu, Daniel J. Jacob, Sebastian D. Eastham, Melissa P. Sulprizio, Xuan Wang, Tomás Sherwen, Mat J. Evans, Qianjie Chen, Becky Alexander, Theodore K. Koenig, Rainer Volkamer, L. Gregory Huey, Michael Le Breton, Thomas J. Bannan, and Carl J. Percival
Atmos. Chem. Phys., 19, 6497–6507, https://doi.org/10.5194/acp-19-6497-2019, https://doi.org/10.5194/acp-19-6497-2019, 2019
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We quantify the effect of sea salt aerosol on tropospheric bromine chemistry with a new mechanistic description of the halogen chemistry in a global atmospheric chemistry model. For the first time, we are able to reproduce the observed levels of bromide activation from the sea salt aerosol in a manner consistent with bromine oxide radical measured from various platforms. Sea salt aerosol plays a far more complex role in global tropospheric chemistry than previously recognized.
Huang Yang, Darryn W. Waugh, Clara Orbe, Guang Zeng, Olaf Morgenstern, Douglas E. Kinnison, Jean-Francois Lamarque, Simone Tilmes, David A. Plummer, Patrick Jöckel, Susan E. Strahan, Kane A. Stone, and Robyn Schofield
Atmos. Chem. Phys., 19, 5511–5528, https://doi.org/10.5194/acp-19-5511-2019, https://doi.org/10.5194/acp-19-5511-2019, 2019
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We evaluate the performance of a suite of models in simulating the large-scale transport from the northern midlatitudes to the Arctic using a CO-like idealized tracer. We find a large multi-model spread of the Arctic concentration of this CO-like tracer that is well correlated with the differences in the location of the midlatitude jet as well as the northern Hadley Cell edge. Our results suggest the Hadley Cell is key and zonal-mean transport by surface meridional flow needs better constraint.
Xuan Wang, Daniel J. Jacob, Sebastian D. Eastham, Melissa P. Sulprizio, Lei Zhu, Qianjie Chen, Becky Alexander, Tomás Sherwen, Mathew J. Evans, Ben H. Lee, Jessica D. Haskins, Felipe D. Lopez-Hilfiker, Joel A. Thornton, Gregory L. Huey, and Hong Liao
Atmos. Chem. Phys., 19, 3981–4003, https://doi.org/10.5194/acp-19-3981-2019, https://doi.org/10.5194/acp-19-3981-2019, 2019
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Chlorine radicals have a broad range of implications for tropospheric chemistry, air quality, and climate. We present a comprehensive simulation of tropospheric chlorine in a global 3-D model, which includes explicit accounting of chloride mobilization from sea salt aerosol. We find the chlorine chemistry contributes 1.0 % of the global oxidation of methane and decreases global burdens of tropospheric ozone by 7 % and OH by 3 % through the associated bromine radical chemistry.
Lu Shen, Daniel J. Jacob, Loretta J. Mickley, Yuxuan Wang, and Qiang Zhang
Atmos. Chem. Phys., 18, 17489–17496, https://doi.org/10.5194/acp-18-17489-2018, https://doi.org/10.5194/acp-18-17489-2018, 2018
Eloise A. Marais, Daniel J. Jacob, Sungyeon Choi, Joanna Joiner, Maria Belmonte-Rivas, Ronald C. Cohen, Steffen Beirle, Lee T. Murray, Luke D. Schiferl, Viral Shah, and Lyatt Jaeglé
Atmos. Chem. Phys., 18, 17017–17027, https://doi.org/10.5194/acp-18-17017-2018, https://doi.org/10.5194/acp-18-17017-2018, 2018
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We intercompare two new products of global upper tropospheric nitrogen dioxide (NO2) retrieved from the Ozone Monitoring Instrument (OMI). We evaluate these products with aircraft observations from NASA DC8 aircraft campaigns and interpret the useful information these products can provide about nitrogen oxides (NOx) in the global upper troposphere using the GEOS-Chem chemical transport model.
Jian-Xiong Sheng, Daniel J. Jacob, Joannes D. Maasakkers, Yuzhong Zhang, and Melissa P. Sulprizio
Atmos. Meas. Tech., 11, 6379–6388, https://doi.org/10.5194/amt-11-6379-2018, https://doi.org/10.5194/amt-11-6379-2018, 2018
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We conduct Observing System Simulation Experiments to compare the ability of future satellite measurements of atmospheric methane columns for constraining methane emissions at the 25 km scale. We find that the geostationary instruments can do much better than TROPOMI and are less sensitive to cloud cover. GeoCARB observing twice a day would provide 70 % of the information from the nominal GEO-CAPE mission considered by NASA in response to the Decadal Survey of the US National Research Council.
Samuel R. Hall, Kirk Ullmann, Michael J. Prather, Clare M. Flynn, Lee T. Murray, Arlene M. Fiore, Gustavo Correa, Sarah A. Strode, Stephen D. Steenrod, Jean-Francois Lamarque, Jonathan Guth, Béatrice Josse, Johannes Flemming, Vincent Huijnen, N. Luke Abraham, and Alex T. Archibald
Atmos. Chem. Phys., 18, 16809–16828, https://doi.org/10.5194/acp-18-16809-2018, https://doi.org/10.5194/acp-18-16809-2018, 2018
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Photolysis (J rates) initiates and drives atmospheric chemistry, and Js are perturbed by factors of 2 by clouds. The NASA Atmospheric Tomography (ATom) Mission provides the first comprehensive observations on how clouds perturb Js through the remote Pacific and Atlantic basins. We compare these cloud-perturbation J statistics with those from nine global chemistry models. While basic patterns agree, there is a large spread across models, and all lack some basic features of the observations.
Yuzhong Zhang, Daniel J. Jacob, Joannes D. Maasakkers, Melissa P. Sulprizio, Jian-Xiong Sheng, Ritesh Gautam, and John Worden
Atmos. Chem. Phys., 18, 15959–15973, https://doi.org/10.5194/acp-18-15959-2018, https://doi.org/10.5194/acp-18-15959-2018, 2018
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We assess the potential of using satellite observations of atmospheric methane to monitor global mean tropospheric OH concentration, a key parameter for the oxidizing power of the atmosphere.
Jean J. Guo, Arlene M. Fiore, Lee T. Murray, Daniel A. Jaffe, Jordan L. Schnell, Charles T. Moore, and George P. Milly
Atmos. Chem. Phys., 18, 12123–12140, https://doi.org/10.5194/acp-18-12123-2018, https://doi.org/10.5194/acp-18-12123-2018, 2018
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We use the GEOS-Chem model to estimate the influence from anthropogenic and background sources to ozone over the USA. Novel findings include the point that year-to-year background variability on the 10 highest observed ozone days is driven mainly by natural sources and not international or intercontinental pollution transport. High positive model biases during summer are associated with regional ozone production. The EPA 3-year average metric falls short of its aim to remove natural variability.
Prasad Kasibhatla, Tomás Sherwen, Mathew J. Evans, Lucy J. Carpenter, Chris Reed, Becky Alexander, Qianjie Chen, Melissa P. Sulprizio, James D. Lee, Katie A. Read, William Bloss, Leigh R. Crilley, William C. Keene, Alexander A. P. Pszenny, and Alma Hodzic
Atmos. Chem. Phys., 18, 11185–11203, https://doi.org/10.5194/acp-18-11185-2018, https://doi.org/10.5194/acp-18-11185-2018, 2018
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Recent measurements of NOx and HONO suggest that photolysis of particulate nitrate in sea-salt aerosols is important in terms of marine boundary layer oxidant chemistry. We present the first global-scale assessment of the significance of this new chemical pathway for NOx, O3, and OH in the marine boundary layer. We also present a preliminary assessment of the potential impact of photolysis of particulate nitrate associated with other aerosol types on continental boundary layer chemistry.
Xiaokang Wu, Huang Yang, Darryn W. Waugh, Clara Orbe, Simone Tilmes, and Jean-Francois Lamarque
Atmos. Chem. Phys., 18, 7439–7452, https://doi.org/10.5194/acp-18-7439-2018, https://doi.org/10.5194/acp-18-7439-2018, 2018
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The seasonal and interannual variability of transport times from northern mid-latitudes into the southern hemisphere is examined using simulations of
agetracers. The largest variability occurs near the surface close to the tropical convergence zones, but the peak is further south and there is a smaller tropical–extratropical contrast for tracers with more rapid loss. Hence the variability of trace gases in the southern extratropics will vary with their chemical lifetime.
Clara Orbe, Huang Yang, Darryn W. Waugh, Guang Zeng, Olaf Morgenstern, Douglas E. Kinnison, Jean-Francois Lamarque, Simone Tilmes, David A. Plummer, John F. Scinocca, Beatrice Josse, Virginie Marecal, Patrick Jöckel, Luke D. Oman, Susan E. Strahan, Makoto Deushi, Taichu Y. Tanaka, Kohei Yoshida, Hideharu Akiyoshi, Yousuke Yamashita, Andreas Stenke, Laura Revell, Timofei Sukhodolov, Eugene Rozanov, Giovanni Pitari, Daniele Visioni, Kane A. Stone, Robyn Schofield, and Antara Banerjee
Atmos. Chem. Phys., 18, 7217–7235, https://doi.org/10.5194/acp-18-7217-2018, https://doi.org/10.5194/acp-18-7217-2018, 2018
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In this study we compare a few atmospheric transport properties among several numerical models that are used to study the influence of atmospheric chemistry on climate. We show that there are large differences among models in terms of the timescales that connect the Northern Hemisphere midlatitudes, where greenhouse gases and ozone-depleting substances are emitted, to the Southern Hemisphere. Our results may have important implications for how models represent atmospheric composition.
Danny M. Leung, Amos P. K. Tai, Loretta J. Mickley, Jonathan M. Moch, Aaron van Donkelaar, Lu Shen, and Randall V. Martin
Atmos. Chem. Phys., 18, 6733–6748, https://doi.org/10.5194/acp-18-6733-2018, https://doi.org/10.5194/acp-18-6733-2018, 2018
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This paper investigates how large-scale weather systems control fine particulate matter (PM2.5) air quality in China. We show that winter monsoons, onshore winds and frontal rains can drive daily PM2.5 variability in different regions of China. We further project future PM2.5 concentration change by 2050s due to climate change, and verify that climate change has little benefit on future PM2.5 in Beijing, implying cutting down emissions is necessary to mitigate pollutions in megacities of China.
Jian-Xiong Sheng, Daniel J. Jacob, Alexander J. Turner, Joannes D. Maasakkers, Melissa P. Sulprizio, A. Anthony Bloom, Arlyn E. Andrews, and Debra Wunch
Atmos. Chem. Phys., 18, 6483–6491, https://doi.org/10.5194/acp-18-6483-2018, https://doi.org/10.5194/acp-18-6483-2018, 2018
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We use observations of boundary layer methane from the SEAC4RS aircraft campaign over the Southeast US to estimate methane emissions in that region. Our results suggest that the EPA inventory is regionally unbiased but there are large local biases, suggesting variable emission factors. Our results also suggest that the choice of landcover map is the dominant source of error for wetland emission estimates.
Michael J. Prather, Clare M. Flynn, Xin Zhu, Stephen D. Steenrod, Sarah A. Strode, Arlene M. Fiore, Gustavo Correa, Lee T. Murray, and Jean-Francois Lamarque
Atmos. Meas. Tech., 11, 2653–2668, https://doi.org/10.5194/amt-11-2653-2018, https://doi.org/10.5194/amt-11-2653-2018, 2018
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A new protocol for merging in situ atmospheric chemistry measurements with 3-D models is developed. This technique can identify the most reactive air parcels in terms of tropospheric production/loss of O3 & CH4. This approach highlights differences in 6 global chemistry models even with composition specified. Thus in situ measurements from, e.g., NASA's ATom mission can be used to develop a chemical climatology of, not only the key species, but also the rates of key reactions in each air parcel.
