Articles | Volume 9, issue 2
https://doi.org/10.5194/gmd-9-799-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-799-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
The description and validation of the computationally Efficient CH4–CO–OH (ECCOHv1.01) chemistry module for 3-D model applications
Yasin F. Elshorbany
CORRESPONDING AUTHOR
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Earth System Science Interdisciplinary Center, University of Maryland,
College Park, Maryland, USA
Bryan N. Duncan
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Sarah A. Strode
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Universities Space Research Association, Columbia, Maryland, USA
James S. Wang
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Universities Space Research Association, Columbia, Maryland, USA
Jules Kouatchou
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Science Systems and Applications Inc., Lanham, Maryland, USA
Related authors
Yasin Elshorbany, Jerald Ziemke, Sarah Strode, Hervé Petetin, Kazuyuki Miyazaki, Isabelle De Smedt, Kenneth Pickering, Rodrigo Seguel, Helen Worden, Tamara Emmerichs, Domenico Taraborrelli, Maria Cazorla, Suvarna Fadnavis, Rebecca Buchholz, Benjamin Gaubert, Néstor Rojas, Thiago Nogueira, Thérèse Salameh, and Min Huang
EGUsphere, https://doi.org/10.5194/egusphere-2024-720, https://doi.org/10.5194/egusphere-2024-720, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We investigated tropospheric ozone spatial variability and trends from 2005 to 2019 and related those to ozone precursors on global and regional scales. We also investigate the spatiotemporal characteristics of the ozone formation regime in relation to ozone chemical sources and sinks. Our analysis is based on remote sensing products of the Tropospheric Column of Ozone and its precursors, nitrogen dioxide, formaldehyde, and total column of CO as well as ozonesonde data and model simulations.
Rodrigo J. Seguel, Lucas Castillo, Charlie Opazo, Néstor Y. Rojas, Thiago Nogueira, María Cazorla, Mario Gavidia-Calderón, Laura Gallardo, René Garreaud, Tomás Carrasco-Escaff, and Yasin Elshorbany
EGUsphere, https://doi.org/10.5194/egusphere-2024-328, https://doi.org/10.5194/egusphere-2024-328, 2024
Short summary
Short summary
Our research found that surface ozone trends in major South American cities increase or remain steady but show no signs of decreasing. Extra-tropical cities (Santiago and São Paulo), in particular, face the highest risk of ozone exposure. Furthermore, we found that prolonged heat waves and large fires explain many of the most extreme ozone values.
Hannah Meusel, Yasin Elshorbany, Uwe Kuhn, Thorsten Bartels-Rausch, Kathrin Reinmuth-Selzle, Christopher J. Kampf, Guo Li, Xiaoxiang Wang, Jos Lelieveld, Ulrich Pöschl, Thorsten Hoffmann, Hang Su, Markus Ammann, and Yafang Cheng
Atmos. Chem. Phys., 17, 11819–11833, https://doi.org/10.5194/acp-17-11819-2017, https://doi.org/10.5194/acp-17-11819-2017, 2017
Short summary
Short summary
In this study we investigated protein nitration and decomposition by light in the presence of NO2 via flow tube measurements. Nitrated proteins have an enhanced allergenic potential but so far nitration was only studied in dark conditions. Under irradiated conditions we found that proteins predominantly decompose while forming nitrous acid (HONO) an important precursor of the OH radical. Unlike other studies on heterogeneous NO2 conversion we found a stable HONO formation over a long period.
Y. F. Elshorbany, P. J. Crutzen, B. Steil, A. Pozzer, H. Tost, and J. Lelieveld
Atmos. Chem. Phys., 14, 1167–1184, https://doi.org/10.5194/acp-14-1167-2014, https://doi.org/10.5194/acp-14-1167-2014, 2014
Yasin Elshorbany, Jerald Ziemke, Sarah Strode, Hervé Petetin, Kazuyuki Miyazaki, Isabelle De Smedt, Kenneth Pickering, Rodrigo Seguel, Helen Worden, Tamara Emmerichs, Domenico Taraborrelli, Maria Cazorla, Suvarna Fadnavis, Rebecca Buchholz, Benjamin Gaubert, Néstor Rojas, Thiago Nogueira, Thérèse Salameh, and Min Huang
EGUsphere, https://doi.org/10.5194/egusphere-2024-720, https://doi.org/10.5194/egusphere-2024-720, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We investigated tropospheric ozone spatial variability and trends from 2005 to 2019 and related those to ozone precursors on global and regional scales. We also investigate the spatiotemporal characteristics of the ozone formation regime in relation to ozone chemical sources and sinks. Our analysis is based on remote sensing products of the Tropospheric Column of Ozone and its precursors, nitrogen dioxide, formaldehyde, and total column of CO as well as ozonesonde data and model simulations.
Amir H. Souri, Bryan N. Duncan, Sarah A. Strode, Daniel C. Anderson, Michael E. Manyin, Junhua Liu, Luke D. Oman, Zhen Zhang, and Brad Weir
EGUsphere, https://doi.org/10.5194/egusphere-2024-410, https://doi.org/10.5194/egusphere-2024-410, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We explore a new method to make use of the wealth of information obtained from satellite observations of Aura OMI NO2, HCHO, along with MERRA2 reanalysis in NASA’s GEOS model equipped with an efficient tropospheric OH (TOH) estimator to enhance the representation of TOH spatial distribution and its long-term trends. This new framework helps us pinpoint regional inaccuracies in TOH and differentiate between established prior knowledge and newly acquired information from satellites on TOH trends.
Rodrigo J. Seguel, Lucas Castillo, Charlie Opazo, Néstor Y. Rojas, Thiago Nogueira, María Cazorla, Mario Gavidia-Calderón, Laura Gallardo, René Garreaud, Tomás Carrasco-Escaff, and Yasin Elshorbany
EGUsphere, https://doi.org/10.5194/egusphere-2024-328, https://doi.org/10.5194/egusphere-2024-328, 2024
Short summary
Short summary
Our research found that surface ozone trends in major South American cities increase or remain steady but show no signs of decreasing. Extra-tropical cities (Santiago and São Paulo), in particular, face the highest risk of ozone exposure. Furthermore, we found that prolonged heat waves and large fires explain many of the most extreme ozone values.
Daniel C. Anderson, Bryan N. Duncan, Julie M. Nicely, Junhua Liu, Sarah A. Strode, and Melanie B. Follette-Cook
Atmos. Chem. Phys., 23, 6319–6338, https://doi.org/10.5194/acp-23-6319-2023, https://doi.org/10.5194/acp-23-6319-2023, 2023
Short summary
Short summary
We describe a methodology that combines machine learning, satellite observations, and 3D chemical model output to infer the abundance of the hydroxyl radical (OH), a chemical that removes many trace gases from the atmosphere. The methodology successfully captures the variability of observed OH, although further observations are needed to evaluate absolute accuracy. Current satellite observations are of sufficient quality to infer OH, but retrieval validation in the remote tropics is needed.
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
Short summary
Short summary
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.
Daniel C. Anderson, Bryan N. Duncan, Arlene M. Fiore, Colleen B. Baublitz, Melanie B. Follette-Cook, Julie M. Nicely, and Glenn M. Wolfe
Atmos. Chem. Phys., 21, 6481–6508, https://doi.org/10.5194/acp-21-6481-2021, https://doi.org/10.5194/acp-21-6481-2021, 2021
Short summary
Short summary
We demonstrate that large-scale climate features are the primary driver of year-to-year variability in simulated values of the hydroxyl radical, the primary atmospheric oxidant, over 1980–2018. The El Niño–Southern Oscillation is the dominant mode of hydroxyl variability, resulting in large-scale global decreases in OH during El Niño events. Other climate modes, such as the Australian monsoon and the North Atlantic Oscillation, have impacts of similar magnitude but on on more localized scales.
Sarah A. Strode, James S. Wang, Michael Manyin, Bryan Duncan, Ryan Hossaini, Christoph A. Keller, Sylvia E. Michel, and James W. C. White
Atmos. Chem. Phys., 20, 8405–8419, https://doi.org/10.5194/acp-20-8405-2020, https://doi.org/10.5194/acp-20-8405-2020, 2020
Short summary
Short summary
The 13C : 12C isotopic ratio in methane (CH4) provides information about CH4 sources, but loss of CH4 by reaction with OH and chlorine (Cl) also affects this ratio. Tropospheric Cl provides a small and uncertain sink for CH4 but has a large effect on its isotopic ratio. We use the GEOS model with several different Cl fields to test the sensitivity of methane's isotopic composition to tropospheric Cl. Cl affects the global mean, hemispheric gradient, and seasonal cycle of the isotopic ratio.
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
Short summary
Short summary
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.
Fei Liu, Bryan N. Duncan, Nickolay A. Krotkov, Lok N. Lamsal, Steffen Beirle, Debora Griffin, Chris A. McLinden, Daniel L. Goldberg, and Zifeng Lu
Atmos. Chem. Phys., 20, 99–116, https://doi.org/10.5194/acp-20-99-2020, https://doi.org/10.5194/acp-20-99-2020, 2020
Short summary
Short summary
We present a novel method to infer CO2 emissions from individual power plants, based on satellite observations of co-emitted NO2. We find that the CO2 emissions estimated by our satellite-based method during 2005–2017 are in reasonable agreement with the CEMS measurements for US power plants. The broader implication of our methodology is that it has the potential to provide an additional constraint on CO2 emissions from power plants in regions of the world without reliable emissions accounting.
Paul I. Palmer, Emily L. Wilson, Geronimo L. Villanueva, Giuliano Liuzzi, Liang Feng, Anthony J. DiGregorio, Jianping Mao, Lesley Ott, and Bryan Duncan
Atmos. Meas. Tech., 12, 2579–2594, https://doi.org/10.5194/amt-12-2579-2019, https://doi.org/10.5194/amt-12-2579-2019, 2019
Short summary
Short summary
We describe the potential impact of a new, low-cost, portable ground instrument (the mini-LHR) that measures methane and carbon dioxide in the atmospheric column. This region is key in quantifying the global carbon budget but has geographical gaps in measurements left by ground-based networks and space-based observations. A deployment of 50 mini-LHRs would add new data products in the Amazon, the Arctic, and southern Asia and significantly improve knowledge of regional and global carbon budgets.
