Articles | Volume 14, issue 9
Geosci. Model Dev., 14, 5789–5823, 2021
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.
Geosci. Model Dev., 14, 5789–5823, 2021
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
GCAP 2.0: a global 3-D chemical-transport model framework for past, present, and future climate scenarios
Lee T. Murray et al.
Related authors
Heejeong Kim, Wendell W. Walters, Claire Bekker, Lee T. Murray, and Meredith G. Hastings
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-622, https://doi.org/10.5194/acp-2022-622, 2022
Preprint under review for ACP
Short summary
Short summary
Atmospheric nitrate has an important impact on human and ecosystem health. We evaluated atmospheric nitrate formation pathways in the northeastern U.S. utilizing oxygen isotope compositions. There was a significant difference between the phases of nitrate, indicating different formation mechanisms. Comparison of the observations with model simulations indicated that N2O5 hydrolysis was overpredicted. Our study has important implications for improving atmospheric chemistry model representation.
Claire Bekker, Wendell W. Walters, Lee T. Murray, and Meredith G. Hastings
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-621, https://doi.org/10.5194/acp-2022-621, 2022
Preprint under review for ACP
Short summary
Short summary
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.
Roisin Commane, Andrew Hallward-Driemeier, and Lee T. Murray
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-272, https://doi.org/10.5194/amt-2022-272, 2022
Preprint under review for AMT
Short summary
Short summary
Methane / ethane ratios can be used to identify and partition the difference 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 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. Discuss., https://doi.org/10.5194/acp-2022-631, https://doi.org/10.5194/acp-2022-631, 2022
Revised manuscript under review for ACP
Short summary
Short summary
We have prepared from the recent ATom aircraft mission a unique and unusual result: 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.
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
Short summary
Short summary
Atmospheric chemistry models like GEOS-Chem are versatile tools widely used in air pollution and climate studies. The simulations used in such studies can be very computationally demanding, and thus it is useful if the model can simulate a specific geographic region at a higher resolution than the rest of the globe. Here, we implement, test, and demonstrate a new variable-resolution capability in GEOS-Chem that is suitable for simulations conducted on supercomputers.
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
Short summary
Short summary
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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. Discuss., https://doi.org/10.5194/acp-2022-749, https://doi.org/10.5194/acp-2022-749, 2022
Preprint under review for ACP
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Over 100 countries plan to cut their methane emissions by 30 % this decade under the 2021 Global Methane Pledge. The oil and gas industry is a high priority for emission reductions, but the temporal variability of oil/gas methane emissions is poorly understood. We used satellite observations to quantify weekly oil/gas methane emissions from the U.S. Permian Basin. We find that Permian emissions are highly variable and stronger than previously known, with diverse economic and activity drivers.
Ruijun Dang, Daniel J. Jacob, Viral Shah, Sebastian D. Eastham, Thibaud M. Fritz, Loretta J. Mickley, Tianjia Liu, Yi Wang, and Jun Wang
EGUsphere, https://doi.org/10.5194/egusphere-2022-1198, https://doi.org/10.5194/egusphere-2022-1198, 2022
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Model underestimate of the background NO2 is largely corrected by considering aerosol nitrate photolysis. Increase in aircraft emissions not only increases the background NO2, but also affects the satellite retrieval by altering the NO2 vertical profile. Increase in wildfire emissions contributes to the flattening of post-2009 OMI NO2 trends in the western US. Our work shows the importance of properly accounting for the free tropospheric background in interpreting satellite NO2 observations.
Heejeong Kim, Wendell W. Walters, Claire Bekker, Lee T. Murray, and Meredith G. Hastings
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-622, https://doi.org/10.5194/acp-2022-622, 2022
Preprint under review for ACP
Short summary
Short summary
Atmospheric nitrate has an important impact on human and ecosystem health. We evaluated atmospheric nitrate formation pathways in the northeastern U.S. utilizing oxygen isotope compositions. There was a significant difference between the phases of nitrate, indicating different formation mechanisms. Comparison of the observations with model simulations indicated that N2O5 hydrolysis was overpredicted. Our study has important implications for improving atmospheric chemistry model representation.
Claire Bekker, Wendell W. Walters, Lee T. Murray, and Meredith G. Hastings
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-621, https://doi.org/10.5194/acp-2022-621, 2022
Preprint under review for ACP
Short summary
Short summary
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.
Roisin Commane, Andrew Hallward-Driemeier, and Lee T. Murray
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-272, https://doi.org/10.5194/amt-2022-272, 2022
Preprint under review for AMT
Short summary
Short summary
Methane / ethane ratios can be used to identify and partition the difference 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 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. Discuss., https://doi.org/10.5194/acp-2022-631, https://doi.org/10.5194/acp-2022-631, 2022
Revised manuscript under review for ACP
Short summary
Short summary
We have prepared from the recent ATom aircraft mission a unique and unusual result: 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.
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
Short summary
Short summary
Atmospheric chemistry models like GEOS-Chem are versatile tools widely used in air pollution and climate studies. The simulations used in such studies can be very computationally demanding, and thus it is useful if the model can simulate a specific geographic region at a higher resolution than the rest of the globe. Here, we implement, test, and demonstrate a new variable-resolution capability in GEOS-Chem that is suitable for simulations conducted on supercomputers.
