Articles | Volume 17, issue 21
https://doi.org/10.5194/gmd-17-7835-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-7835-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
07 Nov 2024
Methods for assessment of models |
| 07 Nov 2024
| 07 Nov 2024
An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6
Trude Eidhammer
CORRESPONDING AUTHOR
NSF National Center for Atmospheric Research, Boulder, CO, USA
Andrew Gettelman
NSF National Center for Atmospheric Research, Boulder, CO, USA
now at: Atmospheric, Climate and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
Katherine Thayer-Calder
NSF National Center for Atmospheric Research, Boulder, CO, USA
Duncan Watson-Parris
Scripps Institution of Oceanography and Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
Gregory Elsaesser
Dept. of Applied Physics and Applied Mathematics, Columbia University, New York, NY, USA
NASA Goddard Institute for Space Studies, New York, NY, USA
Hugh Morrison
NSF National Center for Atmospheric Research, Boulder, CO, USA
Marcus van Lier-Walqui
Center for Climate Systems Research, Columbia University, New York, NY, USA
NASA Goddard Institute for Space Studies, New York, NY, USA
Ci Song
Department of Atmospheric Science, University of Wyoming, Laramie, WY, USA
Daniel McCoy
Department of Atmospheric Science, University of Wyoming, Laramie, WY, USA
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August Mikkelsen, Daniel T. McCoy, Trude Eidhammer, Andrew Gettelman, Ci Song, Hamish Gordon, and Isabel L. McCoy
EGUsphere, https://doi.org/10.5194/egusphere-2024-2158, https://doi.org/10.5194/egusphere-2024-2158, 2024
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The largest uncertainty in inferring the magnitude of future warming comes from ambiguity in the strength of cooling in the historical record from aerosols. Aerosols are small liquid and solid particles that are important for cloud formation. The interactions between aerosols and clouds are complex and difficult to observe. In this study, we use surface observations of cloud and precipitation properties to constrain a climate model and interpret causality in complex aerosol-cloud interactions.
Tamara Pletzer, Jonathan P. Conway, Nicolas J. Cullen, Trude Eidhammer, and Marwan Katurji
Hydrol. Earth Syst. Sci., 28, 459–478, https://doi.org/10.5194/hess-28-459-2024, https://doi.org/10.5194/hess-28-459-2024, 2024
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We applied a glacier and hydrology model in the McMurdo Dry Valleys (MDV) to model the start and duration of melt over a summer in this extreme polar desert. To do so, we found it necessary to prevent the drainage of melt into ice and optimize the albedo scheme. We show that simulating albedo (for the first time in the MDV) is critical to modelling the feedbacks of albedo, snowfall and melt in the region. This paper is a first step towards more complex spatial modelling of melt and streamflow.
Andrew Gettelman, Hugh Morrison, Trude Eidhammer, Katherine Thayer-Calder, Jian Sun, Richard Forbes, Zachary McGraw, Jiang Zhu, Trude Storelvmo, and John Dennis
Geosci. Model Dev., 16, 1735–1754, https://doi.org/10.5194/gmd-16-1735-2023, https://doi.org/10.5194/gmd-16-1735-2023, 2023
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Clouds are a critical part of weather and climate prediction. In this work, we document updates and corrections to the description of clouds used in several Earth system models. These updates include the ability to run the scheme on graphics processing units (GPUs), changes to the numerical description of precipitation, and a correction to the ice number. There are big improvements in the computational performance that can be achieved with GPU acceleration.
Trude Eidhammer, Adam Booth, Sven Decker, Lu Li, Michael Barlage, David Gochis, Roy Rasmussen, Kjetil Melvold, Atle Nesje, and Stefan Sobolowski
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We coupled a detailed snow–ice model (Crocus) to represent glaciers in the Weather Research and Forecasting (WRF)-Hydro model and tested it on a well-studied glacier. Several observational systems were used to evaluate the system, i.e., satellites, ground-penetrating radar (used over the glacier for snow depth) and stake observations for glacier mass balance and discharge measurements in rivers from the glacier. Results showed improvements in the streamflow projections when including the model.