Jingqiu Mao, Annmarie Carlton, Ronald C. Cohen, William H. Brune, Steven S. Brown, Glenn M. Wolfe, Jose L. Jimenez, Havala O. T. Pye, Nga Lee Ng, Lu Xu, V. Faye McNeill, Kostas Tsigaridis, Brian C. McDonald, Carsten Warneke, Alex Guenther, Matthew J. Alvarado, Joost de Gouw, Loretta J. Mickley, Eric M. Leibensperger, Rohit Mathur, Christopher G. Nolte, Robert W. Portmann, Nadine Unger, Mika Tosca, and Larry W. Horowitz
Atmos. Chem. Phys., 18, 2615–2651, https://doi.org/10.5194/acp-18-2615-2018, https://doi.org/10.5194/acp-18-2615-2018, 2018
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This paper is aimed at discussing progress in evaluating, diagnosing, and improving air quality and climate modeling using comparisons to SAS observations as a guide to thinking about improvements to mechanisms and parameterizations in models.
Ruth M. Doherty, Clara Orbe, Guang Zeng, David A. Plummer, Michael J. Prather, Oliver Wild, Meiyun Lin, Drew T. Shindell, and Ian A. Mackenzie
Atmos. Chem. Phys., 17, 14219–14237, https://doi.org/10.5194/acp-17-14219-2017, https://doi.org/10.5194/acp-17-14219-2017, 2017
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We investigate how climate change impacts global air pollution transport. To study transport changes, we use a carbon monoxide (CO) tracer species emitted from global sources. We find robust and consistent changes in CO-tracer distributions in climate change simulations performed by four chemistry–climate models in different seasons. We highlight the importance of the co-location of emission source regions and controlling transport processes in determining future pollution transport.
Daniel H. Cusworth, Loretta J. Mickley, Eric M. Leibensperger, and Michael J. Iacono
Atmos. Chem. Phys., 17, 13559–13572, https://doi.org/10.5194/acp-17-13559-2017, https://doi.org/10.5194/acp-17-13559-2017, 2017
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Since 1990, light-scattering pollution known as aerosols have declined as a result of tightening US air quality regulations. Our study finds that US surface solar radiation has increased simultaneously. We establish a link between aerosols and radiation through physical and statistical models. We find the strongest relationship between aerosols, radiation, and climate at a site in the Midwest. Our work underscores the importance of regional pollution on climate in the US and abroad.
Jenny A. Fisher, Lee T. Murray, Dylan B. A. Jones, and Nicholas M. Deutscher
Geosci. Model Dev., 10, 4129–4144, https://doi.org/10.5194/gmd-10-4129-2017, https://doi.org/10.5194/gmd-10-4129-2017, 2017
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Carbon monoxide (CO) simulation in atmospheric chemistry models is used for source–receptor analysis, emission inversion, and interpretation of observations. We introduce a major update to CO simulation in the GEOS-Chem chemical transport model that removes fundamental inconsistencies relative to the standard model, resolving biases of more than 100 ppb and errors in vertical structure. We also add source tagging of secondary CO and demonstrate it provides added value in low-emission regions.
Michael J. Prather, Xin Zhu, Clare M. Flynn, Sarah A. Strode, Jose M. Rodriguez, Stephen D. Steenrod, Junhua Liu, Jean-Francois Lamarque, Arlene M. Fiore, Larry W. Horowitz, Jingqiu Mao, Lee T. Murray, Drew T. Shindell, and Steven C. Wofsy
Atmos. Chem. Phys., 17, 9081–9102, https://doi.org/10.5194/acp-17-9081-2017, https://doi.org/10.5194/acp-17-9081-2017, 2017
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We present a new approach for comparing atmospheric chemistry models with measurements based on what these models are used to do, i.e., calculate changes in ozone and methane, prime greenhouse gases. This method anticipates a new type of measurements from the NASA Atmospheric Tomography (ATom) mission. In comparing the mixture of species within air parcels, we focus on those responsible for key chemical changes and weight these parcels by their chemical reactivity.
Lu Shen, Loretta J. Mickley, and Lee T. Murray
Atmos. Chem. Phys., 17, 4355–4367, https://doi.org/10.5194/acp-17-4355-2017, https://doi.org/10.5194/acp-17-4355-2017, 2017
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We introduce a new method to characterize the influence of atmospheric circulation on surface PM2.5 concentrations. Applying our statistical model to climate projections, we find a strong influence of 2000–2050 climate change on PM2.5 air quality in the United States. We find that current atmospheric chemistry models may underestimate the strong positive sensitivity of PM2.5 to temperature in the eastern United States in summer, and so may underestimate PM2.5 changes in a warmer climate.
Tomás Sherwen, Mat J. Evans, Lucy J. Carpenter, Johan A. Schmidt, and Loretta J. Mickley
Atmos. Chem. Phys., 17, 1557–1569, https://doi.org/10.5194/acp-17-1557-2017, https://doi.org/10.5194/acp-17-1557-2017, 2017
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We model pre-industrial to present day changes using the GEOS-Chem global chemical transport model with halogens (Cl, Br, I). The model better captures pre-industrial O3 observations with halogens included. Halogens buffer the tropospheric forcing of O3 (RFTO3) from pre-industrial to present day, reducing RFTO3 by 0.087 Wm−2. This reduction is greater than that from halogens on stratospheric O3 (−0.05 Wm−2). This suggests that models that do not include halogens will overestimate RFTO3by ~ 25%.
Katherine R. Travis, Daniel J. Jacob, Jenny A. Fisher, Patrick S. Kim, Eloise A. Marais, Lei Zhu, Karen Yu, Christopher C. Miller, Robert M. Yantosca, Melissa P. Sulprizio, Anne M. Thompson, Paul O. Wennberg, John D. Crounse, Jason M. St. Clair, Ronald C. Cohen, Joshua L. Laughner, Jack E. Dibb, Samuel R. Hall, Kirk Ullmann, Glenn M. Wolfe, Illana B. Pollack, Jeff Peischl, Jonathan A. Neuman, and Xianliang Zhou
Atmos. Chem. Phys., 16, 13561–13577, https://doi.org/10.5194/acp-16-13561-2016, https://doi.org/10.5194/acp-16-13561-2016, 2016
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Ground-level ozone pollution in the Southeast US involves complex chemistry driven by anthropogenic emissions of nitrogen oxides (NOx) and biogenic emissions of isoprene. We find that US NOx emissions are overestimated nationally by as much as 50 % and that reducing model emissions by this amount results in good agreement with SEAC4RS aircraft measurements in August and September 2013. Observations of nitrate wet deposition fluxes and satellite NO2 columns further support this result.
Lei Zhu, Daniel J. Jacob, Patrick S. Kim, Jenny A. Fisher, Karen Yu, Katherine R. Travis, Loretta J. Mickley, Robert M. Yantosca, Melissa P. Sulprizio, Isabelle De Smedt, Gonzalo González Abad, Kelly Chance, Can Li, Richard Ferrare, Alan Fried, Johnathan W. Hair, Thomas F. Hanisco, Dirk Richter, Amy Jo Scarino, James Walega, Petter Weibring, and Glenn M. Wolfe
Atmos. Chem. Phys., 16, 13477–13490, https://doi.org/10.5194/acp-16-13477-2016, https://doi.org/10.5194/acp-16-13477-2016, 2016
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HCHO column data are widely used as a proxy for VOCs emissions, but validation of the data has been extremely limited. We use accurate aircraft observations to validate and intercompare 6 HCHO retrievals with GEOS-Chem as the intercomparison platform. Retrievals are interconsistent in spatial variability over the SE US and in daily variability, but are biased low by 20–51 %. Our work supports the use of HCHO column as a quantitative proxy for isoprene emission after correction of the low bias.
L. Shen, L. J. Mickley, and A. P. K. Tai
Atmos. Chem. Phys., 15, 10925–10938, https://doi.org/10.5194/acp-15-10925-2015, https://doi.org/10.5194/acp-15-10925-2015, 2015
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In this study, we have examined the effect of polar jet and Bermuda High on ozone air quality in the eastern United States. In the Midwest and northeast, the poleward shift of jet wind leads to reduced polar jet frequency, resulting in increased ozone there. In the southeast, the influence of Bermuda High on ozone variability depends on the location of its west edge. Westward movement increases the ozone only when the JJA Bermuda High west edge is located west of 85.4°W.
X. Yue, L. J. Mickley, J. A. Logan, R. C. Hudman, M. V. Martin, and R. M. Yantosca
Atmos. Chem. Phys., 15, 10033–10055, https://doi.org/10.5194/acp-15-10033-2015, https://doi.org/10.5194/acp-15-10033-2015, 2015
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Based on simulated meteorology from 13 GCMs, we projected future wildfire activity in Alaskan and Canadian ecoregions by the mid-century. The most robust change is the increase of 150-390% in area burned over Alaska and western Canada. The models also predict an increase of 45-90% in the central and southern Canadian ecoregions, but a decrease of up to 50% in northern Canada. We further quantify how the changes in wildfire emissions may affect ozone concentrations in North America.
P. Achakulwisut, L. J. Mickley, L. T. Murray, A. P. K. Tai, J. O. Kaplan, and B. Alexander
Atmos. Chem. Phys., 15, 7977–7998, https://doi.org/10.5194/acp-15-7977-2015, https://doi.org/10.5194/acp-15-7977-2015, 2015
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The atmosphere’s oxidative capacity determines the lifetime of many trace gases important to climate, chemistry, and human health. Yet uncertainties remain about its past variations, its controlling factors, and the radiative forcing of short-lived species it influences. To reduce these uncertainties, we must better quantify the natural emissions and chemical reaction mechanisms of organic compounds in the atmosphere, which play a role in governing the oxidative capacity.
L. T. Murray, L. J. Mickley, J. O. Kaplan, E. D. Sofen, M. Pfeiffer, and B. Alexander
Atmos. Chem. Phys., 14, 3589–3622, https://doi.org/10.5194/acp-14-3589-2014, https://doi.org/10.5194/acp-14-3589-2014, 2014
H. Jiang, H. Liao, H. O. T. Pye, S. Wu, L. J. Mickley, J. H. Seinfeld, and X. Y. Zhang
Atmos. Chem. Phys., 13, 7937–7960, https://doi.org/10.5194/acp-13-7937-2013, https://doi.org/10.5194/acp-13-7937-2013, 2013
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Assessment of object-based indices to identify convective organization
The Global Forest Fire Emissions Prediction System version 1.0
NEIVAv1.0: Next-generation Emissions InVentory expansion of Akagi et al. (2011) version 1.0
FLEXPART version 11: improved accuracy, efficiency, and flexibility
Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions
Air quality modeling intercomparison and multiscale ensemble chain for Latin America
Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG
Selecting CMIP6 global climate models (GCMs) for Coordinated Regional Climate Downscaling Experiment (CORDEX) dynamical downscaling over Southeast Asia using a standardised benchmarking framework
Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and its impact on multi-species inversion with GEOS-Chem (v12.5)
RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension
Introducing graupel density prediction in Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics and evaluation of the modified scheme during the ICE-POP field campaign
Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community
Atmospheric-river-induced precipitation in California as simulated by the regionally refined Simple Convective Resolving E3SM Atmosphere Model (SCREAM) Version 0
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Recent improvements and maximum covariance analysis of aerosol and cloud properties in the EC-Earth3-AerChem model
GPU-HADVPPM4HIP V1.0: using the heterogeneous-compute interface for portability (HIP) to speed up the piecewise parabolic method in the CAMx (v6.10) air quality model on China's domestic GPU-like accelerator
Preliminary evaluation of the effect of electro-coalescence with conducting sphere approximation on the formation of warm cumulus clouds using SCALE-SDM version 0.2.5–2.3.0
Similarity-Based Analysis of Atmospheric Organic Compounds for Machine Learning Applications
Cell tracking -based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system
Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system
Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows
Improving the EnSRF in the Community Inversion Framework: a case study with ICON-ART 2024.01
AeroMix v1.0.1: a Python package for modeling aerosol optical properties and mixing states
Impact of ITCZ width on global climate: ITCZ-MIP
Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains
Mixed-precision computing in the GRIST dynamical core for weather and climate modelling
A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
RCEMIP-II: mock-Walker simulations as phase II of the radiative–convective equilibrium model intercomparison project
The MESSy DWARF (based on MESSy v2.55.2)
Objective identification of meteorological fronts and climatologies from ERA-Interim and ERA5
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
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Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
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Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
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The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
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The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
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The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
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We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
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Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
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Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
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This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
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Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
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Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
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Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
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Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
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We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
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Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
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RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
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We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
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We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
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Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-145, https://doi.org/10.5194/gmd-2024-145, 2024
Revised manuscript accepted for GMD
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements in 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Manu Anna Thomas, Klaus Wyser, Shiyu Wang, Marios Chatziparaschos, Paraskevi Georgakaki, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Maria Kanakidou, Carlos Pérez García-Pando, Athanasios Nenes, Twan van Noije, Philippe Le Sager, and Abhay Devasthale
Geosci. Model Dev., 17, 6903–6927, https://doi.org/10.5194/gmd-17-6903-2024, https://doi.org/10.5194/gmd-17-6903-2024, 2024
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Aerosol–cloud interactions occur at a range of spatio-temporal scales. While evaluating recent developments in EC-Earth3-AerChem, this study aims to understand the extent to which the Twomey effect manifests itself at larger scales. We find a reduction in the warm bias over the Southern Ocean due to model improvements. While we see footprints of the Twomey effect at larger scales, the negative relationship between cloud droplet number and liquid water drives the shortwave radiative effect.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
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AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
Ruyi Zhang, Limin Zhou, Shin-ichiro Shima, and Huawei Yang
Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
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Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Hilda Sandström and Patrick Rinke
EGUsphere, https://doi.org/10.48550/arXiv.2406.18171, https://doi.org/10.48550/arXiv.2406.18171, 2024
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Machine learning has the potential to aid the identification organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning model in atmospheric sciences.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-99, https://doi.org/10.5194/gmd-2024-99, 2024
Revised manuscript accepted for GMD
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rainfall. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and then the model skill is evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with 4 open-source models.