Arlene M. Fiore, Emily V. Fischer, George P. Milly, Shubha Pandey Deolal, Oliver Wild, Daniel A. Jaffe, Johannes Staehelin, Olivia E. Clifton, Dan Bergmann, William Collins, Frank Dentener, Ruth M. Doherty, Bryan N. Duncan, Bernd Fischer, Stefan Gilge, Peter G. Hess, Larry W. Horowitz, Alexandru Lupu, Ian A. MacKenzie, Rokjin Park, Ludwig Ries, Michael G. Sanderson, Martin G. Schultz, Drew T. Shindell, Martin Steinbacher, David S. Stevenson, Sophie Szopa, Christoph Zellweger, and Guang Zeng
Atmos. Chem. Phys., 18, 15345–15361, https://doi.org/10.5194/acp-18-15345-2018, https://doi.org/10.5194/acp-18-15345-2018, 2018
Short summary
Short summary
We demonstrate a proof-of-concept approach for applying northern midlatitude mountaintop peroxy acetyl nitrate (PAN) measurements and a multi-model ensemble during April to constrain the influence of continental-scale anthropogenic precursor emissions on PAN. Our findings imply a role for carefully coordinated multi-model ensembles in helping identify observations for discriminating among widely varying (and poorly constrained) model responses of atmospheric constituents to changes in emissions.
James S. Wang, S. Randolph Kawa, G. James Collatz, Motoki Sasakawa, Luciana V. Gatti, Toshinobu Machida, Yuping Liu, and Michael E. Manyin
Atmos. Chem. Phys., 18, 11097–11124, https://doi.org/10.5194/acp-18-11097-2018, https://doi.org/10.5194/acp-18-11097-2018, 2018
Short summary
Short summary
We used measurements of CO2 in the atmosphere from the GOSAT satellite and from surface sites around the world, together with a transport model and a unique estimation technique, to quantify CO2 sources and removals over a recent period. We find that climate variations can strongly influence uptake by vegetation and release in decay and fires. However, regional gaps in observations and inaccuracies to which current satellite technology is susceptible result in important estimation biases.
Pieternel F. Levelt, Joanna Joiner, Johanna Tamminen, J. Pepijn Veefkind, Pawan K. Bhartia, Deborah C. Stein Zweers, Bryan N. Duncan, David G. Streets, Henk Eskes, Ronald van der A, Chris McLinden, Vitali Fioletov, Simon Carn, Jos de Laat, Matthew DeLand, Sergey Marchenko, Richard McPeters, Jerald Ziemke, Dejian Fu, Xiong Liu, Kenneth Pickering, Arnoud Apituley, Gonzalo González Abad, Antti Arola, Folkert Boersma, Christopher Chan Miller, Kelly Chance, Martin de Graaf, Janne Hakkarainen, Seppo Hassinen, Iolanda Ialongo, Quintus Kleipool, Nickolay Krotkov, Can Li, Lok Lamsal, Paul Newman, Caroline Nowlan, Raid Suleiman, Lieuwe Gijsbert Tilstra, Omar Torres, Huiqun Wang, and Krzysztof Wargan
Atmos. Chem. Phys., 18, 5699–5745, https://doi.org/10.5194/acp-18-5699-2018, https://doi.org/10.5194/acp-18-5699-2018, 2018
Short summary
Short summary
The aim of this paper is to highlight the many successes of the Ozone Monitoring Instrument (OMI) spanning more than 13 years. Data from OMI have been used in a wide range of applications. Due to its unprecedented spatial resolution, in combination with daily global coverage, OMI plays a unique role in measuring trace gases important for the ozone layer, air quality, and climate change. OMI data continue to be used for new research and applications.
Hannah Meusel, Yasin Elshorbany, Uwe Kuhn, Thorsten Bartels-Rausch, Kathrin Reinmuth-Selzle, Christopher J. Kampf, Guo Li, Xiaoxiang Wang, Jos Lelieveld, Ulrich Pöschl, Thorsten Hoffmann, Hang Su, Markus Ammann, and Yafang Cheng
Atmos. Chem. Phys., 17, 11819–11833, https://doi.org/10.5194/acp-17-11819-2017, https://doi.org/10.5194/acp-17-11819-2017, 2017
Short summary
Short summary
In this study we investigated protein nitration and decomposition by light in the presence of NO2 via flow tube measurements. Nitrated proteins have an enhanced allergenic potential but so far nitration was only studied in dark conditions. Under irradiated conditions we found that proteins predominantly decompose while forming nitrous acid (HONO) an important precursor of the OH radical. Unlike other studies on heterogeneous NO2 conversion we found a stable HONO formation over a long period.
Hyun-Deok Choi, Hongyu Liu, James H. Crawford, David B. Considine, Dale J. Allen, Bryan N. Duncan, Larry W. Horowitz, Jose M. Rodriguez, Susan E. Strahan, Lin Zhang, Xiong Liu, Megan R. Damon, and Stephen D. Steenrod
Atmos. Chem. Phys., 17, 8429–8452, https://doi.org/10.5194/acp-17-8429-2017, https://doi.org/10.5194/acp-17-8429-2017, 2017
Short summary
Short summary
We evaluate global ozone–carbon monoxide (O3–CO) correlations in a chemistry and transport model during July–August with TES-Aura satellite observations and examine the sensitivity of model simulations to input meteorological data and emissions. Results show that O3–CO correlations may be used effectively to constrain the sources of regional tropospheric O3 in global 3-D models, especially for those regions where convective transport of pollution plays an important role.
Sarah A. Strode, Helen M. Worden, Megan Damon, Anne R. Douglass, Bryan N. Duncan, Louisa K. Emmons, Jean-Francois Lamarque, Michael Manyin, Luke D. Oman, Jose M. Rodriguez, Susan E. Strahan, and Simone Tilmes
Atmos. Chem. Phys., 16, 7285–7294, https://doi.org/10.5194/acp-16-7285-2016, https://doi.org/10.5194/acp-16-7285-2016, 2016
Short summary
Short summary
We use global models to interpret trends in MOPITT observations of CO. Simulations with time-dependent emissions reproduce the observed trends over the eastern USA and Europe, suggesting that the emissions are reasonable for these regions. The simulations produce a positive trend over eastern China, contrary to the observed negative trend. This may indicate that the assumed emission trend over China is too positive. However, large variability in the overhead ozone column also contributes.
Hongyu Liu, David B. Considine, Larry W. Horowitz, James H. Crawford, Jose M. Rodriguez, Susan E. Strahan, Megan R. Damon, Stephen D. Steenrod, Xiaojing Xu, Jules Kouatchou, Claire Carouge, and Robert M. Yantosca
Atmos. Chem. Phys., 16, 4641–4659, https://doi.org/10.5194/acp-16-4641-2016, https://doi.org/10.5194/acp-16-4641-2016, 2016
Short summary
Short summary
We assess the utility of cosmogenic beryllium-7, a natural aerosol tracer, for evaluating cross-tropopause transport in global models. We show that model excessive cross-tropopause transport of beryllium-7 corresponds to overestimated stratospheric contribution to tropospheric ozone. We conclude that the observational constraints for beryllium-7 and observed beryllium-7 total deposition fluxes can be used routinely as a first-order assessment of cross-tropopause transport in global models.
Nickolay A. Krotkov, Chris A. McLinden, Can Li, Lok N. Lamsal, Edward A. Celarier, Sergey V. Marchenko, William H. Swartz, Eric J. Bucsela, Joanna Joiner, Bryan N. Duncan, K. Folkert Boersma, J. Pepijn Veefkind, Pieternel F. Levelt, Vitali E. Fioletov, Russell R. Dickerson, Hao He, Zifeng Lu, and David G. Streets
Atmos. Chem. Phys., 16, 4605–4629, https://doi.org/10.5194/acp-16-4605-2016, https://doi.org/10.5194/acp-16-4605-2016, 2016
Short summary
Short summary
We examine changes in SO2 and NO2 over the world's most polluted regions during the first decade of Aura OMI observations. Over the eastern US, both NO2 and SO2 levels decreased by 40 % and 80 %, respectively. OMI confirmed large reductions in SO2 over eastern Europe's largest coal power plants. The North China Plain has the world's most severe SO2 pollution, but a decreasing trend been observed since 2011, with a 50 % reduction in 2012–2014. India's SO2 and NO2 levels are growing at a fast pace.
S. A. Strode, B. N. Duncan, E. A. Yegorova, J. Kouatchou, J. R. Ziemke, and A. R. Douglass
Atmos. Chem. Phys., 15, 11789–11805, https://doi.org/10.5194/acp-15-11789-2015, https://doi.org/10.5194/acp-15-11789-2015, 2015
Short summary
Short summary
A low bias in carbon monoxide (CO) at northern latitudes is a common feature of chemistry climate models. We find that increasing Northern Hemisphere (NH) CO emissions or reducing NH OH concentrations improves the agreement with CO surface observations, but reducing NH OH leads to a better comparison with MOPITT. Removing model biases in ozone and water vapor increases the simulated methane lifetime, but it does not give the 20% reduction in NH OH suggested by our analysis of the CO bias.
Z. Lu, D. G. Streets, B. de Foy, L. N. Lamsal, B. N. Duncan, and J. Xing
Atmos. Chem. Phys., 15, 10367–10383, https://doi.org/10.5194/acp-15-10367-2015, https://doi.org/10.5194/acp-15-10367-2015, 2015
Short summary
Short summary
Using an exponentially modified Gaussian method and taking into account the effect of wind on NO2 distributions, we estimate 3-year moving-average emissions of summertime NOx from 35 US urban areas directly from NO2 retrievals of the OMI during 2005−2014. Total OMI-derived NOx emissions over US urban areas decreased by 49%, consistent with reductions of 43, 49, and 44% in the bottom-up NOx emissions, the weak-wind OMI NO2 burdens, and the averaged NO2 concentrations, respectively.