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
Short summary
Short summary
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
Related subject area
Atmospheric sciences
Lightning assimilation in the WRF model (Version 4.1.1): technique updates and assessment of the applications from regional to hemispheric scales
Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a)
Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement
A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF
A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions
Importance of different parameterization changes for the updated dust cycle modeling in the Community Atmosphere Model (version 6.1)
Evaluation of the NAQFC driven by the NOAA Global Forecast System (version 16): comparison with the WRF-CMAQ during the summer 2019 FIREX-AQ campaign
Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation
Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China
A lumped species approach for the simulation of secondary organic aerosol production from intermediate-volatility organic compounds (IVOCs): application to road transport in PMCAMx-iv (v1.0)
TrackMatcher – a tool for finding intercepts in tracks of geographical positions
Recovery of sparse urban greenhouse gas emissions
Tropospheric transport and unresolved convection: numerical experiments with CLaMS 2.0/MESSy
MUNICH v2.0: a street-network model coupled with SSH-aerosol (v1.2) for multi-pollutant modelling
A preliminary evaluation of FY-4A visible radiance data assimilation by the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for a tropical storm case
Repeatable high-resolution statistical downscaling through deep learning
Atmospherically Relevant Chemistry and Aerosol box model – ARCA box (version 1.2)
MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films
Introduction of the DISAMAR radiative transfer model: determining instrument specifications and analysing methods for atmospheric retrieval (version 4.1.5)
Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study
Downscaling atmospheric chemistry simulations with physically consistent deep learning
A machine learning methodology for the generation of a parameterization of the hydroxyl radical
Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs
Hybrid ensemble-variational data assimilation in ABC-DA within a tropical framework
OpenIFS/AC: atmospheric chemistry and aerosol in OpenIFS 43r3
Simulations of aerosol pH in China using WRF-Chem (v4.0): sensitivities of aerosol pH and its temporal variations during haze episodes
A daily highest air temperature estimation method and spatial–temporal changes analysis of high temperature in China from 1979 to 2018
TransClim (v1.0): a chemistry–climate response model for assessing the effect of mitigation strategies for road traffic on ozone
A description of the first open-source community release of MISTRA-v9.0: a 0D/1D atmospheric boundary layer chemistry model
Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations
Computationally efficient methods for large-scale atmospheric inverse modeling
Improving the joint estimation of CO2 and surface carbon fluxes using a constrained ensemble Kalman filter in COLA (v1.0)
Evaluation of high-resolution predictions of fine particulate matter and its composition in an urban area using PMCAMx-v2.0
RAP-Net: Region Attention Predictive Network for precipitation nowcasting
Effects of point source emission heights in WRF–STILT: a step towards exploiting nocturnal observations in models
uDALES 1.0: a large-eddy simulation model for urban environments
Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)'s Global Ensemble Forecast System (GEFS-Aerosols v1)
Assimilation of GPM-retrieved ocean surface meteorology data for two snowstorm events during ICE-POP 2018
A multi-pollutant and multi-sectorial approach to screening the consistency of emission inventories
Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain. A case study using WRF-LES V4.0.1
Transporting CRM Variance in a Multiscale Modelling Framework
Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): a protocol for investigating the role of stratospheric polar vortex disturbances in subseasonal to seasonal forecasts
The Mission Support System (MSS v7.0.0) and its use in planning for the SouthTRAC aircraft campaign
Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate δ13C(CH4) and CH4: a case study with model LMDz-SACS
The Comprehensive Automobile Research System (CARS) – a Python-based automobile emissions inventory model
Validation of turbulent heat transfer models against eddy covariance flux measurements over a seasonally ice-covered lake
Regional evaluation of the performance of the global CAMS chemical modeling system over the United States (IFS cycle 47r1)
Order of magnitude wall time improvement of variational methane inversions by physical parallelization: a demonstration using TM5-4DVAR
Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in the Weather Research and Forecasting (v4.1.3) model during the ICE-POP 2018 field campaign
GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): An automatic generation tool of semi-explicit mechanisms
Daiwen Kang, Nicholas K. Heath, Robert C. Gilliam, Tanya L. Spero, and Jonathan E. Pleim
Geosci. Model Dev., 15, 8561–8579, https://doi.org/10.5194/gmd-15-8561-2022, https://doi.org/10.5194/gmd-15-8561-2022, 2022
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A lightning assimilation (LTA) technique implemented in the WRF model's Kain–Fritsch (KF) convective scheme is updated and applied to simulations from regional to hemispheric scales using observed lightning flashes from ground-based lightning detection networks. Different user-toggled options associated with the KF scheme on simulations with and without LTA are assessed. The model's performance is improved significantly by LTA, but it is sensitive to various factors.
Sujeong Lim, Hyeon-Ju Gim, Ebony Lee, Seungyeon Lee, Won Young Lee, Yong Hee Lee, Claudio Cassardo, and Seon Ki Park
Geosci. Model Dev., 15, 8541–8559, https://doi.org/10.5194/gmd-15-8541-2022, https://doi.org/10.5194/gmd-15-8541-2022, 2022
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The land surface model (LSM) contains various uncertain parameters, which are obtained by the empirical relations reflecting the specific local region and can be a source of uncertainty. To seek the optimal parameter values in the snow-related processes of the Noah LSM over South Korea, we have implemented an optimization algorithm, a micro-genetic algorithm using the observations. As a result, the optimized snow parameters improve snowfall prediction.