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024, https://doi.org/10.5194/gmd-17-6571-2024, 2024
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Satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator, highlighting its added value through a case study and diagnostics.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
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The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, https://doi.org/10.5194/gmd-17-6465-2024, 2024
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In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2197, https://doi.org/10.5194/egusphere-2024-2197, 2024
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The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a more efficient implementation of the serial and batch versions of the Ensemble Square Root Filter (EnSRF) algorithm in CIF.
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and Damu Bala Subrahamanyam
Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024, https://doi.org/10.5194/gmd-17-6379-2024, 2024
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A Python successor to the aerosol module of the OPAC model, named AeroMix, has been developed, with enhanced capabilities to better represent real atmospheric aerosol mixing scenarios. AeroMix’s performance in modeling aerosol mixing states has been evaluated against field measurements, substantiating its potential as a versatile aerosol optical model framework for next-generation algorithms to infer aerosol mixing states and chemical composition.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
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The width of the tropical rain belt affects many aspects of our climate, yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes, but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Tianning Su and Yunyan Zhang
Geosci. Model Dev., 17, 6319–6336, https://doi.org/10.5194/gmd-17-6319-2024, https://doi.org/10.5194/gmd-17-6319-2024, 2024
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Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, https://doi.org/10.5194/gmd-17-6301-2024, 2024
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This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The coded equation terms in the governing equations that are sensitive (or insensitive) to the precision level have been identified. The performance of mixed-precision computing in weather and climate simulations was analyzed.
Sam O. Owens, Dipanjan Majumdar, Chris E. Wilson, Paul Bartholomew, and Maarten van Reeuwijk
Geosci. Model Dev., 17, 6277–6300, https://doi.org/10.5194/gmd-17-6277-2024, https://doi.org/10.5194/gmd-17-6277-2024, 2024
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Designing cities that are resilient, sustainable, and beneficial to health requires an understanding of urban climate and air quality. This article presents an upgrade to the multi-physics numerical model uDALES, which can simulate microscale airflow, heat transfer, and pollutant dispersion in urban environments. This upgrade enables it to resolve realistic urban geometries more accurately and to take advantage of the resources available on current and future high-performance computing systems.
Felipe Cifuentes, Henk Eskes, Folkert Boersma, Enrico Dammers, and Charlotte Bryan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2225, https://doi.org/10.5194/egusphere-2024-2225, 2024
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOX emissions using synthetic NO2 satellite column retrievals derived from high-resolution model simulations. The FDA accurately reproduced NOX emissions when column observations were limited to the boundary layer and when the variability of NO2 lifetime, NOX:NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces a strong model dependency, reducing the simplicity of the original FDA formulation.
Allison A. Wing, Levi G. Silvers, and Kevin A. Reed
Geosci. Model Dev., 17, 6195–6225, https://doi.org/10.5194/gmd-17-6195-2024, https://doi.org/10.5194/gmd-17-6195-2024, 2024
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This paper presents the experimental design for a model intercomparison project to study tropical clouds and climate. It is a follow-up from a prior project that used a simplified framework for tropical climate. The new project adds one new component – a specified pattern of sea surface temperatures as the lower boundary condition. We provide example results from one cloud-resolving model and one global climate model and test the sensitivity to the experimental parameters.
Astrid Kerkweg, Timo Kirfel, Doung H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-117, https://doi.org/10.5194/gmd-2024-117, 2024
Revised manuscript accepted for GMD
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This article introduces the MESSy DWARF. Usually, the Modular Earth Submodel System (MESSy) is linked to full dynamical models to build chemistry climate models. However, due to the modular concept of MESSy, and the newly developed DWARF component, it is now possible to create simplified models containing just one or some process descriptions. This renders very useful for technical optimisation (e.g., GPU porting) and can be used to create less complex models, e.g., a chemical box model.
Philip G. Sansom and Jennifer L. Catto
Geosci. Model Dev., 17, 6137–6151, https://doi.org/10.5194/gmd-17-6137-2024, https://doi.org/10.5194/gmd-17-6137-2024, 2024
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Weather fronts bring a lot of rain and strong winds to many regions of the mid-latitudes. We have developed an updated method of identifying these fronts in gridded data that can be used on new datasets with small grid spacing. The method can be easily applied to different datasets due to the use of open-source software for its development and shows improvements over similar previous methods. We present an updated estimate of the average frequency of fronts over the past 40 years.
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
Barrett, S. R. H., Yim, S. H. L., Gilmore, C. K., Murray, L. T., Kuhn, S. R.,
Tai, A. P. K., Yantosca, R. M., Byun, D. W., Ngan, F., Li, X., Levy, J. I.,
Ashok, A., Koo, J., Wong, H. M., Dessens, O., Balasubramanian, S., Fleming,
G. G., Pearlson, M. N., Wollersheim, C., Malina, R., Arunachalam, S.,
Binkowski, F. S., Leibensperger, E. M., Jacob, D. J., Hileman, J. I., and
Waitz, I. A.: Public Health, Climate, and Economic Impacts of Desulfurizing
Jet Fuel, Environ. Sci. Technol., 46, 4275–4282, https://doi.org/10.1021/es203325a,
2012. a, b
Baskaran, M., Coleman, C., and Santschi, P.: Atmospheric Depositional Fluxes
of Be-7 and Pb-210 at Galveston and College-Station, Texas, J. Geophys. Res.-Atmos., 98, 20555–20571, https://doi.org/10.1029/93JD02182, 1993. a
Bauer, N., Calvin, K., Emmerling, J., Fricko, O., Fujimori, S., Hilaire, J.,
Eom, J., Krey, V., Kriegler, E., Mouratiadou, I., Sytze de Boer, H., van den
Berg, M., Carrara, S., Daioglou, V., Drouet, L., Edmonds, J. E., Gernaat, D.,
Havlik, P., Johnson, N., Klein, D., Kyle, P., Marangoni, G., Masui, T.,
Pietzcker, R. C., Strubegger, M., Wise, M., Riahi, K., and van Vuuren, D. P.:
Shared Socio-Economic Pathways of the Energy Sector – Quantifying the
Narratives, Glob. Environ. Change, 42, 316–330,
https://doi.org/10.1016/j.gloenvcha.2016.07.006, 2017. a
Bauer, S. E., Tsigaridis, K., Faluvegi, G., Kelley, M., Lo, K. K., Miller,
R. L., Nazarenko, L., Schmidt, G. A., and Wu, J.: Historical (1850–2014)
Aerosol Evolution and Role on Climate Forcing Using the GISS ModelE2.1
Contribution to CMIP6, J. Adv. Model. Earth Syst., 12, e2019MS001978,
https://doi.org/10.1029/2019ms001978, 2020. 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, b, c, d
Bindle, L., Martin, R. V., Cooper, M. J., Lundgren, E. W., Eastham, S. D., Auer, B. M., Clune, T. L., Weng, H., Lin, J., Murray, L. T., Meng, J., Keller, C. A., Pawson, S., and Jacob, D. J.: Grid-Stretching Capability for the GEOS-Chem 13.0.0 Atmospheric Chemistry Model, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2020-398, in review, 2020. a
Bleichrodt, J.: Mean Tropospheric Residence Time of Cosmic-Ray-Produced
Beryllium-7 at North Temperate Latitudes, J. Geophys. Res.-Oc. Atm., 83,
3058–3062, https://doi.org/10.1029/JC083iC06p03058, 1978. a
Bondietti, E., Brantley, J., and Rangarajan, C.: Size Distributions and Growth of Natural and Chernobyl-Derived Sub-Micron Aerosols in Tennessee, J. Environ. Radioactiv., 6, 99–120, https://doi.org/10.1016/0265-931X(88)90054-9, 1988. a, b
Bradley, W. and Pearson, J.: Aircraft Measurements of Vertical Distribution of Radon in Lower Atmosphere, J. Geophys. Res., 75, 5890,
https://doi.org/10.1029/jc075i030p05890, 1970. a
Brost, R. and Chatfield, R.: Transport of Radon in a 3-Dimensional,
Subhemispheric Model, J. Geophys. Res.-Atmos., 94, 5095–5119,
https://doi.org/10.1029/JD094iD04p05095, 1989. a
Brost, R., Feichter, J., and Heimann, M.: 3-Dimensional Simulation of Be-7 in a Global Climate Model, J. Geophys. Res.-Atmos., 96, 22423–22445,
https://doi.org/10.1029/91JD02283, 1991. a
Brown, L., Stensland, G., Klein, J., and Middleton, R.: Atmospheric Deposition of Be-7 and Be-10, Geochim. Cosmochim. Ac., 53, 135–142,
https://doi.org/10.1016/0016-7037(89)90280-9, 1989. a
Calvin, K., Bond-Lamberty, B., Clarke, L., Edmonds, J., Eom, J., Hartin, C.,
Kim, S., Kyle, P., Link, R., Moss, R., McJeon, H., Patel, P., Smith, S.,
Waldhoff, S., and Wise, M.: The SSP4: A world of deepening inequality, Glob. Environ. Change, 42, 284–296, https://doi.org/10.1016/j.gloenvcha.2016.06.010, 2017. a
Carn, S. A., Yang, K., Prata, A. J., and Krotkov, N. A.: Extending the
long-term record of volcanic SO2 emissions with the Ozone Mapping and
Profiler Suite nadir mapper, Geophys. Res. Lett., 42, 925–932,
https://doi.org/10.1002/2014gl062437, 2015. a
Cecil, D. J., Buechler, D. E., and Blakeslee, R. J.: Gridded lightning
climatology from TRMM-LIS and OTD: Dataset description, Atmos. Res., 135–136, 404–414, https://doi.org/10.1016/j.atmosres.2012.06.028, 2014. a
Chance, K.: OMI/Aura Formaldehyde (HCHO) Total Column Daily L3 Weighted
Mean Global 0.1deg Lat/Lon Grid V003, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/Aura/OMI/DATA3010, 2019. a
Considine, D. B., Bergmann, D. J., and Liu, H.: Sensitivity of Global Modeling Initiative chemistry and transport model simulations of radon-222 and lead-210 to input meteorological data, Atmos. Chem. Phys., 5, 3389–3406, https://doi.org/10.5194/acp-5-3389-2005, 2005. a, b
Crespo Cuaresma, J.: Income projections for climate change research: A
framework based on human capital dynamics, Glob. Environ. Change, 42,
226–236, https://doi.org/10.1016/j.gloenvcha.2015.02.012, 2017. a
Del Genio, A. D., Chen, Y., Kim, D., and Yao, M.-S.: The MJO Transition from
Shallow to Deep Convection in CloudSat/CALIPSO Data and GISS GCM
Simulations, J. Climate, 25, 3755–3770, https://doi.org/10.1175/jcli-d-11-00384.1,
2012. a
Del Genio, A. D., Wu, J., Wolf, A. B., Chen, Y., Yao, M.-S., and Kim, D.:
Constraints on Cumulus Parameterization from Simulations of Observed MJO
Events, J. Climate, 28, 6419–6442, https://doi.org/10.1175/jcli-d-14-00832.1, 2015. a
Dellink, R., Chateau, J., Lanzi, E., and Magné, B.: Long-term economic
growth projections in the Shared Socioeconomic Pathways, Glob. Environ.