L. K. Emmons, S. R. Arnold, S. A. Monks, V. Huijnen, S. Tilmes, K. S. Law, J. L. Thomas, J.-C. Raut, I. Bouarar, S. Turquety, Y. Long, B. Duncan, S. Steenrod, S. Strode, J. Flemming, J. Mao, J. Langner, A. M. Thompson, D. Tarasick, E. C. Apel, D. R. Blake, R. C. Cohen, J. Dibb, G. S. Diskin, A. Fried, S. R. Hall, L. G. Huey, A. J. Weinheimer, A. Wisthaler, T. Mikoviny, J. Nowak, J. Peischl, J. M. Roberts, T. Ryerson, C. Warneke, and D. Helmig
Atmos. Chem. Phys., 15, 6721–6744, https://doi.org/10.5194/acp-15-6721-2015, https://doi.org/10.5194/acp-15-6721-2015, 2015
Short summary
Short summary
Eleven 3-D tropospheric chemistry models have been compared and evaluated with observations in the Arctic during the International Polar Year (IPY 2008). Large differences are seen among the models, particularly related to the model chemistry of volatile organic compounds (VOCs) and reactive nitrogen (NOx, PAN, HNO3) partitioning. Consistency among the models in the underestimation of CO, ethane and propane indicates the emission inventory is too low for these compounds.
S. R. Arnold, L. K. Emmons, S. A. Monks, K. S. Law, D. A. Ridley, S. Turquety, S. Tilmes, J. L. Thomas, I. Bouarar, J. Flemming, V. Huijnen, J. Mao, B. N. Duncan, S. Steenrod, Y. Yoshida, J. Langner, and Y. Long
Atmos. Chem. Phys., 15, 6047–6068, https://doi.org/10.5194/acp-15-6047-2015, https://doi.org/10.5194/acp-15-6047-2015, 2015
Short summary
Short summary
The extent to which forest fires produce the air pollutant and greenhouse gas ozone (O3) in the atmosphere at high latitudes in not well understood. We have compared how fire emissions produce O3 and its precursors in several models of atmospheric chemistry. We find enhancements in O3 in air dominated by fires in all models, which increase on average as fire emissions age. We also find that in situ O3 production in the Arctic is sensitive to details of organic chemistry and vertical lifting.
S. A. Monks, S. R. Arnold, L. K. Emmons, K. S. Law, S. Turquety, B. N. Duncan, J. Flemming, V. Huijnen, S. Tilmes, J. Langner, J. Mao, Y. Long, J. L. Thomas, S. D. Steenrod, J. C. Raut, C. Wilson, M. P. Chipperfield, G. S. Diskin, A. Weinheimer, H. Schlager, and G. Ancellet
Atmos. Chem. Phys., 15, 3575–3603, https://doi.org/10.5194/acp-15-3575-2015, https://doi.org/10.5194/acp-15-3575-2015, 2015
Short summary
Short summary
Multi-model simulations of Arctic CO, O3 and OH are evaluated using observations. Models show highly variable concentrations but the relative importance of emission regions and types is robust across the models, demonstrating the importance of biomass burning as a source. Idealised tracer experiments suggest that some of the model spread is due to variations in simulated transport from Europe in winter and from Asia throughout the year.
J. S. Wang, S. R. Kawa, J. Eluszkiewicz, D. F. Baker, M. Mountain, J. Henderson, T. Nehrkorn, and T. S. Zaccheo
Atmos. Chem. Phys., 14, 12897–12914, https://doi.org/10.5194/acp-14-12897-2014, https://doi.org/10.5194/acp-14-12897-2014, 2014
Short summary
Short summary
Our simulations suggest that CO2 measurements by the planned ASCENDS satellite could improve estimates of emissions and uptake by up to 50% at the weekly 1° by 1° scale, 40-75% at the annual biome scale, and 65-85% for the whole of North America. The results depend on the laser wavelength used and the assumed precision of the measurements. The resulting biome flux uncertainties, 0.01-0.06 billion tons of C per year, would satisfy one definition of mission success.
S. Choi, J. Joiner, Y. Choi, B. N. Duncan, A. Vasilkov, N. Krotkov, and E. Bucsela
Atmos. Chem. Phys., 14, 10565–10588, https://doi.org/10.5194/acp-14-10565-2014, https://doi.org/10.5194/acp-14-10565-2014, 2014
Y. F. Elshorbany, P. J. Crutzen, B. Steil, A. Pozzer, H. Tost, and J. Lelieveld
Atmos. Chem. Phys., 14, 1167–1184, https://doi.org/10.5194/acp-14-1167-2014, https://doi.org/10.5194/acp-14-1167-2014, 2014
J. X. Warner, R. Yang, Z. Wei, F. Carminati, A. Tangborn, Z. Sun, W. Lahoz, J.-L. Attié, L. El Amraoui, and B. Duncan
Atmos. Chem. Phys., 14, 103–114, https://doi.org/10.5194/acp-14-103-2014, https://doi.org/10.5194/acp-14-103-2014, 2014
Related subject area
Atmospheric sciences
Modelling wind farm effects in HARMONIE–AROME (cycle 43.2.2) – Part 1: Implementation and evaluation
Analytical and adaptable initial conditions for dry and moist baroclinic waves in the global hydrostatic model OpenIFS (CY43R3)
Challenges of constructing and selecting the “perfect” boundary conditions for the large-eddy simulation model PALM
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model
Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India
How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe–AERCHEM, v1.0) sub-submodel of MESSy
A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Advances and prospects of deep learning for medium-range extreme weather forecasting
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
Deep learning applied to CO2 power plant emissions quantification using simulated satellite images
Sensitivity of the WRF-Chem v4.4 simulations of ozone and formaldehyde and their precursors to multiple bottom-up emission inventories over East Asia during the KORUS-AQ 2016 field campaign
Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL
Assessment of climate biases in OpenIFS version 43r3 across model horizontal resolutions and time steps
High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning
Effects of vertical grid spacing on the climate simulated in the ICON-Sapphire global storm-resolving model
Development of the tangent linear and adjoint models of the global online chemical transport model MPAS-CO2 v7.3
Impacts of updated reaction kinetics on the global GEOS-Chem simulation of atmospheric chemistry
Spatial spin-up of precipitation in limited-area convection-permitting simulations over North America using the CRCM6/GEM5.0 model
Sensitivity of atmospheric rivers to aerosol treatment in regional climate simulations: insights from the AIRA identification algorithm
The implementation of dust mineralogy in COSMO5.05-MUSCAT
Implementation of the ISORROPIA-lite aerosol thermodynamics model into the EMAC chemistry climate model (based on MESSy v2.55): implications for aerosol composition and acidity
Evaluation of surface shortwave downward radiation forecasts by the numerical weather prediction model AROME
GEO4PALM v1.1: an open-source geospatial data processing toolkit for the PALM model system
Modeling collision–coalescence in particle microphysics: numerical convergence of mean and variance of precipitation in cloud simulations using the University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1
Modeling below-cloud scavenging of size-resolved particles in GEM-MACHv3.1
Impacts of a double-moment bulk cloud microphysics scheme (NDW6-G23) on aerosol fields in NICAM.19 with a global 14 km grid resolution
Sensitivity of air quality model responses to emission changes: comparison of results based on four EU inventories through FAIRMODE benchmarking methodology
A simple and realistic aerosol emission approach for use in the Thompson–Eidhammer microphysics scheme in the NOAA UFS Weather Model (version GSL global-24Feb2022)
Representing effects of surface heterogeneity in a multi-plume eddy diffusivity mass flux boundary layer parameterization
On the formation of biogenic secondary organic aerosol in chemical transport models: an evaluation of the WRF-CHIMERE (v2020r2) model with a focus over the Finnish boreal forest
The first application of a numerically exact, higher-order sensitivity analysis approach for atmospheric modelling: implementation of the hyperdual-step method in the Community Multiscale Air Quality Model (CMAQ) version 5.3.2
The ddeq Python library for point source quantification from remote sensing images (Version 1.0)
GAN-argcPredNet v2.0: a radar echo extrapolation model based on spatiotemporal process enhancement
Analysis of the GEFS-Aerosols annual budget to better understand aerosol predictions simulated in the model
A model for rapid PM2.5 exposure estimates in wildfire conditions using routinely available data: rapidfire v0.1.3
BoundaryLayerDynamics.jl v1.0: a modern codebase for atmospheric boundary-layer simulations
Investigating Ground-Level Ozone Pollution in Semi-Arid and Arid Regions of Arizona Using WRF-Chem v4.4 Modeling
The wave-age-dependent stress parameterisation (WASP) for momentum and heat turbulent fluxes at sea in SURFEX v8.1
FUME 2.0 – Flexible Universal processor for Modeling Emissions
Assessment of tropospheric ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran
Application of regional meteorology and air quality models based on MIPS CPU Platform
Spherical air mass factors in one and two dimensions with SASKTRAN 1.6.0
An improved version of the piecewise parabolic method advection scheme: description and performance assessment in a bidimensional test case with stiff chemistry in toyCTM v1.0.1
INCHEM-Py v1.2: a community box model for indoor air chemistry
Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemistry-transport model using Cloud-J v7.3e
Representation of atmosphere-induced heterogeneity in land–atmosphere interactions in E3SM–MMFv2
How the meteorological spectral nudging impacts on aerosol radiation clouds interactions?
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
Short summary
Short summary
Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Clément Bouvier, Daan van den Broek, Madeleine Ekblom, and Victoria A. Sinclair
Geosci. Model Dev., 17, 2961–2986, https://doi.org/10.5194/gmd-17-2961-2024, https://doi.org/10.5194/gmd-17-2961-2024, 2024
Short summary
Short summary
An analytical initial background state has been developed for moist baroclinic wave simulation on an aquaplanet and implemented into OpenIFS. Seven parameters can be controlled, which are used to generate the background states and the development of baroclinic waves. The meteorological and numerical stability has been assessed. Resulting baroclinic waves have proven to be realistic and sensitive to the jet's width.
Jelena Radović, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024, https://doi.org/10.5194/gmd-17-2901-2024, 2024
Short summary
Short summary
Boundary conditions are of crucial importance for numerical model (e.g., PALM) validation studies and have a large influence on the model results, especially when studying the atmosphere of real, complex, and densely built urban environments. Our experiments with different driving conditions for the large-eddy simulation model PALM show its strong dependency on boundary conditions, which is important for the proper separation of errors coming from the boundary conditions and the model itself.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024, https://doi.org/10.5194/gmd-17-2641-2024, 2024
Short summary
Short summary
In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
Short summary
Short summary
A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, https://doi.org/10.5194/gmd-17-2597-2024, 2024
Short summary
Short summary
The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation, we find that MESSy's bias in modelling routinely observed reduced inorganic aerosol mass concentrations, especially in the United States. Furthermore, the representation of fine-aerosol pH is particularly improved in the marine boundary layer.