Haochen Sun, Jimmy C. H. Fung, Yiang Chen, Zhenning Li, Dehao Yuan, Wanying Chen, and Xingcheng Lu
Geosci. Model Dev., 15, 8439–8452, https://doi.org/10.5194/gmd-15-8439-2022, https://doi.org/10.5194/gmd-15-8439-2022, 2022
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This study developed a novel deep-learning layer, the broadcasting layer, to build an end-to-end LSTM-based deep-learning model for regional air pollution forecast. By combining the ground observation, WRF-CMAQ simulation, and the broadcasting LSTM deep-learning model, forecast accuracy has been significantly improved when compared to other methods. The broadcasting layer and its variants can also be applied in other research areas to supersede the traditional numerical interpolation methods.
Shunji Kotsuki, Takemasa Miyoshi, Keiichi Kondo, and Roland Potthast
Geosci. Model Dev., 15, 8325–8348, https://doi.org/10.5194/gmd-15-8325-2022, https://doi.org/10.5194/gmd-15-8325-2022, 2022
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Data assimilation plays an important part in numerical weather prediction (NWP) in terms of combining forecasted states and observations. While data assimilation methods in NWP usually assume the Gaussian error distribution, some variables in the atmosphere, such as precipitation, are known to have non-Gaussian error statistics. This study extended a widely used ensemble data assimilation algorithm to enable the assimilation of more non-Gaussian observations.
Martin Vojta, Andreas Plach, Rona L. Thompson, and Andreas Stohl
Geosci. Model Dev., 15, 8295–8323, https://doi.org/10.5194/gmd-15-8295-2022, https://doi.org/10.5194/gmd-15-8295-2022, 2022
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In light of recent global warming, we aim to improve methods for modeling greenhouse gas emissions in order to support the successful implementation of the Paris Agreement. In this study, we investigate certain aspects of a Bayesian inversion method that uses computer simulations and atmospheric observations to improve estimates of greenhouse gas emissions. We explore method limitations, discuss problems, and suggest improvements.
Longlei Li, Natalie M. Mahowald, Jasper F. Kok, Xiaohong Liu, Mingxuan Wu, Danny M. Leung, Douglas S. Hamilton, Louisa K. Emmons, Yue Huang, Neil Sexton, Jun Meng, and Jessica Wan
Geosci. Model Dev., 15, 8181–8219, https://doi.org/10.5194/gmd-15-8181-2022, https://doi.org/10.5194/gmd-15-8181-2022, 2022
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This study advances mineral dust parameterizations in the Community Atmospheric Model (CAM; version 6.1). Efforts include 1) incorporating a more physically based dust emission scheme; 2) updating the dry deposition scheme; and 3) revising the gravitational settling velocity to account for dust asphericity. Substantial improvements achieved with these updates can help accurately quantify dust–climate interactions using CAM, such as the dust-radiation and dust–cloud interactions.
Youhua Tang, Patrick C. Campbell, Pius Lee, Rick Saylor, Fanglin Yang, Barry Baker, Daniel Tong, Ariel Stein, Jianping Huang, Ho-Chun Huang, Li Pan, Jeff McQueen, Ivanka Stajner, Jose Tirado-Delgado, Youngsun Jung, Melissa Yang, Ilann Bourgeois, Jeff Peischl, Tom Ryerson, Donald Blake, Joshua Schwarz, Jose-Luis Jimenez, James Crawford, Glenn Diskin, Richard Moore, Johnathan Hair, Greg Huey, Andrew Rollins, Jack Dibb, and Xiaoyang Zhang
Geosci. Model Dev., 15, 7977–7999, https://doi.org/10.5194/gmd-15-7977-2022, https://doi.org/10.5194/gmd-15-7977-2022, 2022
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This paper compares two meteorological datasets for driving a regional air quality model: a regional meteorological model using WRF (WRF-CMAQ) and direct interpolation from an operational global model (GFS-CMAQ). In the comparison with surface measurements and aircraft data in summer 2019, these two methods show mixed performance depending on the corresponding meteorological settings. Direct interpolation is found to be a viable method to drive air quality models.
Zhiquan Liu, Chris Snyder, Jonathan J. Guerrette, Byoung-Joo Jung, Junmei Ban, Steven Vahl, Yali Wu, Yannick Trémolet, Thomas Auligné, Benjamin Ménétrier, Anna Shlyaeva, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 15, 7859–7878, https://doi.org/10.5194/gmd-15-7859-2022, https://doi.org/10.5194/gmd-15-7859-2022, 2022
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JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the MPAS model, was publicly released for community use. This article describes JEDI-MPAS's implementation of the ensemble–variational DA technique and demonstrates its robustness and credible performance by incrementally adding three types of microwave radiances (clear-sky AMSU-A, all-sky AMSU-A, clear-sky MHS) to a non-radiance DA experiment. We intend to periodically release new and improved versions of JEDI-MPAS in upcoming years.
Li Fang, Jianbing Jin, Arjo Segers, Hai Xiang Lin, Mijie Pang, Cong Xiao, Tuo Deng, and Hong Liao
Geosci. Model Dev., 15, 7791–7807, https://doi.org/10.5194/gmd-15-7791-2022, https://doi.org/10.5194/gmd-15-7791-2022, 2022
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This study proposes a regional feature selection-based machine learning system to predict short-term air quality in China. The system has a tool that can figure out the importance of input data for better prediction. It provides large-scale air quality prediction that exhibits improved interpretability, fewer training costs, and higher accuracy compared with a standard machine learning system. It can act as an early warning for citizens and reduce exposure to PM2.5 and other air pollutants.