Change, 42, 200–214, https://doi.org/10.1016/j.gloenvcha.2015.06.004, 2017. a
Dibb, J.: Atmospheric Deposition of Beryllium-7 in the Chesapeake Bay Region, J. Geophys. Res.-Atmos., 94, 2261–2265, https://doi.org/10.1029/JD094iD02p02261, 1989. a
Dibb, J., Talbot, R., and Gregory, G.: Beryllium-7 and Pb-210 in the
Western-Hemisphere Arctic Atmosphere - Observations from Three Recent
Aircraft-Based Sampling Programs, J. Geophys. Res.-Atmos., 97, 16709–16715, https://doi.org/10.1029/91JD01807, 1992. a
Dibb, J., Meeker, L., Finkel, R., Southon, J., Caffee, M., and Barrie, L.:
Estimation of Stratospheric Input to the Arctic Troposphere - Be-7 and Be-10 in Aerosols at Alert, Canada, J. Geophys. Res.-Atmos., 99, 12855–12864,
https://doi.org/10.1029/94JD00742, 1994. a
Dobber, M., Dirksen, R., Levelt, P., van den Oord, G., Voors, R., Kleipool, Q., Jaross, G., Kowalewski, M., Hilsenrath, E., Leppelmeier, G., Johan, d. V., Dierssen, W., and Rozemeijer, N.: Ozone monitoring instrument calibration, IEEE T. Geosci. Remote, 44, 1209–1238, https://doi.org/10.1109/tgrs.2006.869987, 2006. a, b
Du, J., Zhang, J., Zhang, J., and Wu, Y.: Deposition patterns of atmospheric
Be-7 and Pb-210 in coast of East China Sea, Shanghai, China, Atmos. Environ., 42, 5101–5109, https://doi.org/10.1016/j.atmosenv.2008.02.007, 2008. a
Dutkiewicz, V. and Husain, L.: Stratospheric and Tropospheric Components of
Be-7 in Surface Air, J. Geophys. Res.-Atmos., 90, 5783–5788,
https://doi.org/10.1029/JD090iD03p05783, 1985. a
Eastham, S., Doherty, S., Keith, D., Richter, J. H., and Xia, L.: Air quality and climate connections., Eos, 102, https://doi.org/10.1029/2021EO156087, 2021. a
Eastham, S. D., Weisenstein, D. K., and Barrett, S. R.: Development and
evaluation of the unified tropospheric–stratospheric chemistry extension
(UCX) for the global chemistry-transport model GEOS-Chem, Atmos. Environ., 89, 52–63, https://doi.org/10.1016/j.atmosenv.2014.02.001, 2014. a, b, c
Eastham, S. D., Long, M. S., Keller, C. A., Lundgren, E., Yantosca, R. M., Zhuang, J., Li, C., Lee, C. J., Yannetti, M., Auer, B. M., Clune, T. L., Kouatchou, J., Putman, W. M., Thompson, M. A., Trayanov, A. L., Molod, A. M., Martin, R. V., and Jacob, D. J.: GEOS-Chem High Performance (GCHP v11-02c): a next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications, Geosci. Model Dev., 11, 2941–2953, https://doi.org/10.5194/gmd-11-2941-2018, 2018. a, b, c
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Feichter, J. and Crutzen, P.: Parameterization of vertical tracer transport
due to deep cumulus convection in a global transport model and its evaluation
with 222Radon measurements, Tellus B, 42, 100–117,
https://doi.org/10.3402/tellusb.v42i1.15195, 1990. a
Finney, D. L., Doherty, R. M., Wild, O., Stevenson, D. S., MacKenzie, I. A.,
and Blyth, A. M.: A projected decrease in lightning under climate change,
Nat. Clim. Change, 8, 210–213, https://doi.org/10.1038/s41558-018-0072-6, 2018. a
Fiore, A., Naik, V., and Leibensperger, E.: Air quality and climate
connections, J. Air Waste Manag. Assoc., 65, 645–685,
https://doi.org/10.1080/10962247.2015.1040526, 2015. a
Fiore, A. M., Dentener, F. J., Wild, O., Cuvelier, C., Schultz, M. G., Hess,
P., Textor, C., Schulz, M., Doherty, R. M., Horowitz, L. W., MacKenzie,
I. A., Sanderson, M. G., Shindell, D. T., Stevenson, D. S., Szopa, S.,
Van Dingenen, R., Zeng, G., Atherton, C., Bergmann, D., Bey, I., Carmichael,
G., Collins, W. J., Duncan, B. N., Faluvegi, G., Folberth, G., Gauss, M.,
Gong, S., Hauglustaine, D., Holloway, T., Isaksen, I. S. A., Jacob, D. J.,
Jonson, J. E., Kaminski, J. W., Keating, T. J., Lupu, A., Marmer, E.,
Montanaro, V., Park, R. J., Pitari, G., Pringle, K. J., Pyle, J. A.,
Schroeder, S., Vivanco, M. G., Wind, P., Wojcik, G., Wu, S., and Zuber, A.:
Multimodel estimates of intercontinental source-receptor relationships for
ozone pollution, J. Geophys. Res.-Atmos., 114, D04301,
https://doi.org/10.1029/2008JD010816, 2009. a
Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient
thermodynamic equilibrium model for
K+–Ca2+–Mg2+–
Fricko, O., Havlik, P., Rogelj, J., Klimont, Z., Gusti, M., Johnson, N., Kolp, P., Strubegger, M., Valin, H., Amann, M., Ermolieva, T., Forsell, N.,
Herrero, M., Heyes, C., Kindermann, G., Krey, V., McCollum, D. L.,
Obersteiner, M., Pachauri, S., Rao, S., Schmid, E., Schoepp, W., and Riahi,
K.: The marker quantification of the Shared Socioeconomic Pathway 2: A
middle-of-the-road scenario for the 21st century, Glob. Environ. Change, 42,
251–267, https://doi.org/10.1016/j.gloenvcha.2016.06.004, 2017. a
Fujimori, S., Hasegawa, T., Masui, T., Takahashi, K., Herran, D. S., Dai, H.,
Hijioka, Y., and Kainuma, M.: SSP3: AIM implementation of Shared
Socioeconomic Pathways, Glob. Environ. Change, 42, 268–283,
https://doi.org/10.1016/j.gloenvcha.2016.06.009, 2017. a
Garcia-Menendez, F., Monier, E., and Selin, N. E.: The role of natural
variability in projections of climate change impacts on U.S. ozone
pollution, Geophys. Res. Lett., 44, 2911–2921, https://doi.org/10.1002/2016gl071565,
2017. a
GBD 2019 Risk Factor Collaborators: Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019, Lancet, 396, 1223–1249,
https://doi.org/10.1016/S0140-6736(20)30752-2, 2020. a
Gelaro, R., McCarty, W., Suárez, M., Todling, R., Molod, A., Takacs, L.,
Randles, C., Darmenov, A., Bosilovich, M., Reichle, R., Wargan, K., Coy, L.,
Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A.,
Gu, W., Kim, G., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J., Partyka,
G., Pawson, S., Putman, W., Rienecker, M., Schubert, S., Sienkiewicz, M., and
Zhao, B.: The Modern-Era Retrospective Analysis for Research and
Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454,
https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a, b, c
Geng, L., Zatko, M. C., Alexander, B., Fudge, T. J., Schauer, A. J., Murray,
L. T., and Mickley, L. J.: Effects of postdepositional processing on
nitrogen isotopes of nitrate in the Greenland Ice Sheet Project 2 ice core, Geophys. Res. Lett., 42, 5346–5354, https://doi.org/10.1002/2015gl064218, 2015. a
Geng, L., Murray, L. T., Mickley, L. J., Lin, P., Fu, Q., Schauer, A. J., and Alexander, B.: Isotopic evidence of multiple controls on atmospheric oxidants over climate transitions, Nature, 546, 133–136, https://doi.org/10.1038/nature22340, 2017. a
Gidden, M. J., Riahi, K., Smith, S. J., Fujimori, S., Luderer, G., Kriegler, E., van Vuuren, D. P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J. C., Frank, S., Fricko, O., Harmsen, M., Hasegawa, T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., and Takahashi, K.: Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century, Geosci. Model Dev., 12, 1443–1475, https://doi.org/10.5194/gmd-12-1443-2019, 2019. a, b, c
González Abad, G., Liu, X., Chance, K., Wang, H., Kurosu, T. P., and Suleiman, R.: Updated Smithsonian Astrophysical Observatory Ozone Monitoring Instrument (SAO OMI) formaldehyde retrieval, Atmos. Meas. Tech., 8, 19–32, https://doi.org/10.5194/amt-8-19-2015, 2015. a, b
Griffiths, P. T., Murray, L. T., Zeng, G., Shin, Y. M., Abraham, N. L., Archibald, A. T., Deushi, M., Emmons, L. K., Galbally, I. E., Hassler, B., Horowitz, L. W., Keeble, J., Liu, J., Moeini, O., Naik, V., O'Connor, F. M., Oshima, N., Tarasick, D., Tilmes, S., Turnock, S. T., Wild, O., Young, P. J., and Zanis, P.: Tropospheric ozone in CMIP6 simulations, Atmos. Chem. Phys., 21, 4187–4218, https://doi.org/10.5194/acp-21-4187-2021, 2021. a, b
Guelle, W., Balkanski, Y., Dibb, J., Schulz, M., and Dulac, F.: Wet deposition in a global size-dependent aerosol transport model 2. Influence of the scavenging scheme on Pb-210 vertical profiles, surface concentrations, and deposition, J. Geophys. Res.-Atmos., 103, 28875–28891,
https://doi.org/10.1029/98JD01826, 1998a. a
Guelle, W., Balkanski, Y., Schulz, M., Dulac, F., and Monfray, P.: Wet
deposition in a global size-dependent aerosol transport model – 1. Comparison of a 1 year Pb-210 simulation with ground measurements, J. Geophys. Res.-Atmos., 103, 11429–11445, https://doi.org/10.1029/97JD03680, 1998b. a
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012. a
Hall, B. D., Dutton, G. S., Mondeel, D. J., Nance, J. D., Rigby, M., Butler, J. H., Moore, F. L., Hurst, D. F., and Elkins, J. W.: Improving measurements of SF6 for the study of atmospheric transport and emissions, Atmos. Meas. Tech., 4, 2441–2451, https://doi.org/10.5194/amt-4-2441-2011, 2011. a
Hall, T. M. and Waugh, D. W.: Stratospheric residence time and its
relationship to mean age, J. Geophys. Res., 105, 6773,
https://doi.org/10.1029/1999JD901096, 2000. a
Hammer, M., van Donkelaar, A., Li, C., Lyapustin, A., Sayer, A., Hsu, N., Levy, R., Garay, M., Kalashnikova, O., Kahn, R., Brauer, M., Apte, J., Henze, D., Zhang, L., Zhang, Q., Ford, B., Pierce, J., and Martin, R.: Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018), Environ. Sci. Technol., 54, 7879–7890, https://doi.org/10.1021/acs.est.0c01764, 2020. a, b, c, d
Hansen, J., Russell, G., Rind, D., Stone, P., Lacis, A., Lebedeff, S., Ruedy,
R., and Travis, L.: Efficient Three-Dimensional Global Models for Climate
Studies: Models I and II, Mon. Weather Rev., 111, 609–662,
https://doi.org/10.1175/1520-0493(1983)111<0609:ETDGMF>2.0.CO;2, 1983. a, b
Harvey, M. and Matthews, K.: Be-7 Deposition in a High-Rainfall Area of
New-Zealand, J. Atmos. Chem., 8, 299–306, https://doi.org/10.1007/BF00052708, 1989. a
Hasebe, N., Doke, T., Kikuchi, J., Takeuchi, Y., And Sugiyama, T.: Observation of Fallout Rates of Atmospheric Be-7 and Na-22 Produced by Cosmic-Rays Concerning Estimation of the Fallout Rate of Atmospheric Al-26, J. Geophys. Res.-Space, 86, 520–524, https://doi.org/10.1029/JA086iA02p00520, 1981. a
