Articles | Volume 7, issue 3
Geosci. Model Dev., 7, 1137–1157, 2014
https://doi.org/10.5194/gmd-7-1137-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue: The Modular Earth Submodel System (MESSy) (ACP/GMD inter-journal...
Geosci. Model Dev., 7, 1137–1157, 2014
https://doi.org/10.5194/gmd-7-1137-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
17 Jun 2014
Model description paper
| 17 Jun 2014
The MESSy aerosol submodel MADE3 (v2.0b): description and a box model test
J. C. Kaiser et al.
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Xueyin Ruan, Chun Zhao, Rahul A. Zaveri, Pengzhen He, Xinming Wang, Jingyuan Shao, and Lei Geng
Geosci. Model Dev., 15, 6143–6164, https://doi.org/10.5194/gmd-15-6143-2022, https://doi.org/10.5194/gmd-15-6143-2022, 2022
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Accurate prediction of aerosol pH in chemical transport models is essential to aerosol modeling. This study examines the performance of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) on aerosol pH predictions and the sensitivities to emissions of nonvolatile cations and NH3, aerosol-phase state assumption, and heterogeneous sulfate production. Temporal evolution of aerosol pH during haze cycles in Beijing and the driving factors are also presented and discussed.
Christof Gerhard Beer, Johannes Hendricks, and Mattia Righi
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-529, https://doi.org/10.5194/acp-2022-529, 2022
Preprint under review for ACP
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Ice nucleating aerosol particles (INPs) have important influences on cirrus clouds and the climate system. However, their global atmospheric distribution in the cirrus regime is still very uncertain. We present a global climatology of INPs at cirrus conditions derived from model simulations, considering the INP-types mineral dust, soot, crystalline ammonium sulfate and glassy organics. The comparison of respective INP concentrations indicates a large importance of ammonium sulfate particles.
Yu Yao, Jeffrey H. Curtis, Joseph Ching, Zhonghua Zheng, and Nicole Riemer
Atmos. Chem. Phys., 22, 9265–9282, https://doi.org/10.5194/acp-22-9265-2022, https://doi.org/10.5194/acp-22-9265-2022, 2022
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Investigating the impacts of aerosol mixing state on aerosol optical properties has a long history from both the modeling and experimental perspective. In this study, we used particle-resolved simulations as a benchmark to determine the error in optical properties when using simplified aerosol representations. We found that errors in single scattering albedo due to the internal mixture assumptions can have substantial effects on calculating aerosol direct radiative forcing.
Simon Kirschler, Christiane Voigt, Bruce Anderson, Ramon Campos Braga, Gao Chen, Andrea F. Corral, Ewan Crosbie, Hossein Dadashazar, Richard A. Ferrare, Valerian Hahn, Johannes Hendricks, Stefan Kaufmann, Richard Moore, Mira L. Pöhlker, Claire Robinson, Amy J. Scarino, Dominik Schollmayer, Michael A. Shook, K. Lee Thornhill, Edward Winstead, Luke D. Ziemba, and Armin Sorooshian
Atmos. Chem. Phys., 22, 8299–8319, https://doi.org/10.5194/acp-22-8299-2022, https://doi.org/10.5194/acp-22-8299-2022, 2022
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In this study we show that the vertical velocity dominantly impacts the cloud droplet number concentration (NC) of low-level clouds over the western North Atlantic in the winter and summer season, while the cloud condensation nuclei concentration, aerosol size distribution and chemical composition impact NC within a season. The observational data presented in this study can evaluate and improve the representation of aerosol–cloud interactions for a wide range of conditions.
Jerome D. Fast, David Bell, Jiumeng Liu, Fan Mei, Georges Saliba, John E. Shilling, Kaitlyn Suski, Jason Tomlinson, Jian Wang, Rahul Zaveri, and Alla Zelenyuk
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-384, https://doi.org/10.5194/acp-2022-384, 2022
Revised manuscript under review for ACP
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Recent aircraft measurements from the HI-SCALE campaign conducted over the Southern Great Plains (SGP) site in Oklahoma are used to quantify spatial variability of aerosol properties in terms of grid spacings typically used by weather and climate models. Surprisingly large horizontal gradients in aerosol properties were frequently observed in this rural area. This spatial variability can be used as an uncertainty range when comparing surface point measurements to model predictions.
Matthew L. Dawson, Christian Guzman, Jeffrey H. Curtis, Mario Acosta, Shupeng Zhu, Donald Dabdub, Andrew Conley, Matthew West, Nicole Riemer, and Oriol Jorba
Geosci. Model Dev., 15, 3663–3689, https://doi.org/10.5194/gmd-15-3663-2022, https://doi.org/10.5194/gmd-15-3663-2022, 2022
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Progress in identifying complex, mixed-phase physicochemical processes has resulted in an advanced understanding of the evolution of atmospheric systems but has also introduced a level of complexity that few atmospheric models were designed to handle. We present a flexible treatment for multiphase chemical processes for models of diverse scale, from box up to global models. This enables users to build a customized multiphase mechanism that is accessible to a much wider community.
Giorgio Doglioni, Valentina Aquila, Sampa Das, Peter R. Colarco, and Dino Zardi
EGUsphere, https://doi.org/10.5194/egusphere-2022-80, https://doi.org/10.5194/egusphere-2022-80, 2022
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We use a chemistry climate model to analyze the perturbations to the stratospheric dynamics caused by an injection of carbonaceous aerosol comparable to the ones caused by a series of pyrocumulonimbi that formed over British Columbia, Canada on August 13, 2017. The injection of light-absorbing aerosol in an otherwise clean lower stratosphere cause the formation of long-lasting stratospheric anticyclones at the synoptic scale.
Ka Ming Fung, Colette L. Heald, Jesse H. Kroll, Siyuan Wang, Duseong S. Jo, Andrew Gettelman, Zheng Lu, Xiaohong Liu, Rahul A. Zaveri, Eric C. Apel, Donald R. Blake, Jose-Luis Jimenez, Pedro Campuzano-Jost, Patrick R. Veres, Timothy S. Bates, John E. Shilling, and Maria Zawadowicz
Atmos. Chem. Phys., 22, 1549–1573, https://doi.org/10.5194/acp-22-1549-2022, https://doi.org/10.5194/acp-22-1549-2022, 2022
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Understanding the natural aerosol burden in the preindustrial era is crucial for us to assess how atmospheric aerosols affect the Earth's radiative budgets. Our study explores how a detailed description of dimethyl sulfide (DMS) oxidation (implemented in the Community Atmospheric Model version 6 with chemistry, CAM6-chem) could help us better estimate the present-day and preindustrial concentrations of sulfate and other relevant chemicals, as well as the resulting aerosol radiative impacts.
Jingmin Li, Johannes Hendricks, Mattia Righi, and Christof G. Beer
Geosci. Model Dev., 15, 509–533, https://doi.org/10.5194/gmd-15-509-2022, https://doi.org/10.5194/gmd-15-509-2022, 2022
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The growing complexity of global aerosol models results in a large number of parameters that describe the aerosol number, size, and composition. This makes the analysis, evaluation, and interpretation of the model results a challenge. To overcome this difficulty, we apply a machine learning classification method to identify clusters of specific aerosol types in global aerosol simulations. Our results demonstrate the spatial distributions and characteristics of these identified aerosol clusters.
Zhonghua Zheng, Matthew West, Lei Zhao, Po-Lun Ma, Xiaohong Liu, and Nicole Riemer
Atmos. Chem. Phys., 21, 17727–17741, https://doi.org/10.5194/acp-21-17727-2021, https://doi.org/10.5194/acp-21-17727-2021, 2021
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Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. We present a framework for evaluating the error in aerosol mixing state induced by aerosol representation assumptions, which is one of the important contributors to structural uncertainty in aerosol models. Our study provides insights into potential improvements to model process representation for aerosols.
Sudipta Ghosh, Sagnik Dey, Sushant Das, Nicole Riemer, Graziano Giuliani, Dilip Ganguly, Chandra Venkataraman, Filippo Giorgi, Sachchida Nand Tripathi, Srikanthan Ramachandran, Thazhathakal Ayyappen Rajesh, Harish Gadhavi, and Atul Kumar Srivastava
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-330, https://doi.org/10.5194/gmd-2021-330, 2021
Revised manuscript under review for GMD
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Accurate representation of aerosols in climate models is critical in minimizing the uncertainty in climate projections. Here we implement region-specific emission fluxes and a more accurate scheme for carbonaceous aerosol ageing processes in a regional climate model, RegCM4, and show that it improves the model performance significantly against in-situ, reanalysis and satellite data over the Indian subcontinent. We recommend improving the model performance before using them for climate studies.
Mattia Righi, Johannes Hendricks, and Christof Gerhard Beer
Atmos. Chem. Phys., 21, 17267–17289, https://doi.org/10.5194/acp-21-17267-2021, https://doi.org/10.5194/acp-21-17267-2021, 2021
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A global climate model is applied to simulate the impact of aviation soot on natural cirrus clouds. A large number of numerical experiments are performed to analyse how the quantification of the resulting climate impact is affected by known uncertainties. These concern the ability of aviation soot to nucleate ice and the role of model dynamics. Our results show that both aspects are important for the quantification of this effect and that discrepancies among different model studies still exist.
Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyring
Geosci. Model Dev., 14, 3159–3184, https://doi.org/10.5194/gmd-14-3159-2021, https://doi.org/10.5194/gmd-14-3159-2021, 2021
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This work presents new diagnostics for the Earth System Model Evaluation Tool (ESMValTool) v2.0 on the hydrological cycle, extreme events, impact assessment, regional evaluations, and ensemble member selection. The ESMValTool v2.0 diagnostics are developed by a large community of scientists aiming to facilitate the evaluation and comparison of Earth system models (ESMs) with a focus on the ESMs participating in the Coupled Model Intercomparison Project (CMIP).
Maria A. Zawadowicz, Kaitlyn Suski, Jiumeng Liu, Mikhail Pekour, Jerome Fast, Fan Mei, Arthur J. Sedlacek, Stephen Springston, Yang Wang, Rahul A. Zaveri, Robert Wood, Jian Wang, and John E. Shilling
Atmos. Chem. Phys., 21, 7983–8002, https://doi.org/10.5194/acp-21-7983-2021, https://doi.org/10.5194/acp-21-7983-2021, 2021
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This paper describes the results of a recent field campaign in the eastern North Atlantic, where two mass spectrometers were deployed aboard a research aircraft to measure the chemistry of aerosols and trace gases. Very clean conditions were found, dominated by local sulfate-rich acidic aerosol and very aged organics. Evidence of
long-range transport of aerosols from the continents was also identified.