Stella E. I. Manavi and Spyros N. Pandis
Geosci. Model Dev., 15, 7731–7749, https://doi.org/10.5194/gmd-15-7731-2022, https://doi.org/10.5194/gmd-15-7731-2022, 2022
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The paper describes the first step towards the development of a simulation framework for the chemistry and secondary organic aerosol production of intermediate-volatility organic compounds (IVOCs). These compounds can be a significant source of organic particulate matter. Our approach treats IVOCs as lumped compounds that retain their chemical characteristics. Estimated IVOC emissions from road transport were a factor of 8 higher than emissions used in previous applications.
Peter Bräuer and Matthias Tesche
Geosci. Model Dev., 15, 7557–7572, https://doi.org/10.5194/gmd-15-7557-2022, https://doi.org/10.5194/gmd-15-7557-2022, 2022
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This paper presents a tool for (i) finding temporally and spatially resolved intersections between two- or three-dimensional geographical tracks (trajectories) and (ii) extracting of data in the vicinity of intersections to achieve the optimal combination of various data sets.
Benjamin Zanger, Jia Chen, Man Sun, and Florian Dietrich
Geosci. Model Dev., 15, 7533–7556, https://doi.org/10.5194/gmd-15-7533-2022, https://doi.org/10.5194/gmd-15-7533-2022, 2022
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Gaussian priors (GPs) used in least squares inversion do not reflect the true distributions of greenhouse gas emissions well. A method that does not rely on GPs is sparse reconstruction (SR). We show that necessary conditions for SR are satisfied for cities and that the application of a wavelet transform can further enhance sparsity. We apply the theory of compressed sensing to SR. Our results show that SR needs fewer measurements and is superior for assessing unknown emitters compared to GPs.
Paul Konopka, Mengchu Tao, Marc von Hobe, Lars Hoffmann, Corinna Kloss, Fabrizio Ravegnani, C. Michael Volk, Valentin Lauther, Andreas Zahn, Peter Hoor, and Felix Ploeger
Geosci. Model Dev., 15, 7471–7487, https://doi.org/10.5194/gmd-15-7471-2022, https://doi.org/10.5194/gmd-15-7471-2022, 2022
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Pure trajectory-based transport models driven by meteorology derived from reanalysis products (ERA5) take into account only the resolved, advective part of transport. That means neither mixing processes nor unresolved subgrid-scale advective processes like convection are included. The Chemical Lagrangian Model of the Stratosphere (CLaMS) includes these processes. We show that isentropic mixing dominates unresolved transport. The second most important transport process is unresolved convection.
Youngseob Kim, Lya Lugon, Alice Maison, Thibaud Sarica, Yelva Roustan, Myrto Valari, Yang Zhang, Michel André, and Karine Sartelet
Geosci. Model Dev., 15, 7371–7396, https://doi.org/10.5194/gmd-15-7371-2022, https://doi.org/10.5194/gmd-15-7371-2022, 2022
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This paper presents the latest version of the street-network model MUNICH, v2.0. The description of MUNICH v1.0, which models gas-phase pollutants in a street network, was published in GMD in 2018. Since then, major modifications have been made to MUNICH. The comprehensive aerosol model SSH-aerosol is now coupled to MUNICH to simulate primary and secondary aerosol concentrations. New parameterisations have also been introduced. Test cases are defined to illustrate the new model functionalities.
Yongbo Zhou, Yubao Liu, Zhaoyang Huo, and Yang Li
Geosci. Model Dev., 15, 7397–7420, https://doi.org/10.5194/gmd-15-7397-2022, https://doi.org/10.5194/gmd-15-7397-2022, 2022
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The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped with the recently added forward operator Radiative Transfer for TOVS (RTTOV), in assimilating FY-4A visible images into the Weather Research and Forecasting (WRF) model. The ability of the WRF-DART/RTTOV system to improve the forecasting skills for a tropical storm over East Asia and the Western Pacific is demonstrated in an Observing System Simulation Experiment framework.
Dánnell Quesada-Chacón, Klemens Barfus, and Christian Bernhofer
Geosci. Model Dev., 15, 7353–7370, https://doi.org/10.5194/gmd-15-7353-2022, https://doi.org/10.5194/gmd-15-7353-2022, 2022
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We improved the performance of past perfect prognosis statistical downscaling methods while achieving full model repeatability with GPU-calculated deep learning models using the TensorFlow, climate4R, and VALUE frameworks. We employed the ERA5 reanalysis as predictors and ReKIS (eastern Ore Mountains, Germany, 1 km resolution) as precipitation predictand, while incorporating modern deep learning architectures. The achieved repeatability is key to accomplish further milestones with deep learning.
Petri Clusius, Carlton Xavier, Lukas Pichelstorfer, Putian Zhou, Tinja Olenius, Pontus Roldin, and Michael Boy
Geosci. Model Dev., 15, 7257–7286, https://doi.org/10.5194/gmd-15-7257-2022, https://doi.org/10.5194/gmd-15-7257-2022, 2022
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Atmospheric chemistry and aerosol processes form a dynamic and sensitively balanced system, and solving problems regarding air quality or climate requires detailed modelling and coupling of the processes. The models involved are often very complex to use. We have addressed this problem with the new ARCA box model. It puts much of the current knowledge of the nano- and microscale aerosol dynamics and chemistry into usable software and has the potential to become a valuable tool in the community.