T. Eidhammer, H. Morrison, A. Bansemer, A. Gettelman, and A. J. Heymsfield
Atmos. Chem. Phys., 14, 10103–10118, https://doi.org/10.5194/acp-14-10103-2014, https://doi.org/10.5194/acp-14-10103-2014, 2014
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Paul T. Griffiths, Laura J. Wilcox, Robert J. Allen, Vaishali Naik, Fiona M. O'Connor, Michael J. Prather, Alexander T. Archibald, Florence Brown, Makoto Deushi, William Collins, Stephanie Fiedler, Naga Oshima, Lee T. Murray, Christopher J. Smith, Steven T. Turnock, Duncan Watson-Parris, and Paul J. Young
EGUsphere, https://doi.org/10.5194/egusphere-2024-2528, https://doi.org/10.5194/egusphere-2024-2528, 2024
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The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) aimed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. In this paper, we review its contribution to AR6, and the wider understanding of the role of these species in climate and climate change. We identify remaining challenges concluding with recommendations aimed to improve the utility and uptake of climate model data to address the role of short-lived climate forcers in the Earth system.
August Mikkelsen, Daniel T. McCoy, Trude Eidhammer, Andrew Gettelman, Ci Song, Hamish Gordon, and Isabel L. McCoy
EGUsphere, https://doi.org/10.5194/egusphere-2024-2158, https://doi.org/10.5194/egusphere-2024-2158, 2024
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The largest uncertainty in inferring the magnitude of future warming comes from ambiguity in the strength of cooling in the historical record from aerosols. Aerosols are small liquid and solid particles that are important for cloud formation. The interactions between aerosols and clouds are complex and difficult to observe. In this study, we use surface observations of cloud and precipitation properties to constrain a climate model and interpret causality in complex aerosol-cloud interactions.
Duncan Watson-Parris, Laura J. Wilcox, Camilla W. Stjern, Robert J. Allen, Geeta Persad, Massimo A. Bollasina, Annica M. L. Ekman, Carley E. Iles, Manoj Joshi, Marianne T. Lund, Daniel McCoy, Daniel Westervelt, Andrew Williams, and Bjørn H. Samset
EGUsphere, https://doi.org/10.5194/egusphere-2024-1946, https://doi.org/10.5194/egusphere-2024-1946, 2024
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In 2020, regulations by the International Maritime Organization aimed to reduce aerosol emissions from ships. These aerosols previously had a cooling effect, which the regulations might reduce, revealing more greenhouse gas warming. Here we find that while there is regional warming, the global 2020–2040 temperature rise is only +0.03°C. This small change is difficult to distinguish from natural climate variability, indicating the regulations have had a limited effect on observed warming to date.
Weiyu Zhang, Kwinten Van Weverberg, Cyril J. Morcrette, Wuhu Feng, Kalli Furtado, Paul R. Field, Chih-Chieh Chen, Andrew Gettelman, Piers M. Forster, Daniel R. Marsh, and Alexandru Rap
EGUsphere, https://doi.org/10.5194/egusphere-2024-1573, https://doi.org/10.5194/egusphere-2024-1573, 2024
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Contrail cirrus is the largest, but also most uncertain contribution of aviation to global warming. We evaluate for the first time the impact of the host climate model on contrail cirrus properties. Substantial differences exist between contrail cirrus formation, persistence, and radiative effects in the host climate models. Reliable contrail cirrus simulations require advanced representation of cloud optical properties and microphysics, which should be better constrained by observations.
Johannes Mülmenstädt, Edward Gryspeerdt, Sudhakar Dipu, Johannes Quaas, Andrew S. Ackerman, Ann M. Fridlind, Florian Tornow, Susanne E. Bauer, Andrew Gettelman, Yi Ming, Youtong Zheng, Po-Lun Ma, Hailong Wang, Kai Zhang, Matthew W. Christensen, Adam C. Varble, L. Ruby Leung, Xiaohong Liu, David Neubauer, Daniel G. Partridge, Philip Stier, and Toshihiko Takemura
Atmos. Chem. Phys., 24, 7331–7345, https://doi.org/10.5194/acp-24-7331-2024, https://doi.org/10.5194/acp-24-7331-2024, 2024
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Human activities release copious amounts of small particles called aerosols into the atmosphere. These particles change how much sunlight clouds reflect to space, an important human perturbation of the climate, whose magnitude is highly uncertain. We found that the latest climate models show a negative correlation but a positive causal relationship between aerosols and cloud water. This means we need to be very careful when we interpret observational studies that can only see correlation.