Duseong S. Jo, Alma Hodzic, Louisa K. Emmons, Simone Tilmes, Rebecca H. Schwantes, Michael J. Mills, Pedro Campuzano-Jost, Weiwei Hu, Rahul A. Zaveri, Richard C. Easter, Balwinder Singh, Zheng Lu, Christiane Schulz, Johannes Schneider, John E. Shilling, Armin Wisthaler, and Jose L. Jimenez
Atmos. Chem. Phys., 21, 3395–3425, https://doi.org/10.5194/acp-21-3395-2021, https://doi.org/10.5194/acp-21-3395-2021, 2021
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Secondary organic aerosol (SOA) is a major component of submicron particulate matter, but there are a lot of uncertainties in the future prediction of SOA. We used CESM 2.1 to investigate future IEPOX SOA concentration changes. The explicit chemistry predicted substantial changes in IEPOX SOA depending on the future scenario, but the parameterization predicted weak changes due to simplified chemistry, which shows the importance of correct physicochemical dependencies in future SOA prediction.
Christof G. Beer, Johannes Hendricks, Mattia Righi, Bernd Heinold, Ina Tegen, Silke Groß, Daniel Sauer, Adrian Walser, and Bernadett Weinzierl
Geosci. Model Dev., 13, 4287–4303, https://doi.org/10.5194/gmd-13-4287-2020, https://doi.org/10.5194/gmd-13-4287-2020, 2020
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Mineral dust aerosol plays an important role in the climate system. Previously, dust emissions have often been represented in global models by prescribed monthly-mean emission fields representative of a specific year. We now apply an online calculation of wind-driven dust emissions. This results in an improved agreement with observations, due to a better representation of the highly variable dust emissions. Increasing the model resolution led to an additional performance gain.
Axel Lauer, Veronika Eyring, Omar Bellprat, Lisa Bock, Bettina K. Gier, Alasdair Hunter, Ruth Lorenz, Núria Pérez-Zanón, Mattia Righi, Manuel Schlund, Daniel Senftleben, Katja Weigel, and Sabrina Zechlau
Geosci. Model Dev., 13, 4205–4228, https://doi.org/10.5194/gmd-13-4205-2020, https://doi.org/10.5194/gmd-13-4205-2020, 2020
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The Earth System Model Evaluation Tool is a community software tool designed for evaluation and analysis of climate models. New features of version 2.0 include analysis scripts for important large-scale features in climate models, diagnostics for extreme events, regional model and impact evaluation. In this paper, newly implemented climate metrics, emergent constraints for climate-relevant feedbacks and diagnostics for future model projections are described and illustrated with examples.
Veronika Eyring, Lisa Bock, Axel Lauer, Mattia Righi, Manuel Schlund, Bouwe Andela, Enrico Arnone, Omar Bellprat, Björn Brötz, Louis-Philippe Caron, Nuno Carvalhais, Irene Cionni, Nicola Cortesi, Bas Crezee, Edouard L. Davin, Paolo Davini, Kevin Debeire, Lee de Mora, Clara Deser, David Docquier, Paul Earnshaw, Carsten Ehbrecht, Bettina K. Gier, Nube Gonzalez-Reviriego, Paul Goodman, Stefan Hagemann, Steven Hardiman, Birgit Hassler, Alasdair Hunter, Christopher Kadow, Stephan Kindermann, Sujan Koirala, Nikolay Koldunov, Quentin Lejeune, Valerio Lembo, Tomas Lovato, Valerio Lucarini, François Massonnet, Benjamin Müller, Amarjiit Pandde, Núria Pérez-Zanón, Adam Phillips, Valeriu Predoi, Joellen Russell, Alistair Sellar, Federico Serva, Tobias Stacke, Ranjini Swaminathan, Verónica Torralba, Javier Vegas-Regidor, Jost von Hardenberg, Katja Weigel, and Klaus Zimmermann
Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, https://doi.org/10.5194/gmd-13-3383-2020, 2020
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility.
Havala O. T. Pye, Athanasios Nenes, Becky Alexander, Andrew P. Ault, Mary C. Barth, Simon L. Clegg, Jeffrey L. Collett Jr., Kathleen M. Fahey, Christopher J. Hennigan, Hartmut Herrmann, Maria Kanakidou, James T. Kelly, I-Ting Ku, V. Faye McNeill, Nicole Riemer, Thomas Schaefer, Guoliang Shi, Andreas Tilgner, John T. Walker, Tao Wang, Rodney Weber, Jia Xing, Rahul A. Zaveri, and Andreas Zuend
Atmos. Chem. Phys., 20, 4809–4888, https://doi.org/10.5194/acp-20-4809-2020, https://doi.org/10.5194/acp-20-4809-2020, 2020
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Acid rain is recognized for its impacts on human health and ecosystems, and programs to mitigate these effects have had implications for atmospheric acidity. Historical measurements indicate that cloud and fog droplet acidity has changed in recent decades in response to controls on emissions from human activity, while the limited trend data for suspended particles indicate acidity may be relatively constant. This review synthesizes knowledge on the acidity of atmospheric particles and clouds.
Mattia Righi, Johannes Hendricks, Ulrike Lohmann, Christof Gerhard Beer, Valerian Hahn, Bernd Heinold, Romy Heller, Martina Krämer, Michael Ponater, Christian Rolf, Ina Tegen, and Christiane Voigt
Geosci. Model Dev., 13, 1635–1661, https://doi.org/10.5194/gmd-13-1635-2020, https://doi.org/10.5194/gmd-13-1635-2020, 2020
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A new cloud microphysical scheme is implemented in the global EMAC-MADE3 aerosol model and evaluated. The new scheme features a detailed parameterization for aerosol-driven ice formation in cirrus clouds, accounting for the competition between homogeneous and heterogeneous ice formation processes. The comparison against satellite data and in situ measurements shows that the model performance is in line with similar global coupled models featuring ice cloud parameterizations.
Mattia Righi, Bouwe Andela, Veronika Eyring, Axel Lauer, Valeriu Predoi, Manuel Schlund, Javier Vegas-Regidor, Lisa Bock, Björn Brötz, Lee de Mora, Faruk Diblen, Laura Dreyer, Niels Drost, Paul Earnshaw, Birgit Hassler, Nikolay Koldunov, Bill Little, Saskia Loosveldt Tomas, and Klaus Zimmermann
Geosci. Model Dev., 13, 1179–1199, https://doi.org/10.5194/gmd-13-1179-2020, https://doi.org/10.5194/gmd-13-1179-2020, 2020
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This paper describes the second major release of ESMValTool, a community diagnostic and performance metrics tool for the evaluation of Earth system models. This new version features a brand new design, with an improved interface and a revised preprocessor. It takes advantage of state-of-the-art computational libraries and methods to deploy efficient and user-friendly data processing, improving the performance over its predecessor by more than a factor of 30.
J. Christopher Kaiser, Johannes Hendricks, Mattia Righi, Patrick Jöckel, Holger Tost, Konrad Kandler, Bernadett Weinzierl, Daniel Sauer, Katharina Heimerl, Joshua P. Schwarz, Anne E. Perring, and Thomas Popp
Geosci. Model Dev., 12, 541–579, https://doi.org/10.5194/gmd-12-541-2019, https://doi.org/10.5194/gmd-12-541-2019, 2019
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The implementation of the aerosol microphysics submodel MADE3 into the global atmospheric chemistry model EMAC is described and evaluated against an extensive pool of observational data, focusing on aerosol mass and number concentrations, size distributions, composition, and optical properties. EMAC (MADE3) is able to reproduce main aerosol properties reasonably well, in line with the performance of other global aerosol models.
Jian Wang, John E. Shilling, Jiumeng Liu, Alla Zelenyuk, David M. Bell, Markus D. Petters, Ryan Thalman, Fan Mei, Rahul A. Zaveri, and Guangjie Zheng
Atmos. Chem. Phys., 19, 941–954, https://doi.org/10.5194/acp-19-941-2019, https://doi.org/10.5194/acp-19-941-2019, 2019
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Earlier studies showed organic hygroscopicity increases with oxidation level. Such increases have been attributed to higher water solubility for more oxidized organics. By systematically varying the water content of activating droplets, we show that for secondary organic aerosols, essentially all organics are dissolved at the point of droplet activation. Therefore, the organic hygroscopicity is not limited by solubility but is dictated mainly by the molecular weight of organic species.
Swen Metzger, Mohamed Abdelkader, Benedikt Steil, and Klaus Klingmüller
Atmos. Chem. Phys., 18, 16747–16774, https://doi.org/10.5194/acp-18-16747-2018, https://doi.org/10.5194/acp-18-16747-2018, 2018
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This paper discusses the importance of aerosol water for aerosol optical depth calculations using a long-term evaluation of the global chemistry climate and Earth system model EMAC through a comparison of EQSAM4clim and ISORROPIA II with observations at climate timescales. Sensitivity studies with different levels of chemical aging and associated water uptake demonstrate the importance of aerosol water for climate modeling studies.
Claudia Timmreck, Graham W. Mann, Valentina Aquila, Rene Hommel, Lindsay A. Lee, Anja Schmidt, Christoph Brühl, Simon Carn, Mian Chin, Sandip S. Dhomse, Thomas Diehl, Jason M. English, Michael J. Mills, Ryan Neely, Jianxiong Sheng, Matthew Toohey, and Debra Weisenstein
Geosci. Model Dev., 11, 2581–2608, https://doi.org/10.5194/gmd-11-2581-2018, https://doi.org/10.5194/gmd-11-2581-2018, 2018
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The paper describes the experimental design of the Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP). ISA-MIP will improve understanding of stratospheric aerosol processes, chemistry, and dynamics and constrain climate impacts of background aerosol variability and small and large volcanic eruptions. It will help to asses the stratospheric aerosol contribution to the early 21st century global warming hiatus period and the effects from hypothetical geoengineering schemes.
Klaus Klingmüller, Swen Metzger, Mohamed Abdelkader, Vlassis A. Karydis, Georgiy L. Stenchikov, Andrea Pozzer, and Jos Lelieveld
Geosci. Model Dev., 11, 989–1008, https://doi.org/10.5194/gmd-11-989-2018, https://doi.org/10.5194/gmd-11-989-2018, 2018
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More than 1 billion tons of mineral dust particles are raised into the atmosphere every year, which has a significant impact on climate, society and ecosystems. The location, time and amount of dust emissions depend on surface and wind conditions. In the atmospheric chemistry–climate model EMAC, we have updated the relevant surface data and equations. Our validation shows that the updates substantially improve the agreement of model results and observations.
Jeffrey H. Curtis, Nicole Riemer, and Matthew West
Geosci. Model Dev., 10, 4057–4079, https://doi.org/10.5194/gmd-10-4057-2017, https://doi.org/10.5194/gmd-10-4057-2017, 2017
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Traditional aerosol representations rely on simplifying assumptions about the aerosol composition in order to reduce computational cost. This introduces errors in estimates of aerosol impacts on climate. In contrast, the WRF-PartMC-MOSAIC-SCM model, presented here, uses a particle-resolved aerosol representation. It is made feasible by the development of efficient numerical methods, and allows for the capture of complex aerosol mixing states with altitude.