Adam Milsom, Amy Lees, Adam M. Squires, and Christian Pfrang
Geosci. Model Dev., 15, 7139–7151, https://doi.org/10.5194/gmd-15-7139-2022, https://doi.org/10.5194/gmd-15-7139-2022, 2022
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MultilayerPy is a Python-based framework facilitating the creation, running and optimisation of state-of-the-art kinetic multi-layer models of aerosol and film processes. Models can be fit to data with local and global optimisation algorithms along with a statistical sampling algorithm, which quantifies the uncertainty in optimised model parameters. This “modelling study in a box” enables more reproducible and reliable results, with model code and outputs produced in a human-readable way.
Johan F. de Haan, Ping Wang, Maarten Sneep, J. Pepijn Veefkind, and Piet Stammes
Geosci. Model Dev., 15, 7031–7050, https://doi.org/10.5194/gmd-15-7031-2022, https://doi.org/10.5194/gmd-15-7031-2022, 2022
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We present an overview of the DISAMAR radiative transfer code, highlighting the novel semi-analytical derivatives for the doubling–adding formulae and the new DISMAS technique for weak absorbers. DISAMAR includes forward simulations and retrievals for satellite spectral measurements from 270 to 2400 nm to determine instrument specifications for passive remote sensing. It has been used in various Sentinel-4/5P/5 projects and in the TROPOMI aerosol layer height and ozone profile products.
Ivette H. Banos, Will D. Mayfield, Guoqing Ge, Luiz F. Sapucci, Jacob R. Carley, and Louisa Nance
Geosci. Model Dev., 15, 6891–6917, https://doi.org/10.5194/gmd-15-6891-2022, https://doi.org/10.5194/gmd-15-6891-2022, 2022
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A prototype data assimilation system for NOAA’s next-generation rapidly updated, convection-allowing forecast system, or Rapid Refresh Forecast System (RRFS) v0.1, is tested and evaluated. The impact of using data assimilation with a convective storm case study is examined. Although the convection in RRFS tends to be overestimated in intensity and underestimated in extent, the use of data assimilation proves to be crucial to improve short-term forecasts of storms and precipitation.
Andrew Geiss, Sam J. Silva, and Joseph C. Hardin
Geosci. Model Dev., 15, 6677–6694, https://doi.org/10.5194/gmd-15-6677-2022, https://doi.org/10.5194/gmd-15-6677-2022, 2022
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This work demonstrates the use of modern machine learning techniques to enhance the resolution of atmospheric chemistry simulations. We evaluate the schemes for an 8 x 10 increase in resolution and find that they perform substantially better than conventional methods. Methods are introduced to target machine learning methods towards this type of problem, most notably by ensuring they do not break known physical constraints.
Daniel C. Anderson, Melanie B. Follette-Cook, Sarah A. Strode, Julie M. Nicely, Junhua Liu, Peter D. Ivatt, and Bryan N. Duncan
Geosci. Model Dev., 15, 6341–6358, https://doi.org/10.5194/gmd-15-6341-2022, https://doi.org/10.5194/gmd-15-6341-2022, 2022
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The hydroxyl radical (OH) is the most important chemical in the atmosphere for removing certain pollutants, including methane, the second-most-important greenhouse gas. We present a methodology to create an easily modifiable parameterization that can calculate OH concentrations in a computationally efficient way. The parameterization, which predicts OH within 5 %, can be integrated into larger climate models to allow for calculation of the interactions between OH, methane, and other chemicals.
Akshay Sridhar, Yassine Tissaoui, Simone Marras, Zhaoyi Shen, Charles Kawczynski, Simon Byrne, Kiran Pamnany, Maciej Waruszewski, Thomas H. Gibson, Jeremy E. Kozdon, Valentin Churavy, Lucas C. Wilcox, Francis X. Giraldo, and Tapio Schneider
Geosci. Model Dev., 15, 6259–6284, https://doi.org/10.5194/gmd-15-6259-2022, https://doi.org/10.5194/gmd-15-6259-2022, 2022
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ClimateMachine is a new open-source Julia-language atmospheric modeling code. We describe its limited-area configuration and the model equations, and we demonstrate applicability through benchmark problems, including atmospheric flow in the shallow cumulus regime. We show that the discontinuous Galerkin numerics and model equations allow global conservation of key variables (up to sources and sinks). We assess CPU strong scaling and GPU weak scaling to show its suitability for large simulations.
Joshua Chun Kwang Lee, Javier Amezcua, and Ross Noel Bannister
Geosci. Model Dev., 15, 6197–6219, https://doi.org/10.5194/gmd-15-6197-2022, https://doi.org/10.5194/gmd-15-6197-2022, 2022
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In this article, we implement a novel data assimilation method for the ABC–DA system which combines traditional data assimilation approaches in a hybrid approach. We document the technical development and test the hybrid approach in idealised experiments within a tropical framework of the ABC–DA system. Our findings indicate that the hybrid approach outperforms individual traditional approaches. Its potential benefits have been highlighted and should be explored further within this framework.
Vincent Huijnen, Philippe Le Sager, Marcus O. Köhler, Glenn Carver, Samuel Rémy, Johannes Flemming, Simon Chabrillat, Quentin Errera, and Twan van Noije
Geosci. Model Dev., 15, 6221–6241, https://doi.org/10.5194/gmd-15-6221-2022, https://doi.org/10.5194/gmd-15-6221-2022, 2022
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We report on the first implementation of atmospheric chemistry and aerosol as part of the OpenIFS model, based on the CAMS global model. We give an overview of the model and evaluate two reference model configurations, with and without the stratospheric chemistry extension, against a variety of observational datasets. This OpenIFS version with atmospheric composition components is open to the scientific user community under a standard OpenIFS license.