Maria Rosa Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-73, https://doi.org/10.5194/gmd-2024-73, 2024
Revised manuscript accepted for GMD
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Ross J. Herbert, Andrew I. L. Williams, Philipp Weiss, Duncan Watson-Parris, Elisabeth Dingley, Daniel Klocke, and Philip Stier
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Clouds exist at scales that climate models struggle to represent, limiting our knowledge of how climate change may impact clouds. Here we use a new km-scale global model representing an important step towards the necessary scale. We focus on how aerosol particles modify clouds, radiation, and precipitation. We find the magnitude and manner of responses tend to vary from region to region, highlighting the potential of global km-scale simulations and a need to represent aerosols in climate models.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Bradley Hall, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan P. Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Blair Trewin, Myles Allen, Robbie Andrew, Richard A. Betts, Alex Borger, Tim Boyer, Jiddu A. Broersma, Carlo Buontempo, Samantha Burgess, Chiara Cagnazzo, Lijing Cheng, Pierre Friedlingstein, Andrew Gettelman, Johannes Gütschow, Masayoshi Ishii, Stuart Jenkins, Xin Lan, Colin Morice, Jens Mühle, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Jan C. Minx, Gunnar Myhre, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, Sophie Szopa, Peter Thorne, Mahesh V. M. Kovilakam, Elisa Majamäki, Jukka-Pekka Jalkanen, Margreet van Marle, Rachel M. Hoesly, Robert Rohde, Dominik Schumacher, Guido van der Werf, Russell Vose, Kirsten Zickfeld, Xuebin Zhang, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 16, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024, https://doi.org/10.5194/essd-16-2625-2024, 2024
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Peer Nowack and Duncan Watson-Parris
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In our Opinion article, we review uncertainties in global climate change projections and current methods using Earth observations to constrain them, which is crucial for climate risk assessments and for informing society. We then discuss how machine learning can advance the field, discussing recent work that provides potentially stronger and more robust links between observed data and future climate projections. We further discuss challenges of applying machine learning to climate science.
Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes Kaiser, Zak Kipling, Harri Kokkola, Xiaohong Liu, Keren Mezuman, Tero Mielonen, Gunnar Myhre, Xiaohua Pan, Anna Protonotariou, Samuel Remy, Ragnhild Bieltvedt Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, and Kai Zhang
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We compared smoke plume simulations from 11 global models to each other and to satellite smoke-amount observations, aimed at constraining smoke source strength. In regions where plumes are thick and background aerosol is low, models and satellites compare well. However, the input emission inventory tends to underestimate in many places, and particle property and loss-rate assumptions vary enormously among models, causing uncertainties that require systematic in-situ measurements to resolve.
Thirza Feenstra, Miren Vizcaino, Bert Wouters, Michele Petrini, Raymond Sellevold, and Katherine Thayer-Calder
EGUsphere, https://doi.org/10.5194/egusphere-2024-1126, https://doi.org/10.5194/egusphere-2024-1126, 2024
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Ming Luo, Helen M. Worden, Robert D. Field, Kostas Tsigaridis, and Gregory S. Elsaesser
Atmos. Meas. Tech., 17, 2611–2624, https://doi.org/10.5194/amt-17-2611-2024, https://doi.org/10.5194/amt-17-2611-2024, 2024
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The TROPESS CrIS single-pixel CO profile retrievals are compared to the MOPITT CO products in steps of adjusting them to the common a priori assumptions. The two data sets are found to agree within 5 %. We also demonstrated and analyzed the proper steps in evaluating GISS ModelE CO simulations using satellite CO retrieval products for the western US wildfire events in September 2020.