Hauglustaine, D., Hourdin, F., Jourdain, L., Filiberti, M., Walters, S.,
Lamarque, J., and Holland, E.: Interactive chemistry in the Laboratoire de
Météorologie Dynamique general circulation model: Description and
background tropospheric chemistry evaluation, J. Geophys. Res.-Atmos., 109,
D04314, https://doi.org/10.1029/2003JD003957, 2004. a
Henze, D. K., Hakami, A., and Seinfeld, J. H.: Development of the adjoint of GEOS-Chem, Atmos. Chem. Phys., 7, 2413–2433, https://doi.org/10.5194/acp-7-2413-2007, 2007. a, b
Hirose, K., Honda, T., Yagishita, S., Igarashi, Y., and Aoyama, M.: Deposition behaviors of Pb-210, Be-7 and thorium isotopes observed in Tsukuba and Nagasaki, Japan, Atmos. Environ., 38, 6601–6608,
https://doi.org/10.1016/j.atmosenv.2004.08.012, 2004. a
Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018. a, b, c
Holton, J., Haynes, P., Mcintyre, M., Douglass, A., Rood, R., and Pfister, L.: Stratosphere-Troposphere Exchange, Rev. Geophys., 33, 403–439,
https://doi.org/10.1029/95RG02097, 1995. a
Horowitz, L. W., Liang, J., Gardner, G. M., and Jacob, D. J.: Export of
reactive nitrogen from North America during summertime: Sensitivity to
hydrocarbon chemistry, J. Geophys. Res.-Atmos., 103, 13451–13476,
https://doi.org/10.1029/97jd03142, 1998. a
Hu, L., Jacob, D. J., Liu, X., Zhang, Y., Zhang, L., Kim, P. S., Sulprizio,
M. P., and Yantosca, R. M.: Global budget of tropospheric ozone: Evaluating
recent model advances with satellite (OMI), aircraft (IAGOS), and ozonesonde
observations, Atmos. Environ., 167, 323–334,
https://doi.org/10.1016/j.atmosenv.2017.08.036, 2017. a
Hudman, R. C., Moore, N. E., Mebust, A. K., Martin, R. V., Russell, A. R., Valin, L. C., and Cohen, R. C.: Steps towards a mechanistic model of global soil nitric oxide emissions: implementation and space based-constraints, Atmos. Chem. Phys., 12, 7779–7795, https://doi.org/10.5194/acp-12-7779-2012, 2012. a
Hui, J. and Hong, L.: Projected Changes in NOx Emissions from Lightning as a Result of 2000–2050 Climate Change, Atmospheric and Oceanic Science
Letters, 6, 284–289, https://doi.org/10.3878/j.issn.1674-2834.13.0042, 2013. a
Husain, L., Coffey, P., Meyers, R., and Cederwall, R.: Ozone Transport from
Stratosphere to Troposphere, Geophys. Res. Lett., 4, 363–365,
https://doi.org/10.1029/GL004i009p00363, 1977. a
Igarashi, Y., Hirose, I., and Otsuji-Hatori, M.: Beryllium-7 deposition and
its relation to sulfate deposition, J. Atmos. Chem., 29, 217–231,
https://doi.org/10.1023/A:1005921113496, 1998. a
Jacob, D. J. and Prather, M. J.: Radon-222 as a test of convective transport
in a general circulation model, Tellus B, 42, 118–134,
https://doi.org/10.3402/tellusb.v42i1.15196, 1990. a
Jacob, D. J., Prather, M., Rasch, P., Shia, R., Balkanski, Y., Beagley, S.,
Bergmann, D., Blackshear, W., Brown, M., Chiba, M., Chipperfield, M.,
deGrandpre, J., Dignon, J., Feichter, J., Genthon, C., Grose, W., Kasibhatla,
P., Kohler, I., Kritz, M., Law, K., PENNER, J., Ramonet, M., Reeves, C.,
Rotman, D., Stockwell, D., VanVelthoven, P., Verver, G., Wild, O., Yang, H.,
and Zimmermann, P.: Evaluation and intercomparison of global atmospheric
transport models using Rn-222 and other short-lived tracers, J. Geophys. Res.-Atmos, 102, 5953–5970, https://doi.org/10.1029/96JD02955, 1997. a, b
Jaeglé, L., Quinn, P. K., Bates, T. S., Alexander, B., and Lin, J.-T.: Global distribution of sea salt aerosols: new constraints from in situ and remote sensing observations, Atmos. Chem. Phys., 11, 3137–3157, https://doi.org/10.5194/acp-11-3137-2011, 2011. a
Jiang, L. and O'Neill, B. C.: Global urbanization projections for the Shared
Socioeconomic Pathways, Glob. Environ. Change, 42, 193–199,
https://doi.org/10.1016/j.gloenvcha.2015.03.008, 2017. a
Kaplan, J. O., Folberth, G., and Hauglustaine, D. A.: Role of methane and
biogenic volatile organic compound sources in late glacial and Holocene
fluctuations of atmospheric methane concentrations, Global Biogeochem. Cy.,
20, GB2016, https://doi.org/10.1029/2005gb002590, 2006. a
Keller, C. A., Long, M. S., Yantosca, R. M., Da Silva, A. M., Pawson, S., and Jacob, D. J.: HEMCO v1.0: a versatile, ESMF-compliant component for calculating emissions in atmospheric models, Geosci. Model Dev., 7, 1409–1417, https://doi.org/10.5194/gmd-7-1409-2014, 2014. a, b, c
Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck, R., Canuto, V., Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz, C. A., Del Genio,
A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim, D., Lacis, A. A.,
Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J., Matthews, E. E.,
McDermid, S., Mezuman, K., Miller, R. L., Murray, L. T., Oinas, V., Orbe, C.,
García-Pando, C. P., Perlwitz, J. P., Puma, M. J., Rind, D., Romanou,
A., Shindell, D. T., Sun, S., Tausnev, N., Tsigaridis, K., Tselioudis, G.,
Weng, E., Wu, J., and Yao, M.-S.: GISS-E2.1: Configurations and
Climatology, J. Adv. Model. Earth Syst., 12, e2019MS002025, https://doi.org/10.1029/2019MS002025, 2020. a, b, c, d
Kim, D., Sobel, A. H., Del Genio, A. D., Chen, Y., Camargo, S. J., Yao, M.-S., Kelley, M., and Nazarenko, L.: The Tropical Subseasonal Variability
Simulated in the NASA GISS General Circulation Model, J. Climate, 25,
4641–4659, https://doi.org/10.1175/jcli-d-11-00447.1, 2012. a
Koch, D., Jacob, D. J., and Graustein, W.: Vertical transport of tropospheric aerosols as indicated by Be-7 and Pb-210 in a chemical tracer model, J. Geophys. Res.-Atmos., 101, 18651–18666, https://doi.org/10.1029/96JD01176, 1996. a, b, c, d
Kopacz, M., Jacob, D. J., Henze, D. K., Heald, C. L., Streets, D. G., and
Zhang, Q.: Comparison of adjoint and analytical Bayesian inversion methods
for constraining Asian sources of carbon monoxide using satellite (MOPITT)
measurements of CO columns, J. Geophys. Res., 114, D04305, https://doi.org/10.1029/2007jd009264, 2009. a
Kriegler, E., Bauer, N., Popp, A., Humpenöder, F., Leimbach, M., Strefler, J., Baumstark, L., Bodirsky, B. L., Hilaire, J., Klein, D., Mouratiadou, I., Weindl, I., Bertram, C., Dietrich, J.-P., Luderer, G., Pehl, M., Pietzcker, R., Piontek, F., Lotze-Campen, H., Biewald, A., Bonsch, M., Giannousakis, A., Kreidenweis, U., Müller, C., Rolinski, S., Schultes, A., Schwanitz, J., Stevanovic, M., Calvin, K., Emmerling, J., Fujimori, S., and Edenhofer, O.: Fossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century, Glob. Environ. Change, 42, 297–315, https://doi.org/10.1016/j.gloenvcha.2016.05.015, 2017. a
Kritz, M., Rosner, S., and Stockwell, D.: Validation of an off-line
three-dimensional chemical transport model using observed radon profiles – 1. Observations, J. Geophys. Res.-Atmos., 103, 8425–8432, https://doi.org/10.1029/97JD02655,
1998. a
Krotkov, N. A., Lamsal, L. N., Celarier, E. A., Swartz, W. H., Marchenko, S. V., Bucsela, E. J., Chan, K. L., Wenig, M., and Zara, M.: The version 3 OMI NO2 standard product, Atmos. Meas. Tech., 10, 3133–3149, https://doi.org/10.5194/amt-10-3133-2017, 2017. a, b
Krotkov, N. A., Lamsal, L. N., Marchenko, S. V., Celarier, E. A., Bucsela, E.
J., Swartz, W. H., Joiner, J., and the OMI core team: OMI/Aura NO2
Cloud-Screened Total and Tropospheric Column L3 Global Gridded 0.25 degree x 0.25 degree V3, NASA Goddard Space Flight Center, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/Aura/OMI/DATA3007, 2019. a
Lal, D., Malhotra, P., and Peters, B.: On the Production of Radioisotopes in
the Atmosphere by Cosmic Radiation and Their Application to Meteorology, J. Atmos. Terr. Phys., 12, 306–328, https://doi.org/10.1016/0021-9169(58)90062-X, 1958. a
Leimbach, M., Kriegler, E., Roming, N., and Schwanitz, J.: Future growth
patterns of world regions – A GDP scenario approach, Glob. Environ. Change,
42, 215–225, https://doi.org/10.1016/j.gloenvcha.2015.02.005, 2017. a
Li, C., Krotkov, N. A., Leonard, P. J. T., Carn, S., Joiner, J., Spurr, R. J. D., and Vasilkov, A.: Version 2 Ozone Monitoring Instrument SO2 product (OMSO2 V2): new anthropogenic SO2 vertical column density dataset, Atmos. Meas. Tech., 13, 6175–6191, https://doi.org/10.5194/amt-13-6175-2020, 2020a. a, b
Li, C., Krotkov, N. A., Leonard, P.: OMI/Aura Sulfur Dioxide (SO2)
Total Column L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/Aura/OMI/DATA3008, 2020b. a
Lin, S.-J. and Rood, R. B.: Multidimensional Flux-Form Semi-Lagrangian
Transport Schemes, Mon. Weather Rev., 124, 2046–2070,
https://doi.org/10.1175/1520-0493(1996)124<2046:MFFSLT>2.0.CO;2, 1996. a, b
Liu, H., Jacob, D. J., Bey, I., and Yantosca, R.: Constraints from Pb-210 and Be-7 on wet deposition and transport in a global three-dimensional chemical tracer model driven by assimilated meteorological fields, J. Geophys. Res.-Atmos, 106, 12109–12128, https://doi.org/10.1029/2000JD900839, 2001. a, b, c, d
Liu, H., Considine, D. B., Horowitz, L. W., Crawford, J. H., Rodriguez, J. M., Strahan, S. E., Damon, M. R., Steenrod, S. D., Xu, X., Kouatchou, J., Carouge, C., and Yantosca, R. M.: Using beryllium-7 to assess cross-tropopause transport in global models, Atmos. Chem. Phys., 16, 4641–4659, https://doi.org/10.5194/acp-16-4641-2016, 2016. a, b, c
Lundgren, L., Bindle, L., Yantosca, B., Sulprizio, M., Downs, W., and Eastham, S. D.: geoschem/GCHP: GCHP 13.1.0, Zenodo [code], https://doi.org/10.5281/zenodo.4984437, 2021. a
Maenhaut, W., Zoller, W., and Coles, D.: Radionuclides in the South Pole
Atmosphere, J. Geophys. Res.-Oc. Atm., 84, 3131–3138,
https://doi.org/10.1029/JC084iC06p03131, 1979. a, b
Mahowald, N., Rasch, P., and Prinn, R.: Cumulus parameterizations in chemical transport models, J. Geophys. Res.-Atmos., 100, 26173–26189,
https://doi.org/10.1029/95JD02606, 1995. a
Maiss, M. and Brenninkmeijer, C. A. M.: Atmospheric SF6: Trends, Sources,