Xueyin Ruan, Chun Zhao, Rahul A. Zaveri, Pengzhen He, Xinming Wang, Jingyuan Shao, and Lei Geng
Geosci. Model Dev., 15, 6143–6164, https://doi.org/10.5194/gmd-15-6143-2022, https://doi.org/10.5194/gmd-15-6143-2022, 2022
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Accurate prediction of aerosol pH in chemical transport models is essential to aerosol modeling. This study examines the performance of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) on aerosol pH predictions and the sensitivities to emissions of nonvolatile cations and NH3, aerosol-phase state assumption, and heterogeneous sulfate production. Temporal evolution of aerosol pH during haze cycles in Beijing and the driving factors are also presented and discussed.
Ping Wang, Kebiao Mao, Fei Meng, Zhihao Qin, Shu Fang, and Sayed M. Bateni
Geosci. Model Dev., 15, 6059–6083, https://doi.org/10.5194/gmd-15-6059-2022, https://doi.org/10.5194/gmd-15-6059-2022, 2022
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In order to obtain the key parameters of high-temperature spatial–temporal variation analysis, this study proposed a daily highest air temperature (Tmax) estimation frame to build a Tmax dataset in China from 1979 to 2018. We found that the annual and seasonal mean Tmax in most areas of China showed an increasing trend. The abnormal temperature changes mainly occurred in El Nin~o years or La Nin~a years. IOBW had a stronger influence on China's warming events than other factors.
Vanessa Simone Rieger and Volker Grewe
Geosci. Model Dev., 15, 5883–5903, https://doi.org/10.5194/gmd-15-5883-2022, https://doi.org/10.5194/gmd-15-5883-2022, 2022
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Road traffic emissions of nitrogen oxides, volatile organic compounds and carbon monoxide produce ozone in the troposphere and thus influence Earth's climate. To assess the ozone response to a broad range of mitigation strategies for road traffic, we developed a new chemistry–climate response model called TransClim. It is based on lookup tables containing climate–response relations and thus is able to quickly determine the climate response of a mitigation option.
Josué Bock, Jan Kaiser, Max Thomas, Andreas Bott, and Roland von Glasow
Geosci. Model Dev., 15, 5807–5828, https://doi.org/10.5194/gmd-15-5807-2022, https://doi.org/10.5194/gmd-15-5807-2022, 2022
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MISTRA-v9.0 is an atmospheric boundary layer chemistry model. The model includes a detailed particle description with regards to the microphysics, gas–particle interactions, and liquid phase chemistry within particles. Version 9.0 is the first release of MISTRA as an open-source community model. This paper presents a thorough description of the model characteristics and components. We show some examples of simulations reproducing previous studies with MISTRA with good consistency.
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.
Taewon Cho, Julianne Chung, Scot M. Miller, and Arvind K. Saibaba
Geosci. Model Dev., 15, 5547–5565, https://doi.org/10.5194/gmd-15-5547-2022, https://doi.org/10.5194/gmd-15-5547-2022, 2022
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Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for more detailed maps of surface fluxes have yielded numerous computational and statistical challenges. This article describes computationally efficient methods for large-scale atmospheric inverse modeling.
Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Bo Wu, Qixiang Cai, Di Liu, and Pengfei Han
Geosci. Model Dev., 15, 5511–5528, https://doi.org/10.5194/gmd-15-5511-2022, https://doi.org/10.5194/gmd-15-5511-2022, 2022
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We described the application of a constrained ensemble Kalman filter (CEnKF) in a joint CO2 and surface carbon fluxes estimation study. By assimilating the pseudo-surface and OCO-2 observations, the annual global flux estimation is significantly biased without mass conservation. With the additional CEnKF process, the CO2 mass is strictly constrained, and the estimation of annual fluxes is significantly improved.
Brian T. Dinkelacker, Pablo Garcia Rivera, Ioannis Kioutsioukis, Peter J. Adams, and Spyros N. Pandis
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-145, https://doi.org/10.5194/gmd-2022-145, 2022
Revised manuscript accepted for GMD
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The performance of a chemical transport model in reproducing PM2.5 concentrations and composition was evaluated at the finest scale using measurements from regulatory sites as well as a network of low-cost monitors. Total PM2.5 mass is reproduced well by the model during the winter when compared to regulatory measurements, but in the summer PM2.5 is underpredicted mainly due to difficulties in reproducing the regional secondary organic aerosol levels.
Zheng Zhang, Chuyao Luo, Shanshan Feng, Rui Ye, Yunming Ye, and Xutao Li
Geosci. Model Dev., 15, 5407–5419, https://doi.org/10.5194/gmd-15-5407-2022, https://doi.org/10.5194/gmd-15-5407-2022, 2022
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In this paper, we develop a model to predict radar echo sequences and apply it in the precipitation nowcasting field. Different from existing models, we propose two new attention modules. By introducing them, the performance of RAP-Net outperforms other models, especially in those regions with moderate and heavy rainfall. Considering that these regions cause more threats to human activities, the research in our work is significant for preventing natural disasters caused by heavy rainfall.
Fabian Maier, Christoph Gerbig, Ingeborg Levin, Ingrid Super, Julia Marshall, and Samuel Hammer
Geosci. Model Dev., 15, 5391–5406, https://doi.org/10.5194/gmd-15-5391-2022, https://doi.org/10.5194/gmd-15-5391-2022, 2022
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We show that the default representation of point source emissions in WRF–STILT leads to large overestimations when modelling fossil fuel CO2 concentrations for a 30 m high observation site during stable atmospheric conditions. We therefore introduce a novel point source modelling approach in WRF-STILT that takes into account their effective emission heights and results in a much better agreement with observations.