Benjamin N. Murphy, Matthew C. Woody, Jose L. Jimenez, Ann Marie G. Carlton, Patrick L. Hayes, Shang Liu, Nga L. Ng, Lynn M. Russell, Ari Setyan, Lu Xu, Jeff Young, Rahul A. Zaveri, Qi Zhang, and Havala O. T. Pye
Atmos. Chem. Phys., 17, 11107–11133, https://doi.org/10.5194/acp-17-11107-2017, https://doi.org/10.5194/acp-17-11107-2017, 2017
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We incorporate recent findings about the behavior of organic pollutants in urban airsheds into the Community Multiscale Air Quality (CMAQ) model to refine predictions of organic particulate pollution in the United States. The new techniques, which account for the volatility and ongoing chemistry of airborne organic compounds, substantially reduce biases, particularly in the winter time and near emission sources.
Simon O'Meara, David O. Topping, Rahul A. Zaveri, and Gordon McFiggans
Atmos. Chem. Phys., 17, 10477–10494, https://doi.org/10.5194/acp-17-10477-2017, https://doi.org/10.5194/acp-17-10477-2017, 2017
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To simulate particle-phase diffusion, an analytical expression is desired because it takes less calculation time than a differential equation. Here a correction is found for the analytical solution for when diffusivity is dependent on composition, thereby making it more widely applicable than before. Consequently, we are able to more realistically evaluate the rate limitation (if any) imposed by particle-phase diffusion on component partitioning between the gas and particle phase.
Joseph Ching, Jerome Fast, Matthew West, and Nicole Riemer
Atmos. Chem. Phys., 17, 7445–7458, https://doi.org/10.5194/acp-17-7445-2017, https://doi.org/10.5194/acp-17-7445-2017, 2017
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The composition of individual aerosols affects their cloud condensation nuclei (CCN) properties, but is challenging to represent in models. This study quantifies the error in CCN calculations when per-particle information is neglected by using a metric for the composition diversity within a population. With more particle-level measurements from field campaigns, the approach is useful for quantifying uncertainties in composition-dependent quantities regarding aerosol–cloud–climate interactions.
Daniele Visioni, Giovanni Pitari, and Valentina Aquila
Atmos. Chem. Phys., 17, 3879–3889, https://doi.org/10.5194/acp-17-3879-2017, https://doi.org/10.5194/acp-17-3879-2017, 2017
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This review paper summarizes the state-of-the-art knowledge of the direct and indirect side effects of sulfate geoengineering, that is, the injection of sulfur dioxide into the stratosphere in order to offset the warming caused by the anthropic increase in greenhouse gasses. An overview of the various effects and their uncertainties, using results from published scientific articles, may help fine-tune the best amount of sulfate to be injected in an eventual realization of the experiment.
Mohamed Abdelkader, Swen Metzger, Benedikt Steil, Klaus Klingmüller, Holger Tost, Andrea Pozzer, Georgiy Stenchikov, Leonard Barrie, and Jos Lelieveld
Atmos. Chem. Phys., 17, 3799–3821, https://doi.org/10.5194/acp-17-3799-2017, https://doi.org/10.5194/acp-17-3799-2017, 2017
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We present a modeling study on the impacts of the key processes (dust emission flux, convection and dust aging parameterizations) that control the transatlantic dust transport using an advanced version of the EMAC atmospheric chemistry general circulation model. We define the
direct effect of dust agingas an increase in the AOD as a result of hygroscopic growth. We define the
indirect effectas a reduction in the dust AOD due to the higher removal of the aged dust particles.
Adam P. Bateman, Zhaoheng Gong, Tristan H. Harder, Suzane S. de Sá, Bingbing Wang, Paulo Castillo, Swarup China, Yingjun Liu, Rachel E. O'Brien, Brett B. Palm, Hung-Wei Shiu, Glauber G. Cirino, Ryan Thalman, Kouji Adachi, M. Lizabeth Alexander, Paulo Artaxo, Allan K. Bertram, Peter R. Buseck, Mary K. Gilles, Jose L. Jimenez, Alexander Laskin, Antonio O. Manzi, Arthur Sedlacek, Rodrigo A. F. Souza, Jian Wang, Rahul Zaveri, and Scot T. Martin
Atmos. Chem. Phys., 17, 1759–1773, https://doi.org/10.5194/acp-17-1759-2017, https://doi.org/10.5194/acp-17-1759-2017, 2017
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The occurrence of nonliquid and liquid physical states of submicron atmospheric particulate matter (PM) downwind of an urban region in central Amazonia was investigated. Air masses representing background conditions, urban pollution, and regional- and continental-scale biomass were measured. Anthropogenic influences contributed to the presence of nonliquid PM in the atmospheric particle population, while liquid PM dominated during periods of biogenic influence.
Chaim I. Garfinkel, Valentina Aquila, Darryn W. Waugh, and Luke D. Oman
Atmos. Chem. Phys., 17, 1313–1327, https://doi.org/10.5194/acp-17-1313-2017, https://doi.org/10.5194/acp-17-1313-2017, 2017
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Previous work has noted a discrepancy between models and observations in trends of the large-scale overturning circulation in the stratosphere. Here, we show that a model can simulate trends that are reminiscent of those observed, including space- and time-varying trends in different regions of the stratosphere. We therefore clarify that the statement that is often made that models simulate an accelerated circulation only applies over long time periods and is not true for the past 25 years.
Emma L. D'Ambro, Ben H. Lee, Jiumeng Liu, John E. Shilling, Cassandra J. Gaston, Felipe D. Lopez-Hilfiker, Siegfried Schobesberger, Rahul A. Zaveri, Claudia Mohr, Anna Lutz, Zhenfa Zhang, Avram Gold, Jason D. Surratt, Jean C. Rivera-Rios, Frank N. Keutsch, and Joel A. Thornton
Atmos. Chem. Phys., 17, 159–174, https://doi.org/10.5194/acp-17-159-2017, https://doi.org/10.5194/acp-17-159-2017, 2017
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We studied the formation and properties of secondary organic aerosol produced from isoprene. We find that a significant fraction (~50 %) of the mass is composed of low-volatility, highly oxidized compounds such as C5H12O6. A significant fraction of the remainder appears to be in the form of oligomeric material. Adding NOx maintained or decreased SOA yields while increasing the fraction of low-volatility material, possibly due to oligomers.
Veronika Eyring, Peter J. Gleckler, Christoph Heinze, Ronald J. Stouffer, Karl E. Taylor, V. Balaji, Eric Guilyardi, Sylvie Joussaume, Stephan Kindermann, Bryan N. Lawrence, Gerald A. Meehl, Mattia Righi, and Dean N. Williams
Earth Syst. Dynam., 7, 813–830, https://doi.org/10.5194/esd-7-813-2016, https://doi.org/10.5194/esd-7-813-2016, 2016
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We argue that the CMIP community has reached a critical juncture at which many baseline aspects of model evaluation need to be performed much more efficiently to enable a systematic and rapid performance assessment of the large number of models participating in CMIP, and we announce our intention to implement such a system for CMIP6. At the same time, continuous scientific research is required to develop innovative metrics and diagnostics that help narrowing the spread in climate projections.
Raquel A. Silva, J. Jason West, Jean-François Lamarque, Drew T. Shindell, William J. Collins, Stig Dalsoren, Greg Faluvegi, Gerd Folberth, Larry W. Horowitz, Tatsuya Nagashima, Vaishali Naik, Steven T. Rumbold, Kengo Sudo, Toshihiko Takemura, Daniel Bergmann, Philip Cameron-Smith, Irene Cionni, Ruth M. Doherty, Veronika Eyring, Beatrice Josse, Ian A. MacKenzie, David Plummer, Mattia Righi, David S. Stevenson, Sarah Strode, Sophie Szopa, and Guang Zengast
Atmos. Chem. Phys., 16, 9847–9862, https://doi.org/10.5194/acp-16-9847-2016, https://doi.org/10.5194/acp-16-9847-2016, 2016
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Using ozone and PM2.5 concentrations from the ACCMIP ensemble of chemistry-climate models for the four Representative Concentration Pathway scenarios (RCPs), together with projections of future population and baseline mortality rates, we quantify the human premature mortality impacts of future ambient air pollution in 2030, 2050 and 2100, relative to 2000 concentrations. We also estimate the global mortality burden of ozone and PM2.5 in 2000 and each future period.
Mijung Song, Pengfei F. Liu, Sarah J. Hanna, Rahul A. Zaveri, Katie Potter, Yuan You, Scot T. Martin, and Allan K. Bertram
Atmos. Chem. Phys., 16, 8817–8830, https://doi.org/10.5194/acp-16-8817-2016, https://doi.org/10.5194/acp-16-8817-2016, 2016
Simone Dietmüller, Patrick Jöckel, Holger Tost, Markus Kunze, Catrin Gellhorn, Sabine Brinkop, Christine Frömming, Michael Ponater, Benedikt Steil, Axel Lauer, and Johannes Hendricks
Geosci. Model Dev., 9, 2209–2222, https://doi.org/10.5194/gmd-9-2209-2016, https://doi.org/10.5194/gmd-9-2209-2016, 2016
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Four new radiation related submodels (RAD, AEROPT, CLOUDOPT, and ORBIT) are available within the MESSy framework now. They are largely based on the original radiation scheme of ECHAM5. RAD simulates radiative transfer, AEROPT calculates aerosol optical properties, CLOUDOPT calculates cloud optical properties, and ORBIT is responsible for Earth orbit calculations. Multiple diagnostic calls of the radiation routine are possible, so radiative forcing can be calculated during the model simulation.
Swen Metzger, Benedikt Steil, Mohamed Abdelkader, Klaus Klingmüller, Li Xu, Joyce E. Penner, Christos Fountoukis, Athanasios Nenes, and Jos Lelieveld
Atmos. Chem. Phys., 16, 7213–7237, https://doi.org/10.5194/acp-16-7213-2016, https://doi.org/10.5194/acp-16-7213-2016, 2016
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We introduce an unique single parameter framework to efficiently parameterize the aerosol water uptake for mixtures of semi-volatile and non-volatile compounds, being entirely based on the single solute specific coefficient introduced in Metzger et al. (2012).
Christopher D. Cappa, Katheryn R. Kolesar, Xiaolu Zhang, Dean B. Atkinson, Mikhail S. Pekour, Rahul A. Zaveri, Alla Zelenyuk, and Qi Zhang
Atmos. Chem. Phys., 16, 6511–6535, https://doi.org/10.5194/acp-16-6511-2016, https://doi.org/10.5194/acp-16-6511-2016, 2016
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Measurements of size-dependent aerosol optical properties at visible wavelengths made during the 2010 CARES study are reported on, with a special focus on the characterization of supermicron particles. The relationships with and dependence upon particle composition, particle size, photochemical aging, water uptake and heating are discussed, along with broader implications of these in situ measurements for the interpretation of remote sensing products.