and Prospects, Environ. Sci. Technol., 32, 3077–3086, https://doi.org/10.1021/es9802807, 1998. a
Meinshausen, M., Vogel, E., Nauels, A., Lorbacher, K., Meinshausen, N., Etheridge, D. M., Fraser, P. J., Montzka, S. A., Rayner, P. J., Trudinger, C. M., Krummel, P. B., Beyerle, U., Canadell, J. G., Daniel, J. S., Enting, I. G., Law, R. M., Lunder, C. R., O'Doherty, S., Prinn, R. G., Reimann, S., Rubino, M., Velders, G. J. M., Vollmer, M. K., Wang, R. H. J., and Weiss, R.: Historical greenhouse gas concentrations for climate modelling (CMIP6), Geosci. Model Dev., 10, 2057–2116, https://doi.org/10.5194/gmd-10-2057-2017, 2017. a, b, c
Menon, S., Del Genio, A. D., Kaufman, Y., Bennartz, R., Koch, D., Loeb, N., and Orlikowski, D.: Analyzing signatures of aerosol-cloud interactions from
satellite retrievals and the GISS GCM to constrain the aerosol indirect
effect, J. Geophys. Res., 113, D14S22, https://doi.org/10.1029/2007jd009442, 2008. a
Miller, R. L., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Kelley, M.,
Ruedy, R., Russell, G. L., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck,
R., Canuto, V., Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz,
C. A., Del Genio, A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim,
D., Lacis, A. A., Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J.,
Matthews, E. E., McDermid, S., Mezuman, K., Murray, L. T., Oinas, V., Orbe,
C., Pérez García-Pando, C., Perlwitz, J. P., Puma, M. J., Rind, D.,
Romanou, A., Shindell, D. T., Sun, S., Tausnev, N., Tsigaridis, K.,
Tselioudis, G., Weng, E., Wu, J., and Yao, M.-S.: CMIP6 Historical
Simulations (1850–2014) With GISS-E2.1, J. Adv. Model. Earth Syst., 13, e2019MS002034, https://doi.org/10.1029/2019ms002034, 2021. a, b, c
Moore, H., Poet, S., and Martell, E.: Rn-222, Pb-210, Bi-210, and Po-210
Profiles and Aerosol Residence Times Versus Altitude, J. Geophys. Res., 78,
7065–7075, https://doi.org/10.1029/JC078i030p07065, 1973. a
Murray, L. T.: Lightning NOx and Impacts on Air Quality, Curr. Pollut. Rep., 2, 115–133, https://doi.org/10.1007/s40726-016-0031-7, 2016. a
Murray, L. T.: An uncertain future for lightning, Nat. Clim. Change, 8,
191–192, https://doi.org/10.1038/s41558-018-0094-0, 2018. a
Murray, L. T.: GCAP 2.0 input files, GCAP Data Repository [data set], available at: http://atmos.earth.rochester.edu/input/gc/ExtData/, last access: 9 September 2021. a
Murray, L. T., Jacob, D. J., Logan, J. A., Hudman, R. C., and Koshak, W. J.:
Optimized regional and interannual variability of lightning in a global
chemical transport model constrained by LIS/OTD satellite data, J. Geophys. Res.-Atmos., 117, D20307, https://doi.org/10.1029/2012jd017934, 2012. a, b
Murray, L. T., Logan, J. A., and Jacob, D. J.: Interannual variability in
tropical tropospheric ozone and OH: The role of lightning, J. Geophys. Res.-Atmos., 118, 11468–11480, https://doi.org/10.1002/jgrd.50857, 2013. a
Murray, L. T., Mickley, L. J., Kaplan, J. O., Sofen, E. D., Pfeiffer, M., and Alexander, B.: Factors controlling variability in the oxidative capacity of the troposphere since the Last Glacial Maximum, Atmos. Chem. Phys., 14, 3589–3622, https://doi.org/10.5194/acp-14-3589-2014, 2014. a, b, c, d, e, f, g, h, i, j
Murray, L. T., Leibensperger, E. M., and Mickley, L. J.: MERRA-2 like diagnostics for the GISS ModelE2.1 GCM, Zenodo [code], https://doi.org/10.5281/zenodo.4783672, 2021. a
Naik, V., Voulgarakis, A., Fiore, A. M., Horowitz, L. W., Lamarque, J.-F., Lin, M., Prather, M. J., Young, P. J., Bergmann, D., Cameron-Smith, P. J., Cionni, I., Collins, W. J., Dalsøren, S. B., Doherty, R., Eyring, V., Faluvegi, G., Folberth, G. A., Josse, B., Lee, Y. H., MacKenzie, I. A., Nagashima, T., van Noije, T. P. C., Plummer, D. A., Righi, M., Rumbold, S. T., Skeie, R., Shindell, D. T., Stevenson, D. S., Strode, S., Sudo, K., Szopa, S., and Zeng, G.: Preindustrial to present-day changes in tropospheric hydroxyl radical and methane lifetime from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), Atmos. Chem. Phys., 13, 5277–5298, https://doi.org/10.5194/acp-13-5277-2013, 2013. a
Nakicenovic, N. and Swart, R. (Eds.): IPCC Special Report on Emissions
Scenarios, Cambridge University Press, Cambridge, UK, 2000. a
Narazaki, Y. and Fujitaka, K.: The Geographical Distribution and Features of
7Be Deposition in Japan, Japan Health Physics Society, 37, 317–324, 2010. a
Nazarov, L., Kuzenkov, A., Malakhov, S., Volokitina, L., Gaziyev, Y., and
Vasilyev, A.: Radioactive Aerosol Distribution in Middle and Upper
Troposphere over USSR in 1963-1968, J. Geophys. Res., 75, 3575,
https://doi.org/10.1029/JC075i018p03575, 1970. a
Nightingale, P. D., Liss, P. S., and Schlosser, P.: Measurements of air-sea
gas transfer during an open ocean algal bloom, Geophys. Res. Lett., 27,
2117–2120, https://doi.org/10.1029/2000gl011541, 2000a. a
Nightingale, P. D., Malin, G., Law, C. S., Watson, A. J., Liss, P. S.,
Liddicoat, M. I., Boutin, J., and Upstill-Goddard, R. C.: In situ evaluation
of air-sea gas exchange parameterizations using novel conservative and
volatile tracers, Global Biogeochem. Cy., 14, 373–387,
https://doi.org/10.1029/1999gb900091, 2000b. a
Nijampurkar, V. and Rao, D.: Polar Fallout of Radionuclides Si-32, Be-7 and
Pb-210 and Past Accumulation Rate of Ice at Indian Station, Dakshin Gangotri,
East Antarctica, J. Environ. Radioactiv., 21, 107–117,
https://doi.org/10.1016/0265-931X(93)90048-C, 1993. a
NOAA Carbon Cycle Group ObsPack Team: Multi-laboratory compilation of atmospheric sulfur hexafluoride data for the period
1983–2017; obspack_sf6_1_v2.1.1_2018-08-17, NOAA Earth System Research Laboratory, Global Monitoring Division, https://doi.org/10.25925/20180817, 2018. a
Olsen, C., Larsen, I., Lowry, P., Cutshall, N., Todd, J., Wong, G., and Casey, W.: Atmospheric Fluxes and Marsh-Soil Inventories of Be-7 and Pb-210, J. Geophys. Res.-Atmos., 90, 10487–10495, https://doi.org/10.1029/JD090iD06p10487, 1985. a
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. a
Orbe, C., Plummer, D. A., Waugh, D. W., Yang, H., Jöckel, P., Kinnison, D. E., Josse, B., Marecal, V., Deushi, M., Abraham, N. L., Archibald, A. T., Chipperfield, M. P., Dhomse, S., Feng, W., and Bekki, S.: Description and Evaluation of the specified-dynamics experiment in the Chemistry-Climate Model Initiative , Atmos. Chem. Phys., 20, 3809–3840, https://doi.org/10.5194/acp-20-3809-2020, 2020a. a, b
Orbe, C., Rind, D., Jonas, J., Nazarenko, L., Faluvegi, G., Murray, L. T.,
Shindell, D. T., Tsigaridis, K., Zhou, T., Kelley, M., and Schmidt, G. A.:
GISS Model E2.2: A Climate Model Optimized for the Middle Atmosphere – 2.
Validation of Large-Scale Transport and Evaluation of Climate Response, J.
Geophys. Res.-Atmos., 125, e2020JD033151, https://doi.org/10.1029/2020jd033151, 2020b. a, b, c, d
Papastefanou, C.: Beryllium-7 Aerosols in Ambient Air, Aerosol Air Qual. Res., 9, 187–197, https://doi.org/10.4209/aaqr.2009.01.0004, 2009. a
Papastefanou, C. and Ioannidou, A.: Beryllium-7 aerosols in ambient air, Environ. Int., 22, S125–S130, https://doi.org/10.1016/S0160-4120(97)80366-2, 1996. a
Papastefanou, C., Ioannidou, A., Stoulos, S., and Manolopoulou, M.:
Atmospheric Deposition of Cosmogenic Be-7 and Cs-137 from Fallout of the
Chernobyl Accident, Sci. Total Environ., 170, 151–156,
https://doi.org/10.1016/0048-9697(95)04608-4, 1995. a
Park, R. J., Jacob, D. J., Field, B. D., Yantosca, R., and Chin, M.: Natural
and transboundary pollution influences on sulfate-nitrate-ammonium aerosols
in the United States: Implications for policy, J. Geophys. Res., 109, D15204, https://doi.org/10.1029/2003jd004473, 2004. a, b, c
Pfeiffer, M., Spessa, A., and Kaplan, J. O.: A model for global biomass burning in preindustrial time: LPJ-LMfire (v1.0), Geosci. Model Dev., 6, 643–685, https://doi.org/10.5194/gmd-6-643-2013, 2013. a
Platnick, S., et al.: MODIS Atmosphere L3 Monthly Product. NASA MODIS
Adaptive Processing System, Goddard Space Flight Center, USA, https://doi.org/10.5067/MODIS/MYD08_M3.061, 2015. a
Popp, A., Calvin, K., Fujimori, S., Havlik, P., Humpenöder, F., Stehfest,
E., Bodirsky, B. L., Dietrich, J. P., Doelmann, J. C., Gusti, M., Hasegawa,
T., Kyle, P., Obersteiner, M., Tabeau, A., Takahashi, K., Valin, H.,
Waldhoff, S., Weindl, I., Wise, M., Kriegler, E., Lotze-Campen, H., Fricko,
O., Riahi, K., and Vuuren, D. P. v.: Land-use futures in the shared
socio-economic pathways, Glob. Environ. Change, 42, 331–345,
https://doi.org/10.1016/j.gloenvcha.2016.10.002, 2017. a
Prather, M. J.: Numerical advection by conservation of second-order moments, J. Geophys. Res., 91, 6671, https://doi.org/10.1029/jd091id06p06671, 1986. a
Prather, M. J., Holmes, C. D., and Hsu, J.: Reactive greenhouse gas scenarios: Systematic exploration of uncertainties and the role of atmospheric chemistry, Geophys. Res. Lett., 39, L09803, https://doi.org/10.1029/2012GL051440, 2012. a, b
Preiss, N., Melieres, M., and Pourchet, M.: A compilation of data on lead 210 concentration in surface air and fluxes at the air-surface and water-sediment interfaces, J. Geophys. Res.-Atmos., 101, 28847–28862,
https://doi.org/10.1029/96JD01836, 1996. a, b, c
Price, C. and Rind, D.: A simple lightning parameterization for calculating
global lightning distributions, J. Geophys. Res.-Atmos., 97, 9919–9933,
https://doi.org/10.1029/92jd00719, 1992. a
Price, C. G.: Lightning Applications in Weather and Climate Research, Surv.
Geophys., 34, 755–767, https://doi.org/10.1007/s10712-012-9218-7, 2013. a
Prinn, R., Huang, J., Weiss, R., Cunnold, D., Fraser, P., Simmonds, P.,
McCulloch, A., Harth, C., Reimann, S., Salameh, P., O'Doherty, S., Wang, R.,
Porter, L., Miller, B., and Krummel, P.: Evidence for variability of
atmospheric hydroxyl radicals over the past quarter century, Geophys. Res.