Ivo Suter, Tom Grylls, Birgit S. Sützl, Sam O. Owens, Chris E. Wilson, and Maarten van Reeuwijk
Geosci. Model Dev., 15, 5309–5335, https://doi.org/10.5194/gmd-15-5309-2022, https://doi.org/10.5194/gmd-15-5309-2022, 2022
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Cities are increasingly moving to the fore of climate and air quality research due to their central role in the population’s health and well-being, while suitable models remain scarce. This article describes the development of a new urban LES model, which allows examining the effects of various processes, infrastructure and vegetation on the local climate and air quality. Possible applications are demonstrated and a comparison to an experiment is shown.
Li Zhang, Raffaele Montuoro, Stuart A. McKeen, Barry Baker, Partha S. Bhattacharjee, Georg A. Grell, Judy Henderson, Li Pan, Gregory J. Frost, Jeff McQueen, Rick Saylor, Haiqin Li, Ravan Ahmadov, Jun Wang, Ivanka Stajner, Shobha Kondragunta, Xiaoyang Zhang, and Fangjun Li
Geosci. Model Dev., 15, 5337–5369, https://doi.org/10.5194/gmd-15-5337-2022, https://doi.org/10.5194/gmd-15-5337-2022, 2022
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The NOAA’s air quality predictions contribute to protecting lives and health in the US, which requires sustainable development and improvement of forecast systems. GEFS-Aerosols v1 has been developed in a collaboration between the NOAA research laboratories for operational forecast since September 2020 in the NCEP. The predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational system.
Xuanli Li, Jason B. Roberts, Jayanthi Srikishen, Jonathan L. Case, Walter A. Petersen, Gyuwon Lee, and Christopher R. Hain
Geosci. Model Dev., 15, 5287–5308, https://doi.org/10.5194/gmd-15-5287-2022, https://doi.org/10.5194/gmd-15-5287-2022, 2022
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This research assimilated the Global Precipitation Measurement (GPM) satellite-retrieved ocean surface meteorology data into the Weather Research and Forecasting (WRF) model with the Gridpoint Statistical Interpolation (GSI) system. This was for two snowstorms during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games' (ICE-POP 2018) field experiments. The results indicated a positive impact of the data for short-term forecasts for heavy snowfall.
Philippe Thunis, Alain Clappier, Enrico Pisoni, Bertrand Bessagnet, Jeroen Kuenen, Marc Guevara, and Susana Lopez-Aparicio
Geosci. Model Dev., 15, 5271–5286, https://doi.org/10.5194/gmd-15-5271-2022, https://doi.org/10.5194/gmd-15-5271-2022, 2022
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In this work, we propose a screening method to improve the quality of emission inventories, which are responsible for large uncertainties in air-quality modeling. The first step of screening consists of keeping only emission contributions that are relevant enough. In a second step, the method identifies large differences that provide evidence of methodological divergence or errors. We used the approach to compare two versions of the CAMS-REG European-scale inventory over 150 European cities.
Julian Quimbayo-Duarte, Johannes Wagner, Norman Wildmann, Thomas Gerz, and Juerg Schmidli
Geosci. Model Dev., 15, 5195–5209, https://doi.org/10.5194/gmd-15-5195-2022, https://doi.org/10.5194/gmd-15-5195-2022, 2022
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The ultimate objective of this model evaluation is to improve boundary layer flow representation over complex terrain. The numerical model is tested against observations retrieved during the Perdigão 2017 field campaign (moderate complex terrain). We observed that the inclusion of a forest parameterization in the numerical model significantly improves the representation of the wind field in the atmospheric boundary layer.
Walter Hannah and Kyle Pressel
EGUsphere, https://doi.org/10.5194/egusphere-2022-397, https://doi.org/10.5194/egusphere-2022-397, 2022
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A multiscale modelling framework couples two models of the atmosphere that each cover different scale ranges. Traditionally, fluctuations in the small-scale model are not transported by the flow on the large-scale model grid, but this is hypothesized to be responsible for a persistent, unphysical checkerboard pattern. A method is presented to facilitate the transport of these small-scale fluctuations, analogous to how small-scale clouds and turbulence are transported in the real atmosphere.
Peter Hitchcock, Amy Butler, Andrew Charlton-Perez, Chaim I. Garfinkel, Tim Stockdale, James Anstey, Dann Mitchell, Daniela I. V. Domeisen, Tongwen Wu, Yixiong Lu, Daniele Mastrangelo, Piero Malguzzi, Hai Lin, Ryan Muncaster, Bill Merryfield, Michael Sigmond, Baoqiang Xiang, Liwei Jia, Yu-Kyung Hyun, Jiyoung Oh, Damien Specq, Isla R. Simpson, Jadwiga H. Richter, Cory Barton, Jeff Knight, Eun-Pa Lim, and Harry Hendon
Geosci. Model Dev., 15, 5073–5092, https://doi.org/10.5194/gmd-15-5073-2022, https://doi.org/10.5194/gmd-15-5073-2022, 2022
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This paper describes an experimental protocol focused on sudden stratospheric warmings to be carried out by subseasonal forecast modeling centers. These will allow for inter-model comparisons of these major disruptions to the stratospheric polar vortex and their impacts on the near-surface flow. The protocol will lead to new insights into the contribution of the stratosphere to subseasonal forecast skill and new approaches to the dynamical attribution of extreme events.