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li
Geosci. Model Dev., 17, 2569–2581, https://doi.org/10.5194/gmd-17-2569-2024, https://doi.org/10.5194/gmd-17-2569-2024, 2024
Short summary
Short summary
In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV) variation with altitude in the study area. RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024, https://doi.org/10.5194/gmd-17-2419-2024, 2024
Short summary
Short summary
The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary
Short summary
In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
Short summary
Short summary
Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, https://doi.org/10.5194/gmd-17-2247-2024, 2024
Short summary
Short summary
This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, https://doi.org/10.5194/gmd-17-2053-2024, 2024
Short summary
Short summary
PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols, and hydrometeors). PyRTlib is a so-called radiative transfer model; these are commonly used to simulate and understand remote sensing observations from ground-based, airborne, or satellite instruments.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024, https://doi.org/10.5194/gmd-17-1995-2024, 2024
Short summary
Short summary
Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024, https://doi.org/10.5194/gmd-17-1931-2024, 2024
Short summary
Short summary
Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
Sanam Noreen Vardag and Robert Maiwald
Geosci. Model Dev., 17, 1885–1902, https://doi.org/10.5194/gmd-17-1885-2024, https://doi.org/10.5194/gmd-17-1885-2024, 2024
Short summary
Short summary
We use the atmospheric transport model GRAMM/GRAL in a Bayesian inversion to estimate urban CO2 emissions on a neighbourhood scale. We analyse the effect of varying number, precision and location of CO2 sensors for CO2 flux estimation. We further test the inclusion of co-emitted species and correlation in the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
Abhishek Savita, Joakim Kjellsson, Robin Pilch Kedzierski, Mojib Latif, Tabea Rahm, Sebastian Wahl, and Wonsun Park
Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024, https://doi.org/10.5194/gmd-17-1813-2024, 2024
Short summary
Short summary
The OpenIFS model is used to examine the impact of horizontal resolutions (HR) and model time steps. We find that the surface wind biases over the oceans, in particular the Southern Ocean, are sensitive to the model time step and HR, with the HR having the smallest biases. When using a coarse-resolution model with a shorter time step, a similar improvement is also found. Climate biases can be reduced in the OpenIFS model at a cheaper cost by reducing the time step rather than increasing the HR.
Ferdinand Briegel, Jonas Wehrle, Dirk Schindler, and Andreas Christen
Geosci. Model Dev., 17, 1667–1688, https://doi.org/10.5194/gmd-17-1667-2024, https://doi.org/10.5194/gmd-17-1667-2024, 2024
Short summary
Short summary
We present a new approach to model heat stress in cities using artificial intelligence (AI). We show that the AI model is fast in terms of prediction but accurate when evaluated with measurements. The fast-predictive AI model enables several new potential applications, including heat stress prediction and warning; downscaling of potential future climates; evaluation of adaptation effectiveness; and, more fundamentally, development of guidelines to support urban planning and policymaking.
Hauke Schmidt, Sebastian Rast, Jiawei Bao, Amrit Cassim, Shih-Wei Fang, Diego Jimenez-de la Cuesta, Paul Keil, Lukas Kluft, Clarissa Kroll, Theresa Lang, Ulrike Niemeier, Andrea Schneidereit, Andrew I. L. Williams, and Bjorn Stevens
Geosci. Model Dev., 17, 1563–1584, https://doi.org/10.5194/gmd-17-1563-2024, https://doi.org/10.5194/gmd-17-1563-2024, 2024
Short summary
Short summary
A recent development in numerical simulations of the global atmosphere is the increase in horizontal resolution to grid spacings of a few kilometers. However, the vertical grid spacing of these models has not been reduced at the same rate as the horizontal grid spacing. Here, we assess the effects of much finer vertical grid spacings, in particular the impacts on cloud quantities and the atmospheric energy balance.
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev., 17, 1543–1562, https://doi.org/10.5194/gmd-17-1543-2024, https://doi.org/10.5194/gmd-17-1543-2024, 2024
Short summary
Short summary
The tangent linear and adjoint models have been successfully implemented in the MPAS-CO2 system, which has undergone rigorous accuracy testing. This development lays the groundwork for a global carbon flux data assimilation system, which offers the flexibility of high-resolution focus on specific areas, while maintaining a coarser resolution elsewhere. This approach significantly reduces computational costs and is thus perfectly suited for future CO2 geostationery and imager satellites.
Kelvin H. Bates, Mathew J. Evans, Barron H. Henderson, and Daniel J. Jacob
Geosci. Model Dev., 17, 1511–1524, https://doi.org/10.5194/gmd-17-1511-2024, https://doi.org/10.5194/gmd-17-1511-2024, 2024
Short summary
Short summary
Accurate representation of rates and products of chemical reactions in atmospheric models is crucial for simulating concentrations of pollutants and climate forcers. We update the widely used GEOS-Chem atmospheric chemistry model with reaction parameters from recent compilations of experimental data and demonstrate the implications for key atmospheric chemical species. The updates decrease tropospheric CO mixing ratios and increase stratospheric nitrogen oxide mixing ratios, among other changes.
François Roberge, Alejandro Di Luca, René Laprise, Philippe Lucas-Picher, and Julie Thériault
Geosci. Model Dev., 17, 1497–1510, https://doi.org/10.5194/gmd-17-1497-2024, https://doi.org/10.5194/gmd-17-1497-2024, 2024
Short summary
Short summary
Our study addresses a challenge in dynamical downscaling using regional climate models, focusing on the lack of small-scale features near the boundaries. We introduce a method to identify this “spatial spin-up” in precipitation simulations. Results show spin-up distances up to 300 km, varying by season and driving variable. Double nesting with comprehensive variables (e.g. microphysical variables) offers advantages. Findings will help optimize simulations for better climate projections.
Eloisa Raluy-López, Juan Pedro Montávez, and Pedro Jiménez-Guerrero
Geosci. Model Dev., 17, 1469–1495, https://doi.org/10.5194/gmd-17-1469-2024, https://doi.org/10.5194/gmd-17-1469-2024, 2024
Short summary
Short summary
Atmospheric rivers (ARs) represent a significant source of water but are also related to extreme precipitation events. Here, we present a new regional-scale AR identification algorithm and apply it to three simulations that include aerosol interactions at different levels. The results show that aerosols modify the intensity and trajectory of ARs and redistribute the AR-related precipitation. Thus, the correct inclusion of aerosol effects is important in the simulation of AR behavior.
Sofía Gómez Maqueo Anaya, Dietrich Althausen, Matthias Faust, Holger Baars, Bernd Heinold, Julian Hofer, Ina Tegen, Albert Ansmann, Ronny Engelmann, Annett Skupin, Birgit Heese, and Kerstin Schepanski
Geosci. Model Dev., 17, 1271–1295, https://doi.org/10.5194/gmd-17-1271-2024, https://doi.org/10.5194/gmd-17-1271-2024, 2024
Short summary
Short summary
Mineral dust aerosol particles vary greatly in their composition depending on source region, which leads to different physicochemical properties. Most atmosphere–aerosol models consider mineral dust aerosols to be compositionally homogeneous, which ultimately increases model uncertainty. Here, we present an approach to explicitly consider the heterogeneity of the mineralogical composition for simulations of the Saharan atmospheric dust cycle with regard to dust transport towards the Atlantic.
Alexandros Milousis, Alexandra P. Tsimpidi, Holger Tost, Spyros N. Pandis, Athanasios Nenes, Astrid Kiendler-Scharr, and Vlassis A. Karydis
Geosci. Model Dev., 17, 1111–1131, https://doi.org/10.5194/gmd-17-1111-2024, https://doi.org/10.5194/gmd-17-1111-2024, 2024
Short summary
Short summary
This study aims to evaluate the newly developed ISORROPIA-lite aerosol thermodynamic module within the EMAC model and explore discrepancies in global atmospheric simulations of aerosol composition and acidity by utilizing different aerosol phase states. Even though local differences were found in regions where the RH ranged from 20 % to 60 %, on a global scale the results are similar. Therefore, ISORROPIA-lite can be a reliable and computationally effective alternative to ISORROPIA II in EMAC.
Marie-Adèle Magnaldo, Quentin Libois, Sébastien Riette, and Christine Lac
Geosci. Model Dev., 17, 1091–1109, https://doi.org/10.5194/gmd-17-1091-2024, https://doi.org/10.5194/gmd-17-1091-2024, 2024
Short summary
Short summary
With the worldwide development of the solar energy sector, the need for reliable solar radiation forecasts has significantly increased. However, meteorological models that predict, among others things, solar radiation have errors. Therefore, we wanted to know in which situtaions these errors are most significant. We found that errors mostly occur in cloudy situations, and different errors were highlighted depending on the cloud altitude. Several potential sources of errors were identified.
Dongqi Lin, Jiawei Zhang, Basit Khan, Marwan Katurji, and Laura E. Revell
Geosci. Model Dev., 17, 815–845, https://doi.org/10.5194/gmd-17-815-2024, https://doi.org/10.5194/gmd-17-815-2024, 2024
Short summary
Short summary
GEO4PALM is an open-source tool to generate static input for the Parallelized Large-Eddy Simulation (PALM) model system. Geospatial static input is essential for realistic PALM simulations. However, existing tools fail to generate PALM's geospatial static input for most regions. GEO4PALM is compatible with diverse geospatial data sources and provides access to free data sets. In addition, this paper presents two application examples, which show successful PALM simulations using GEO4PALM.
Piotr Zmijewski, Piotr Dziekan, and Hanna Pawlowska
Geosci. Model Dev., 17, 759–780, https://doi.org/10.5194/gmd-17-759-2024, https://doi.org/10.5194/gmd-17-759-2024, 2024
Short summary
Short summary
In computer simulations of clouds it is necessary to model the myriad of droplets that constitute a cloud. A popular method for this is to use so-called super-droplets (SDs), each representing many real droplets. It has remained a challenge to model collisions of SDs. We study how precipitation in a cumulus cloud depends on the number of SDs. Surprisingly, we do not find convergence in mean precipitation even for numbers of SDs much larger than typically used in simulations.
