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Geoscientific Model Development
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Volume 10, issue 8
Geosci. Model Dev., 10, 2971–2999, 2017
https://doi.org/10.5194/gmd-10-2971-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-2971-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Special issue: Coupled chemistry–meteorology modelling: status and...
Geosci. Model Dev., 10, 2971–2999, 2017
https://doi.org/10.5194/gmd-10-2971-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-2971-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Model description paper 08 Aug 2017
Model description paper | 08 Aug 2017
Enviro-HIRLAM online integrated meteorology–chemistry modelling system: strategy, methodology, developments and applications (v7.2)
Alexander Baklanov et al.
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Alexander Kurganskiy, Carsten Ambelas Skjøth, Alexander Baklanov, Mikhail Sofiev, Annika Saarto, Elena Severova, Sergei Smyshlyaev, and Eigil Kaas
Atmos. Chem. Phys., 20, 2099–2121, https://doi.org/10.5194/acp-20-2099-2020, https://doi.org/10.5194/acp-20-2099-2020, 2020
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The aim of the study was to evaluate three birch pollen source maps using a state-of-the-art atmospheric model Enviro-HIRLAM. Enviro-HIRLAM is a so-called online model where both weather and air pollution are calculated at all time steps.
The evaluation has been performed for 12 pollen observation sites located in Denmark, Finland, and Russia.
Mikhail Varentsov, Pavel Konstantinov, Alexander Baklanov, Igor Esau, Victoria Miles, and Richard Davy
Atmos. Chem. Phys., 18, 17573–17587, https://doi.org/10.5194/acp-18-17573-2018, https://doi.org/10.5194/acp-18-17573-2018, 2018
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This study reports on the urban heat island (UHI) in a typical Arctic city in winter. Using in situ observations, remote sensing data and modeling, we show that the urban temperature anomaly reaches up to 11 K with a mean value of 1.9 K. At least 50 % of this anomaly is caused by the UHI effect, driven mostly by heating. The rest is created by natural microclimatic variability over the hilly terrain. This is a strong argument in support of energy efficiency measures in the Arctic cities.
Angela Benedetti, Jeffrey S. Reid, Peter Knippertz, John H. Marsham, Francesca Di Giuseppe, Samuel Rémy, Sara Basart, Olivier Boucher, Ian M. Brooks, Laurent Menut, Lucia Mona, Paolo Laj, Gelsomina Pappalardo, Alfred Wiedensohler, Alexander Baklanov, Malcolm Brooks, Peter R. Colarco, Emilio Cuevas, Arlindo da Silva, Jeronimo Escribano, Johannes Flemming, Nicolas Huneeus, Oriol Jorba, Stelios Kazadzis, Stefan Kinne, Thomas Popp, Patricia K. Quinn, Thomas T. Sekiyama, Taichu Tanaka, and Enric Terradellas
Atmos. Chem. Phys., 18, 10615–10643, https://doi.org/10.5194/acp-18-10615-2018, https://doi.org/10.5194/acp-18-10615-2018, 2018
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Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation authorities, solar energy plant managers, climate service providers, and health professionals. This paper describes the advances in the field and sets out requirements for observations for the sustainability of these activities.
Laura Palacios-Peña, Rocío Baró, Alexander Baklanov, Alessandra Balzarini, Dominik Brunner, Renate Forkel, Marcus Hirtl, Luka Honzak, José María López-Romero, Juan Pedro Montávez, Juan Luis Pérez, Guido Pirovano, Roberto San José, Wolfram Schröder, Johannes Werhahn, Ralf Wolke, Rahela Žabkar, and Pedro Jiménez-Guerrero
Atmos. Chem. Phys., 18, 5021–5043, https://doi.org/10.5194/acp-18-5021-2018, https://doi.org/10.5194/acp-18-5021-2018, 2018
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Atmospheric aerosols modify the radiative budget of the Earth, and it is therefore mandatory to have an accurate representation of their optical properties for understanding their climatic role. This work therefore evaluates the skill in the representation of optical properties by different remote-sensing sensors and regional online coupled chemistry–climate models over Europe.
Rocío Baró, Laura Palacios-Peña, Alexander Baklanov, Alessandra Balzarini, Dominik Brunner, Renate Forkel, Marcus Hirtl, Luka Honzak, Juan Luis Pérez, Guido Pirovano, Roberto San José, Wolfram Schröder, Johannes Werhahn, Ralf Wolke, Rahela Žabkar, and Pedro Jiménez-Guerrero
Atmos. Chem. Phys., 17, 9677–9696, https://doi.org/10.5194/acp-17-9677-2017, https://doi.org/10.5194/acp-17-9677-2017, 2017
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The influence on modeled max., mean and min. temperature over Europe of including aerosol–radiation–cloud interactions has been assessed for two case studies in 2010. Data were taken from an ensemble of online regional chemistry–climate models from EuMetChem COST Action. The results indicate that including these interactions clearly improves the spatiotemporal variability in the temperature signal simulated by the models, with implications for reducing the uncertainty in climate projections.
Alexander Baklanov
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-1174, https://doi.org/10.5194/acp-2016-1174, 2017
Preprint retracted
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The article provides an introduction to the COST Action ES1004 – European framework for online integrated air quality and meteorology modelling (EuMetChem) – goals and outcomes for this Special Issue "Coupled chemistry–meteorology modelling: status and relevance for numerical weather prediction, air quality and climate communities" which collects key scientific papers of EuMetChem and its collaborators from different continents.
Hanna K. Lappalainen, Veli-Matti Kerminen, Tuukka Petäjä, Theo Kurten, Aleksander Baklanov, Anatoly Shvidenko, Jaana Bäck, Timo Vihma, Pavel Alekseychik, Meinrat O. Andreae, Stephen R. Arnold, Mikhail Arshinov, Eija Asmi, Boris Belan, Leonid Bobylev, Sergey Chalov, Yafang Cheng, Natalia Chubarova, Gerrit de Leeuw, Aijun Ding, Sergey Dobrolyubov, Sergei Dubtsov, Egor Dyukarev, Nikolai Elansky, Kostas Eleftheriadis, Igor Esau, Nikolay Filatov, Mikhail Flint, Congbin Fu, Olga Glezer, Aleksander Gliko, Martin Heimann, Albert A. M. Holtslag, Urmas Hõrrak, Juha Janhunen, Sirkku Juhola, Leena Järvi, Heikki Järvinen, Anna Kanukhina, Pavel Konstantinov, Vladimir Kotlyakov, Antti-Jussi Kieloaho, Alexander S. Komarov, Joni Kujansuu, Ilmo Kukkonen, Ella-Maria Duplissy, Ari Laaksonen, Tuomas Laurila, Heikki Lihavainen, Alexander Lisitzin, Alexsander Mahura, Alexander Makshtas, Evgeny Mareev, Stephany Mazon, Dmitry Matishov, Vladimir Melnikov, Eugene Mikhailov, Dmitri Moisseev, Robert Nigmatulin, Steffen M. Noe, Anne Ojala, Mari Pihlatie, Olga Popovicheva, Jukka Pumpanen, Tatjana Regerand, Irina Repina, Aleksei Shcherbinin, Vladimir Shevchenko, Mikko Sipilä, Andrey Skorokhod, Dominick V. Spracklen, Hang Su, Dmitry A. Subetto, Junying Sun, Arkady Y. Terzhevik, Yuri Timofeyev, Yuliya Troitskaya, Veli-Pekka Tynkkynen, Viacheslav I. Kharuk, Nina Zaytseva, Jiahua Zhang, Yrjö Viisanen, Timo Vesala, Pertti Hari, Hans Christen Hansson, Gennady G. Matvienko, Nikolai S. Kasimov, Huadong Guo, Valery Bondur, Sergej Zilitinkevich, and Markku Kulmala
Atmos. Chem. Phys., 16, 14421–14461, https://doi.org/10.5194/acp-16-14421-2016, https://doi.org/10.5194/acp-16-14421-2016, 2016
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After kick off in 2012, the Pan-Eurasian Experiment (PEEX) program has expanded fast and today the multi-disciplinary research community covers ca. 80 institutes and a network of ca. 500 scientists from Europe, Russia, and China. Here we introduce scientific topics relevant in this context. This is one of the first multi-disciplinary overviews crossing scientific boundaries, from atmospheric sciences to socio-economics and social sciences.
M. Kulmala, H. K. Lappalainen, T. Petäjä, T. Kurten, V.-M. Kerminen, Y. Viisanen, P. Hari, S. Sorvari, J. Bäck, V. Bondur, N. Kasimov, V. Kotlyakov, G. Matvienko, A. Baklanov, H. D. Guo, A. Ding, H.-C. Hansson, and S. Zilitinkevich
Atmos. Chem. Phys., 15, 13085–13096, https://doi.org/10.5194/acp-15-13085-2015, https://doi.org/10.5194/acp-15-13085-2015, 2015
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The Pan-European Experiment (PEEX) is introduced. PEEX is a multidisciplinary, multiscale and multicomponent research, research infrastructure and capacity-building program. This paper outlines the mission, vision and objectives of PEEX and introduces its main components, including the research agenda, research infrastructure, knowledge transfer and potential impacts on society. The paper also summarizes the main scientific questions that PEEX is going to tackle in the future.