Marje Prank, Mikhail Sofiev, Svetlana Tsyro, Carlijn Hendriks, Valiyaveetil Semeena, Xavier Vazhappilly Francis, Tim Butler, Hugo Denier van der Gon, Rainer Friedrich, Johannes Hendricks, Xin Kong, Mark Lawrence, Mattia Righi, Zissis Samaras, Robert Sausen, Jaakko Kukkonen, and Ranjeet Sokhi
Atmos. Chem. Phys., 16, 6041–6070, https://doi.org/10.5194/acp-16-6041-2016, https://doi.org/10.5194/acp-16-6041-2016, 2016
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Aerosol composition in Europe was simulated by four chemistry transport models and compared to observations to identify the most prominent areas for model improvement. Notable differences were found between the models' predictions, attributable to different treatment or omission of aerosol sources and processes. All models underestimated the observed concentrations by 10–60 %, mostly due to under-predicting the carbonaceous and mineral particles and omitting the aerosol-bound water.
Veronika Eyring, Mattia Righi, Axel Lauer, Martin Evaldsson, Sabrina Wenzel, Colin Jones, Alessandro Anav, Oliver Andrews, Irene Cionni, Edouard L. Davin, Clara Deser, Carsten Ehbrecht, Pierre Friedlingstein, Peter Gleckler, Klaus-Dirk Gottschaldt, Stefan Hagemann, Martin Juckes, Stephan Kindermann, John Krasting, Dominik Kunert, Richard Levine, Alexander Loew, Jarmo Mäkelä, Gill Martin, Erik Mason, Adam S. Phillips, Simon Read, Catherine Rio, Romain Roehrig, Daniel Senftleben, Andreas Sterl, Lambertus H. van Ulft, Jeremy Walton, Shiyu Wang, and Keith D. Williams
Geosci. Model Dev., 9, 1747–1802, https://doi.org/10.5194/gmd-9-1747-2016, https://doi.org/10.5194/gmd-9-1747-2016, 2016
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A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) in CMIP has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations.
Klaus Klingmüller, Andrea Pozzer, Swen Metzger, Georgiy L. Stenchikov, and Jos Lelieveld
Atmos. Chem. Phys., 16, 5063–5073, https://doi.org/10.5194/acp-16-5063-2016, https://doi.org/10.5194/acp-16-5063-2016, 2016
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During the last decade, the Middle East experienced the strongest increase in atmospheric aerosol concentrations worldwide. Based on satellite observations, the present study corroborates this trend and reveals correlations with soil moisture and precipitation in and surrounding the Fertile Crescent. This suggests that the increasing drought conditions in this region have enhanced dust emissions, a tendency which is expected to be intensified by climate change.
Megan D. Willis, Robert M. Healy, Nicole Riemer, Matthew West, Jon M. Wang, Cheol-Heon Jeong, John C. Wenger, Greg J. Evans, Jonathan P. D. Abbatt, and Alex K. Y. Lee
Atmos. Chem. Phys., 16, 4693–4706, https://doi.org/10.5194/acp-16-4693-2016, https://doi.org/10.5194/acp-16-4693-2016, 2016
Mattia Righi, Johannes Hendricks, and Robert Sausen
Atmos. Chem. Phys., 16, 4481–4495, https://doi.org/10.5194/acp-16-4481-2016, https://doi.org/10.5194/acp-16-4481-2016, 2016
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Using a global aerosol model, we estimate the impact of aviation emissions on aerosol and climate in 2030 for different scenarios. The aviation impacts on particle number are found to be much more important than the impact on aerosol mass and significantly contribute to the overall increase in particle number concentration between 2000 and 2030. This leads to a large aviation-induced climate effect (mostly driven by aerosol-cloud interactions), a factor of 2 to 4 larger than in the year 2000.
L. Kleinman, C. Kuang, A. Sedlacek, G. Senum, S. Springston, J. Wang, Q. Zhang, J. Jayne, J. Fast, J. Hubbe, J. Shilling, and R. Zaveri
Atmos. Chem. Phys., 16, 1729–1746, https://doi.org/10.5194/acp-16-1729-2016, https://doi.org/10.5194/acp-16-1729-2016, 2016
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Atmospheric measurements of total organic aerosol (OA) and tracers of anthropogenic and biogenic emissions are used to quantify synergistic effects (A–B interactions) between two classes of precursors in the formation of OA. Regressions are consistent with the Sacramento plume composed mainly of modern carbon, and OA correlating best with an anthropogenic tracer. It is found that meteorological conditions during a pollution episode can mimic effects of A–B interactions.
A. Lupascu, R. Easter, R. Zaveri, M. Shrivastava, M. Pekour, J. Tomlinson, Q. Yang, H. Matsui, A. Hodzic, Q. Zhang, and J. D. Fast
Atmos. Chem. Phys., 15, 12283–12313, https://doi.org/10.5194/acp-15-12283-2015, https://doi.org/10.5194/acp-15-12283-2015, 2015
M. Abdelkader, S. Metzger, R. E. Mamouri, M. Astitha, L. Barrie, Z. Levin, and J. Lelieveld
Atmos. Chem. Phys., 15, 9173–9189, https://doi.org/10.5194/acp-15-9173-2015, https://doi.org/10.5194/acp-15-9173-2015, 2015
M. Righi, V. Eyring, K.-D. Gottschaldt, C. Klinger, F. Frank, P. Jöckel, and I. Cionni
Geosci. Model Dev., 8, 733–768, https://doi.org/10.5194/gmd-8-733-2015, https://doi.org/10.5194/gmd-8-733-2015, 2015
L. Fierce, N. Riemer, and T. C. Bond
Atmos. Chem. Phys., 15, 3173–3191, https://doi.org/10.5194/acp-15-3173-2015, https://doi.org/10.5194/acp-15-3173-2015, 2015
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The timescale for particles containing black carbon to age from hydrophobic to hygroscopic strongly influences black carbon's atmospheric lifetime and climate impact. This paper identifies the minimal set of independent variables needed to explain variance in this aging timescale. This work distills the complex interactions captured by a particle-resolved aerosol model to a few input variables and is a first step toward developing physically based parameterizations of aerosol aging.
D. Lowe, S. Archer-Nicholls, W. Morgan, J. Allan, S. Utembe, B. Ouyang, E. Aruffo, M. Le Breton, R. A. Zaveri, P. Di Carlo, C. Percival, H. Coe, R. Jones, and G. McFiggans
Atmos. Chem. Phys., 15, 1385–1409, https://doi.org/10.5194/acp-15-1385-2015, https://doi.org/10.5194/acp-15-1385-2015, 2015
M. Righi, J. Hendricks, and R. Sausen
Atmos. Chem. Phys., 15, 633–651, https://doi.org/10.5194/acp-15-633-2015, https://doi.org/10.5194/acp-15-633-2015, 2015
S. Archer-Nicholls, D. Lowe, S. Utembe, J. Allan, R. A. Zaveri, J. D. Fast, Ø. Hodnebrog, H. Denier van der Gon, and G. McFiggans
Geosci. Model Dev., 7, 2557–2579, https://doi.org/10.5194/gmd-7-2557-2014, https://doi.org/10.5194/gmd-7-2557-2014, 2014
J. D. Fast, J. Allan, R. Bahreini, J. Craven, L. Emmons, R. Ferrare, P. L. Hayes, A. Hodzic, J. Holloway, C. Hostetler, J. L. Jimenez, H. Jonsson, S. Liu, Y. Liu, A. Metcalf, A. Middlebrook, J. Nowak, M. Pekour, A. Perring, L. Russell, A. Sedlacek, J. Seinfeld, A. Setyan, J. Shilling, M. Shrivastava, S. Springston, C. Song, R. Subramanian, J. W. Taylor, V. Vinoj, Q. Yang, R. A. Zaveri, and Q. Zhang
Atmos. Chem. Phys., 14, 10013–10060, https://doi.org/10.5194/acp-14-10013-2014, https://doi.org/10.5194/acp-14-10013-2014, 2014
R. M. Healy, N. Riemer, J. C. Wenger, M. Murphy, M. West, L. Poulain, A. Wiedensohler, I. P. O'Connor, E. McGillicuddy, J. R. Sodeau, and G. J. Evans
Atmos. Chem. Phys., 14, 6289–6299, https://doi.org/10.5194/acp-14-6289-2014, https://doi.org/10.5194/acp-14-6289-2014, 2014
J. Tian, N. Riemer, M. West, L. Pfaffenberger, H. Schlager, and A. Petzold
Atmos. Chem. Phys., 14, 5327–5347, https://doi.org/10.5194/acp-14-5327-2014, https://doi.org/10.5194/acp-14-5327-2014, 2014
R. A. Zaveri, R. C. Easter, J. E. Shilling, and J. H. Seinfeld
Atmos. Chem. Phys., 14, 5153–5181, https://doi.org/10.5194/acp-14-5153-2014, https://doi.org/10.5194/acp-14-5153-2014, 2014
M. Kuebbeler, U. Lohmann, J. Hendricks, and B. Kärcher
Atmos. Chem. Phys., 14, 3027–3046, https://doi.org/10.5194/acp-14-3027-2014, https://doi.org/10.5194/acp-14-3027-2014, 2014
N. Riemer and M. West
Atmos. Chem. Phys., 13, 11423–11439, https://doi.org/10.5194/acp-13-11423-2013, https://doi.org/10.5194/acp-13-11423-2013, 2013
C. Zhao, S. Chen, L. R. Leung, Y. Qian, J. F. Kok, R. A. Zaveri, and J. Huang
Atmos. Chem. Phys., 13, 10733–10753, https://doi.org/10.5194/acp-13-10733-2013, https://doi.org/10.5194/acp-13-10733-2013, 2013
R. C. Moffet, T. C. Rödel, S. T. Kelly, X. Y. Yu, G. T. Carroll, J. Fast, R. A. Zaveri, A. Laskin, and M. K. Gilles
Atmos. Chem. Phys., 13, 10445–10459, https://doi.org/10.5194/acp-13-10445-2013, https://doi.org/10.5194/acp-13-10445-2013, 2013
M. Righi, J. Hendricks, and R. Sausen
Atmos. Chem. Phys., 13, 9939–9970, https://doi.org/10.5194/acp-13-9939-2013, https://doi.org/10.5194/acp-13-9939-2013, 2013
V. Naik, A. Voulgarakis, A. M. Fiore, L. W. Horowitz, J.-F. Lamarque, M. Lin, M. J. Prather, P. J. Young, D. Bergmann, P. J. Cameron-Smith, I. Cionni, W. J. Collins, S. B. Dalsøren, R. Doherty, V. Eyring, G. Faluvegi, G. A. Folberth, B. Josse, Y. H. Lee, I. A. MacKenzie, T. Nagashima, T. P. C. van Noije, D. A. Plummer, M. Righi, S. T. Rumbold, R. Skeie, D. T. Shindell, D. S. Stevenson, S. Strode, K. Sudo, S. Szopa, and G. Zeng
Atmos. Chem. Phys., 13, 5277–5298, https://doi.org/10.5194/acp-13-5277-2013, https://doi.org/10.5194/acp-13-5277-2013, 2013
M. Gyawali, W. P. Arnott, R. A. Zaveri, C. Song, M. Pekour, B. Flowers, M. K. Dubey, A. Setyan, Q. Zhang, J. W. Harworth, J. G. Radney, D. B. Atkinson, S. China, C. Mazzoleni, K. Gorkowski, R. Subramanian, B. T. Jobson, and H. Moosmüller
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-13-7113-2013, https://doi.org/10.5194/acpd-13-7113-2013, 2013
Revised manuscript not accepted
K. Gottschaldt, C. Voigt, P. Jöckel, M. Righi, R. Deckert, and S. Dietmüller
Atmos. Chem. Phys., 13, 3003–3025, https://doi.org/10.5194/acp-13-3003-2013, https://doi.org/10.5194/acp-13-3003-2013, 2013