Johannes Mülmenstädt, Andrew S. Ackerman, Ann M. Fridlind, Meng Huang, Po-Lun Ma, Naser Mahfouz, Susanne E. Bauer, Susannah M. Burrows, Matthew W. Christensen, Sudhakar Dipu, Andrew Gettelman, L. Ruby Leung, Florian Tornow, Johannes Quaas, Adam C. Varble, Hailong Wang, Kai Zhang, and Youtong Zheng
EGUsphere, https://doi.org/10.5194/egusphere-2024-778, https://doi.org/10.5194/egusphere-2024-778, 2024
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Stratocumulus clouds play a large role in Earth's climate by reflecting incoming solar energy back to space. Turbulence at stratocumulus cloud top mixes in drier, warmer air, which can lead to a reduction in cloud. This process is challenging for coarse-resolution global models to represent. We show that global models nevertheless agree well with our process understanding. Global models also think the process is less important for the climate than other lines of evidence had led us to conclude.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
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Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
George Jordan, Florent Malavelle, Ying Chen, Amy Peace, Eliza Duncan, Daniel G. Partridge, Paul Kim, Duncan Watson-Parris, Toshihiko Takemura, David Neubauer, Gunnar Myhre, Ragnhild Skeie, Anton Laakso, and James Haywood
Atmos. Chem. Phys., 24, 1939–1960, https://doi.org/10.5194/acp-24-1939-2024, https://doi.org/10.5194/acp-24-1939-2024, 2024
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The 2014–15 Holuhraun eruption caused a huge aerosol plume in an otherwise unpolluted region, providing a chance to study how aerosol alters cloud properties. This two-part study uses observations and models to quantify this relationship’s impact on the Earth’s energy budget. Part 1 suggests the models capture the observed spatial and chemical evolution of the plume, yet no model plume is exact. Understanding these differences is key for Part 2, where changes to cloud properties are explored.
Tamara Pletzer, Jonathan P. Conway, Nicolas J. Cullen, Trude Eidhammer, and Marwan Katurji
Hydrol. Earth Syst. Sci., 28, 459–478, https://doi.org/10.5194/hess-28-459-2024, https://doi.org/10.5194/hess-28-459-2024, 2024
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We applied a glacier and hydrology model in the McMurdo Dry Valleys (MDV) to model the start and duration of melt over a summer in this extreme polar desert. To do so, we found it necessary to prevent the drainage of melt into ice and optimize the albedo scheme. We show that simulating albedo (for the first time in the MDV) is critical to modelling the feedbacks of albedo, snowfall and melt in the region. This paper is a first step towards more complex spatial modelling of melt and streamflow.
Adam C. Varble, Adele L. Igel, Hugh Morrison, Wojciech W. Grabowski, and Zachary J. Lebo
Atmos. Chem. Phys., 23, 13791–13808, https://doi.org/10.5194/acp-23-13791-2023, https://doi.org/10.5194/acp-23-13791-2023, 2023
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As atmospheric particles called aerosols increase in number, the number of droplets in clouds tends to increase, which has been theorized to increase storm intensity. We critically evaluate the evidence for this theory, showing that flaws and limitations of previous studies coupled with unaddressed cloud process complexities draw it into question. We provide recommendations for future observations and modeling to overcome current uncertainties.
Peter Manshausen, Duncan Watson-Parris, Matthew W. Christensen, Jukka-Pekka Jalkanen, and Philip Stier
Atmos. Chem. Phys., 23, 12545–12555, https://doi.org/10.5194/acp-23-12545-2023, https://doi.org/10.5194/acp-23-12545-2023, 2023
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Aerosol from burning fuel changes cloud properties, e.g., the number of droplets and the content of water. Here, we study how clouds respond to different amounts of shipping aerosol. Droplet numbers increase linearly with increasing aerosol over a broad range until they stop increasing, while the amount of liquid water always increases, independently of emission amount. These changes in cloud properties can make them reflect more or less sunlight, which is important for the earth's climate.
Andrew Gettelman
Geosci. Model Dev., 16, 4937–4956, https://doi.org/10.5194/gmd-16-4937-2023, https://doi.org/10.5194/gmd-16-4937-2023, 2023
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A representation of rainbows is developed for a climate model. The diagnostic raises many common issues. Simulated rainbows are evaluated against limited observations. The pattern of rainbows in the model matches observations and theory about when and where rainbows are most common. The diagnostic is used to assess the past and future state of rainbows. Changes to clouds from climate change are expected to increase rainbows as cloud cover decreases in a warmer world.