M. Beekmann, A. S. H. Prévôt, F. Drewnick, J. Sciare, S. N. Pandis, H. A. C. Denier van der Gon, M. Crippa, F. Freutel, L. Poulain, V. Ghersi, E. Rodriguez, S. Beirle, P. Zotter, S.-L. von der Weiden-Reinmüller, M. Bressi, C. Fountoukis, H. Petetin, S. Szidat, J. Schneider, A. Rosso, I. El Haddad, A. Megaritis, Q. J. Zhang, V. Michoud, J. G. Slowik, S. Moukhtar, P. Kolmonen, A. Stohl, S. Eckhardt, A. Borbon, V. Gros, N. Marchand, J. L. Jaffrezo, A. Schwarzenboeck, A. Colomb, A. Wiedensohler, S. Borrmann, M. Lawrence, A. Baklanov, and U. Baltensperger
Atmos. Chem. Phys., 15, 9577–9591, https://doi.org/10.5194/acp-15-9577-2015, https://doi.org/10.5194/acp-15-9577-2015, 2015
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A detailed characterization of air quality in the Paris (France) agglomeration, a megacity, during two summer and winter intensive campaigns and from additional 1-year observations, revealed that about 70% of the fine particulate matter (PM) at urban background is transported into the megacity from upwind regions. Unexpectedly, a major part of organic PM is of modern origin (woodburning and cooking activities, secondary formation from biogenic VOC).
M. Bocquet, H. Elbern, H. Eskes, M. Hirtl, R. Žabkar, G. R. Carmichael, J. Flemming, A. Inness, M. Pagowski, J. L. Pérez Camaño, P. E. Saide, R. San Jose, M. Sofiev, J. Vira, A. Baklanov, C. Carnevale, G. Grell, and C. Seigneur
Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, https://doi.org/10.5194/acp-15-5325-2015, 2015
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Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of concentrations, and perform inverse modeling. Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. We review here the current status of data assimilation in atmospheric chemistry models, with a particular focus on future prospects for data assimilation in CCMM.
A. Baklanov, K. Schlünzen, P. Suppan, J. Baldasano, D. Brunner, S. Aksoyoglu, G. Carmichael, J. Douros, J. Flemming, R. Forkel, S. Galmarini, M. Gauss, G. Grell, M. Hirtl, S. Joffre, O. Jorba, E. Kaas, M. Kaasik, G. Kallos, X. Kong, U. Korsholm, A. Kurganskiy, J. Kushta, U. Lohmann, A. Mahura, A. Manders-Groot, A. Maurizi, N. Moussiopoulos, S. T. Rao, N. Savage, C. Seigneur, R. S. Sokhi, E. Solazzo, S. Solomos, B. Sørensen, G. Tsegas, E. Vignati, B. Vogel, and Y. Zhang
Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014, https://doi.org/10.5194/acp-14-317-2014, 2014
Tuukka Petäjä, Ella-Maria Duplissy, Ksenia Tabakova, Julia Schmale, Barbara Altstädter, Gerard Ancellet, Mikhail Arshinov, Yurii Balin, Urs Baltensperger, Jens Bange, Alison Beamish, Boris Belan, Antoine Berchet, Rossana Bossi, Warren R. L. Cairns, Ralf Ebinghaus, Imad El Haddad, Beatriz Ferreira-Araujo, Anna Franck, Lin Huang, Antti Hyvärinen, Angelika Humbert, Athina-Cerise Kalogridis, Pavel Konstantinov, Astrid Lampert, Matthew MacLeod, Olivier Magand, Alexander Mahura, Louis Marelle, Vladimir Masloboev, Dmitri Moisseev, Vaios Moschos, Niklas Neckel, Tatsuo Onishi, Stefan Osterwalder, Aino Ovaska, Pauli Paasonen, Mikhail Panchenko, Fidel Pankratov, Jakob B. Pernov, Andreas Platis, Olga Popovicheva, Jean-Christophe Raut, Aurélie Riandet, Torsten Sachs, Rosamaria Salvatori, Roberto Salzano, Ludwig Schröder, Martin Schön, Vladimir Shevchenko, Henrik Skov, Jeroen E. Sonke, Andrea Spolaor, Vasileios K. Stathopoulos, Mikko Strahlendorff, Jennie L. Thomas, Vito Vitale, Sterios Vratolis, Carlo Barbante, Sabine Chabrillat, Aurélien Dommergue, Konstantinos Eleftheriadis, Jyri Heilimo, Kathy S. Law, Andreas Massling, Steffen M. Noe, Jean-Daniel Paris, André S. H. Prévôt, Ilona Riipinen, Birgit Wehner, Zhiyong Xie, and Hanna K. Lappalainen
Atmos. Chem. Phys., 20, 8551–8592, https://doi.org/10.5194/acp-20-8551-2020, https://doi.org/10.5194/acp-20-8551-2020, 2020
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The role of polar regions is increasing in terms of megatrends such as globalization, new transport routes, demography, and the use of natural resources with consequent effects on regional and transported pollutant concentrations. Here we summarize initial results from our integrative project exploring the Arctic environment and pollution to deliver data products, metrics, and indicators for stakeholders.
Alexander Kurganskiy, Carsten Ambelas Skjøth, Alexander Baklanov, Mikhail Sofiev, Annika Saarto, Elena Severova, Sergei Smyshlyaev, and Eigil Kaas
Atmos. Chem. Phys., 20, 2099–2121, https://doi.org/10.5194/acp-20-2099-2020, https://doi.org/10.5194/acp-20-2099-2020, 2020
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The aim of the study was to evaluate three birch pollen source maps using a state-of-the-art atmospheric model Enviro-HIRLAM. Enviro-HIRLAM is a so-called online model where both weather and air pollution are calculated at all time steps.
The evaluation has been performed for 12 pollen observation sites located in Denmark, Finland, and Russia.
Andreas Müller, Willem Deconinck, Christian Kühnlein, Gianmarco Mengaldo, Michael Lange, Nils Wedi, Peter Bauer, Piotr K. Smolarkiewicz, Michail Diamantakis, Sarah-Jane Lock, Mats Hamrud, Sami Saarinen, George Mozdzynski, Daniel Thiemert, Michael Glinton, Pierre Bénard, Fabrice Voitus, Charles Colavolpe, Philippe Marguinaud, Yongjun Zheng, Joris Van Bever, Daan Degrauwe, Geert Smet, Piet Termonia, Kristian P. Nielsen, Bent H. Sass, Jacob W. Poulsen, Per Berg, Carlos Osuna, Oliver Fuhrer, Valentin Clement, Michael Baldauf, Mike Gillard, Joanna Szmelter, Enda O'Brien, Alastair McKinstry, Oisín Robinson, Parijat Shukla, Michael Lysaght, Michał Kulczewski, Milosz Ciznicki, Wojciech Piątek, Sebastian Ciesielski, Marek Błażewicz, Krzysztof Kurowski, Marcin Procyk, Pawel Spychala, Bartosz Bosak, Zbigniew P. Piotrowski, Andrzej Wyszogrodzki, Erwan Raffin, Cyril Mazauric, David Guibert, Louis Douriez, Xavier Vigouroux, Alan Gray, Peter Messmer, Alexander J. Macfaden, and Nick New
Geosci. Model Dev., 12, 4425–4441, https://doi.org/10.5194/gmd-12-4425-2019, https://doi.org/10.5194/gmd-12-4425-2019, 2019
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This paper presents an overview of the ESCAPE project. Dwarfs (key patterns in terms of computation and communication) are identified in weather prediction models. They are optimised for different hardware architectures. New algorithms are developed that are specifically designed for better energy efficiency and improved portability through domain-specific languages. Different numerical techniques are compared in terms of energy efficiency and performance for a variety of computing technologies.
Fidel Pankratov, Alexander Mahura, Tuukka Petäjä, Valentin Popov, and Vladimir Masloboev
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-1228, https://doi.org/10.5194/acp-2018-1228, 2018
Revised manuscript not accepted
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Analysis of long-term observations for atmospheric mercury in the Russian Arctic, considering inter-annual, seasonal and monthly variabilities is in focus. Linkage of elevated concentrations with volcanic eruptions in Iceland is explored. Results showed that active volcanoes can play a role of sources for increased mercury levels in the Northern Hemisphere due to long-range atmospheric transport of volcanic clouds towards the Arctic.
Mikhail Varentsov, Pavel Konstantinov, Alexander Baklanov, Igor Esau, Victoria Miles, and Richard Davy
Atmos. Chem. Phys., 18, 17573–17587, https://doi.org/10.5194/acp-18-17573-2018, https://doi.org/10.5194/acp-18-17573-2018, 2018
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This study reports on the urban heat island (UHI) in a typical Arctic city in winter. Using in situ observations, remote sensing data and modeling, we show that the urban temperature anomaly reaches up to 11 K with a mean value of 1.9 K. At least 50 % of this anomaly is caused by the UHI effect, driven mostly by heating. The rest is created by natural microclimatic variability over the hilly terrain. This is a strong argument in support of energy efficiency measures in the Arctic cities.