D. S. Stevenson, P. J. Young, V. Naik, J.-F. Lamarque, D. T. Shindell, A. Voulgarakis, R. B. Skeie, S. B. Dalsoren, G. Myhre, T. K. Berntsen, G. A. Folberth, S. T. Rumbold, W. J. Collins, I. A. MacKenzie, R. M. Doherty, G. Zeng, T. P. C. van Noije, A. Strunk, D. Bergmann, P. Cameron-Smith, D. A. Plummer, S. A. Strode, L. Horowitz, Y. H. Lee, S. Szopa, K. Sudo, T. Nagashima, B. Josse, I. Cionni, M. Righi, V. Eyring, A. Conley, K. W. Bowman, O. Wild, and A. Archibald
Atmos. Chem. Phys., 13, 3063–3085, https://doi.org/10.5194/acp-13-3063-2013, https://doi.org/10.5194/acp-13-3063-2013, 2013
A. Voulgarakis, V. Naik, J.-F. Lamarque, D. T. Shindell, P. J. Young, M. J. Prather, O. Wild, R. D. Field, D. Bergmann, P. Cameron-Smith, I. Cionni, W. J. Collins, S. B. Dalsøren, R. M. Doherty, V. Eyring, G. Faluvegi, G. A. Folberth, L. W. Horowitz, B. Josse, I. A. MacKenzie, T. Nagashima, D. A. Plummer, M. Righi, S. T. Rumbold, D. S. Stevenson, S. A. Strode, K. Sudo, S. Szopa, and G. Zeng
Atmos. Chem. Phys., 13, 2563–2587, https://doi.org/10.5194/acp-13-2563-2013, https://doi.org/10.5194/acp-13-2563-2013, 2013
P. J. Young, A. T. Archibald, K. W. Bowman, J.-F. Lamarque, V. Naik, D. S. Stevenson, S. Tilmes, A. Voulgarakis, O. Wild, D. Bergmann, P. Cameron-Smith, I. Cionni, W. J. Collins, S. B. Dalsøren, R. M. Doherty, V. Eyring, G. Faluvegi, L. W. Horowitz, B. Josse, Y. H. Lee, I. A. MacKenzie, T. Nagashima, D. A. Plummer, M. Righi, S. T. Rumbold, R. B. Skeie, D. T. Shindell, S. A. Strode, K. Sudo, S. Szopa, and G. Zeng
Atmos. Chem. Phys., 13, 2063–2090, https://doi.org/10.5194/acp-13-2063-2013, https://doi.org/10.5194/acp-13-2063-2013, 2013
J. E. Shilling, R. A. Zaveri, J. D. Fast, L. Kleinman, M. L. Alexander, M. R. Canagaratna, E. Fortner, J. M. Hubbe, J. T. Jayne, A. Sedlacek, A. Setyan, S. Springston, D. R. Worsnop, and Q. Zhang
Atmos. Chem. Phys., 13, 2091–2113, https://doi.org/10.5194/acp-13-2091-2013, https://doi.org/10.5194/acp-13-2091-2013, 2013
J.-F. Lamarque, D. T. Shindell, B. Josse, P. J. Young, I. Cionni, V. Eyring, D. Bergmann, P. Cameron-Smith, W. J. Collins, R. Doherty, S. Dalsoren, G. Faluvegi, G. Folberth, S. J. Ghan, L. W. Horowitz, Y. H. Lee, I. A. MacKenzie, T. Nagashima, V. Naik, D. Plummer, M. Righi, S. T. Rumbold, M. Schulz, R. B. Skeie, D. S. Stevenson, S. Strode, K. Sudo, S. Szopa, A. Voulgarakis, and G. Zeng
Geosci. Model Dev., 6, 179–206, https://doi.org/10.5194/gmd-6-179-2013, https://doi.org/10.5194/gmd-6-179-2013, 2013
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Hybrid ensemble-variational data assimilation in ABC-DA within a tropical framework
OpenIFS/AC: atmospheric chemistry and aerosol in OpenIFS 43r3
Simulations of aerosol pH in China using WRF-Chem (v4.0): sensitivities of aerosol pH and its temporal variations during haze episodes
A daily highest air temperature estimation method and spatial–temporal changes analysis of high temperature in China from 1979 to 2018
TransClim (v1.0): a chemistry–climate response model for assessing the effect of mitigation strategies for road traffic on ozone
A description of the first open-source community release of MISTRA-v9.0: a 0D/1D atmospheric boundary layer chemistry model
Integrated Methane Inversion (IMI 1.0): a user-friendly, cloud-based facility for inferring high-resolution methane emissions from TROPOMI satellite observations
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RAP-Net: Region Attention Predictive Network for precipitation nowcasting
Effects of point source emission heights in WRF–STILT: a step towards exploiting nocturnal observations in models
uDALES 1.0: a large-eddy simulation model for urban environments
Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)'s Global Ensemble Forecast System (GEFS-Aerosols v1)
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A multi-pollutant and multi-sectorial approach to screening the consistency of emission inventories
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Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): a protocol for investigating the role of stratospheric polar vortex disturbances in subseasonal to seasonal forecasts
Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate δ13C(CH4) and CH4: a case study with model LMDz-SACS
The Comprehensive Automobile Research System (CARS) – a Python-based automobile emissions inventory model
Validation of turbulent heat transfer models against eddy covariance flux measurements over a seasonally ice-covered lake
Regional evaluation of the performance of the global CAMS chemical modeling system over the United States (IFS cycle 47r1)
Order of magnitude wall time improvement of variational methane inversions by physical parallelization: a demonstration using TM5-4DVAR
Simulated microphysical properties of winter storms from bulk-type microphysics schemes and their evaluation in the Weather Research and Forecasting (v4.1.3) model during the ICE-POP 2018 field campaign
A novel method for objective identification of 3-D potential vorticity anomalies
Multiple same-level and telescoping nesting in GFDL's dynamical core
Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
Assessing the roles emission sources and atmospheric processes play in simulating δ15N of atmospheric NOx and NO3− using CMAQ (version 5.2.1) and SMOKE (version 4.6)
The Regional Coupled Suite (RCS-IND1): application of a flexible regional coupled modelling framework to the Indian region at kilometre scale
A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China
Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 1: assessing E3SM aerosol predictions using aircraft, ship, and surface measurements
Effects of vertical ship exhaust plume distributions on urban pollutant concentration – a sensitivity study with MITRAS v2.0 and EPISODE-CityChem v1.4
An emergency response model for the formation and dispersion of plumes originating from major fires (BUOYANT v4.20)
Description and evaluation of the community aerosol dynamics model MAFOR v2.0
Modeling the high-mercury wet deposition in the southeastern US with WRF-GC-Hg v1.0
Development of a deep neural network for predicting 6 h average PM2.5 concentrations up to 2 subsequent days using various training data
Chemistry Across Multiple Phases (CAMP) version 1.0: an integrated multiphase chemistry model
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Coupling a weather model directly to GNSS orbit determination – case studies with OpenIFS
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Massive-Parallel Trajectory Calculations version 2.2 (MPTRAC-2.2): Lagrangian transport simulations on graphics processing units (GPUs)
Bedymo: a combined quasi-geostrophic and primitive equation model in σ coordinates
Simulation of organics in the atmosphere: evaluation of EMACv2.54 with the Mainz Organic Mechanism (MOM) coupled to the ORACLE (v1.0) submodel
Joshua Chun Kwang Lee, Javier Amezcua, and Ross Noel Bannister
Geosci. Model Dev., 15, 6197–6219, https://doi.org/10.5194/gmd-15-6197-2022, https://doi.org/10.5194/gmd-15-6197-2022, 2022
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In this article, we implement a novel data assimilation method for the ABC–DA system which combines traditional data assimilation approaches in a hybrid approach. We document the technical development and test the hybrid approach in idealised experiments within a tropical framework of the ABC–DA system. Our findings indicate that the hybrid approach outperforms individual traditional approaches. Its potential benefits have been highlighted and should be explored further within this framework.
Vincent Huijnen, Philippe Le Sager, Marcus O. Köhler, Glenn Carver, Samuel Rémy, Johannes Flemming, Simon Chabrillat, Quentin Errera, and Twan van Noije
Geosci. Model Dev., 15, 6221–6241, https://doi.org/10.5194/gmd-15-6221-2022, https://doi.org/10.5194/gmd-15-6221-2022, 2022
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We report on the first implementation of atmospheric chemistry and aerosol as part of the OpenIFS model, based on the CAMS global model. We give an overview of the model and evaluate two reference model configurations, with and without the stratospheric chemistry extension, against a variety of observational datasets. This OpenIFS version with atmospheric composition components is open to the scientific user community under a standard OpenIFS license.
Xueyin Ruan, Chun Zhao, Rahul A. Zaveri, Pengzhen He, Xinming Wang, Jingyuan Shao, and Lei Geng
Geosci. Model Dev., 15, 6143–6164, https://doi.org/10.5194/gmd-15-6143-2022, https://doi.org/10.5194/gmd-15-6143-2022, 2022
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Accurate prediction of aerosol pH in chemical transport models is essential to aerosol modeling. This study examines the performance of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) on aerosol pH predictions and the sensitivities to emissions of nonvolatile cations and NH3, aerosol-phase state assumption, and heterogeneous sulfate production. Temporal evolution of aerosol pH during haze cycles in Beijing and the driving factors are also presented and discussed.
Ping Wang, Kebiao Mao, Fei Meng, Zhihao Qin, Shu Fang, and Sayed M. Bateni
Geosci. Model Dev., 15, 6059–6083, https://doi.org/10.5194/gmd-15-6059-2022, https://doi.org/10.5194/gmd-15-6059-2022, 2022
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In order to obtain the key parameters of high-temperature spatial–temporal variation analysis, this study proposed a daily highest air temperature (Tmax) estimation frame to build a Tmax dataset in China from 1979 to 2018. We found that the annual and seasonal mean Tmax in most areas of China showed an increasing trend. The abnormal temperature changes mainly occurred in El Nin~o years or La Nin~a years. IOBW had a stronger influence on China's warming events than other factors.