Leighton A. Regayre, Lucia Deaconu, Daniel P. Grosvenor, David M. H. Sexton, Christopher Symonds, Tom Langton, Duncan Watson-Paris, Jane P. Mulcahy, Kirsty J. Pringle, Mark Richardson, Jill S. Johnson, John W. Rostron, Hamish Gordon, Grenville Lister, Philip Stier, and Ken S. Carslaw
Atmos. Chem. Phys., 23, 8749–8768, https://doi.org/10.5194/acp-23-8749-2023, https://doi.org/10.5194/acp-23-8749-2023, 2023
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Aerosol forcing of Earth’s energy balance has persisted as a major cause of uncertainty in climate simulations over generations of climate model development. We show that structural deficiencies in a climate model are exposed by comprehensively exploring parametric uncertainty and that these deficiencies limit how much the model uncertainty can be reduced through observational constraint. This provides a future pathway towards building models with greater physical realism and lower uncertainty.
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
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We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Andrew Gettelman, Hugh Morrison, Trude Eidhammer, Katherine Thayer-Calder, Jian Sun, Richard Forbes, Zachary McGraw, Jiang Zhu, Trude Storelvmo, and John Dennis
Geosci. Model Dev., 16, 1735–1754, https://doi.org/10.5194/gmd-16-1735-2023, https://doi.org/10.5194/gmd-16-1735-2023, 2023
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Clouds are a critical part of weather and climate prediction. In this work, we document updates and corrections to the description of clouds used in several Earth system models. These updates include the ability to run the scheme on graphics processing units (GPUs), changes to the numerical description of precipitation, and a correction to the ice number. There are big improvements in the computational performance that can be achieved with GPU acceleration.
Leighton A. Regayre, Lucia Deaconu, Daniel P. Grosvenor, David Sexton, Christopher C. Symonds, Tom Langton, Duncan Watson-Paris, Jane P. Mulcahy, Kirsty J. Pringle, Mark Richardson, Jill S. Johnson, John Rostron, Hamish Gordon, Grenville Lister, Philip Stier, and Ken S. Carslaw
EGUsphere, https://doi.org/10.5194/egusphere-2022-1330, https://doi.org/10.5194/egusphere-2022-1330, 2022
Preprint archived
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We show that potential structural deficiencies in a climate model can be exposed by comprehensively exploring its parametric uncertainty, and that these deficiencies limit how much the model uncertainty can be reduced through observational constraint. Combined consideration of parametric and structural uncertainties provides a future pathway towards building models that have greater physical realism and lower uncertainty.
Xingying Huang, Andrew Gettelman, William C. Skamarock, Peter Hjort Lauritzen, Miles Curry, Adam Herrington, John T. Truesdale, and Michael Duda
Geosci. Model Dev., 15, 8135–8151, https://doi.org/10.5194/gmd-15-8135-2022, https://doi.org/10.5194/gmd-15-8135-2022, 2022
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We focus on the recent development of a state-of-the-art storm-resolving global climate model and investigate how this next-generation model performs for precipitation prediction over the western USA. Results show realistic representations of precipitation with significantly enhanced snowpack over complex terrains. The model evaluation advances the unified modeling of large-scale forcing constraints and realistic fine-scale features to advance multi-scale climate predictions and changes.
Zhipeng Qu, Alexei Korolev, Jason A. Milbrandt, Ivan Heckman, Yongjie Huang, Greg M. McFarquhar, Hugh Morrison, Mengistu Wolde, and Cuong Nguyen
Atmos. Chem. Phys., 22, 12287–12310, https://doi.org/10.5194/acp-22-12287-2022, https://doi.org/10.5194/acp-22-12287-2022, 2022
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Secondary ice production (SIP) is an important physical phenomenon that results in an increase in the cloud ice particle concentration and can have a significant impact on the evolution of clouds. Here, idealized simulations of a tropical convective system were conducted. Agreement between the simulations and observations highlights the impacts of SIP on the maintenance of tropical convection in nature and the importance of including the modelling of SIP in numerical weather prediction models.
Haochi Che, Philip Stier, Duncan Watson-Parris, Hamish Gordon, and Lucia Deaconu
Atmos. Chem. Phys., 22, 10789–10807, https://doi.org/10.5194/acp-22-10789-2022, https://doi.org/10.5194/acp-22-10789-2022, 2022
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Extensive stratocumulus clouds over the south-eastern Atlantic (SEA) can lead to a cooling effect on the climate. A key pathway by which aerosols affect cloud properties is by acting as cloud condensation nuclei (CCN). Here, we investigated the source attribution of CCN in the SEA as well as the cloud responses. Our results show that aerosol nucleation contributes most to CCN in the marine boundary layer. In terms of emissions, anthropogenic sources contribute most to the CCN and cloud droplets.