Dion Häfner, René Løwe Jacobsen, Carsten Eden, Mads R. B. Kristensen, Markus Jochum, Roman Nuterman, and Brian Vinter
Geosci. Model Dev., 11, 3299–3312, https://doi.org/10.5194/gmd-11-3299-2018, https://doi.org/10.5194/gmd-11-3299-2018, 2018
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Well-performing, easy-to-use ocean models are a central ingredient to further the understanding of our Earth and climate. Veros, the versatile ocean simulator, is the first full-blown ocean model entirely written in the high-level programming language Python. It is considerably more approachable than traditional Fortran models and leverages modern best practices; at the same time, thanks to the Bohrium framework, Veros is about half as fast as a reference implementation in Fortran 90.
Angela Benedetti, Jeffrey S. Reid, Peter Knippertz, John H. Marsham, Francesca Di Giuseppe, Samuel Rémy, Sara Basart, Olivier Boucher, Ian M. Brooks, Laurent Menut, Lucia Mona, Paolo Laj, Gelsomina Pappalardo, Alfred Wiedensohler, Alexander Baklanov, Malcolm Brooks, Peter R. Colarco, Emilio Cuevas, Arlindo da Silva, Jeronimo Escribano, Johannes Flemming, Nicolas Huneeus, Oriol Jorba, Stelios Kazadzis, Stefan Kinne, Thomas Popp, Patricia K. Quinn, Thomas T. Sekiyama, Taichu Tanaka, and Enric Terradellas
Atmos. Chem. Phys., 18, 10615–10643, https://doi.org/10.5194/acp-18-10615-2018, https://doi.org/10.5194/acp-18-10615-2018, 2018
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Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation authorities, solar energy plant managers, climate service providers, and health professionals. This paper describes the advances in the field and sets out requirements for observations for the sustainability of these activities.
Laura Palacios-Peña, Rocío Baró, Alexander Baklanov, Alessandra Balzarini, Dominik Brunner, Renate Forkel, Marcus Hirtl, Luka Honzak, José María López-Romero, Juan Pedro Montávez, Juan Luis Pérez, Guido Pirovano, Roberto San José, Wolfram Schröder, Johannes Werhahn, Ralf Wolke, Rahela Žabkar, and Pedro Jiménez-Guerrero
Atmos. Chem. Phys., 18, 5021–5043, https://doi.org/10.5194/acp-18-5021-2018, https://doi.org/10.5194/acp-18-5021-2018, 2018
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Atmospheric aerosols modify the radiative budget of the Earth, and it is therefore mandatory to have an accurate representation of their optical properties for understanding their climatic role. This work therefore evaluates the skill in the representation of optical properties by different remote-sensing sensors and regional online coupled chemistry–climate models over Europe.
Ruth Mottram, Kristian Pagh Nielsen, Emily Gleeson, and Xiaohua Yang
Adv. Sci. Res., 14, 323–334, https://doi.org/10.5194/asr-14-323-2017, https://doi.org/10.5194/asr-14-323-2017, 2017
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The HARMONIE weather forecasting model is used successfully in Greenland, but there are some problems over the ice sheet due to the lack of realistic glacier surface characteristics. By introducing a correction to the model, preventing glacier surface temperatures over 0 °C, we improve both 2 m air temperature and the surface winds (both strength and direction) forecast by the model.
We also identify other corrections needed before HARMONIE can be used for climate and ice sheet modelling.
Rocío Baró, Laura Palacios-Peña, Alexander Baklanov, Alessandra Balzarini, Dominik Brunner, Renate Forkel, Marcus Hirtl, Luka Honzak, Juan Luis Pérez, Guido Pirovano, Roberto San José, Wolfram Schröder, Johannes Werhahn, Ralf Wolke, Rahela Žabkar, and Pedro Jiménez-Guerrero
Atmos. Chem. Phys., 17, 9677–9696, https://doi.org/10.5194/acp-17-9677-2017, https://doi.org/10.5194/acp-17-9677-2017, 2017
Short summary
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The influence on modeled max., mean and min. temperature over Europe of including aerosol–radiation–cloud interactions has been assessed for two case studies in 2010. Data were taken from an ensemble of online regional chemistry–climate models from EuMetChem COST Action. The results indicate that including these interactions clearly improves the spatiotemporal variability in the temperature signal simulated by the models, with implications for reducing the uncertainty in climate projections.
Bo Christiansen, Nis Jepsen, Rigel Kivi, Georg Hansen, Niels Larsen, and Ulrik Smith Korsholm
Atmos. Chem. Phys., 17, 9347–9364, https://doi.org/10.5194/acp-17-9347-2017, https://doi.org/10.5194/acp-17-9347-2017, 2017
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Ozone soundings in the troposphere from nine Arctic stations covering the period 1984–2014 have been analyzed. Stations with the best data coverage show a consistent and significant temporal variation with a maximum near 2005 followed by a decrease. Some significant changes are found in the annual cycle in agreement with the notion that the ozone summer maximum is appearing earlier in the year. Such changes in Arctic ozone in the free troposphere have not been reported before.
Laura Rontu, Emily Gleeson, Petri Räisänen, Kristian Pagh Nielsen, Hannu Savijärvi, and Bent Hansen Sass
Adv. Sci. Res., 14, 195–215, https://doi.org/10.5194/asr-14-195-2017, https://doi.org/10.5194/asr-14-195-2017, 2017
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This paper provides an overview of the HLRADIA shortwave (SW) and longwave (LW) broadband radiation schemes used in the HIRLAM numerical weather prediction (NWP) model and available in the HARMONIE-AROME mesoscale NWP model. The advantage of broadband, over spectral, schemes is that they can be called more frequently within the NWP model, without compromising on computational efficiency. Fast physically based radiation parametrizations are also valuable for high-resolution ensemble forecasting.
Alexander Baklanov
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-1174, https://doi.org/10.5194/acp-2016-1174, 2017
Preprint retracted
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The article provides an introduction to the COST Action ES1004 – European framework for online integrated air quality and meteorology modelling (EuMetChem) – goals and outcomes for this Special Issue "Coupled chemistry–meteorology modelling: status and relevance for numerical weather prediction, air quality and climate communities" which collects key scientific papers of EuMetChem and its collaborators from different continents.
Hanna K. Lappalainen, Veli-Matti Kerminen, Tuukka Petäjä, Theo Kurten, Aleksander Baklanov, Anatoly Shvidenko, Jaana Bäck, Timo Vihma, Pavel Alekseychik, Meinrat O. Andreae, Stephen R. Arnold, Mikhail Arshinov, Eija Asmi, Boris Belan, Leonid Bobylev, Sergey Chalov, Yafang Cheng, Natalia Chubarova, Gerrit de Leeuw, Aijun Ding, Sergey Dobrolyubov, Sergei Dubtsov, Egor Dyukarev, Nikolai Elansky, Kostas Eleftheriadis, Igor Esau, Nikolay Filatov, Mikhail Flint, Congbin Fu, Olga Glezer, Aleksander Gliko, Martin Heimann, Albert A. M. Holtslag, Urmas Hõrrak, Juha Janhunen, Sirkku Juhola, Leena Järvi, Heikki Järvinen, Anna Kanukhina, Pavel Konstantinov, Vladimir Kotlyakov, Antti-Jussi Kieloaho, Alexander S. Komarov, Joni Kujansuu, Ilmo Kukkonen, Ella-Maria Duplissy, Ari Laaksonen, Tuomas Laurila, Heikki Lihavainen, Alexander Lisitzin, Alexsander Mahura, Alexander Makshtas, Evgeny Mareev, Stephany Mazon, Dmitry Matishov, Vladimir Melnikov, Eugene Mikhailov, Dmitri Moisseev, Robert Nigmatulin, Steffen M. Noe, Anne Ojala, Mari Pihlatie, Olga Popovicheva, Jukka Pumpanen, Tatjana Regerand, Irina Repina, Aleksei Shcherbinin, Vladimir Shevchenko, Mikko Sipilä, Andrey Skorokhod, Dominick V. Spracklen, Hang Su, Dmitry A. Subetto, Junying Sun, Arkady Y. Terzhevik, Yuri Timofeyev, Yuliya Troitskaya, Veli-Pekka Tynkkynen, Viacheslav I. Kharuk, Nina Zaytseva, Jiahua Zhang, Yrjö Viisanen, Timo Vesala, Pertti Hari, Hans Christen Hansson, Gennady G. Matvienko, Nikolai S. Kasimov, Huadong Guo, Valery Bondur, Sergej Zilitinkevich, and Markku Kulmala
Atmos. Chem. Phys., 16, 14421–14461, https://doi.org/10.5194/acp-16-14421-2016, https://doi.org/10.5194/acp-16-14421-2016, 2016
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After kick off in 2012, the Pan-Eurasian Experiment (PEEX) program has expanded fast and today the multi-disciplinary research community covers ca. 80 institutes and a network of ca. 500 scientists from Europe, Russia, and China. Here we introduce scientific topics relevant in this context. This is one of the first multi-disciplinary overviews crossing scientific boundaries, from atmospheric sciences to socio-economics and social sciences.