Vanessa Simone Rieger and Volker Grewe
Geosci. Model Dev., 15, 5883–5903, https://doi.org/10.5194/gmd-15-5883-2022, https://doi.org/10.5194/gmd-15-5883-2022, 2022
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Road traffic emissions of nitrogen oxides, volatile organic compounds and carbon monoxide produce ozone in the troposphere and thus influence Earth's climate. To assess the ozone response to a broad range of mitigation strategies for road traffic, we developed a new chemistry–climate response model called TransClim. It is based on lookup tables containing climate–response relations and thus is able to quickly determine the climate response of a mitigation option.
Josué Bock, Jan Kaiser, Max Thomas, Andreas Bott, and Roland von Glasow
Geosci. Model Dev., 15, 5807–5828, https://doi.org/10.5194/gmd-15-5807-2022, https://doi.org/10.5194/gmd-15-5807-2022, 2022
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MISTRA-v9.0 is an atmospheric boundary layer chemistry model. The model includes a detailed particle description with regards to the microphysics, gas–particle interactions, and liquid phase chemistry within particles. Version 9.0 is the first release of MISTRA as an open-source community model. This paper presents a thorough description of the model characteristics and components. We show some examples of simulations reproducing previous studies with MISTRA with good consistency.
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles
Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, https://doi.org/10.5194/gmd-15-5787-2022, 2022
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Reducing atmospheric methane emissions is critical to slow near-term climate change. Globally surveying satellite instruments like the TROPOspheric Monitoring Instrument (TROPOMI) have unique capabilities for monitoring atmospheric methane around the world. Here we present a user-friendly cloud-computing tool that enables researchers and stakeholders to quantify methane emissions across user-selected regions of interest using TROPOMI satellite observations.
Taewon Cho, Julianne Chung, Scot M. Miller, and Arvind K. Saibaba
Geosci. Model Dev., 15, 5547–5565, https://doi.org/10.5194/gmd-15-5547-2022, https://doi.org/10.5194/gmd-15-5547-2022, 2022
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Atmospheric inverse modeling describes the process of estimating greenhouse gas fluxes or air pollution emissions at the Earth's surface using observations of these gases collected in the atmosphere. The launch of new satellites, the expansion of surface observation networks, and a desire for more detailed maps of surface fluxes have yielded numerous computational and statistical challenges. This article describes computationally efficient methods for large-scale atmospheric inverse modeling.
Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Bo Wu, Qixiang Cai, Di Liu, and Pengfei Han
Geosci. Model Dev., 15, 5511–5528, https://doi.org/10.5194/gmd-15-5511-2022, https://doi.org/10.5194/gmd-15-5511-2022, 2022
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We described the application of a constrained ensemble Kalman filter (CEnKF) in a joint CO2 and surface carbon fluxes estimation study. By assimilating the pseudo-surface and OCO-2 observations, the annual global flux estimation is significantly biased without mass conservation. With the additional CEnKF process, the CO2 mass is strictly constrained, and the estimation of annual fluxes is significantly improved.
Zheng Zhang, Chuyao Luo, Shanshan Feng, Rui Ye, Yunming Ye, and Xutao Li
Geosci. Model Dev., 15, 5407–5419, https://doi.org/10.5194/gmd-15-5407-2022, https://doi.org/10.5194/gmd-15-5407-2022, 2022
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In this paper, we develop a model to predict radar echo sequences and apply it in the precipitation nowcasting field. Different from existing models, we propose two new attention modules. By introducing them, the performance of RAP-Net outperforms other models, especially in those regions with moderate and heavy rainfall. Considering that these regions cause more threats to human activities, the research in our work is significant for preventing natural disasters caused by heavy rainfall.
Fabian Maier, Christoph Gerbig, Ingeborg Levin, Ingrid Super, Julia Marshall, and Samuel Hammer
Geosci. Model Dev., 15, 5391–5406, https://doi.org/10.5194/gmd-15-5391-2022, https://doi.org/10.5194/gmd-15-5391-2022, 2022
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We show that the default representation of point source emissions in WRF–STILT leads to large overestimations when modelling fossil fuel CO2 concentrations for a 30 m high observation site during stable atmospheric conditions. We therefore introduce a novel point source modelling approach in WRF-STILT that takes into account their effective emission heights and results in a much better agreement with observations.
Ivo Suter, Tom Grylls, Birgit S. Sützl, Sam O. Owens, Chris E. Wilson, and Maarten van Reeuwijk
Geosci. Model Dev., 15, 5309–5335, https://doi.org/10.5194/gmd-15-5309-2022, https://doi.org/10.5194/gmd-15-5309-2022, 2022
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Cities are increasingly moving to the fore of climate and air quality research due to their central role in the population’s health and well-being, while suitable models remain scarce. This article describes the development of a new urban LES model, which allows examining the effects of various processes, infrastructure and vegetation on the local climate and air quality. Possible applications are demonstrated and a comparison to an experiment is shown.
Li Zhang, Raffaele Montuoro, Stuart A. McKeen, Barry Baker, Partha S. Bhattacharjee, Georg A. Grell, Judy Henderson, Li Pan, Gregory J. Frost, Jeff McQueen, Rick Saylor, Haiqin Li, Ravan Ahmadov, Jun Wang, Ivanka Stajner, Shobha Kondragunta, Xiaoyang Zhang, and Fangjun Li
Geosci. Model Dev., 15, 5337–5369, https://doi.org/10.5194/gmd-15-5337-2022, https://doi.org/10.5194/gmd-15-5337-2022, 2022
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The NOAA’s air quality predictions contribute to protecting lives and health in the US, which requires sustainable development and improvement of forecast systems. GEFS-Aerosols v1 has been developed in a collaboration between the NOAA research laboratories for operational forecast since September 2020 in the NCEP. The predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational system.
Xuanli Li, Jason B. Roberts, Jayanthi Srikishen, Jonathan L. Case, Walter A. Petersen, Gyuwon Lee, and Christopher R. Hain
Geosci. Model Dev., 15, 5287–5308, https://doi.org/10.5194/gmd-15-5287-2022, https://doi.org/10.5194/gmd-15-5287-2022, 2022
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This research assimilated the Global Precipitation Measurement (GPM) satellite-retrieved ocean surface meteorology data into the Weather Research and Forecasting (WRF) model with the Gridpoint Statistical Interpolation (GSI) system. This was for two snowstorms during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games' (ICE-POP 2018) field experiments. The results indicated a positive impact of the data for short-term forecasts for heavy snowfall.
Philippe Thunis, Alain Clappier, Enrico Pisoni, Bertrand Bessagnet, Jeroen Kuenen, Marc Guevara, and Susana Lopez-Aparicio
Geosci. Model Dev., 15, 5271–5286, https://doi.org/10.5194/gmd-15-5271-2022, https://doi.org/10.5194/gmd-15-5271-2022, 2022
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In this work, we propose a screening method to improve the quality of emission inventories, which are responsible for large uncertainties in air-quality modeling. The first step of screening consists of keeping only emission contributions that are relevant enough. In a second step, the method identifies large differences that provide evidence of methodological divergence or errors. We used the approach to compare two versions of the CAMS-REG European-scale inventory over 150 European cities.
Julian Quimbayo-Duarte, Johannes Wagner, Norman Wildmann, Thomas Gerz, and Juerg Schmidli
Geosci. Model Dev., 15, 5195–5209, https://doi.org/10.5194/gmd-15-5195-2022, https://doi.org/10.5194/gmd-15-5195-2022, 2022
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The ultimate objective of this model evaluation is to improve boundary layer flow representation over complex terrain. The numerical model is tested against observations retrieved during the Perdigão 2017 field campaign (moderate complex terrain). We observed that the inclusion of a forest parameterization in the numerical model significantly improves the representation of the wind field in the atmospheric boundary layer.
Peter Hitchcock, Amy Butler, Andrew Charlton-Perez, Chaim I. Garfinkel, Tim Stockdale, James Anstey, Dann Mitchell, Daniela I. V. Domeisen, Tongwen Wu, Yixiong Lu, Daniele Mastrangelo, Piero Malguzzi, Hai Lin, Ryan Muncaster, Bill Merryfield, Michael Sigmond, Baoqiang Xiang, Liwei Jia, Yu-Kyung Hyun, Jiyoung Oh, Damien Specq, Isla R. Simpson, Jadwiga H. Richter, Cory Barton, Jeff Knight, Eun-Pa Lim, and Harry Hendon
Geosci. Model Dev., 15, 5073–5092, https://doi.org/10.5194/gmd-15-5073-2022, https://doi.org/10.5194/gmd-15-5073-2022, 2022
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This paper describes an experimental protocol focused on sudden stratospheric warmings to be carried out by subseasonal forecast modeling centers. These will allow for inter-model comparisons of these major disruptions to the stratospheric polar vortex and their impacts on the near-surface flow. The protocol will lead to new insights into the contribution of the stratosphere to subseasonal forecast skill and new approaches to the dynamical attribution of extreme events.
Joël Thanwerdas, Marielle Saunois, Antoine Berchet, Isabelle Pison, Bruce H. Vaughn, Sylvia Englund Michel, and Philippe Bousquet
Geosci. Model Dev., 15, 4831–4851, https://doi.org/10.5194/gmd-15-4831-2022, https://doi.org/10.5194/gmd-15-4831-2022, 2022
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Estimating CH4 sources by exploiting observations within an inverse modeling framework is a powerful approach. Here, a new system designed to assimilate δ13C(CH4) observations together with CH4 observations is presented. By optimizing both the emissions and associated source signatures of multiple emission categories, this new system can efficiently differentiate the co-located emission categories and provide estimates of CH4 sources that are consistent with isotopic data.
Bok H. Baek, Rizzieri Pedruzzi, Minwoo Park, Chi-Tsan Wang, Younha Kim, Chul-Han Song, and Jung-Hun Woo
Geosci. Model Dev., 15, 4757–4781, https://doi.org/10.5194/gmd-15-4757-2022, https://doi.org/10.5194/gmd-15-4757-2022, 2022
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The Comprehensive Automobile Research System (CARS) is an open-source Python-based automobile emissions inventory model designed to efficiently estimate high-quality emissions. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road-link-level network information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced inventory for policymakers, stakeholders, and the air quality modeling community.