Roya Ghahreman, Wanmin Gong, Paul A. Makar, Alexandru Lupu, Amanda Cole, Kulbir Banwait, Colin Lee, and Ayodeji Akingunola
Geosci. Model Dev., 17, 685–707, https://doi.org/10.5194/gmd-17-685-2024, https://doi.org/10.5194/gmd-17-685-2024, 2024
Short summary
Short summary
The article explores the impact of different representations of below-cloud scavenging on model biases. A new scavenging scheme and precipitation-phase partitioning improve the model's performance, with better SO42- scavenging and wet deposition of NO3- and NH4+.
Daisuke Goto, Tatsuya Seiki, Kentaroh Suzuki, Hisashi Yashiro, and Toshihiko Takemura
Geosci. Model Dev., 17, 651–684, https://doi.org/10.5194/gmd-17-651-2024, https://doi.org/10.5194/gmd-17-651-2024, 2024
Short summary
Short summary
Global climate models with coarse grid sizes include uncertainties about the processes in aerosol–cloud–precipitation interactions. To reduce these uncertainties, here we performed numerical simulations using a new version of our global aerosol transport model with a finer grid size over a longer period than in our previous study. As a result, we found that the cloud microphysics module influences the aerosol distributions through both aerosol wet deposition and aerosol–cloud interactions.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, Enrico Pisoni, and Bertrand Bessagnet
Geosci. Model Dev., 17, 587–606, https://doi.org/10.5194/gmd-17-587-2024, https://doi.org/10.5194/gmd-17-587-2024, 2024
Short summary
Short summary
In our study the robustness of the model responses to emission reductions in the EU is assessed when the emission data are changed. Our findings are particularly important to better understand the uncertainties associated to the emission inventories and how these uncertainties impact the level of accuracy of the resulting air quality modelling, which is a key for designing air quality plans. Also crucial is the choice of indicator to avoid misleading interpretations of the results.
Haiqin Li, Georg A. Grell, Ravan Ahmadov, Li Zhang, Shan Sun, Jordan Schnell, and Ning Wang
Geosci. Model Dev., 17, 607–619, https://doi.org/10.5194/gmd-17-607-2024, https://doi.org/10.5194/gmd-17-607-2024, 2024
Short summary
Short summary
We developed a simple and realistic method to provide aerosol emissions for aerosol-aware microphysics in a numerical weather forecast model. The cloud-radiation differences between the experimental (EXP) and control (CTL) experiments responded to the aerosol differences. The strong positive precipitation biases over North America and Europe from the CTL run were significantly reduced in the EXP run. This study shows that a realistic representation of aerosol emissions should be considered.
Nathan Patrick Arnold
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-245, https://doi.org/10.5194/gmd-2023-245, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Earth System Models often represent the land surface at smaller scales than the atmosphere, but surface-atmosphere coupling uses only aggregated surface properties. This study presents a method to allow heterogeneous surface properties to modify boundary layer updrafts. The method is tested in single column experiments. Updraft properties are found to reasonably covary with surface conditions, and simulated boundary layer variability is enhanced over more heterogeneous land surfaces.
Giancarlo Ciarelli, Sara Tahvonen, Arineh Cholakian, Manuel Bettineschi, Bruno Vitali, Tuukka Petäjä, and Federico Bianchi
Geosci. Model Dev., 17, 545–565, https://doi.org/10.5194/gmd-17-545-2024, https://doi.org/10.5194/gmd-17-545-2024, 2024
Short summary
Short summary
The terrestrial ecosystem releases large quantities of biogenic gases in the Earth's Atmosphere. These gases can effectively be converted into so-called biogenic aerosol particles and, eventually, affect the Earth's climate. Climate prediction varies greatly depending on how these processes are represented in model simulations. In this study, we present a detailed model evaluation analysis aimed at understanding the main source of uncertainty in predicting the formation of biogenic aerosols.
Jiachen Liu, Eric Chen, and Shannon L. Capps
Geosci. Model Dev., 17, 567–585, https://doi.org/10.5194/gmd-17-567-2024, https://doi.org/10.5194/gmd-17-567-2024, 2024
Short summary
Short summary
Air pollution harms human life and ecosystems, but its sources are complex. Scientists and policy makers use air pollution models to advance knowledge and inform control strategies. We implemented a recently developed numeral system to relate any set of model inputs, like pollutant emissions from a given activity, to all model outputs, like concentrations of pollutants harming human health. This approach will be straightforward to update when scientists discover new processes in the atmosphere.
Gerrit Kuhlmann, Erik F. M. Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2023-2936, https://doi.org/10.5194/egusphere-2023-2936, 2024
Short summary
Short summary
We present a Python software library for data-driven emission quantification (ddeq). It can be used to determine the emissions of hot spots (cities, power plants and industry) from remote sensing images using different methods. ddeq can be extended for new datasets and methods, providing a powerful community tool for users and developers. The application of the methods is shown using Jupyter Notebooks included in the library.
Kun Zheng, Qiya Tan, Huihua Ruan, Jinbiao Zhang, Cong Luo, Siyu Tang, Yunlei Yi, Yugang Tian, and Jianmei Cheng
Geosci. Model Dev., 17, 399–413, https://doi.org/10.5194/gmd-17-399-2024, https://doi.org/10.5194/gmd-17-399-2024, 2024
Short summary
Short summary
Radar echo extrapolation is the common method in precipitation nowcasting. Deep learning has potential in extrapolation. However, the existing models have low prediction accuracy for heavy rainfall. In this study, the prediction accuracy is improved by suppressing the blurring effect of rain distribution and reducing the negative bias. The results show that our model has better performance, which is useful for urban operation and flood prevention.
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev., 17, 431–447, https://doi.org/10.5194/gmd-17-431-2024, https://doi.org/10.5194/gmd-17-431-2024, 2024
Short summary
Short summary
A GEFS-Aerosols simulation was conducted from 1 September 2019 to 30 September 2020 to evaluate the model performance of GEFS-Aerosols. The purpose of this study was to understand how aerosol chemical and physical processes affect ambient aerosol concentrations by placing aerosol wet deposition, dry deposition, reactions, gravitational deposition, and emissions into the aerosol mass balance equation.
Sean Raffuse, Susan O'Neill, and Rebecca Schmidt
Geosci. Model Dev., 17, 381–397, https://doi.org/10.5194/gmd-17-381-2024, https://doi.org/10.5194/gmd-17-381-2024, 2024
Short summary
Short summary
Large wildfires are increasing throughout the western United States, and wildfire smoke is hazardous to public health. We developed a suite of tools called rapidfire for estimating particle pollution during wildfires using routinely available data sets. rapidfire uses official air monitoring, satellite data, meteorology, smoke modeling, and low-cost sensors. Estimates from rapidfire compare well with ground monitors and are being used in public health studies across California.
Manuel F. Schmid, Marco G. Giometto, Gregory A. Lawrence, and Marc B. Parlange
Geosci. Model Dev., 17, 321–333, https://doi.org/10.5194/gmd-17-321-2024, https://doi.org/10.5194/gmd-17-321-2024, 2024
Short summary
Short summary
Turbulence-resolving flow models have strict performance requirements, as simulations often run for weeks using hundreds of processes. Many flow scenarios also require the flexibility to modify physical and numerical models for problem-specific requirements. With a new code written in Julia we hope to make such adaptations easier without compromising on performance. In this paper we discuss the modeling approach and present validation and performance results.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-234, https://doi.org/10.5194/gmd-2023-234, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This research focuses on surface ozone (O3) pollution in Arizona, a historically air quality-challenged arid/semi-arid region in the US. The unique characteristics of semi-arid/arid regions, e.g., intense heat, minimal moisture, persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Marie-Noëlle Bouin, Cindy Lebeaupin Brossier, Sylvie Malardel, Aurore Voldoire, and César Sauvage
Geosci. Model Dev., 17, 117–141, https://doi.org/10.5194/gmd-17-117-2024, https://doi.org/10.5194/gmd-17-117-2024, 2024
Short summary
Short summary
In numerical models, the turbulent exchanges of heat and momentum at the air–sea interface are not represented explicitly but with parameterisations depending on the surface parameters. A new parameterisation of turbulent fluxes (WASP) has been implemented in the surface model SURFEX v8.1 and validated on four case studies. It combines a close fit to observations including cyclonic winds, a dependency on the wave growth rate, and the possibility of being used in atmosphere–wave coupled models.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
EGUsphere, https://doi.org/10.5194/egusphere-2023-2740, https://doi.org/10.5194/egusphere-2023-2740, 2024
Short summary
Short summary
For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure facilitating further processing to allow emission processing from continental to street scale.
Najmeh Kaffashzadeh and Abbas Ali Aliakbari Bidokhti
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-226, https://doi.org/10.5194/gmd-2023-226, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Reanalysis data have been widely used as an initial condition for the daily forecast of the atmosphere or boundary conditions in regional models, for the study of climate change, and as proxies to complement insufficient in situ measurements. This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2023-2962, https://doi.org/10.5194/egusphere-2023-2962, 2024
Short summary
Short summary
There are relatively limited researches on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPU, have distinct advantages in energy efficiency and scalability. In this study, the air quality modeling system can run stably on MIPS CPU platform, and the experiment results verify the stability of scientific computing on the platform. The work provides a technical foundation for the scientific application based on MIPS CPU platforms.
Lukas Fehr, Chris McLinden, Debora Griffin, Daniel Zawada, Doug Degenstein, and Adam Bourassa
Geosci. Model Dev., 16, 7491–7507, https://doi.org/10.5194/gmd-16-7491-2023, https://doi.org/10.5194/gmd-16-7491-2023, 2023
Short summary
Short summary
This work highlights upgrades to SASKTRAN, a model that simulates sunlight interacting with the atmosphere to help measure trace gases. The upgrades were verified by detailed comparisons between different numerical methods. A case study was performed using SASKTRAN’s multidimensional capabilities, which found that ignoring horizontal variation in the atmosphere (a common practice in the field) can introduce non-negligible errors where there is snow or high pollution.
Sylvain Mailler, Romain Pennel, Laurent Menut, and Arineh Cholakian
Geosci. Model Dev., 16, 7509–7526, https://doi.org/10.5194/gmd-16-7509-2023, https://doi.org/10.5194/gmd-16-7509-2023, 2023
Short summary
Short summary
We show that a new advection scheme named PPM + W (piecewise parabolic method + Walcek) offers geoscientific modellers an alternative, high-performance scheme designed for Cartesian-grid advection, with improved performance over the classical PPM scheme. The computational cost of PPM + W is not higher than that of PPM. With improved accuracy and controlled computational cost, this new scheme may find applications in chemistry-transport models, ocean models or atmospheric circulation models.