Emily Gleeson, Velle Toll, Kristian Pagh Nielsen, Laura Rontu, and Ján Mašek
Atmos. Chem. Phys., 16, 5933–5948, https://doi.org/10.5194/acp-16-5933-2016, https://doi.org/10.5194/acp-16-5933-2016, 2016
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The direct shortwave (SW) radiative effect of aerosols under clear-sky conditions in the ALADIN-HIRLAM numerical weather prediction system was investigated using three SW radiation schemes in diagnostic single-column experiments. Each scheme accurately simulates the direct SW effect when observed aerosols are used, particularly for heavy pollution scenarios.
M. Kulmala, H. K. Lappalainen, T. Petäjä, T. Kurten, V.-M. Kerminen, Y. Viisanen, P. Hari, S. Sorvari, J. Bäck, V. Bondur, N. Kasimov, V. Kotlyakov, G. Matvienko, A. Baklanov, H. D. Guo, A. Ding, H.-C. Hansson, and S. Zilitinkevich
Atmos. Chem. Phys., 15, 13085–13096, https://doi.org/10.5194/acp-15-13085-2015, https://doi.org/10.5194/acp-15-13085-2015, 2015
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The Pan-European Experiment (PEEX) is introduced. PEEX is a multidisciplinary, multiscale and multicomponent research, research infrastructure and capacity-building program. This paper outlines the mission, vision and objectives of PEEX and introduces its main components, including the research agenda, research infrastructure, knowledge transfer and potential impacts on society. The paper also summarizes the main scientific questions that PEEX is going to tackle in the future.
M. Beekmann, A. S. H. Prévôt, F. Drewnick, J. Sciare, S. N. Pandis, H. A. C. Denier van der Gon, M. Crippa, F. Freutel, L. Poulain, V. Ghersi, E. Rodriguez, S. Beirle, P. Zotter, S.-L. von der Weiden-Reinmüller, M. Bressi, C. Fountoukis, H. Petetin, S. Szidat, J. Schneider, A. Rosso, I. El Haddad, A. Megaritis, Q. J. Zhang, V. Michoud, J. G. Slowik, S. Moukhtar, P. Kolmonen, A. Stohl, S. Eckhardt, A. Borbon, V. Gros, N. Marchand, J. L. Jaffrezo, A. Schwarzenboeck, A. Colomb, A. Wiedensohler, S. Borrmann, M. Lawrence, A. Baklanov, and U. Baltensperger
Atmos. Chem. Phys., 15, 9577–9591, https://doi.org/10.5194/acp-15-9577-2015, https://doi.org/10.5194/acp-15-9577-2015, 2015
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A detailed characterization of air quality in the Paris (France) agglomeration, a megacity, during two summer and winter intensive campaigns and from additional 1-year observations, revealed that about 70% of the fine particulate matter (PM) at urban background is transported into the megacity from upwind regions. Unexpectedly, a major part of organic PM is of modern origin (woodburning and cooking activities, secondary formation from biogenic VOC).
M. Bocquet, H. Elbern, H. Eskes, M. Hirtl, R. Žabkar, G. R. Carmichael, J. Flemming, A. Inness, M. Pagowski, J. L. Pérez Camaño, P. E. Saide, R. San Jose, M. Sofiev, J. Vira, A. Baklanov, C. Carnevale, G. Grell, and C. Seigneur
Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, https://doi.org/10.5194/acp-15-5325-2015, 2015
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Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of concentrations, and perform inverse modeling. Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. We review here the current status of data assimilation in atmospheric chemistry models, with a particular focus on future prospects for data assimilation in CCMM.
K. P. Nielsen, E. Gleeson, and L. Rontu
Geosci. Model Dev., 7, 1433–1449, https://doi.org/10.5194/gmd-7-1433-2014, https://doi.org/10.5194/gmd-7-1433-2014, 2014
P. H. Lauritzen, P. A. Ullrich, C. Jablonowski, P. A. Bosler, D. Calhoun, A. J. Conley, T. Enomoto, L. Dong, S. Dubey, O. Guba, A. B. Hansen, E. Kaas, J. Kent, J.-F. Lamarque, M. J. Prather, D. Reinert, V. V. Shashkin, W. C. Skamarock, B. Sørensen, M. A. Taylor, and M. A. Tolstykh
Geosci. Model Dev., 7, 105–145, https://doi.org/10.5194/gmd-7-105-2014, https://doi.org/10.5194/gmd-7-105-2014, 2014
A. Baklanov, K. Schlünzen, P. Suppan, J. Baldasano, D. Brunner, S. Aksoyoglu, G. Carmichael, J. Douros, J. Flemming, R. Forkel, S. Galmarini, M. Gauss, G. Grell, M. Hirtl, S. Joffre, O. Jorba, E. Kaas, M. Kaasik, G. Kallos, X. Kong, U. Korsholm, A. Kurganskiy, J. Kushta, U. Lohmann, A. Mahura, A. Manders-Groot, A. Maurizi, N. Moussiopoulos, S. T. Rao, N. Savage, C. Seigneur, R. S. Sokhi, E. Solazzo, S. Solomos, B. Sørensen, G. Tsegas, E. Vignati, B. Vogel, and Y. Zhang
Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014, https://doi.org/10.5194/acp-14-317-2014, 2014
E. Kaas, B. Sørensen, P. H. Lauritzen, and A. B. Hansen
Geosci. Model Dev., 6, 2023–2047, https://doi.org/10.5194/gmd-6-2023-2013, https://doi.org/10.5194/gmd-6-2023-2013, 2013
B. Sørensen, E. Kaas, and U. S. Korsholm
Geosci. Model Dev., 6, 1029–1042, https://doi.org/10.5194/gmd-6-1029-2013, https://doi.org/10.5194/gmd-6-1029-2013, 2013
Related subject area
Atmospheric Sciences
Role of atmospheric horizontal resolution in simulating tropical and subtropical South American precipitation in HadGEM3-GC31
Image-processing-based atmospheric river tracking method version 1 (IPART-1)
Simulating the forest fire plume dispersion, chemistry, and aerosol formation using SAM-ASP version 1.0
A mass- and energy-conserving framework for using machine learning to speed computations: a photochemistry example
The urban dispersion model EPISODE v10.0 – Part 1: An Eulerian and sub-grid-scale air quality model and its application in Nordic winter conditions
Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?
PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere
Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0, -2.2.1, and -2.2.2
An exploratory performance assessment of the CHIMERE model (version 2017r4) for the northwestern Iberian Peninsula and the summer season
Development of the global atmospheric chemistry general circulation model BCC-GEOS-Chem v1.0: model description and evaluation
Characterizing model errors in chemical transport modeling of methane: impact of model resolution in versions v9-02 of GEOS-Chem and v35j of its adjoint model
The ABC-DA system (v1.4): a variational data assimilation system for convective-scale assimilation research with a study of the impact of a balance constraint
Global aerosol simulations using NICAM.16 on a 14 km grid spacing for a climate study: improved and remaining issues relative to a lower-resolution model
Concentration Trajectory Route of Air pollution with an Integrated Lagrangian model (C-TRAIL Model v1.0) derived from the Community Multiscale Air Quality Model (CMAQ Model v5.2)
Evaluation of three new surface irrigation parameterizations in the WRF-ARW v3.8.1 model: the Po Valley (Italy) case study
An ensemble Kalman filter data assimilation system for the whole neutral atmosphere
Calculating human thermal comfort and thermal stress in the PALM model system 6.0
APIFLAME v2.0 biomass burning emissions model: impact of refined input parameters on atmospheric concentration in Portugal in summer 2016
A multiphase CMAQ version 5.0 adjoint
Further improvement of wet process treatments in GEOS-Chem v12.6.0: impact on global distributions of aerosols and aerosol precursors
Impact of scale-aware deep convection on the cloud liquid and ice water paths and precipitation using the Model for Prediction Across Scales (MPAS-v5.2)
Single-precision arithmetic in ECHAM radiation reduces runtime and energy consumption
Modeling lightning observations from space-based platforms (CloudScat.jl 1.0)
Three-dimensional normal mode functions: open-access tools for their computation in isobaric coordinates (p-3DNMF.v1)
Optimizing a dynamic fossil fuel CO2 emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO2, CO, NOx, and SO2
H2SO4–H2O binary and H2SO4–H2O–NH3 ternary homogeneous and ion-mediated nucleation: lookup tables version 1.0 for 3-D modeling application
Simulated wind farm wake sensitivity to configuration choices in the Weather Research and Forecasting model version 3.8.1
RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting
CAPRAM reduction towards an operational multiphase halogen and dimethyl sulfide chemistry treatment in the chemistry transport model COSMO-MUSCAT(5.04e)
Development of a reduced-complexity plant canopy physics surrogate model for use in chemical transport models: a case study with GEOS-Chem v12.3.0
An adaptive method for speeding up the numerical integration of chemical mechanisms in atmospheric chemistry models: application to GEOS-Chem version 12.0.0
Silicone v1.0.0: an open-source Python package for inferring missing emissions data for climate change research
Satellite-derived leaf area index and roughness length information for surface–atmosphere exchange modelling: a case study for reactive nitrogen deposition in north-western Europe using LOTOS-EUROS v2.0
An online emission module for atmospheric chemistry transport models: implementation in COSMO-GHG v5.6a and COSMO-ART v5.1-3.1
Representing model uncertainty for global atmospheric CO2 flux inversions using ECMWF-IFS-46R1
The Kinetic Energy Budget of the Atmosphere (KEBA) model 1.0: A simple yet physical approach for estimating regional wind energy resource potentials that includes the kinetic energy removal effect by wind turbines
Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0)
Evaluating a fire smoke simulation algorithm in the National Air Quality Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign
Dynamic Anthropogenic activitieS impacting Heat emissions (DASHv1.0): Development and evaluation
WRF-Chem v3.9 simulations of the East Asian dust storm in May 2017: modeling sensitivities to dust emission and dry deposition schemes
Bayesian spatio-temporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields
Configuration and intercomparison of deep learning neural models for statistical downscaling
Modelling the mineralogical composition and solubility of mineral dust in the Mediterranean area with CHIMERE 2017r4
Description and evaluation of a detailed gas-phase chemistry scheme in the TM5-MP global chemistry transport model (r112)
The first Met Office Unified Model–JULES Regional Atmosphere and Land configuration, RAL1
The Cloud-resolving model Radar SIMulator (CR-SIM) Version 3.3: description and applications of a virtual observatory
Near-global-scale high-resolution seasonal simulations with WRF-Noah-MP v.3.8.1
Land Surface Model influence on the simulated climatologies of temperature and precipitation extremes in the WRF v.3.9 model over North America
New strategies for vertical transport in chemistry-transport models: application to the case of the Mount Etna eruption on March 18, 2012 with CHIMERE v2017r4
Hindcasting and forecasting of regional methane from coal mine emissions in the Upper Silesian Coal Basin using the online nested global regional chemistry–climate model MECO(n) (MESSy v2.53)
Paul-Arthur Monerie, Amulya Chevuturi, Peter Cook, Nicholas P. Klingaman, and Christopher E. Holloway
Geosci. Model Dev., 13, 4749–4771, https://doi.org/10.5194/gmd-13-4749-2020, https://doi.org/10.5194/gmd-13-4749-2020, 2020
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In this study, we assess how increasing the horizontal resolution of HadGEM3-GC31 can allow simulating better tropical and subtropical South American precipitation. We compare simulations of HadGEM3-GC3.1, performed at three different horizontal resolutions. We show that increasing resolution allows decreasing precipitation biases over the Andes and northeast Brazil and improves the simulation of daily precipitation distribution.
Guangzhi Xu, Xiaohui Ma, Ping Chang, and Lin Wang
Geosci. Model Dev., 13, 4639–4662, https://doi.org/10.5194/gmd-13-4639-2020, https://doi.org/10.5194/gmd-13-4639-2020, 2020
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We observed considerable limitations in existing atmospheric river (AR) detection methods and looked into other disciplines for inspirations of tackling the AR detection problem. A new method is derived from an image-processing technique and encodes the spatiotemporal-scale information of AR systems, which is a key physical ingredient of ARs that is more stable than the vapor flux intensities, making it more suitable for climate-scale studies when models often have different biases.
Chantelle R. Lonsdale, Matthew J. Alvarado, Anna L. Hodshire, Emily Ramnarine, and Jeffrey R. Pierce
Geosci. Model Dev., 13, 4579–4593, https://doi.org/10.5194/gmd-13-4579-2020, https://doi.org/10.5194/gmd-13-4579-2020, 2020
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The System for Atmospheric Modelling (SAM) has been coupled with the detailed gas/aerosol chemistry model, the Aerosol Simulation Program (ASP), to capture cross-plume concentration gradients as fire plumes evolve downwind. SAM-ASP v1.0 will lead to the development of parameterizations of near-source biomass burning chemistry that can be used to more accurately simulate biomass burning chemical and physical transformations of trace gases and aerosols within coarser chemical transport models.
Patrick Obin Sturm and Anthony S. Wexler
Geosci. Model Dev., 13, 4435–4442, https://doi.org/10.5194/gmd-13-4435-2020, https://doi.org/10.5194/gmd-13-4435-2020, 2020
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Large air quality and climate models calculate different physical and chemical phenomena in separate operators within the overall model, some of which are computationally intensive. Machine learning tools can memorize the behavior of these operators and replace them, but the replacements must still obey physical laws, like conservation principles. This work derives a mathematical framework for machine learning replacements that conserves properties, such as mass or energy, to machine precision.
Paul D. Hamer, Sam-Erik Walker, Gabriela Sousa-Santos, Matthias Vogt, Dam Vo-Thanh, Susana Lopez-Aparicio, Philipp Schneider, Martin O. P. Ramacher, and Matthias Karl
Geosci. Model Dev., 13, 4323–4353, https://doi.org/10.5194/gmd-13-4323-2020, https://doi.org/10.5194/gmd-13-4323-2020, 2020
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EPISODE is an air quality model designed to give information on air pollution in cities down to distances measured in metres from the roadside and other pollution sources. We demonstrate that EPISODE can adequately describe nitrogen dioxide air pollution in a case study in six Norwegian cities. From this, we conclude that EPISODE can be used to provide air quality information to public bodies and society in order to help in the understanding and management of air pollution in urban environments.
Nicola Bodini, Julie K. Lundquist, and Mike Optis
Geosci. Model Dev., 13, 4271–4285, https://doi.org/10.5194/gmd-13-4271-2020, https://doi.org/10.5194/gmd-13-4271-2020, 2020
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While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind speed, its current representation in weather prediction models is inaccurate, especially in complex terrain. In this study, we leverage the potential of machine-learning techniques to provide a more accurate representation of turbulence dissipation rate. Our results show a 30 % reduction in the average error compared to the current model representation of ε and a total elimination of its average bias.
Mario Mech, Maximilian Maahn, Stefan Kneifel, Davide Ori, Emiliano Orlandi, Pavlos Kollias, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, https://doi.org/10.5194/gmd-13-4229-2020, 2020
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The Passive and Active Microwave TRAnsfer tool (PAMTRA) is a public domain software package written in Python and Fortran for the simulation of microwave remote sensing observations. PAMTRA models the interaction of radiation with gases, clouds, precipitation, and the surface using either in situ observations or model output as input parameters. The wide range of applications is demonstrated for passive (radiometer) and active (radar) instruments on ground, airborne, and satellite platforms.
Shin-ichiro Shima, Yousuke Sato, Akihiro Hashimoto, and Ryohei Misumi
Geosci. Model Dev., 13, 4107–4157, https://doi.org/10.5194/gmd-13-4107-2020, https://doi.org/10.5194/gmd-13-4107-2020, 2020
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Using the super-droplet method, we constructed a detailed numerical model of mixed-phase clouds based on kinetic description and subsequently demonstrated that a large-eddy simulation of a cumulonimbus which predicts ice particle morphology without assuming ice categories or mass–dimension relationships is possible. Our results strongly support the particle-based modeling methodology’s efficacy for simulating mixed-phase clouds.
Swen Brands, Guillermo Fernández-García, Marta García Vivanco, Marcos Tesouro Montecelo, Nuria Gallego Fernández, Anthony David Saunders Estévez, Pablo Enrique Carracedo García, Anabela Neto Venâncio, Pedro Melo Da Costa, Paula Costa Tomé, Cristina Otero, María Luz Macho, and Juan Taboada
Geosci. Model Dev., 13, 3947–3973, https://doi.org/10.5194/gmd-13-3947-2020, https://doi.org/10.5194/gmd-13-3947-2020, 2020
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The capability of numerical models to predict air quality depends on many factors. Here, the role of the applied model resolution, emission configuration and model chemistry is assessed for the CHIMERE model and the northwestern Iberian Peninsula. Although heterogeneous results are obtained, the forecasts can be systematically improved by increasing the vertical resolution in the lower and middle troposphere. This finding might also apply to other regions with similar characteristics.
Xiao Lu, Lin Zhang, Tongwen Wu, Michael S. Long, Jun Wang, Daniel J. Jacob, Fang Zhang, Jie Zhang, Sebastian D. Eastham, Lu Hu, Lei Zhu, Xiong Liu, and Min Wei
Geosci. Model Dev., 13, 3817–3838, https://doi.org/10.5194/gmd-13-3817-2020, https://doi.org/10.5194/gmd-13-3817-2020, 2020
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This study presents the development and evaluation of a new climate chemistry model, BCC-GEOS-Chem v1.0, which couples the GEOS-Chem chemical transport model as an atmospheric chemistry component in the Beijing Climate Center atmospheric general circulation model. A 3-year (2012–2014) simulation of BCC-GEOS-Chem v1.0 shows that the model captures well the spatiotemporal distributions of tropospheric ozone, other gaseous pollutants, and aerosols.