Joonatan Ala-Könni, Kukka-Maaria Kohonen, Matti Leppäranta, and Ivan Mammarella
Geosci. Model Dev., 15, 4739–4755, https://doi.org/10.5194/gmd-15-4739-2022, https://doi.org/10.5194/gmd-15-4739-2022, 2022
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Properties of seasonally ice-covered lakes are not currently sufficiently included in global climate models. To fill this gap, this study evaluates three models that could be used to quantify the amount of heat that moves from and into the lake by the air above it and through evaporation of the ice cover. The results show that the complex nature of the surrounding environment as well as difficulties in accurately measuring the surface temperature of ice introduce errors to these models.
Jason E. Williams, Vincent Huijnen, Idir Bouarar, Mehdi Meziane, Timo Schreurs, Sophie Pelletier, Virginie Marécal, Beatrice Josse, and Johannes Flemming
Geosci. Model Dev., 15, 4657–4687, https://doi.org/10.5194/gmd-15-4657-2022, https://doi.org/10.5194/gmd-15-4657-2022, 2022
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The global CAMS air quality model is used for providing tropospheric ozone information to end users. This paper updates the chemical mechanism employed (CBA) and compares it against two other mechanisms (MOCAGE, MOZART) and a multi-decadal dataset based on a previous version of CBA. We perform extensive validation for the US using multiple surface and aircraft datasets, providing an assessment of biases and the extent of correlation across different seasons during 2014.
Sudhanshu Pandey, Sander Houweling, and Arjo Segers
Geosci. Model Dev., 15, 4555–4567, https://doi.org/10.5194/gmd-15-4555-2022, https://doi.org/10.5194/gmd-15-4555-2022, 2022
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Inversions are used to calculate methane emissions using atmospheric mole-fraction measurements. Multidecadal inversions are needed to extract information from the long measurement records of methane. However, multidecadal inversion computations can take months to finish. Here, we demonstrate an order of magnitude improvement in wall clock time for an iterative multidecadal inversion by physical parallelization of chemical transport model.
Jeong-Su Ko, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, Gregory Thompson, and Alexis Berne
Geosci. Model Dev., 15, 4529–4553, https://doi.org/10.5194/gmd-15-4529-2022, https://doi.org/10.5194/gmd-15-4529-2022, 2022
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This study evaluates the performance of the four microphysics parameterizations, the WDM6, WDM7, Thompson, and Morrison schemes, in simulating snowfall events during the ICE-POP 2018 field campaign. Eight snowfall events are selected and classified into three categories (cold-low, warm-low, and air–sea interaction cases). The evaluation focuses on the simulated hydrometeors, microphysics budgets, wind fields, and precipitation using the measurement data.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Roderick van der Linden, Michael Maier-Gerber, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 15, 4447–4468, https://doi.org/10.5194/gmd-15-4447-2022, https://doi.org/10.5194/gmd-15-4447-2022, 2022
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Potential vorticity (PV) analysis plays a central role in studying atmospheric dynamics. For example, anomalies in the PV field near the tropopause are linked to extreme weather events. In this study, an objective strategy to identify these anomalies is presented and evaluated. As a novel concept, it can be applied to three-dimensional (3-D) data sets. Supported by 3-D visualizations, we illustrate advantages of this new analysis over existing studies along a case study.
Joseph Mouallem, Lucas Harris, and Rusty Benson
Geosci. Model Dev., 15, 4355–4371, https://doi.org/10.5194/gmd-15-4355-2022, https://doi.org/10.5194/gmd-15-4355-2022, 2022
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The single-nest capability in GFDL's dynamical core, FV3, is upgraded to support multiple same-level and telescoping nests. Grid nesting adds a refined grid over an area of interest to better resolve small-scale flow features necessary to accurately predict special weather events such as severe storms and hurricanes. This work allows concurrent execution of multiple same-level and telescoping multi-level nested grids in both global and regional setups.
Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, and Scarlet Stadtler
Geosci. Model Dev., 15, 4331–4354, https://doi.org/10.5194/gmd-15-4331-2022, https://doi.org/10.5194/gmd-15-4331-2022, 2022
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Ozone is a toxic greenhouse gas with high spatial variability. We present a machine-learning-based ozone-mapping workflow generating a transparent and reliable product. Going beyond standard mapping methods, this work combines explainable machine learning with uncertainty assessment to increase the integrity of the produced map.
Huan Fang and Greg Michalski
Geosci. Model Dev., 15, 4239–4258, https://doi.org/10.5194/gmd-15-4239-2022, https://doi.org/10.5194/gmd-15-4239-2022, 2022
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A new emission input dataset that incorporates nitrogen isotopes has been used in the CMAQ (Community Multiscale Air Quality) modeling system simulation to qualitatively analyze the changes in δ15N values, due to the dispersion, mixing, and transport of the atmospheric NOx emitted from different sources. The dispersion, mixing, and transport of the atmospheric NOx were based on the meteorology files generated from the WRF (Weather Research and Forecasting) model.
Juan Manuel Castillo, Huw W. Lewis, Akhilesh Mishra, Ashis Mitra, Jeff Polton, Ashley Brereton, Andrew Saulter, Alex Arnold, Segolene Berthou, Douglas Clark, Julia Crook, Ananda Das, John Edwards, Xiangbo Feng, Ankur Gupta, Sudheer Joseph, Nicholas Klingaman, Imranali Momin, Christine Pequignet, Claudio Sanchez, Jennifer Saxby, and Maria Valdivieso da Costa
Geosci. Model Dev., 15, 4193–4223, https://doi.org/10.5194/gmd-15-4193-2022, https://doi.org/10.5194/gmd-15-4193-2022, 2022
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A new environmental modelling system has been developed to represent the effect of feedbacks between atmosphere, land, and ocean in the Indian region. Different approaches to simulating tropical cyclones Titli and Fani are demonstrated. It is shown that results are sensitive to the way in which the ocean response to cyclone evolution is captured in the system. Notably, we show how a more rigorous formulation for the near-surface energy budget can be included when air–sea coupling is included.
Weichao Han, Tai-Long He, Zhaojun Tang, Min Wang, Dylan Jones, and Zhe Jiang
Geosci. Model Dev., 15, 4225–4237, https://doi.org/10.5194/gmd-15-4225-2022, https://doi.org/10.5194/gmd-15-4225-2022, 2022
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We present an application of a hybrid deep learning (DL) model on prediction of surface CO in China from 2015 to 2020, which utilizes both convolutional neural networks and long short-term memory neural networks. The DL model performance is better than a Kalman filter (KF) system in the training period (2005–2018). Furthermore, the DL model demonstrates good temporal extensibility: the mean bias and correlation coefficients are 95.7 ppb and 0.93 in the test period (2019–2020) over eastern China.
Shuaiqi Tang, Jerome D. Fast, Kai Zhang, Joseph C. Hardin, Adam C. Varble, John E. Shilling, Fan Mei, Maria A. Zawadowicz, and Po-Lun Ma
Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, https://doi.org/10.5194/gmd-15-4055-2022, 2022
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We developed an Earth system model (ESM) diagnostics package to compare various types of aerosol properties simulated in ESMs with aircraft, ship, and surface measurements from six field campaigns across spatial scales. The diagnostics package is coded and organized to be flexible and modular for future extension to other field campaign datasets and adapted to higher-resolution model simulations. Future releases will include comprehensive cloud and aerosol–cloud interaction diagnostics.
Ronny Badeke, Volker Matthias, Matthias Karl, and David Grawe
Geosci. Model Dev., 15, 4077–4103, https://doi.org/10.5194/gmd-15-4077-2022, https://doi.org/10.5194/gmd-15-4077-2022, 2022
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For air quality modeling studies, it is very important to distribute pollutants correctly into the model system. This has not yet been done for shipping pollution in great detail. We studied the effects of different vertical distributions of shipping pollutants on the urban air quality and derived advanced formulas for it. These formulas take weather conditions and ship-specific parameters like the exhaust gas temperature into account.
Jaakko Kukkonen, Juha Nikmo, Kari Riikonen, Ilmo Westerholm, Pekko Ilvessalo, Tuomo Bergman, and Klaus Haikarainen
Geosci. Model Dev., 15, 4027–4054, https://doi.org/10.5194/gmd-15-4027-2022, https://doi.org/10.5194/gmd-15-4027-2022, 2022
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A mathematical model has been developed for the dispersion of plumes originating from major fires. We have refined the model for the early evolution of the fire plumes; such a module has not been previously presented. We have evaluated the model against experimental field-scale data. The predicted concentrations agreed well with the aircraft measurements. We have also compiled an operational version of the model, which can be used for emergency contingency planning in the case of major fires.
Matthias Karl, Liisa Pirjola, Tiia Grönholm, Mona Kurppa, Srinivasan Anand, Xiaole Zhang, Andreas Held, Rolf Sander, Miikka Dal Maso, David Topping, Shuai Jiang, Leena Kangas, and Jaakko Kukkonen
Geosci. Model Dev., 15, 3969–4026, https://doi.org/10.5194/gmd-15-3969-2022, https://doi.org/10.5194/gmd-15-3969-2022, 2022
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The community aerosol dynamics model MAFOR includes several advanced features: coupling with an up-to-date chemistry mechanism for volatile organic compounds, a revised Brownian coagulation kernel that takes into account the fractal geometry of soot particles, a multitude of nucleation parameterizations, size-resolved partitioning of semi-volatile inorganics, and a hybrid method for the formation of secondary organic aerosols within the framework of condensation and evaporation.
Xiaotian Xu, Xu Feng, Haipeng Lin, Peng Zhang, Shaojian Huang, Zhengcheng Song, Yiming Peng, Tzung-May Fu, and Yanxu Zhang
Geosci. Model Dev., 15, 3845–3859, https://doi.org/10.5194/gmd-15-3845-2022, https://doi.org/10.5194/gmd-15-3845-2022, 2022
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Mercury is one of the most toxic pollutants in the environment, and wet deposition is a major process for atmospheric mercury to enter, causing ecological and human health risks. High-mercury wet deposition in the southeastern US has been a problem for many years. Here we employed a newly developed high-resolution WRF-GC model with the capability to simulate mercury to study this problem. We conclude that deep convection caused enhanced mercury wet deposition in the southeastern US.
Jeong-Beom Lee, Jae-Bum Lee, Youn-Seo Koo, Hee-Yong Kwon, Min-Hyeok Choi, Hyun-Ju Park, and Dae-Gyun Lee
Geosci. Model Dev., 15, 3797–3813, https://doi.org/10.5194/gmd-15-3797-2022, https://doi.org/10.5194/gmd-15-3797-2022, 2022
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The predication of PM2.5 has been carried out using a numerical air quality model in South Korea. Despite recent progress of numerical air quality models, accurate prediction of PM2.5 is still challenging. In this study, we developed a data-based model using a deep neural network (DNN) to overcome the limitations of numerical air quality models. The results showed that the DNN model outperformed the CMAQ when it was trained by using observation and forecasting data from the numerical models.