David R. Shaw, Toby J. Carter, Helen L. Davies, Ellen Harding-Smith, Elliott C. Crocker, Georgia Beel, Zixu Wang, and Nicola Carslaw
Geosci. Model Dev., 16, 7411–7431, https://doi.org/10.5194/gmd-16-7411-2023, https://doi.org/10.5194/gmd-16-7411-2023, 2023
Short summary
Short summary
Exposure to air pollution is one of the greatest risks to human health, and it is indoors, where we spend upwards of 90 % of our time, that our exposure is greatest. The INdoor CHEMical model in Python (INCHEM-Py) is a new, community-led box model that tracks the evolution and fate of atmospheric chemical pollutants indoors. We have shown the processes simulated by INCHEM-Py, its ability to model experimental data and how it may be used to develop further understanding of indoor air chemistry.
Willem E. van Caspel, David Simpson, Jan Eiof Jonson, Anna M. K. Benedictow, Yao Ge, Alcide di Sarra, Giandomenico Pace, Massimo Vieno, Hannah L. Walker, and Mathew R. Heal
Geosci. Model Dev., 16, 7433–7459, https://doi.org/10.5194/gmd-16-7433-2023, https://doi.org/10.5194/gmd-16-7433-2023, 2023
Short summary
Short summary
Radiation coming from the sun is essential to atmospheric chemistry, driving the breakup, or photodissociation, of atmospheric molecules. This in turn affects the chemical composition and reactivity of the atmosphere. The representation of photodissociation effects is therefore essential in atmospheric chemistry modeling. One such model is the EMEP MSC-W model, for which a new way of calculating the photodissociation rates is tested and evaluated in this paper.
Jungmin Lee, Walter M. Hannah, and David C. Bader
Geosci. Model Dev., 16, 7275–7287, https://doi.org/10.5194/gmd-16-7275-2023, https://doi.org/10.5194/gmd-16-7275-2023, 2023
Short summary
Short summary
Representing accurate land–atmosphere interaction processes is overlooked in weather and climate models. In this study, we propose three methods to represent land–atmosphere coupling in the Energy Exascale Earth System Model (E3SM) with the Multi-scale Modeling Framework (MMF) approach. In this study, we introduce spatially homogeneous and heterogeneous land–atmosphere interaction processes within the cloud-resolving model domain. Our 5-year simulations reveal only small differences.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-209, https://doi.org/10.5194/gmd-2023-209, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
This study is about the modelling of the atmospheric composition in Europe and during the summer 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impact of two modelling processes able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Cited articles
Amnuaylojaroen, T., Barth, M. C., Emmons, L. K., Carmichael, G. R., Kreasuwun, J., Prasitwattanaseree, S., and Chantara, S.: Effect of different emission inventories on modeled
ozone and carbon monoxide in Southeast Asia, Atmos. Chem. Phys., 14, 12983–13012, https://doi.org/10.5194/acp-14-12983-2014, 2014.
Bousquet, P., Ciais, P., Miller, J. B., Dlugokencky, E. J., Hauglustaine,
D. A., Prigent, C., Van der Werf, G. R., Peylin, P., Brunke, E. G., Carouge,
C., Langenfelds, R. L., Lathiere, J., Papa, F., Ramonet, M., Schmidt, M.,
Steele, L. P., Tyler, S. C., and White, J.: Contribution of anthropogenic and
natural sources to atmospheric methane variability, Nature, 443, 439–443,
2006.
Bovensmann, H., Burrows, J. P., Buchwitz, M., Frerick, J., Noël, S., Rozanov, V. V., Chance, K. V., and Goede, A.: SCIAMACHY – Mission Objectives and measurement Modes, J. Atmos. Sci., 56,
127–150, 1999.
Chameides, W., Liu, S. C., and Cicerone, R. J.: Possible variations in
atmospheric methane, J. Geophys. Res., 81, 4997–5001, 1976.
Chen, Y.-H. and Prinn, R. G.: Estimation of atmospheric methane emissions between 1996 and 2001 using a three-dimensional global chemical transport model, J. Geophys. Res., 111, D10307, https://doi.org/10.1029/2005JD006058, 2006.
Deeter, M. N.: MOPITT (Measurement of Pollution in the Troposphere) Version6
Product User's Guide, available at:
http://www2.acd.ucar.edu/sites/default/files/mopitt/v6_users_guide_201309.pdf
(last access: 28 May 2015), 2013.
Deeter, M. N., Worden, H. M., Edwards, D. P., Gille, J. C., and Andrews, A.
E.: Evaluation of MOPITT retrievals of lower tropospheric carbon monoxide
over the United States, J. Geophys. Res., 117, D13306,
https://doi.org/10.1029/2012JD017553, 2012.
Dlugokencky, E. J., Lang, P. M., and Masarie, K. A.: Atmospheric Methane Dry
Air Mole Fractions from the NOAA ESRL Carbon Cycle Cooperative Global Air
Sampling Network, 1983–2014, Version: 2015-08-03, available at: ftp://aftp.cmdl.noaa.gov/data/trace_gases/ch4/flask/surface, last access: 22 February 2016.
Dlugokencky, E. J., Lang, P. M., Crotwell, A. M., Masarie, K. A., and Crotwell,
M. J.: Atmospheric Methane Dry Air Mole Fractions from the NOAA ESRL Carbon
Cycle, Cooperative Global Air Sampling Network, 1983–2013, Version:
2014-06-24, 2014.
Duncan, B. N. and Logan, J. A.: Model analysis of the factors regulating the
trends and variability of carbon monoxide between 1988 and 1997, Atmos. Chem.
Phys., 8, 7389–7403, https://doi.org/10.5194/acp-8-7389-2008, 2008.
Duncan, B. N., Portman, D., Bey, I., and Spivakovsky, C. M.:
Parameterization of OH for efficient computation in chemical tracer models,
J. Geophys. Res., 105, 12259–12262, 2000.
Duncan, B. N., Martin, R. V., Staudt, A. C., Yevich, R. M., and Logan, J.
A.: Interannual and Seasonal Variability of Biomass Burning Emissions
Constrained by Satellite Observations, J. Geophys. Res., 108, 4040,
https://doi.org/10.1029/2002JD002378, 2003a.
Duncan, B. N., Bey, I., Chin, M., Mickley, L. J., Fairlie, T. D., Martin, R. V., and
Matsueda, H.: Indonesian Wildfires of 1997: Impact on Tropospheric Chemistry,
J. Geophys. Res., 108, 4458, https://doi.org/10.1029/2002JD003195, 2003b.
Duncan, B. N., Logan, J. A., Bey, I., Megretskaia, I. A., Yantosca, R. M.,
Novelli, P. C., Jones, N. B., and Rinsland, C. P.: Global budget of CO,
1988–1997: Source estimates and validation with a global model, J. Geophys.
Res., 112, D22301, https://doi.org/10.1029/2007JD008459, 2007a.
Duncan, B. N., Strahan, S. E., Yoshida, Y., Steenrod, S. D., and Livesey, N.:
Model study of the cross-tropopause transport of biomass burning pollution,
Atmos. Chem. Phys., 7, 3713–3736, https://doi.org/10.5194/acp-7-3713-2007, 2007b.
Elshorbany, Y. F., Barnes, I., Becker, K. H., Kleffmann, J., and Wiesen, P.:
Sources and Cycling of Tropospheric Hydroxyl Radicals – An Overview, Z.
Phys. Chem., 224, 967–987, https://doi.org/10.1524/zpch.2010.6136, 2010.
Elshorbany, Y. F., Kleffmann, J., Hofzumahaus, A., Kurtenbach, R., Wiesen,
P., Dorn,H.-P., Schlosser, E., Brauers, T., Fuchs, H., Rohrer, F., Wahner,
A., Kanaya, Y., Yoshino, A., Nishida, S., Kajii, Y., Martinez, M., Rudolf,
M., Harder, H., Lelieveld, J., Elste, T., Plass-Dülmer, C., Stange, G.,
and Berresheim, H.: HOx Budgets during HOxComp: a Case Study of HOx
Chemistry under NOx limited Conditions, J. Geoophys. Res., 117, D03307,
https://doi.org/10.1029/2011JD017008, 2012a.
Elshorbany, Y. F., Steil, B., Brühl, C., and Lelieveld, J.: Impact of HONO
on global atmospheric chemistry calculated with an empirical parameterization
in the EMAC model, Atmos. Chem. Phys., 12, 9977–10000,
https://doi.org/10.5194/acp-12-9977-2012, 2012b.
Elshorbany, Y. F., Crutzen, P. J., Steil, B., Pozzer, A., Tost, H., and
Lelieveld, J.: Global and regional impacts of HONO on the chemical
composition of clouds and aerosols, Atmos. Chem. Phys., 14, 1167–1184,
https://doi.org/10.5194/acp-14-1167-2014, 2014.
Fiore, A. M., Jacob, D. J., Field, B. D., Streets, D. G., Fernandes, S. D.,
and Jang, C.: Linking air pollution and climate change: The case for
controlling methane, Geophys. Res. Lett., 29, 1919, https://doi.org/10.1029/2002GL015601,
2002.
Fiore, A. M., Horowitz, L. W., Dlugokencky, E. J., and West, J. J.: Impact of
meteorology and emissions on methane trends, 1990–2004, Geophys. Res. Lett.,
33, L12809, https://doi.org/10.1029/2006GL026199, 2006.
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., 114, D04301, https://doi.org/10.1029/2008JD010816, 2009.
Frankenberg, C., Aben, I., Bergamaschi, P., Dlugokencky, E. J., van Hees,
R., Houweling, S., van der Meer, P., Snel, R., and Tol, P.: Global
column-averaged methane mixing ratios from 2003 to 2009 as derived from
SCIAMACHY: Trends and variability, J. of Geophys. Res., 116, D04302,
https://doi.org/10.1029/2010JD014849, 2011.
Fuchs, H., Hofzumahaus, A., Rohrer, F., Bohn, B., Brauers, T., Dorn, H.-P.,
Hasseler, R., Holland, F., Kaminski, M., Li, X., Lu, K., Nehr, S.,
Tillmann, R., Wegener, R., and Wahner, A.: Experimental evidence for
efficient hydroxyl radical regeneration in isoprene oxidation, Nat. Geosci.