Ilya Stanevich, Dylan B. A. Jones, Kimberly Strong, Robert J. Parker, Hartmut Boesch, Debra Wunch, Justus Notholt, Christof Petri, Thorsten Warneke, Ralf Sussmann, Matthias Schneider, Frank Hase, Rigel Kivi, Nicholas M. Deutscher, Voltaire A. Velazco, Kaley A. Walker, and Feng Deng
Geosci. Model Dev., 13, 3839–3862, https://doi.org/10.5194/gmd-13-3839-2020, https://doi.org/10.5194/gmd-13-3839-2020, 2020
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Systematic errors in atmospheric models pose a challenge for inverse modeling studies of methane (CH4) emissions. We evaluated the CH4 simulation in the GEOS-Chem model at the horizontal resolutions of 4° × 5° and 2° × 2.5°. Our analysis identified resolution-dependent biases in the model, which we attributed to discrepancies between the two model resolutions in vertical transport in the troposphere and in stratosphere–troposphere exchange.
Ross Noel Bannister
Geosci. Model Dev., 13, 3789–3816, https://doi.org/10.5194/gmd-13-3789-2020, https://doi.org/10.5194/gmd-13-3789-2020, 2020
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Forecasting models start from initial conditions, and data assimilation (DA) is the way that initial conditions are found from a combination of previous model data and latest observations. The ABC model is a simplified convective-scale model developed previously, and ABC-DA is the version of this system that includes the DA capability. This system is described in the present paper, and its performance is demonstrated with a range of options that control how the data assimilation is done.
Daisuke Goto, Yousuke Sato, Hisashi Yashiro, Kentaroh Suzuki, Eiji Oikawa, Rei Kudo, Takashi M. Nagao, and Teruyuki Nakajima
Geosci. Model Dev., 13, 3731–3768, https://doi.org/10.5194/gmd-13-3731-2020, https://doi.org/10.5194/gmd-13-3731-2020, 2020
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We executed a global aerosol model over 3 years with the finest grid size in the world. The results elucidated that global annual averages of parameters associated with the aerosols were generally comparable to those obtained from a low-resolution model (LRM), but spatiotemporal variabilities of the aerosol components and their associated parameters provided better results closer to the observations than those from the LRM. This study clarified the advantages of the high-resolution model.
Arman Pouyaei, Yunsoo Choi, Jia Jung, Bavand Sadeghi, and Chul Han Song
Geosci. Model Dev., 13, 3489–3505, https://doi.org/10.5194/gmd-13-3489-2020, https://doi.org/10.5194/gmd-13-3489-2020, 2020
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This paper introduces a novel Lagrangian model (Concentration Trajectory of Air pollution with an Integrated Lagrangian model, C-TRAIL) for showing the source and receptor areas by following polluted air masses. To investigate the concentrations and trajectories of air masses simultaneously, we use the trajectory-grid (TG) Lagrangian advection model. The TG model follows the concentrations of representative air
packetsof species along trajectories determined by the wind field.
Arianna Valmassoi, Jimy Dudhia, Silvana Di Sabatino, and Francesco Pilla
Geosci. Model Dev., 13, 3179–3201, https://doi.org/10.5194/gmd-13-3179-2020, https://doi.org/10.5194/gmd-13-3179-2020, 2020
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Irrigation affects the atmosphere and models are required to understand its full impact. However, there is no agreed procedure to describe irrigation within regional models. The present study introduces three new methods to integrate this process into the models and validates it for the Po Valley in northern Italy. All the tests done show that the results are improved with the new irrigation techniques when compared against some measures (e.g., temperature, potential evapotranspiration).
Dai Koshin, Kaoru Sato, Kazuyuki Miyazaki, and Shingo Watanabe
Geosci. Model Dev., 13, 3145–3177, https://doi.org/10.5194/gmd-13-3145-2020, https://doi.org/10.5194/gmd-13-3145-2020, 2020
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A new data assimilation system with a 4D local ensemble transform Kalman filter for the whole neutral atmosphere is developed using a T42L124 general circulation model. A conventional observation dataset and bias-corrected satellite temperature data are assimilated. After the improvements of the forecast model, the assimilation parameters are optimized. The minimum optimal number of ensembles is also examined. Results are evaluated using the reanalysis data and independent radar observations.
Dominik Fröhlich and Andreas Matzarakis
Geosci. Model Dev., 13, 3055–3065, https://doi.org/10.5194/gmd-13-3055-2020, https://doi.org/10.5194/gmd-13-3055-2020, 2020
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In the frame of the project "MOSAIK - Model-based city planning and application in climate change", a biometeorology module was implemented into the Parallelized Large-Eddy Simulation Model (PALM). It is comprised of methods for the estimation of three popular thermal indices for the assessment of human thermal comfort or thermal stress. Results of the new module are compared to those by the SkyHelios model as well as the version of the indices published in VDI guideline 3787, part 2.
Solène Turquety, Laurent Menut, Guillaume Siour, Sylvain Mailler, Juliette Hadji-Lazaro, Maya George, Cathy Clerbaux, Daniel Hurtmans, and Pierre-François Coheur
Geosci. Model Dev., 13, 2981–3009, https://doi.org/10.5194/gmd-13-2981-2020, https://doi.org/10.5194/gmd-13-2981-2020, 2020
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Biomass burning emissions are a major source of trace gases and aerosols that need to be accounted for in air quality assessment and forecasting. The APIFLAME model presented in this paper allows the calculation of these emissions based on merged satellite observations at hourly time steps and kilometer scales. Implementing emissions in a chemistry transport model allows realistic simulations of fire plumes as illustrated for wildfires in Portugal in August 2016 using the CHIMERE model.
Shunliu Zhao, Matthew G. Russell, Amir Hakami, Shannon L. Capps, Matthew D. Turner, Daven K. Henze, Peter B. Percell, Jaroslav Resler, Huizhong Shen, Armistead G. Russell, Athanasios Nenes, Amanda J. Pappin, Sergey L. Napelenok, Jesse O. Bash, Kathleen M. Fahey, Gregory R. Carmichael, Charles O. Stanier, and Tianfeng Chai
Geosci. Model Dev., 13, 2925–2944, https://doi.org/10.5194/gmd-13-2925-2020, https://doi.org/10.5194/gmd-13-2925-2020, 2020
Gan Luo, Fangqun Yu, and Jonathan M. Moch
Geosci. Model Dev., 13, 2879–2903, https://doi.org/10.5194/gmd-13-2879-2020, https://doi.org/10.5194/gmd-13-2879-2020, 2020
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This work improved pH calculation for cloud, rain, and wet surfaces, fraction of cloud available for aqueous-phase chemistry, rainout efficiencies for various types of cloud, empirical washout by rain and snow, and wet surface uptake in GEOS-Chem v12.6.0. We compared simulated mass concentrations of aerosol precursors and aerosols with surface monitoring networks, Arctic sites, and ATom observations, and showed that the model results with the updated wet processes agree better for most species.
Laura D. Fowler, Mary C. Barth, and Kiran Alapaty
Geosci. Model Dev., 13, 2851–2877, https://doi.org/10.5194/gmd-13-2851-2020, https://doi.org/10.5194/gmd-13-2851-2020, 2020
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The cloud liquid and ice water path and precipitation simulated with the Model for Prediction Across Scales are compared against satellite data over the tropical Pacific Ocean. Uniform and variable-resolution experiments using scale-aware convection schemes produce strong biases between simulated and observed diagnostics. Results underscore the importance of evaluating clouds, their optical properties, and radiation budget in addition to precipitation in mesh refinement global simulations.
Alessandro Cotronei and Thomas Slawig
Geosci. Model Dev., 13, 2783–2804, https://doi.org/10.5194/gmd-13-2783-2020, https://doi.org/10.5194/gmd-13-2783-2020, 2020
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We converted the radiation part of the atmospheric model ECHAM to single-precision arithmetic, using a step-by-step change in all modules. A small code portion still requires higher precision. The generated code can be easily changed from double to single precision and vice versa. The quality of the output of the single-precision version is comparable to observational data and the one of the original code. The runtime was reduced by 40 %, and the energy consumption could also be decreased.
Alejandro Luque, Francisco José Gordillo-Vázquez, Dongshuai Li, Alejandro Malagón-Romero, Francisco Javier Pérez-Invernón, Anthony Schmalzried, Sergio Soler, Olivier Chanrion, Matthias Heumesser, Torsten Neubert, Víctor Reglero, and Nikolai Østgaard
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-161, https://doi.org/10.5194/gmd-2020-161, 2020
Revised manuscript accepted for GMD
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Lightning flashes are often recorded from space-based platforms. Besides being valuable inputs to weather forecasting, these observations also enable research into fundamental questions in lightning physics. To exploit them it is essential to understand how light propagates from a lightning flash to a space-based observing instrument. Here we present an open-source software tool to model this process that extends previous works and overcomes some of their limitations.