Lett., 32, L07809, https://doi.org/10.1029/2004GL022228, 2005. a, b
Pye, H. O. T. and Seinfeld, J. H.: A global perspective on aerosol from low-volatility organic compounds, Atmos. Chem. Phys., 10, 4377–4401, https://doi.org/10.5194/acp-10-4377-2010, 2010. a
Pye, H. O. T., Liao, H., Wu, S., Mickley, L. J., Jacob, D. J., Henze, D. K.,
and Seinfeld, J. H.: Effect of changes in climate and emissions on future
sulfate-nitrate-ammonium aerosol levels in the United States, J. Geophys. Res.-Atmos, 114, D01205, https://doi.org/10.1029/2008jd010701, 2009. a
Rao, S., Klimont, Z., Smith, S. J., Van Dingenen, R., Dentener, F., Bouwman,
L., Riahi, K., Amann, M., Bodirsky, B. L., van Vuuren, D. P., Aleluia Reis,
L., Calvin, K., Drouet, L., Fricko, O., Fujimori, S., Gernaat, D., Havlik,
P., Harmsen, M., Hasegawa, T., Heyes, C., Hilaire, J., Luderer, G., Masui,
T., Stehfest, E., Strefler, J., van der Sluis, S., and Tavoni, M.: Future
air pollution in the Shared Socio-economic Pathways, Glob. Environ. Change,
42, 346–358, https://doi.org/10.1016/j.gloenvcha.2016.05.012, 2017. a
Ray, E. A., Moore, F. L., Rosenlof, K. H., Davis, S. M., Boenisch, H.,
Morgenstern, O., Smale, D., Rozanov, E., Hegglin, M., Pitari, G., Mancini,
E., Braesicke, P., Butchart, N., Hardiman, S., Li, F., Shibata, K., and
Plummer, D. A.: Evidence for changes in stratospheric transport and mixing
over the past three decades based on multiple data sets and tropical leaky
pipe analysis, J. Geophys. Res., 115, D21304, https://doi.org/10.1029/2010JD014206,
2010. a
Rehfeld, S. and Heimann, M.: Three dimensional atmospheric transport
simulation of the radioactive tracers Pb-210, Be-7, Be-10, and Sr-90, J.
Geophys. Res.-Atmos., 100, 26141–26161, https://doi.org/10.1029/95JD01003, 1995. a
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C.,
Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp,
A., Cuaresma, J. C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S.,
Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F.,
Da Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D.,
Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G.,
Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M.,
Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A.,
and Tavoni, M.: The Shared Socioeconomic Pathways and their energy, land
use, and greenhouse gas emissions implications: An overview, Glob. Environ.
Change, 42, 153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009, 2017. a, b, c
Rigby, M., Mühle, J., Miller, B. R., Prinn, R. G., Krummel, P. B., Steele, L. P., Fraser, P. J., Salameh, P. K., Harth, C. M., Weiss, R. F., Greally, B. R., O'Doherty, S., Simmonds, P. G., Vollmer, M. K., Reimann, S., Kim, J., Kim, K.-R., Wang, H. J., Olivier, J. G. J., Dlugokencky, E. J., Dutton, G. S., Hall, B. D., and Elkins, J. W.: History of atmospheric SF6 from 1973 to 2008, Atmos. Chem. Phys., 10, 10305–10320, https://doi.org/10.5194/acp-10-10305-2010, 2010. a
Rind, D. and Lerner, J.: Use of on-line tracers as a diagnostic tool in
general circulation model development: 1. Horizontal and vertical transport
in the troposphere, J. Geophys. Res.-Atmos., 101, 12667–12683,
https://doi.org/10.1029/96jd00551, 1996. a
Rind, D., Suozzo, R., Balachandran, N. K., Lacis, A., and Russell, G.: The GISS Global Climate-Middle Atmosphere Model. Part I: Model Structure and
Climatology, J. Atmos. Sci., 45, 329–370,
https://doi.org/10.1175/1520-0469(1988)045<0329:TGGCMA>2.0.CO;2, 1988. a
Rind, D., Lerner, J., Jonas, J., and McLinden, C.: Effects of resolution and
model physics on tracer transports in the NASA Goddard Institute for Space
Studies general circulation models, J. Geophys. Res., 112, D09315,
https://doi.org/10.1029/2006jd007476, 2007. a
Rind, D., Orbe, C., Jonas, J., Nazarenko, L., Zhou, T., Kelley, M., Lacis, A., Shindell, D., Faluvegi, G., Romanou, A., Russell, G., Tausnev, N., Bauer, M., and Schmidt, G.: GISS Model E2.2: A Climate Model Optimized for the Middle Atmosphere – Model Structure, Climatology, Variability, and Climate
Sensitivity, J. Geophys. Res.-Atmos., 125, e2019JD032204, https://doi.org/10.1029/2019jd032204, 2020. a
Samanta, D., Karnauskas, K. B., and Goodkin, N. F.: Tropical Pacific SST and
ITCZ Biases in Climate Models: Double Trouble for Future Rainfall
Projections, Geophys. Res. Lett., 46, 2242–2252, https://doi.org/10.1029/2018gl081363,
2019. a
Samir, K. and Lutz, W.: The human core of the shared socioeconomic pathways:
Population scenarios by age, sex and level of education for all countries to
2100., Glob. Environ. Change, 42, 181–192,
https://doi.org/10.1016/j.gloenvcha.2014.06.004, 2017. a
Sanak, J., Gaudry, A., and Lambert, G.: Size Distribution of Pb-210 Aerosols
Over Oceans, Geophys. Res. Lett., 8, 1067–1069, https://doi.org/10.1029/GL008i010p01067,
1981. a, b
Sanak, J., Lambert, G., and Ardouin, B.: Measurement of
Stratosphere-to-Troposphere Exchange in Antarctica by Using Short-Lived
Cosmonuclides, Tellus B, 37, 109–115,
https://doi.org/10.1111/j.1600-0889.1985.tb00059.x, 1985. a
Schmidt, G. A., Ruedy, R., Hansen, J. E., Aleinov, I., Bell, N., Bauer, M.,
Bauer, S., Cairns, B., Canuto, V., Cheng, Y., Del Genio, A., Faluvegi, G.,
Friend, A. D., Hall, T. M., Hu, Y., Kelley, M., Kiang, N. Y., Koch, D.,
Lacis, A. A., Lerner, J., Lo, K. K., Miller, R. L., Nazarenko, L., Oinas, V.,
Perlwitz, J., Perlwitz, J., Rind, D., Romanou, A., Russell, G. L., Sato, M.,
Shindell, D. T., Stone, P. H., Sun, S., Tausnev, N., Thresher, D., and Yao,
M.-S.: Present-Day Atmospheric Simulations Using GISS ModelE: Comparison to
In Situ, Satellite, and Reanalysis Data, J. Climate, 19, 153–192,
https://doi.org/10.1175/jcli3612.1, 2006. a
Schubert, S., Rood, R. B., and Pfaendtner, J.: An Assimilated Dataset for
Earth Science Applications, Bull. Am. Meteorol. Soc., 74, 2331–2342,
https://doi.org/10.1175/1520-0477(1993)074<2331:AADFES>2.0.CO;2, 1993. a
Schuler, C., Wieland, E., Santschi, P., Sturm, M., Lueck, A., Bollhalder, S.,
Beer, J., Bonani, G., Hofmann, H., Suter, M., and Wolfli, W.: A Multitracer
Studyof Radionuclides in Lake Zurich, Switzerland .1. Comparison of
Atmospheric and Sedimentary Fluxes of Be-7, Be-10, Pb-210, Po-210, and
Cs-137, J. Geophys. Res.pOceans, 96, 17051–17065, https://doi.org/10.1029/91JC01765,
1991. a
Schultz, M. G., Schröder, S., Lyapina, O., Cooper, O., Galbally, I.,
Petropavlovskikh, I., Von Schneidemesser, E., Tanimoto, H., Elshorbany, Y.,
Naja, M., Seguel, R., Dauert, U., Eckhardt, P., Feigenspahn, S., Fiebig, M.,
Hjellbrekke, A.-G., Hong, Y.-D., Christian Kjeld, P., Koide, H., Lear, G.,
Tarasick, D., Ueno, M., Wallasch, M., Baumgardner, D., Chuang, M.-T.,
Gillett, R., Lee, M., Molloy, S., Moolla, R., Wang, T., Sharps, K., Adame,
J. A., Ancellet, G., Apadula, F., Artaxo, P., Barlasina, M., Bogucka, M.,
Bonasoni, P., Chang, L., Colomb, A., Cuevas, E., Cupeiro, M., Degorska, A.,
Ding, A., Fröhlich, M., Frolova, M., Gadhavi, H., Gheusi, F., Gilge, S.,
Gonzalez, M. Y., Gros, V., Hamad, S. H., Helmig, D., Henriques, D.,
Hermansen, O., Holla, R., Huber, J., Im, U., Jaffe, D. A., Komala, N.,
Kubistin, D., Lam, K.-S., Laurila, T., Lee, H., Levy, I., Mazzoleni, C.,
Mazzoleni, L., McClure-Begley, A., Mohamad, M., Murovic, M., Navarro-Comas,
M., Nicodim, F., Parrish, D., Read, K. A., Reid, N., Ries, L., Saxena, P.,
Schwab, J. J., Scorgie, Y., Senik, I., Simmonds, P., Sinha, V., Skorokhod,
A., Spain, G., Spangl, W., Spoor, R., Springston, S. R., Steer, K.,
Steinbacher, M., Suharguniyawan, E., Torre, P., Trickl, T., Weili, L.,
Weller, R., Xu, X., Xue, L., and Zhiqiang, M.: Tropospheric Ozone Assessment
Report: Database and Metrics Data of Global Surface Ozone Observations, Elem. Sci. Anth., 5, 58, https://doi.org/10.1525/elementa.244, 2017a. a, b
Schultz, M. G., Schröder, S., Lyapina, O., Cooper, O. R., Galbally, I., Petropavlovskikh, I., von Schneidemesser, E., Tanimoto, H., Elshorbany, Y., Naja, M., Seguel, R. J., Dauert, U., Eckhardt, P., Feigenspan, S., Fiebig, M., Hjellbrekke, A.-G., Hong, Y.-D., Kjeld, P. C., Koide, H., Lear, G., Tarasick, D., Ueno, M., Wallasch, M., Baumgardner, D., Chuang, M.-T., Gillett, R., Lee, M., Molloy, S., Moolla, R., Wang, T., Sharps, K., Adame, J. A., Ancellet, G., Apadula, F., Artaxo, P., Barlasina, M. E., Bogucka, M., Bonasoni, P., Chang, L., Colomb, A., Cuevas-Agulló, E., Cupeiro, M., Degorska, A., Ding, A., Fröhlich, M., Frolova, M., Gadhavi, H., Gheusi, F., Gilge, S., Gonzalez, M. Y., Gros, V., Hamad, S. H., Helmig, D., Henriques, D., Hermansen, O., Holla, R., Hueber, J., Im, U., Jaffe, D. A., Komala, N., Kubistin, D., Lam, K.-S., Laurila, T., Lee, H., Levy, I., Mazzoleni, C., Mazzoleni, L. R., McClure-Begley, A., Mohamad, M., Murovec, M., Navarro-Comas, M., Nicodim, F., Parrish, D., Read, K. A., Reid, N., Ries, L., Saxena, P., Schwab, J. J., Scorgie, Y., Senik, I., Simmonds, P., Sinha, V., Skorokhod, A. I., Spain, G., Spangl, W., Spoor, R., Springston, S. R., Steer, K., Steinbacher, M., Suharguniyawan, E., Torre, P., Trickl, T., Weili, L., Weller, R., Xu, X., Xue, L., and Zhiqiang, M.: Tropospheric Ozone Assessment Report, links to Global surface ozone datasets, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.876108, 2017b. a
Selin, N. E., Wu, S., Nam, K. M., Reilly, J. M., Paltsev, S., Prinn, R. G., and Webster, M. D.: Global health and economic impacts of future ozone
pollution, Environ. Res. Lett., 4, 044014,
https://doi.org/10.1088/1748-9326/4/4/044014, 2009. a
Stettler, M., Eastham, S., and Barrett, S.: Air quality and public health
impacts of UK airports. Part I: Emissions, Atmos. Environ., 45, 5415–5424,
https://doi.org/10.1016/j.atmosenv.2011.07.012, 2011. a
Stockwell, D., Kritz, M., Chipperfield, M., and Pyle, J.: Validation of an
off-line three-dimensional chemical transport model using observed radon
profiles – 2. Model results, J. Geophys. Res.-Atmos., 103, 8433–8445,
https://doi.org/10.1029/97JD02631, 1998. a
Tai, A. P. K., Mickley, L. J., Heald, C. L., and Wu, S.: Effect of