6, 1023–1026, https://doi.org/10.1038/ngeo1964, 2013.
Fujino, J., Nair, R., Kainuma, M., Masui, T., and Matsuoka, Y.: Multigas mitigation
analysis on stabilization scenarios using aim global model, Energy J.,
Special issue, 3, 343–354, 2006.
Giglio, L., Randerson, J. T., van der Werf, G. R., Kasibhatla, P. S.,
Collatz, G. J., Morton, D. C., and DeFries, R. S.: Assessing variability and
long-term trends in burned area by merging multiple satellite fire products,
Biogeosciences, 7, 1171–1186, https://doi.org/10.5194/bg-7-1171-2010, 2010.
Gloudemans, A. M. S., Schrijver, H., Hasekamp, O. P., and Aben, I.: Error
analysis for CO and CH4 total column retrievals from SCIAMACHY
2.3 µm spectra, Atmos. Chem. Phys., 8, 3999–4017,
https://doi.org/10.5194/acp-8-3999-2008, 2008.
Hijioka, Y., Matsuoka, Y., Nishimoto, H., Masui, T., and Kainuma, M.: Global GHG emission
scenarios under GHG concentration stabilization targets, J. Glob. Environ.
Eng., 13, 97–108, 2008.
Ho, S.-P., Edwards, D. P., Gille, J. C., Luo, M., Osterman, G. B., Kulawik,
S. S., and Worden, H.: A global comparison of carbon monoxide profiles and
column amounts from Tropospheric Emission Spectrometer (TES) and Measurements
of Pollution in the Troposphere (MOPITT), J. Geophys. Res., 114, D21307,
https://doi.org/10.1029/2009JD012242, 2009.
Holmes, C. D., Prather, M. J., Søvde, O. A., and Myhre, G.: Future methane,
hydroxyl, and their uncertainties: key climate and emission parameters for
future predictions, Atmos. Chem. Phys., 13, 285–302,
https://doi.org/10.5194/acp-13-285-2013, 2013.
Houweling, S., Krol, M., Bergamaschi, P., Frankenberg, C., Dlugokencky, E.
J., Morino, I., Notholt, J., Sherlock, V., Wunch, D., Beck, V., Gerbig, C.,
Chen, H., Kort, E. A., Röckmann, T., and Aben, I.: A multi-year methane
inversion using SCIAMACHY, accounting for systematic errors using TCCON
measurements, Atmos. Chem. Phys., 14, 3991–4012,
https://doi.org/10.5194/acp-14-3991-2014, 2014.
Kirschke, S., Bousquet, P., Ciais, P., Saunois, M., Canadell, J. G., Dlugokencky, E. J., Bergamaschi,
P., Bergmann, D., Blake, D. R., Bruhwiler, L., Cameron-Smith, P., Castaldi, S., Chevallier,
F., Feng, L., Fraser, A., Heimann, M., Hodson, E. L., Houweling, S., Josse, B., Fraser, P.
J., Krummel, P. B., Lamarque, J.-F., Langenfelds, R. L., Le Quéré, C., Naik, V., O'Doherty, S.,
15 Palmer, P. I., Pison, I., Plummer, D., Poulter, B., Prinn, R. G., Rigby, M., Ringeval, B., Santini,
M., Schmidt, M., Shindell, D. T., Simpson, I. J., Spahni, R., Steele, L. P., Strode, S. A., Sudo,
K., Szopa, S., van der Werf, G. R., Voulgarakis, A., van Weele, M., Weiss, R. F., Williams,
J. E., and Zeng, G.: Three decades of global methane sources and sinks, Nat. Geosci., 6,
813–823, https://doi.org/10.1038/ngeo1955, 2013.
Lamarque, J.-F., Shindell, D. T., Josse, B., Young, P. J., Cionni, I.,
Eyring, V., Bergmann, D., Cameron-Smith, P., Collins, W. J., Doherty, R.,
Dalsoren, S., Faluvegi, G., Folberth, G., Ghan, S. J., Horowitz, L. W., Lee,
Y. H., MacKenzie, I. A., Nagashima, T., Naik, V., Plummer, D., Righi, M.,
Rumbold, S. T., Schulz, M., Skeie, R. B., Stevenson, D. S., Strode, S., Sudo,
K., Szopa, S., Voulgarakis, A., and Zeng, G.: The Atmospheric Chemistry and
Climate Model Intercomparison Project (ACCMIP): overview and description of
models, simulations and climate diagnostics, Geosci. Model Dev., 6, 179–206,
https://doi.org/10.5194/gmd-6-179-2013, 2013.
Lawrence, M. G., Jöckel, P., and von Kuhlmann, R.: What does the global
mean OH concentration tell us?, Atmos. Chem. Phys., 1, 37–49,
https://doi.org/10.5194/acp-1-37-2001, 2001.
Lelieveld, J., Peters, W., Dentener, F. J., and Krol, M. C.: Stability of
tropospheric hydroxyl chemistry, J. Geophys. Res., 107, 4715,
https://doi.org/10.1029/2002JD002272, 2002.
Lin, S.-J.: A “vertically Lagrangian” finite-volume dynamical core for
global models, Mon. Weather Rev., 132, 2293–2307, 2004.
Luo, M., Read, W., Kulawik, S., Worden, J., Livesey, N., Bowman, K., and
Herman, R.: Carbon monoxide (CO) vertical profiles derived from joined TES
and MLS measurements, J. Geophys. Res. Atmos., 118, 10601–10613,
https://doi.org/10.1002/jgrd.50800, 2013.
Molod, A., Takacs, L., Suarez, M., Bacmeister, J., Song, I.-S., and
Eichmann, A.: The GEOS-5 Atmospheric General Circulation Model: Mean Climate
and Development from MERRA to Fortuna, NASA/TM–2012-104606, Technical Report
Series on Global Modeling and Data Assimilation, edited by: Suarez, M., Vol.
28, available at: http://gmao.gsfc.nasa.gov/pubs/docs/tm28.pdf (last
access: 27 October 2015), 2012.
Monks, S. A., Arnold, S. R., Emmons, L. K., Law, K. S., Turquety, S., Duncan,
B. N., Flemming, J., Huijnen, V., Tilmes, S., Langner, J., Mao, J., Long, Y.,
Thomas, J. L., Steenrod, S. D., Raut, J. C., Wilson, C., Chipperfield, M. P.,
Diskin, G. S., Weinheimer, A., Schlager, H., and Ancellet, G.: Multi-model
study of chemical and physical controls on transport of anthropogenic and
biomass burning pollution to the Arctic, Atmos. Chem. Phys., 15, 3575–3603,
https://doi.org/10.5194/acp-15-3575-2015, 2015.
Montzka, S. A., Krol, M., Dlugokencky, E., Hall, B., Joeckel, P., and
Lelieveld, J.: Small interannual variability of global atmospheric hydroxyl,
Science, 331, 67–69, https://doi.org/10.1126/science.1197640, 2011.
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.
Myhre, G., Shindell, D., Breìon, F.-M., Collins, W., Fuglestvedt, J.,
Huang, J., Koch, D., Lamarque, J.-F., Lee, D.,
Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang,
H.: Anthropogenic and natural radiative forcing, in: Climate Change 2013: The
Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, edited
by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K.,
Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., availa1able
at: http://www.climatechange2013.org/images/report/WG1AR5_ALL_FINAL.pdf
(last access: 27 October 2015), 2013.
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.
Novelli, P., Steele, P., and Tans, P. P.: Mixing ratios of carbon monoxide in
the troposphere, J. Geophys. Res., 102, 12855–12861, https://doi.org/10.1029/92JD02010,
1992.
Novelli, P., Masarie, K. A., and Lang, P. M.: Distributions and recent changes
in carbon monoxide in the lower troposphere, J. Geosphys. Res., 103,
19015–19033, 1998.
Oman, L. D., Ziemke, J. R., Douglass, A. R., Waugh, D. W., Lang, C.,
Rodriguez, J. M., and Nielsen, J. E.: The response of tropical tropospheric
ozone to ENSO, Geophys. Res. Lett., 38, L13706, https://doi.org/10.1029/2011GL047865,
2011.
Ott, L., Duncan, B., Pawson, S., Colarco, P., Chin, M., Randles, C., Diehl,
T., and Nielsen, E.: Influence of the 2006 Indonesian biomass burning
aerosols on tropical dynamics studied with the GEOS5 AGCM, J. Geophys. Res.,
115, D14121, https://doi.org/10.1029/2009JD013181, 2010.
Patra, P. K., Houweling, S., Krol, M., Bousquet, P., Belikov, D., Bergmann,
D., Bian, H., Cameron-Smith, P., Chipperfield, M. P., Corbin, K.,
Fortems-Cheiney, A., Fraser, A., Gloor, E., Hess, P., Ito, A., Kawa, S. R.,
Law, R. M., Loh, Z., Maksyutov, S., Meng, L., Palmer, P. I., Prinn, R. G.,
Rigby, M., Saito, R., and Wilson, C.: TransCom model simulations of CH4 and
related species: linking transport, surface flux and chemical loss with
CH4
variability in the troposphere and lower stratosphere, Atmos. Chem. Phys.,
11, 12813–12837, https://doi.org/10.5194/acp-11-12813-2011, 2011.
Patra, P. K., Krol, M. C., Montzka, S. A., Arnold, T., Atlas, E. L.,
Lintner, B. R., Xiang, B., Elkins, J. W., Fraser, P. J., Ghosh, A., Hintsa,
E. J., Hurst, D. F., Ishijima, K., Krummel, P. B., Miller, B. R., Miyazaki,
K., Moore, F. L., Mühle, J., O'Doherty, S., Prinn, R. G., Steele, L. P.,
Takigawa, M., Wang, H. J., Weiss, R. F., Wofsy, S. C., and Young, D.:
Observational evidence for interhemispheric hydroxyl-radical parity, Nature,
513, 219–223, https://doi.org/10.1038/nature13721, 2014.
Pawson, S. R., Stolarski, S., Douglass, A. R., Newman, P. A., Nielsen, J.