Walter Hannah, Kyle Pressel, Mikhail Ovchinnikov, and Gregory Elsaesser
Geosci. Model Dev., 15, 6243–6257, https://doi.org/10.5194/gmd-15-6243-2022, https://doi.org/10.5194/gmd-15-6243-2022, 2022
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An unphysical checkerboard signal is identified in two configurations of the atmospheric component of E3SM. The signal is very persistent and visible after averaging years of data. The signal is very difficult to study because it is often mixed with realistic weather. A method is presented to detect checkerboard patterns and compare the model with satellite observations. The causes of the signal are identified, and a solution for one configuration is discussed.
Edward Gryspeerdt, Daniel T. McCoy, Ewan Crosbie, Richard H. Moore, Graeme J. Nott, David Painemal, Jennifer Small-Griswold, Armin Sorooshian, and Luke Ziemba
Atmos. Meas. Tech., 15, 3875–3892, https://doi.org/10.5194/amt-15-3875-2022, https://doi.org/10.5194/amt-15-3875-2022, 2022
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Droplet number concentration is a key property of clouds, influencing a variety of cloud processes. It is also used for estimating the cloud response to aerosols. The satellite retrieval depends on a number of assumptions – different sampling strategies are used to select cases where these assumptions are most likely to hold. Here we investigate the impact of these strategies on the agreement with in situ data, the droplet number climatology and estimates of the indirect radiative forcing.
Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay Manish Damani, Xinyi Dong, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kühn, Joakim Langner, Kathy S. Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A. Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N. Saunders, Julia Schmale, Sangeeta Sharma, Ragnhild Bieltvedt Skeie, Henrik Skov, Fumikazu Taketani, Manu A. Thomas, Rita Traversi, Kostas Tsigaridis, Svetlana Tsyro, Steven Turnock, Vito Vitale, Kaley A. Walker, Minqi Wang, Duncan Watson-Parris, and Tahya Weiss-Gibbons
Atmos. Chem. Phys., 22, 5775–5828, https://doi.org/10.5194/acp-22-5775-2022, https://doi.org/10.5194/acp-22-5775-2022, 2022
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Air pollutants, like ozone and soot, play a role in both global warming and air quality. Atmospheric models are often used to provide information to policy makers about current and future conditions under different emissions scenarios. In order to have confidence in those simulations, in this study we compare simulated air pollution from 18 state-of-the-art atmospheric models to measured air pollution in order to assess how well the models perform.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Yongjie Huang, Wei Wu, Greg M. McFarquhar, Ming Xue, Hugh Morrison, Jason Milbrandt, Alexei V. Korolev, Yachao Hu, Zhipeng Qu, Mengistu Wolde, Cuong Nguyen, Alfons Schwarzenboeck, and Ivan Heckman
Atmos. Chem. Phys., 22, 2365–2384, https://doi.org/10.5194/acp-22-2365-2022, https://doi.org/10.5194/acp-22-2365-2022, 2022
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Numerous small ice crystals in tropical convective storms are difficult to detect and could be potentially hazardous for commercial aircraft. Previous numerical simulations failed to reproduce this phenomenon and hypothesized that key microphysical processes are still lacking in current models to realistically simulate the phenomenon. This study uses numerical experiments to confirm the dominant role of secondary ice production in the formation of these large numbers of small ice crystals.
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.
Matthew W. Christensen, Andrew Gettelman, Jan Cermak, Guy Dagan, Michael Diamond, Alyson Douglas, Graham Feingold, Franziska Glassmeier, Tom Goren, Daniel P. Grosvenor, Edward Gryspeerdt, Ralph Kahn, Zhanqing Li, Po-Lun Ma, Florent Malavelle, Isabel L. McCoy, Daniel T. McCoy, Greg McFarquhar, Johannes Mülmenstädt, Sandip Pal, Anna Possner, Adam Povey, Johannes Quaas, Daniel Rosenfeld, Anja Schmidt, Roland Schrödner, Armin Sorooshian, Philip Stier, Velle Toll, Duncan Watson-Parris, Robert Wood, Mingxi Yang, and Tianle Yuan
Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, https://doi.org/10.5194/acp-22-641-2022, 2022
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Trace gases and aerosols (tiny airborne particles) are released from a variety of point sources around the globe. Examples include volcanoes, industrial chimneys, forest fires, and ship stacks. These sources provide opportunistic experiments with which to quantify the role of aerosols in modifying cloud properties. We review the current state of understanding on the influence of aerosol on climate built from the wide range of natural and anthropogenic laboratories investigated in recent decades.