Matthew L. Dawson, Christian Guzman, Jeffrey H. Curtis, Mario Acosta, Shupeng Zhu, Donald Dabdub, Andrew Conley, Matthew West, Nicole Riemer, and Oriol Jorba
Geosci. Model Dev., 15, 3663–3689, https://doi.org/10.5194/gmd-15-3663-2022, https://doi.org/10.5194/gmd-15-3663-2022, 2022
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Progress in identifying complex, mixed-phase physicochemical processes has resulted in an advanced understanding of the evolution of atmospheric systems but has also introduced a level of complexity that few atmospheric models were designed to handle. We present a flexible treatment for multiphase chemical processes for models of diverse scale, from box up to global models. This enables users to build a customized multiphase mechanism that is accessible to a much wider community.
Haibo Wang, Ting Yang, Zifa Wang, Jianjun Li, Wenxuan Chai, Guigang Tang, Lei Kong, and Xueshun Chen
Geosci. Model Dev., 15, 3555–3585, https://doi.org/10.5194/gmd-15-3555-2022, https://doi.org/10.5194/gmd-15-3555-2022, 2022
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In this paper, we develop an online data coupled assimilation system (NAQPMS-PDAF) with the Eulerian atmospheric chemistry-transport model. NAQPMS-PDAF allows efficient use of large computational resources. The application and performance of the system are investigated by assimilating 1 month of vertical aerosol observations. The results show that NAQPMS-PDAF can significantly improve the performance of aerosol vertical structure simulation and reduce the uncertainty to a large extent.
Peng Zhang and Yanxu Zhang
Geosci. Model Dev., 15, 3587–3601, https://doi.org/10.5194/gmd-15-3587-2022, https://doi.org/10.5194/gmd-15-3587-2022, 2022
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Mercury is a global pollutant that can be transported over long distance through the atmosphere. We develop a new online global model for atmospheric mercury. The model reproduces the observed global atmospheric mercury concentrations and deposition distributions by simulating the emissions, transport, and physicochemical processes of atmospheric mercury. And we find that the seasonal variations of atmospheric Hg are the result of multiple processes and have obvious regional characteristics.
Patrick Obin Sturm and Anthony S. Wexler
Geosci. Model Dev., 15, 3417–3431, https://doi.org/10.5194/gmd-15-3417-2022, https://doi.org/10.5194/gmd-15-3417-2022, 2022
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Large air quality and climate models require vast amounts of computational power. Machine learning tools like neural networks can be used to make these models more efficient, with the downside that their results might not make physical sense or be easy to interpret. This work develops a physically interpretable neural network that obeys scientific laws like conservation of mass and models atmospheric composition more accurately than a traditional neural network.
Michael Weger, Holger Baars, Henriette Gebauer, Maik Merkel, Alfred Wiedensohler, and Bernd Heinold
Geosci. Model Dev., 15, 3315–3345, https://doi.org/10.5194/gmd-15-3315-2022, https://doi.org/10.5194/gmd-15-3315-2022, 2022
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Numerical models are an important tool to assess the air quality in cities,
as they can provide near-continouos data in time and space. In this paper,
air pollution for an entire city is simulated at a high spatial resolution of 40 m.
At this spatial scale, the effects of buildings on the atmosphere,
like channeling or blocking of the air flow, are directly represented by diffuse obstacles in the used model CAIRDIO. For model validation, measurements from air-monitoring sites are used.
Patrick C. Campbell, Youhua Tang, Pius Lee, Barry Baker, Daniel Tong, Rick Saylor, Ariel Stein, Jianping Huang, Ho-Chun Huang, Edward Strobach, Jeff McQueen, Li Pan, Ivanka Stajner, Jamese Sims, Jose Tirado-Delgado, Youngsun Jung, Fanglin Yang, Tanya L. Spero, and Robert C. Gilliam
Geosci. Model Dev., 15, 3281–3313, https://doi.org/10.5194/gmd-15-3281-2022, https://doi.org/10.5194/gmd-15-3281-2022, 2022
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NOAA's National Air Quality Forecast Capability (NAQFC) continues to protect Americans from the harmful effects of air pollution, while saving billions of dollars per year. Here we describe and evaluate the development of the most advanced version of the NAQFC to date, which became operational at NOAA on 20 July 2021. The new NAQFC is based on a coupling of NOAA's operational Global Forecast System (GFS) version 16 with the Community Multiscale Air Quality (CMAQ) model version 5.3.1.
Athanasios Tsikerdekis, Nick A. J. Schutgens, Guangliang Fu, and Otto P. Hasekamp
Geosci. Model Dev., 15, 3253–3279, https://doi.org/10.5194/gmd-15-3253-2022, https://doi.org/10.5194/gmd-15-3253-2022, 2022
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In our study we quantify the ability of the future satellite sensor SPEXone, part of the NASA PACE mission, to estimate aerosol emissions. The sensor will be able to retrieve accurate information of aerosol light extinction and most importantly light absorption. We simulate SPEXone spatial coverage and combine it with an aerosol model. We found that SPEXone will be able to estimate species-specific (e.g. dust, sea salt, organic or black carbon, sulfates) aerosol emissions very accurately.
Christian Zeman and Christoph Schär
Geosci. Model Dev., 15, 3183–3203, https://doi.org/10.5194/gmd-15-3183-2022, https://doi.org/10.5194/gmd-15-3183-2022, 2022
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Our atmosphere is a chaotic system, where even a tiny change can have a big impact. This makes it difficult to assess if small changes, such as the move to a new hardware architecture, will significantly affect a weather and climate model. We present a methodology that allows to objectively verify this. The methodology is applied to several test cases, showing a high sensitivity. Results also show that a major system update of the underlying supercomputer did not significantly affect our model.
Stelios Myriokefalitakis, Elisa Bergas-Massó, María Gonçalves-Ageitos, Carlos Pérez García-Pando, Twan van Noije, Philippe Le Sager, Akinori Ito, Eleni Athanasopoulou, Athanasios Nenes, Maria Kanakidou, Maarten C. Krol, and Evangelos Gerasopoulos
Geosci. Model Dev., 15, 3079–3120, https://doi.org/10.5194/gmd-15-3079-2022, https://doi.org/10.5194/gmd-15-3079-2022, 2022
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We here describe the implementation of atmospheric multiphase processes in the EC-Earth Earth system model. We provide global budgets of oxalate, sulfate, and iron-containing aerosols, along with an analysis of the links among atmospheric composition, aqueous-phase processes, and aerosol dissolution, supported by comparison to observations. This work is a first step towards an interactive calculation of the deposition of bioavailable atmospheric iron coupled to the model’s ocean component.
Santos J. González-Rojí, Martina Messmer, Christoph C. Raible, and Thomas F. Stocker
Geosci. Model Dev., 15, 2859–2879, https://doi.org/10.5194/gmd-15-2859-2022, https://doi.org/10.5194/gmd-15-2859-2022, 2022
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Different configurations of physics parameterizations of a regional climate model are tested over southern Peru at fine resolution. The most challenging regions compared to observational data are the slopes of the Andes. Model configurations for Europe and East Africa are not perfectly suitable for southern Peru. The experiment with the Stony Brook University microphysics scheme and the Grell–Freitas cumulus parameterization provides the most accurate results over Madre de Dios.
Angel Navarro Trastoy, Sebastian Strasser, Lauri Tuppi, Maksym Vasiuta, Markku Poutanen, Torsten Mayer-Gürr, and Heikki Järvinen
Geosci. Model Dev., 15, 2763–2771, https://doi.org/10.5194/gmd-15-2763-2022, https://doi.org/10.5194/gmd-15-2763-2022, 2022
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Production of satellite products relies on information from different centers. By coupling a weather model and an orbit determination solver we eliminate the dependence on one of the centers. The coupling has proven to be possible in the first stage, where no formatting has been applied to any of the models involved. This opens a window for further development and improvement to a coupling that has proven to be as good as the predecessor model.
Soon-Young Park, Uzzal Kumar Dash, Jinhyeok Yu, Keiya Yumimoto, Itsushi Uno, and Chul Han Song
Geosci. Model Dev., 15, 2773–2790, https://doi.org/10.5194/gmd-15-2773-2022, https://doi.org/10.5194/gmd-15-2773-2022, 2022
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An EnKF was applied to CMAQ for assimilating ground PM2.5 observations from China and South Korea. The EnKF performed better than that without assimilation and even superior to 3D-Var. The reduced MBs in 24 h predictions were 48 % and 27 % by improving ICs and BCs, respectively.
Lars Hoffmann, Paul F. Baumeister, Zhongyin Cai, Jan Clemens, Sabine Griessbach, Gebhard Günther, Yi Heng, Mingzhao Liu, Kaveh Haghighi Mood, Olaf Stein, Nicole Thomas, Bärbel Vogel, Xue Wu, and Ling Zou
Geosci. Model Dev., 15, 2731–2762, https://doi.org/10.5194/gmd-15-2731-2022, https://doi.org/10.5194/gmd-15-2731-2022, 2022
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We describe the new version (2.2) of the Lagrangian transport model MPTRAC, which has been ported for application on GPUs. The model was verified by comparing kinematic trajectories and synthetic tracer simulations for the free troposphere and stratosphere from GPUs and CPUs. Benchmarking showed a speed-up of a factor of 16 of GPU-enabled simulations compared to CPU-only runs, indicating the great potential of applying GPUs for Lagrangian transport simulations on upcoming HPC systems.
Clemens Spensberger, Trond Thorsteinsson, and Thomas Spengler
Geosci. Model Dev., 15, 2711–2729, https://doi.org/10.5194/gmd-15-2711-2022, https://doi.org/10.5194/gmd-15-2711-2022, 2022
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In order to understand the atmosphere, we rely on a hierarchy of models ranging from very simple to very complex. Comparing different steps in this hierarchy usually entails comparing different models. Here we combine two such steps that are commonly used in one modelling framework. This makes comparisons both much easier and much more direct.
Andrea Pozzer, Simon F. Reifenberg, Vinod Kumar, Bruno Franco, Matthias Kohl, Domenico Taraborrelli, Sergey Gromov, Sebastian Ehrhart, Patrick Jöckel, Rolf Sander, Veronica Fall, Simon Rosanka, Vlassis Karydis, Dimitris Akritidis, Tamara Emmerichs, Monica Crippa, Diego Guizzardi, Johannes W. Kaiser, Lieven Clarisse, Astrid Kiendler-Scharr, Holger Tost, and Alexandra Tsimpidi
Geosci. Model Dev., 15, 2673–2710, https://doi.org/10.5194/gmd-15-2673-2022, https://doi.org/10.5194/gmd-15-2673-2022, 2022
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A newly developed setup of the chemistry general circulation model EMAC (ECHAM5/MESSy for Atmospheric Chemistry) is evaluated here. A comprehensive organic degradation mechanism is used and coupled with a volatility base model.
The results show that the model reproduces most of the tracers and aerosols satisfactorily but shows discrepancies for oxygenated organic gases. It is also shown that this model configuration can be used for further research in atmospheric chemistry.
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