Reimar Bauer, Jens-Uwe Grooß, Jörn Ungermann, May Bär, Markus Geldenhuys, and Lars Hoffmann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-155, https://doi.org/10.5194/gmd-2022-155, 2022
Revised manuscript accepted for GMD
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The Mission Support System (MSS) is an open source software package that has been used for planning flight tracks of scientific aircraft in multiple measurements campaigns during the last decade. Here, we describe the MSS software and its use during the SouthTRAC measurement campaign in 2019. As example for how the MSS software is used in conjunction with many data sets, we describe the planning of a single flight probing orographic gravity waves propagating up into the lower mesosphere.
Joël Thanwerdas, Marielle Saunois, Antoine Berchet, Isabelle Pison, Bruce H. Vaughn, Sylvia Englund Michel, and Philippe Bousquet
Geosci. Model Dev., 15, 4831–4851, https://doi.org/10.5194/gmd-15-4831-2022, https://doi.org/10.5194/gmd-15-4831-2022, 2022
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Estimating CH4 sources by exploiting observations within an inverse modeling framework is a powerful approach. Here, a new system designed to assimilate δ13C(CH4) observations together with CH4 observations is presented. By optimizing both the emissions and associated source signatures of multiple emission categories, this new system can efficiently differentiate the co-located emission categories and provide estimates of CH4 sources that are consistent with isotopic data.
Bok H. Baek, Rizzieri Pedruzzi, Minwoo Park, Chi-Tsan Wang, Younha Kim, Chul-Han Song, and Jung-Hun Woo
Geosci. Model Dev., 15, 4757–4781, https://doi.org/10.5194/gmd-15-4757-2022, https://doi.org/10.5194/gmd-15-4757-2022, 2022
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The Comprehensive Automobile Research System (CARS) is an open-source Python-based automobile emissions inventory model designed to efficiently estimate high-quality emissions. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road-link-level network information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced inventory for policymakers, stakeholders, and the air quality modeling community.
Joonatan Ala-Könni, Kukka-Maaria Kohonen, Matti Leppäranta, and Ivan Mammarella
Geosci. Model Dev., 15, 4739–4755, https://doi.org/10.5194/gmd-15-4739-2022, https://doi.org/10.5194/gmd-15-4739-2022, 2022
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Properties of seasonally ice-covered lakes are not currently sufficiently included in global climate models. To fill this gap, this study evaluates three models that could be used to quantify the amount of heat that moves from and into the lake by the air above it and through evaporation of the ice cover. The results show that the complex nature of the surrounding environment as well as difficulties in accurately measuring the surface temperature of ice introduce errors to these models.
Jason E. Williams, Vincent Huijnen, Idir Bouarar, Mehdi Meziane, Timo Schreurs, Sophie Pelletier, Virginie Marécal, Beatrice Josse, and Johannes Flemming
Geosci. Model Dev., 15, 4657–4687, https://doi.org/10.5194/gmd-15-4657-2022, https://doi.org/10.5194/gmd-15-4657-2022, 2022
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The global CAMS air quality model is used for providing tropospheric ozone information to end users. This paper updates the chemical mechanism employed (CBA) and compares it against two other mechanisms (MOCAGE, MOZART) and a multi-decadal dataset based on a previous version of CBA. We perform extensive validation for the US using multiple surface and aircraft datasets, providing an assessment of biases and the extent of correlation across different seasons during 2014.
Sudhanshu Pandey, Sander Houweling, and Arjo Segers
Geosci. Model Dev., 15, 4555–4567, https://doi.org/10.5194/gmd-15-4555-2022, https://doi.org/10.5194/gmd-15-4555-2022, 2022
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Inversions are used to calculate methane emissions using atmospheric mole-fraction measurements. Multidecadal inversions are needed to extract information from the long measurement records of methane. However, multidecadal inversion computations can take months to finish. Here, we demonstrate an order of magnitude improvement in wall clock time for an iterative multidecadal inversion by physical parallelization of chemical transport model.
Jeong-Su Ko, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, Gregory Thompson, and Alexis Berne
Geosci. Model Dev., 15, 4529–4553, https://doi.org/10.5194/gmd-15-4529-2022, https://doi.org/10.5194/gmd-15-4529-2022, 2022
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This study evaluates the performance of the four microphysics parameterizations, the WDM6, WDM7, Thompson, and Morrison schemes, in simulating snowfall events during the ICE-POP 2018 field campaign. Eight snowfall events are selected and classified into three categories (cold-low, warm-low, and air–sea interaction cases). The evaluation focuses on the simulated hydrometeors, microphysics budgets, wind fields, and precipitation using the measurement data.
Zhizhao Wang, Florian Couvidat, and Karine Sartelet
EGUsphere, https://doi.org/10.5194/egusphere-2022-245, https://doi.org/10.5194/egusphere-2022-245, 2022
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3D air-quality models need to represent the complexity of SOA formation with a manageable computational cost. Therefore, we developed GENOA v1.0, an algorithm to generate semi-explicit SOA mechanisms preserving the accuracy of the explicit VOC chemical mechanisms on SOA formation. When applying to the MCM degradation scheme of beta-caryophyllene, GENOA managed to reduce the mechanism from 1626 to 23 reactions and from 579 to 15 species with an average error lower than 3 %.
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....