Carlos A. F. Marques, Martinho Marta-Almeida, and José M. Castanheira
Geosci. Model Dev., 13, 2763–2781, https://doi.org/10.5194/gmd-13-2763-2020, https://doi.org/10.5194/gmd-13-2763-2020, 2020
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A free software package for the computation of the 3-D normal modes of an hydrostatic atmosphere is presented. This software performs the computations in isobaric coordinates and was developed for two user-friendly languages: MATLAB and Python. The software can be used to expand the global atmospheric circulation onto the 3-D normal modes.
Ingrid Super, Hugo A. C. Denier van der Gon, Michiel K. van der Molen, Stijn N. C. Dellaert, and Wouter Peters
Geosci. Model Dev., 13, 2695–2721, https://doi.org/10.5194/gmd-13-2695-2020, https://doi.org/10.5194/gmd-13-2695-2020, 2020
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Understanding urban CO2 fluxes is increasingly important to support emission reduction policies. In this work we extended an existing framework for emission verification to increase its suitability for urban areas and source-sector-based decision-making by using a dynamic emission model. We find that the dynamic emission model provides a better understanding of emissions at small scales and their uncertainties. By including co-emitted species, emission can be related to specific source sectors.
Fangqun Yu, Alexey B. Nadykto, Gan Luo, and Jason Herb
Geosci. Model Dev., 13, 2663–2670, https://doi.org/10.5194/gmd-13-2663-2020, https://doi.org/10.5194/gmd-13-2663-2020, 2020
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Secondary particles formed via nucleation have important implications for air quality and climate. Here we describe nucleation rate lookup tables for four different nucleation mechanisms that can be readily used in chemistry transport and climate models. The nucleation rates predicted have been assessed against state-of-the-art laboratory measurements. The lookup tables cover a wide range of key parameters controlling binary, ternary, and ion-mediated nucleation in the Earth's atmosphere.
Jessica M. Tomaszewski and Julie K. Lundquist
Geosci. Model Dev., 13, 2645–2662, https://doi.org/10.5194/gmd-13-2645-2020, https://doi.org/10.5194/gmd-13-2645-2020, 2020
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Wind farms can briefly impact the nearby environment by reducing wind speeds and mixing warmer air down to the surface. The wind farm parameterization (WFP) in the Weather Research and Forecasting (WRF) model is a tool that numerically simulates wind farms and these meteorological impacts. We highlight the importance of choice in model settings and find that sufficiently fine vertical and horizontal grids with turbine turbulence are needed to accurately simulate wind farm meteorological impacts.
Georgy Ayzel, Tobias Scheffer, and Maik Heistermann
Geosci. Model Dev., 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, https://doi.org/10.5194/gmd-13-2631-2020, 2020
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In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting, which was trained to predict continuous precipitation intensities at a lead time of 5 min. RainNet significantly outperformed the benchmark models at all lead times up to 60 min. Yet, an undesirable property of RainNet predictions is the level of spatial smoothing. Obviously, RainNet learned an optimal level of smoothing to produce a nowcast at 5 min lead time.
Erik H. Hoffmann, Roland Schrödner, Andreas Tilgner, Ralf Wolke, and Hartmut Herrmann
Geosci. Model Dev., 13, 2587–2609, https://doi.org/10.5194/gmd-13-2587-2020, https://doi.org/10.5194/gmd-13-2587-2020, 2020
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A condensed multiphase halogen and DMS chemistry mechanism for application in chemical transport models has been developed and applied by 2D simulations to explore multiphase marine chemistry above the pristine open ocean. The model simulations have demonstrated the ability of the mechanism in studying aerosol cloud processing effects in the marine atmosphere. First 2D simulations have shown significant differences in the DMS processing under convective and stratiform cloud conditions.
Sam J. Silva, Colette L. Heald, and Alex B. Guenther
Geosci. Model Dev., 13, 2569–2585, https://doi.org/10.5194/gmd-13-2569-2020, https://doi.org/10.5194/gmd-13-2569-2020, 2020
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Simulating the influence of the biosphere on atmospheric chemistry has traditionally been computationally intensive. We describe a surrogate canopy physics model parameterized using a statistical learning technique and specifically designed for use in large-scale chemical transport models. Our surrogate model reproduces a more detailed model to within 10 % without a large computational demand, improving the process representation of biosphere–atmosphere exchange.
Lu Shen, Daniel J. Jacob, Mauricio Santillana, Xuan Wang, and Wei Chen
Geosci. Model Dev., 13, 2475–2486, https://doi.org/10.5194/gmd-13-2475-2020, https://doi.org/10.5194/gmd-13-2475-2020, 2020
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Chemical mechanisms in air quality models tend to get more complicated with time, reflecting both increasing knowledge and the need for greater scope. This objectively improves the models but increases the computational burden. In this work, we present an approach that can reduce the computational cost of chemical integration by 30–40 % while maintaining an accuracy better than 1 %. It retains the complexity of the full mechanism where it is needed and preserves full diagnostic information.
Robin D. Lamboll, Zebedee R. J. Nicholls, Jarmo S. Kikstra, Malte Meinshausen, and Joeri Rogelj
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-138, https://doi.org/10.5194/gmd-2020-138, 2020
Revised manuscript accepted for GMD
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Many models project how human activity can lead to more or less climate change, but most of these models do not project all climate-relevant emissions, potentially biasing climate projections. This paper outlines a Python package called Silicone which can add missing emissions in a flexible yet high-throughput manner. It does this 'infilling' based on more complete literature projections. It facilitates a more complete understanding of the climate impact of alternative emission pathways.
Shelley C. van der Graaf, Richard Kranenburg, Arjo J. Segers, Martijn Schaap, and Jan Willem Erisman
Geosci. Model Dev., 13, 2451–2474, https://doi.org/10.5194/gmd-13-2451-2020, https://doi.org/10.5194/gmd-13-2451-2020, 2020
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Chemical transport models (CTMs) are important tools to determine the fate of reactive nitrogen (Nr) emissions. The parameterization of the surface–atmosphere exchange in CTMs is often only linked to fixed, land-use-dependent values. In this paper, we present an approach to derive more realistic, dynamic leaf area index (LAI) and roughness length (z0) input maps using multiple satellite products. We evaluate the effect on Nr concentration and deposition fields modelled in the LOTOS-EUROS CTM.
Michael Jähn, Gerrit Kuhlmann, Qing Mu, Jean-Matthieu Haussaire, David Ochsner, Katherine Osterried, Valentin Clément, and Dominik Brunner
Geosci. Model Dev., 13, 2379–2392, https://doi.org/10.5194/gmd-13-2379-2020, https://doi.org/10.5194/gmd-13-2379-2020, 2020
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Emission inventories of air pollutants and greenhouse gases are widely used as input for atmospheric chemistry transport models. However, the pre-processing of these data is both time-consuming and requires a large amount of disk storage. To overcome this issue, a Python package has been developed and tested for two different models. There, the inventory is projected to the model grid and scaling factors are provided. This approach saves computational time while remaining numerically equivalent.
Joe R. McNorton, Nicolas Bousserez, Anna Agustí-Panareda, Gianpaolo Balsamo, Margarita Choulga, Andrew Dawson, Richard Engelen, Zak Kipling, and Simon Lang
Geosci. Model Dev., 13, 2297–2313, https://doi.org/10.5194/gmd-13-2297-2020, https://doi.org/10.5194/gmd-13-2297-2020, 2020
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To infer carbon emissions from observations using atmospheric models, detailed knowledge of uncertainty is required. The uncertainties associated with models are often estimated because they are difficult to attribute. Here we use a state-of-the-art weather model to assess the impact of uncertainty in the wind fields on atmospheric concentrations of carbon dioxide. These results can be used to help quantify the uncertainty in estimated carbon emissions from atmospheric observations.
Axel Kleidon and Lee M. Miller
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-77, https://doi.org/10.5194/gmd-2020-77, 2020
Revised manuscript accepted for GMD
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When winds are used as renewable energy by more and more wind turbines, one needs to account for the effect of wind turbines on the atmospheric flow. The Kinetic Energy Budget of the Atmosphere (KEBA) model provides a simple, physics-based approach to account for this effect very well when compared to much more detailed numerical simulations with an atmospheric model. KEBA should be useful to derive lower, more realistic wind energy resource potentials of different regions.
Stephan Rasp
Geosci. Model Dev., 13, 2185–2196, https://doi.org/10.5194/gmd-13-2185-2020, https://doi.org/10.5194/gmd-13-2185-2020, 2020
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Subgrid parameterizations are largely responsible for uncertainties in climate models. Recently, several studies tried to improve the representation of subgrid processes by learning parameterization directly from high-resolution modeling data. In this paper, the current state of the art of this research direction is summarized, and an algorithm is proposed to combat major problems with existing approaches, namely instabilities and biases.
Li Pan, HyunCheol Kim, Pius Lee, Rick Saylor, YouHua Tang, Daniel Tong, Barry Baker, Shobha Kondragunta, Chuanyu Xu, Mark G. Ruminski, Weiwei Chen, Jeff Mcqueen, and Ivanka Stajner
Geosci. Model Dev., 13, 2169–2184, https://doi.org/10.5194/gmd-13-2169-2020, https://doi.org/10.5194/gmd-13-2169-2020, 2020
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Compared to anthropogenic emissions, emissions from wildfires are largely uncontrolled and unpredictable. Quantitatively describing wildfire emissions and their contributions to air pollution remains a substantial challenge for air quality forecasting efforts. In this study, we test the wildfire calculation algorithm used by the National Air Quality Forecasting Capability (NAQFC) by comparison with ground, satellite and flight measurements during the Southeast Nexus (SENEX) field experiment.