E., Frith, S. M., and Gupta, M. L.: Goddard Earth Observing System
chemistry-climate model simulations of stratospheric ozone-temperature
coupling between 1950 and 2005, J. Geophys. Res., 113, D12103,
https://doi.org/10.1029/2007JD009511, 2008.
Prather, M.: Lifetimes and Eigen states in atmospheric chemistry, Geophys.
Res. Lett., 21, 801–804, 1994.
Prather, M.: Time scales in atmospheric chemistry: Theory, GWPs for CH4
and CO, and runaway growth, Geophys. Res. Lett., 23, 2597–2600,
https://doi.org/10.1029/96GL02371, 1996.
Prather, M. and Spivakovsky, C. M.: Tropospheric OH and the lifetimes of
hydrochlorofluorocarbons, J. Geophys. Res., 95, 18723–18729,
https://doi.org/10.1029/JD095iD11p18723, 1990.
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.
Prinn, R. G., Huang, J., Weiss, R. F., Cunnold, D. M., Fraser, P. J.,
Simmonds, P. G., McCulloch, A., Harth, C., Reimann, S., Salameh, P.,
O'Doherty, S., Wang, R. H. J., Porter, L. W., Miller, B. R., and Krummel, P.
B.: Evidence for variability of atmospheric hydroxyl radicals over the past
quarter century, Geophys. Res. Lett., 32, L07809, https://doi.org/10.1029/2004GL022228,
2005.
Randerson, J. T., van der Werf, G. R., Giglio, L., Collatz, G. J., and
Kasibhatl, P. S.: Global Fire Emissions Database, Version 3 (GFEDv3.1), Data
set, Oak Ridge National Laboratory Distributed Active Archive Center, Oak
Ridge, Tennessee, USA, available at: http://daac.ornl.gov (last access:
27 October 2015), https://doi.org/10.3334/ORNLDAAC/1191, 2013.
Rienecker, M. M., Suarez, M. J., Todling, R., Bacmeister, J., Takacs, L.,
Liu, H.-C., Gu, W., Sienkiewicz, M., Koster, R. D., Gelaro, R., Stajner, I.,
and Nielsen, J. E.: The GEOS-5 data assimilation system – Documentation of
Versions 5.0.1, 5.1.0, and 5.2.0, Technical Report Series on Global Modeling
and Data Assimilation, Vol. 27, available at:
http://gmao.gsfc.nasa.gov/pubs/docs/GEOS5_104606-Vol27.pdf (last
access: 27 October 2015), 2008.
Rohrer, F. and Berresheim, H.: Strong correlations between levels of
tropospheric hydroxyl radicals and solar ultraviolet radiation, Nature, 442,
184–187, https://doi.org/10.1038/nature04924, 2006.
Rohrer, F., Lu, K., Hofzumahaus, A., Bohn, B., Brauers, T., Chang, C.-C.,
Fuchs, H., Häseler, R., Holland, F., Hu, M., Kita, K., Kondo, Y., Li, X.,
Lou, S., Oebel, A., Shao, M., Zeng, L., Zhu, T., Zhang, Y., and Wahner, A.:
Maximum efficiency in the hydroxyl-radical-based self-cleansing of the
troposphere, Nat. Geosci. 7, 559–563, https://doi.org/10.1038/ngeo2199, 2014.
Sander, S. P., Abbatt, J., Barker, J. R., Burkholder, J. B., Friedl, R. R.,
Golden, D. M., Huie, R. E., Kolb, C. E., Kurylo, M. J., Moortgat, G. K.,
Orkin, V. L., and Wine, P. H.: Chemical Kinetics and Photochemical Data for
Use in Atmospheric Studies, Evaluation No. 17, JPL Publication 10-6, Jet
Propulsion Laboratory, Pasadena, available at:
http://jpldataeval.jpl.nasa.gov (last access: 27 October 2015), 2011.
Schneising, O., Buchwitz, M., Burrows, J. P., Bovensmann, H., Bergamaschi,
P., and Peters, W.: Three years of greenhouse gas column-averaged dry air
mole fractions retrieved from satellite – Part 2: Methane, Atmos. Chem.
Phys., 9, 443–465, https://doi.org/10.5194/acp-9-443-2009, 2009.
Schneising, O., Buchwitz, M., Reuter, M., Heymann, J., Bovensmann, H., and
Burrows, J. P.: Long-term analysis of carbon dioxide and methane
column-averaged mole fractions retrieved from SCIAMACHY, Atmos. Chem. Phys.,
11, 2863–2880, https://doi.org/10.5194/acp-11-2863-2011, 2011.
Schultz, M., Rast, S., van het Bolscher, M., Pulles, T., Brand, R.,
Pereira, J., Mota, B., Spessa, A., Dalsøren, S., van Nojie, T., and Szopa,
S.: Emission data sets and methodologies for estimating emissions, RETRO
project report D1-6, Hamburg, 26 February 2007, available at:
http://gcmd.gsfc.nasa.gov/records/GCMD_GEIA_RETRO.html (last access:
27 October 2015), 2007.
Shindell, D. T., Faluvegi, G., Stevenson, D. S., Krol, M. C., Emmons, L. K.,
Lamarque, J.-F., Petron, G., Dentener, F. J., Ellingsne, K., Schultz, M. G.,
Wild, O., Amann, M., Atherton, C. S., Bergmann, D. J., Bey, I., Butler, T.,
Cofala, J., Collins, W. J., Derwent, R. G., Doherty, R. M., Drevet, J.,
Eskes, H. J., Fiore, A. M., Gauss, M., Hauglustaine, D. A., Horowitz, L. W.,
Isaksen, I. S. A., Lawrence, M. G., Montanaro, V., Müller, J.-F., Pitari,
G., Prather, M. J., Pyle, J. A., Rast, S., Rodriguez, J. M., Sanderson, M.
G., Savage, N. H., Strahan, S. E., Sudo, K., Szopa, S., Unger, N., van Noije,
T. P. C., and Zeng, G.: Multimodel simulations of carbon monoxide: Comparison
with observations and projected near-future changes, J. Geophys. Res., 111,
D19306, https://doi.org/10.1029/2006JD007100, 2006.
Spivakovsky, C., Wofsy, S., and Prather, M.: A numerical method for the
parameterization of atmospheric chemistry: Computation of tropospheric OH, J.
Geophys. Res., 95, 18433–18439, 1990a.
Spivakovsky, C. M., Yevich, R., Logan, J. A., Wofsy, S. C., McElroy, M. B., and
Prather, M. J.: Tropospheric OH in a three-dimensional chemical tracer model:
An assessment based on observations of CH3CC13, J. Geophys. Res.,
95, 18441–18471, https://doi.org/10.1029/JD095iD11p18441, 1990b.
Spivakovsky, C. M., Logan, J. A., Montzka, S. A., Balkanski, Y.
J., Foreman-Fowler, M., Jones, D. B. A., Horowitz, L. W., Fusco, A. C.,
Brenninkmeijer, C. A. M., Prather, M. J., Wofsy, S. C., and McElroy, M. B.:
Three-dimensional climatological distribution of tropospheric OH: Update and
evaluation, J. Geophys. Res., 105, 8931–8980, https://doi.org/10.1029/1999JD901006,
2000.
Stone, D., Whalley, L. K., and Heard, D. E.: Tropospheric OH and HO2 radicals:
field measurements and model comparisons, Chem. Soc. Rev., 41, 6348,
https://doi.org/10.1039/c2cs35140d, 2012.
Strahan, S. E., Duncan, B. N., and Hoor, P.: Observationally derived
transport diagnostics for the lowermost stratosphere and their application to
the GMI chemistry and transport model, Atmos. Chem. Phys., 7, 2435–2445,
https://doi.org/10.5194/acp-7-2435-2007, 2007.
Strode, S. A., Duncan, B. N., Yegorova, E. A., Kouatchou, J., Ziemke, J. R.,
and Douglass, A. R.: Implications of carbon monoxide bias for methane
lifetime and atmospheric composition in chemistry climate models, Atmos.
Chem. Phys., 15, 11789–11805, https://doi.org/10.5194/acp-15-11789-2015,
2015.
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.
Voulgarakis, A., Marlier, M. E., Faluvegi, G., Shindell, D. T.,
Tsigaridis, K., and Mangeon, S.: Interannual variability of tropospheric
trace gases and aerosols: The role of biomass burning emissions, J. Geophys.
Res. Atmos., 120, 7157–7173, https://doi.org/10.1002/2014JD022926, 2015.
Wang, J. S., Logan, J. A., McElroy, M. B., Duncan, B. N., Megretskaia, I.
A., and Yantosca, R. M.: A 3-D model analysis of the slowdown and interannual
variability in the methane growth rate from 1988 to 1997, Global Biogeochem.
Cy., 18, GB3011, https://doi.org/10.1029/2003GB002180, 2004.
Wang, J. S., McElroy, M. B., Logan, J. A., Palmer, P. I., Chameides, W. L.,
Wang, Y., and Megretskaia, I. A.: A quantitative assessment of uncertainties
affecting estimates of global mean OH derived from methyl chloroform
observations, J. Geophys. Res., 113, D12302, https://doi.org/10.1029/2007JD008496, 2008.
Wild, O. and Palmer, P. I.: How sensitive is tropospheric oxidation to
anthropogenic emissions?, Geophys. Res. Lett., 35, L22802,
https://doi.org/10.1029/2008GL035718, 2008.
Wolter, K. and Timlin, M. S.: El Niño/Southern Oscillation behaviour
since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext),
Int. J. Climatol., 31, 1074–1087, https://doi.org/10.1002/joc.2336, 2011.
Worden, H. M., Deeter, M. N., Edwards, D. P., Gille, J. C., Drummond, J. R.,
and Nédélec, P.: Observations of near-surface carbon monoxide from
space using MOPITT multispectral retrievals, J. Geophys., Res., 115, D18314,
https://doi.org/10.1029/2010JD014242, 2010.
Short summary
The ECCOH (pronounced "echo") chemistry module interactively simulates the photochemistry of the CH4–CO–OH system within a chemistry climate model, carbon cycle model, or Earth system model. The computational efficiency of the module allows many multi-decadal sensitivity simulations of the CH4–CO–OH system. This capability is important for capturing nonlinear feedbacks of the CH4–CO–OH system and understanding the perturbations to methane, CO, and OH and the concomitant climate impacts.
The ECCOH (pronounced "echo") chemistry module interactively simulates the photochemistry of the...