CO2 inhibition on biogenic isoprene emission: Implications for air quality under 2000 to 2050 changes in climate, vegetation, and land use, Geophys. Res. Lett., 40, 3479–3483, https://doi.org/10.1002/grl.50650, 2013. a
Tai, A. P. K., Sadiq, M., Pang, J. Y. S., Yung, D. H. Y., and Feng, Z.:
Impacts of Surface Ozone Pollution on Global Crop Yields: Comparing
Different Ozone Exposure Metrics and Incorporating Co-effects of CO2,
Frontiers in Sustainable Food Systems, 5, 534616, https://doi.org/10.3389/fsufs.2021.534616, 2021. a
The International GEOS-Chem User Community: geoschem/geos-chem: GEOS-Chem 12.4.0, Zenodo [code], https://doi.org/10.5281/zenodo.3360635, 2019. a
The International GEOS-Chem User Community: geoschem/geos-chem: GEOS-Chem 12.9.3, Zenodo [code], https://doi.org/10.5281/zenodo.3974569, 2020. a
The International GEOS-Chem User Community: geoschem/GCClassic: GEOS-Chem 13.1.0, Zenodo [code], https://doi.org/10.5281/zenodo.4984436, 2021a. a, b
The International GEOS-Chem User Community: geoschem/HEMCO: HEMCO 3.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.4984639, 2021b. a
The International GEOS-Chem User Community: geoschem/GCClassic: GEOS-Chem 13.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.4618180, 2021c. a
Turekian, K., Nozaki, Y., and Benninger, L.: Geochemistry of Atmospheric Radon and Radon Products, Annu. Rev. Earth Planet. Sci., 5, 227–255,
https://doi.org/10.1146/annurev.ea.05.050177.001303, 1977. a
Turekian, K., Benninger, L., and Dion, E.: 7Be and 210Pb Total Deposition Fluxes at New-Haven, Connecticut and at Bermuda, J. Geophys. Res.-Oc. Atm., 88, 5411–5415, https://doi.org/10.1029/JC088iC09p05411, 1983. a
Usoskin, I., Alanko-Huotari, K., Kovaltsov, G., and Mursula, K.: Heliospheric modulation of cosmic rays: Monthly reconstruction for 1951-2004, J. Geophys. Res.-Space, 110, A12108, https://doi.org/10.1029/2005JA011250, 2005. a, b
Usoskin, I. G. and Kovaltsov, G. A.: Production of cosmogenic Be-7 isotope in the atmosphere: Full 3-D modeling, J. Geophys. Res.-Atmos., 113, D12107,
https://doi.org/10.1029/2007JD009725, 2008. a
van der Werf, G. R., Randerson, J. T., Giglio, L., van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M., van Marle, M. J. E., Morton, D. C., Collatz, G. J., Yokelson, R. J., and Kasibhatla, P. S.: Global fire emissions estimates during 1997–2016, Earth Syst. Sci. Data, 9, 697–720, https://doi.org/10.5194/essd-9-697-2017, 2017. a
van Marle, M. J. E., Kloster, S., Magi, B. I., Marlon, J. R., Daniau, A.-L., Field, R. D., Arneth, A., Forrest, M., Hantson, S., Kehrwald, N. M., Knorr, W., Lasslop, G., Li, F., Mangeon, S., Yue, C., Kaiser, J. W., and van der Werf, G. R.: Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750–2015), Geosci. Model Dev., 10, 3329–3357, https://doi.org/10.5194/gmd-10-3329-2017, 2017. a, b
van Vuuren, D. P., Stehfest, E., Gernaat, D. E., Doelman, J. C., van den Berg, M., Harmsen, M., de Boer, H. S., Bouwman, L. F., Daioglou, V., Edelenbosch, O. Y., Girod, B., Kram, T., Lassaletta, L., Lucas, P. L., van Meijl, H., Müller, C., van Ruijven, B. J., van der Sluis, S., and Tabeau, A.: Energy, land-use and greenhouse gas emissions trajectories under a green
growth paradigm, Glob. Environ. Change, 42, 237–250,
https://doi.org/10.1016/j.gloenvcha.2016.05.008, 2017. a
Veefkind, P.: OMI/Aura Ozone (O3) DOAS Total Column L3 1 day 0.25 degree
x 0.25 degree V3, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/Aura/OMI/DATA3005, 2012. a
Viezee, W. and Singh, H.: The Distribution of Beryllium-7 in the Troposphere – Implications on Stratospheric-Tropospheric Air Exchange, Geophys. Res. Lett., 7, 805–808, https://doi.org/10.1029/GL007i010p00805, 1980. a
Voulgarakis, A., Naik, V., Lamarque, J.-F., Shindell, D. T., Young, P. J., Prather, M. J., Wild, O., Field, R. D., Bergmann, D., Cameron-Smith, P., Cionni, I., Collins, W. J., Dalsøren, S. B., Doherty, R. M., Eyring, V., Faluvegi, G., Folberth, G. A., Horowitz, L. W., Josse, B., MacKenzie, I. A., Nagashima, T., Plummer, D. A., Righi, M., Rumbold, S. T., Stevenson, D. S., Strode, S. A., Sudo, K., Szopa, S., and Zeng, G.: Analysis of present day and future OH and methane lifetime in the ACCMIP simulations, Atmos. Chem. Phys., 13, 2563–2587, https://doi.org/10.5194/acp-13-2563-2013, 2013. a, b
Wallbrink, P. and Murray, A.: Fallout of Be-7 in South Eastern Australia, J. Environ. Radioactiv., 25, 213–228, https://doi.org/10.1016/0265-931X(94)90074-4, 1994. a
Wang, X., Jacob, D. J., Eastham, S. D., Sulprizio, M. P., Zhu, L., Chen, Q., Alexander, B., Sherwen, T., Evans, M. J., Lee, B. H., Haskins, J. D., Lopez-Hilfiker, F. D., Thornton, J. A., Huey, G. L., and Liao, H.: The role of chlorine in global tropospheric chemistry, Atmos. Chem. Phys., 19, 3981–4003, https://doi.org/10.5194/acp-19-3981-2019, 2019. a
Wang, Y. and Jacob, D. J.: Anthropogenic forcing on tropospheric ozone and OH since preindustrial times, J. Geophys. Res.-Atmos., 103, 31123–31135,
https://doi.org/10.1029/1998jd100004, 1998. a
Wang, Y., Jacob, D. J., and Logan, J. A.: Global simulation of tropospheric
O3-NOx-hydrocarbon chemistry: 1. Model formulation, J. Geophys. Res.-Atmos, 103, 10713–10725, https://doi.org/10.1029/98jd00158, 1998a. a
Wang, Y., Jacob, D. J., and Logan, J. A.: Global simulation of tropospheric
O3-NOx-hydrocarbon chemistry: 3. Origin of tropospheric ozone and
effects of nonmethane hydrocarbons, J. Geophys. Res.-Atmos., 103,
10757–10767, https://doi.org/10.1029/98jd00156, 1998b. a
Wang, Y., Logan, J. A., and Jacob, D. J.: Global simulation of tropospheric
O3-NOx-hydrocarbon chemistry: 2. Model evaluation and global ozone
budget, J. Geophys. Res.-Atmos., 103, 10727–10755, https://doi.org/10.1029/98jd00157,
1998c. a
Wang, Y. X., McElroy, M. B., Jacob, D. J., and Yantosca, R. M.: A nested grid formulation for chemical transport over Asia: Applications to CO, J. Geophys. Res.-Atmos., 109, D22307, https://doi.org/10.1029/2004jd005237, 2004. a
Waugh, D.: Atmospheric dynamics: The age of stratospheric air, Nat. Geosci., 2, 14–16, https://doi.org/10.1038/ngeo397, 2009. a
Waugh, D. W. and Hall, T. M.: Age of stratospheric air: Theory, observations, and models, Rev. Geophys., 40, 1010, https://doi.org/10.1029/2000RG000101, 2002. a, b, c
Waugh, D. W., Crotwell, A. M., Dlugokencky, E. J., Dutton, G. S., Elkins,
J. W., Hall, B. D., Hintsa, E. J., Hurst, D. F., Montzka, S. A., Mondeel,
D. J., Moore, F. L., Nance, J. D., Ray, E. A., Steenrod, S. D., Strahan,
S. E., and Sweeney, C.: Tropospheric SF6: Age of air from the Northern
Hemisphere midlatitude surface, J. Geophys. Res.-Atmos., 118, 11429–11441, https://doi.org/10.1002/jgrd.50848, 2013. a
Wilkening, M.: Rn-222 Concentrations in Convective Patterns of a Mountain
Environment, J. Geophys. Res., 75, 1733, https://doi.org/10.1029/JC075i009p01733, 1970. a
Williams, E.: Lightning and climate: A review, Atmos. Res., 76, 272–287,
https://doi.org/10.1016/j.atmosres.2004.11.014, 2005. a
World Meteorological Society (WMO): Scientific Assessment of Ozone
Depletion: 2018, Global Ozone Research and Monitoring Project – Report No.
58, Tech. rep., https://doi.org/10.1080/10962247.2015.1040526, 2018. a
WMO/GAW Ozone Monitoring Community, World Meteorological Organization-Global
Atmosphere Watch Program (WMO-GAW)/World Ozone and Ultraviolet Radiation Data Centre (WOUDC) [data set], retrieved from: https://woudc.org (last access: 4 November 2019), a list of all contributors is available on the website, https://doi.org/10.14287/10000001, 2019. a
Wu, S., Mickley, L. J., Jacob, D. J., Logan, J. A., Yantosca, R. M., and Rind, D.: Why are there large differences between models in global budgets of tropospheric ozone, J. Geophys. Res., 112, D05302, https://doi.org/10.1029/2006jd007801, 2007.
a, b, c
Wu, S., Mickley, L. J., Jacob, D. J., Rind, D., and Streets, D. G.: Effects of 2000–2050 changes in climate and emissions on global tropospheric ozone and the policy-relevant background surface ozone in the United States, J. Geophys. Res., 113, D18312, https://doi.org/10.1029/2007jd009639, 2008a. a
Wu, S., Mickley, L. J., Leibensperger, E. M., Jacob, D. J., Rind, D., and
Streets, D. G.: Effects of 2000–2050 global change on ozone air quality in
the United States, J. Geophys. Res., 113, D06302, https://doi.org/10.1029/2007jd008917, 2008b. a
Yan, Y., Lin, J., Chen, J., and Hu, L.: Improved simulation of tropospheric ozone by a global-multi-regional two-way coupling model system, Atmos. Chem. Phys., 16, 2381–2400, https://doi.org/10.5194/acp-16-2381-2016, 2016. a
Young, P. J., Naik, V., Fiore, A. M., Gaudel, A., Guo, J., Lin, M. Y., Neu,
J. L., Parrish, D. D., Rieder, H. E., Schnell, J. L., Tilmes, S., Wild, O.,
Zhang, L., Ziemke, J. R., Brandt, J., Delcloo, A., Doherty, R. M., Geels, C.,
Hegglin, M. I., Hu, L., Im, U., Kumar, R., Luhar, A., Murray, L. T., Plummer,
D., Rodriguez, J., Saiz-Lopez, A., Schultz, M. G., Woodhouse, M. T., and
Zeng, G.: Tropospheric Ozone Assessment Report: Assessment of global-scale
model performance for global and regional ozone distributions, variability,
and trends, Elem. Sci. Anth., 6, 10, https://doi.org/10.1525/elementa.265, 2018. a
Zender, C. S., Bian, H., and Newman, D.: Mineral Dust Entrainment and
Deposition (DEAD) model: Description and 1990s dust climatology, J. Geophys.
Res., 108, 4416, https://doi.org/10.1029/2002jd002775, 2003. a
Zhu, J., Liao, H., Mao, Y., Yang, Y., and Jiang, H.: Interannual variation, decadal trend, and future change in ozone outflow from East Asia, Atmos. Chem. Phys., 17, 3729–3747, https://doi.org/10.5194/acp-17-3729-2017, 2017. a
Ziemke, J. R., Chandra, S., Duncan, B. N., Froidevaux, L., Bhartia, P. K.,
Levelt, P. F., and Waters, J. W.: Tropospheric ozone determined from aura
OMI and MLS: Evaluation of measurements and comparison with the Global
Modeling Initiative's Chemical Transport Model, J. Geophys. Res.-Atmos., 111, D19303, https://doi.org/10.1029/2006JD007089, 2006. a, b
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.
Chemical-transport models are tools used to study air pollution and inform public policy....