Isabella Capel-Timms, Stefán Thor Smith, Ting Sun, and Sue Grimmond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-52, https://doi.org/10.5194/gmd-2020-52, 2020
Revised manuscript accepted for GMD
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Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local and larger scales urban climate. DASH considers both urban form and function in simulating QF by use of an agent-based structure that includes behavioural characteristics of city populations. This allows social practices to drive the calculation of QF as occupants move, varying by day type, demographic, location, activity, socio-economic factors and in response to environmental conditions.
Yi Zeng, Minghuai Wang, Chun Zhao, Siyu Chen, Zhoukun Liu, Xin Huang, and Yang Gao
Geosci. Model Dev., 13, 2125–2147, https://doi.org/10.5194/gmd-13-2125-2020, https://doi.org/10.5194/gmd-13-2125-2020, 2020
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Dust aerosol can impact many processes of the Earth system, but large uncertainties still remain in dust simulations. In this study, we investigated dust simulation sensitivity to two dust emission schemes and three dry deposition schemes using WRF-Chem. An optimal combination of dry deposition scheme and dust emission scheme has been identified to best simulate the dust storm in comparison with observation. Our results highlight the importance of dry deposition schemes for dust simulation.
Luke M. Western, Zhe Sha, Matthew Rigby, Anita L. Ganesan, Alistair J. Manning, Kieran M. Stanley, Simon J. O'Doherty, Dickon Young, and Jonathan Rougier
Geosci. Model Dev., 13, 2095–2107, https://doi.org/10.5194/gmd-13-2095-2020, https://doi.org/10.5194/gmd-13-2095-2020, 2020
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Assessments of greenhouse gas emissions using atmospheric measurements and meteorological models, or
top-downmethods, are important to verify national inventories or produce a stand-alone estimate where no inventory exists. We present a novel top-down method to estimate emissions. This approach uses a fast method called an integrated nested Laplacian approximation to estimate how these emissions are correlated with other emissions in different locations and at different times.
Jorge Baño-Medina, Rodrigo Manzanas, and José Manuel Gutiérrez
Geosci. Model Dev., 13, 2109–2124, https://doi.org/10.5194/gmd-13-2109-2020, https://doi.org/10.5194/gmd-13-2109-2020, 2020
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In this study we intercompare different deep learning topologies for statistical downscaling purposes. As compared to the top-ranked methods in the largest-to-date downscaling intercomparison study, our results better predict the local climate variability. Moreover, deep learning approaches can be suitably applied to large regions (e.g., continents), which can therefore foster the use of statistical downscaling in flagship initiatives such as CORDEX.
Laurent Menut, Guillaume Siour, Bertrand Bessagnet, Florian Couvidat, Emilie Journet, Yves Balkanski, and Karine Desboeufs
Geosci. Model Dev., 13, 2051–2071, https://doi.org/10.5194/gmd-13-2051-2020, https://doi.org/10.5194/gmd-13-2051-2020, 2020
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Modelling of mineral dust is often done using one single mean species. In this study, differentiated mineral species with their chemical composition are implemented in the CHIMERE regional chemistry-transport model by using global databases. Simulations are carried out to quantify the realism and gain of such mineralogy.
Stelios Myriokefalitakis, Nikos Daskalakis, Angelos Gkouvousis, Andreas Hilboll, Twan van Noije, Jason E. Williams, Philippe Le Sager, Vincent Huijnen, Sander Houweling, Tommi Bergman, Johann Rasmus Nüß, Mihalis Vrekoussis, Maria Kanakidou, and Maarten C. Krol
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-110, https://doi.org/10.5194/gmd-2020-110, 2020
Revised manuscript accepted for GMD
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This work documents and evaluates the detailed tropospheric gas-phase chemical mechanism MOGUNTIA in the three-dimensional chemistry transport model (CTM) TM5-MP. Moreover, the Rosenbrock solver, as generated by the KPP software, is implemented in the chemistry code, which can replace the classical Euler Backward Integration method. Overall, compared to other chemistry schemes in use in CTMs, the MOGUNTIA simulates a large suite of oxygenated VOCs, expanding the applications of the model.
Mike Bush, Tom Allen, Caroline Bain, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Humphrey Lean, Adrian Lock, James Manners, Marion Mittermaier, Cyril Morcrette, Rachel North, Jon Petch, Chris Short, Simon Vosper, David Walters, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Nigel Wood, and Mohamed Zerroukat
Geosci. Model Dev., 13, 1999–2029, https://doi.org/10.5194/gmd-13-1999-2020, https://doi.org/10.5194/gmd-13-1999-2020, 2020
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In this paper we define the first Regional Atmosphere and Land (RAL) science configuration for kilometre-scale modelling using the Unified Model (UM) as the basis for the atmosphere and the Joint UK Land Environment Simulator (JULES) for the land. RAL1 defines the science configuration of the dynamics and physics schemes of the atmosphere and land. This configuration will provide a model baseline for any future weather or climate model developments to be described against.
Mariko Oue, Aleksandra Tatarevic, Pavlos Kollias, Dié Wang, Kwangmin Yu, and Andrew M. Vogelmann
Geosci. Model Dev., 13, 1975–1998, https://doi.org/10.5194/gmd-13-1975-2020, https://doi.org/10.5194/gmd-13-1975-2020, 2020
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We developed the Cloud-resolving model Radar SIMulator (CR-SIM) capable of apples-to-apples comparisons between the multiwavelength, zenith-pointing, and scanning radar and multi-remote-sensing (radar and lidar) observations and the high-resolution atmospheric model output. Applications of CR-SIM as a virtual observatory operator aid interpretation of the differences and improve understanding of the representativeness errors due to the sampling limitations of the ground-based measurements.
Thomas Schwitalla, Kirsten Warrach-Sagi, Volker Wulfmeyer, and Michael Resch
Geosci. Model Dev., 13, 1959–1974, https://doi.org/10.5194/gmd-13-1959-2020, https://doi.org/10.5194/gmd-13-1959-2020, 2020
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Performing seasonal simulations on horizontal grid resolutions of a few kilometres over the entire globe remains challenging. We demonstrate the added value of simulating large-scale patterns and feedbacks at 3 km resolution compared to a coarser-resolution forecast using the WRF numerical weather model on a latitude-belt domain. Results show an improvement of cloud coverage in the tropics, better representation of teleconnection, and improvements of precipitation patterns in different regions.
Almudena García-García, Francisco José Cuesta-Valero, Hugo Beltrami, J. Fidel González-Rouco, Elena García-Bustamante, and Joel Finnis
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-86, https://doi.org/10.5194/gmd-2020-86, 2020
Revised manuscript accepted for GMD
Mathieu Lachatre, Sylvain Mailler, Laurent Menut, Solène Turquety, Pasquale Sellitto, Henda Guermazi, Giuseppe Salerno, Tommaso Caltabiano, and Elisa Carboni
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-62, https://doi.org/10.5194/gmd-2020-62, 2020
Revised manuscript accepted for GMD
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Excessive numerical diffusion is a major limitations in the representation of long-range transport atmospheric models. In the present study, we focus on excessive diffusion in the vertical direction. We explore three possible ways to address this problem: increase vertical resolution, use an advection scheme with antidiffusive properties, and represent more accurately the vertical wind. This study focused on a particular volcanic eruption event to improve atmospheric transport modelling.
Anna-Leah Nickl, Mariano Mertens, Anke Roiger, Andreas Fix, Axel Amediek, Alina Fiehn, Christoph Gerbig, Michal Galkowski, Astrid Kerkweg, Theresa Klausner, Maximilian Eckl, and Patrick Jöckel
Geosci. Model Dev., 13, 1925–1943, https://doi.org/10.5194/gmd-13-1925-2020, https://doi.org/10.5194/gmd-13-1925-2020, 2020
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Based on the global and regional chemistry–climate model system MECO(n), we implemented a forecast system to support the planning of measurement campaign research flights with chemical weather forecasts. We applied this system for the first time to provide 6 d forecasts in support of the CoMet 1.0
campaign targeting methane emitted from coal mining ventilation shafts in the Upper Silesian Coal Basin in Poland. We describe the new forecast system and evaluate its forecast skill.
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The Environment – HIgh Resolution Limited Area Model (Enviro-HIRLAM) is developed as a fully online integrated numerical weather prediction and atmospheric chemical transport model for research and forecasting of joint meteorological, chemical and biological weather. Different aspects of online coupling methodology, research strategy and possible applications of the modelling system, and ''fit-for-purpose'' model configurations for the meteorological and air quality communities are discussed.
The Environment – HIgh Resolution Limited Area Model (Enviro-HIRLAM) is developed as a fully...
Geoscientific Model Development
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