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
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The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Maria Sand, Bjørn H. Samset, Gunnar Myhre, Jonas Gliß, Susanne E. Bauer, Huisheng Bian, Mian Chin, Ramiro Checa-Garcia, Paul Ginoux, Zak Kipling, Alf Kirkevåg, Harri Kokkola, Philippe Le Sager, Marianne T. Lund, Hitoshi Matsui, Twan van Noije, Dirk J. L. Olivié, Samuel Remy, Michael Schulz, Philip Stier, Camilla W. Stjern, Toshihiko Takemura, Kostas Tsigaridis, Svetlana G. Tsyro, and Duncan Watson-Parris
Atmos. Chem. Phys., 21, 15929–15947, https://doi.org/10.5194/acp-21-15929-2021, https://doi.org/10.5194/acp-21-15929-2021, 2021
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Absorption of shortwave radiation by aerosols can modify precipitation and clouds but is poorly constrained in models. A total of 15 different aerosol models from AeroCom phase III have reported total aerosol absorption, and for the first time, 11 of these models have reported in a consistent experiment the contributions to absorption from black carbon, dust, and organic aerosol. Here, we document the model diversity in aerosol absorption.
Wojciech W. Grabowski and Hugh Morrison
Atmos. Chem. Phys., 21, 13997–14018, https://doi.org/10.5194/acp-21-13997-2021, https://doi.org/10.5194/acp-21-13997-2021, 2021
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The paper provides a discussion of key elements of moist convective dynamics: cloud buoyancy, latent heating, precipitation, and entrainment. The motivation comes from recent discussions concerning differences in convective dynamics in polluted and pristine environments.
Trude Eidhammer, Adam Booth, Sven Decker, Lu Li, Michael Barlage, David Gochis, Roy Rasmussen, Kjetil Melvold, Atle Nesje, and Stefan Sobolowski
Hydrol. Earth Syst. Sci., 25, 4275–4297, https://doi.org/10.5194/hess-25-4275-2021, https://doi.org/10.5194/hess-25-4275-2021, 2021
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We coupled a detailed snow–ice model (Crocus) to represent glaciers in the Weather Research and Forecasting (WRF)-Hydro model and tested it on a well-studied glacier. Several observational systems were used to evaluate the system, i.e., satellites, ground-penetrating radar (used over the glacier for snow depth) and stake observations for glacier mass balance and discharge measurements in rivers from the glacier. Results showed improvements in the streamflow projections when including the model.
Shipeng Zhang, Philip Stier, and Duncan Watson-Parris
Atmos. Chem. Phys., 21, 10179–10197, https://doi.org/10.5194/acp-21-10179-2021, https://doi.org/10.5194/acp-21-10179-2021, 2021
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The relationship between aerosol-induced changes in atmospheric energetics and precipitation responses across different scales is studied in terms of fast (radiatively or microphysically mediated) and slow (temperature-mediated) responses. We introduced a method to decompose rainfall changes into contributions from clouds, aerosols, and clear–clean sky from an energetic perspective. It provides a way to better interpret and quantify the precipitation changes caused by aerosol perturbations.
Andrew Gettelman, Chieh-Chieh Chen, and Charles G. Bardeen
Atmos. Chem. Phys., 21, 9405–9416, https://doi.org/10.5194/acp-21-9405-2021, https://doi.org/10.5194/acp-21-9405-2021, 2021
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The COVID-19 pandemic caused significant economic disruption in 2020 and severely impacted air traffic. We use a climate model to evaluate the effect of the reductions in aviation on climate in 2020. Contrails, in general, warm the planet, and COVID-19-related reductions in contrails cooled the land surface in 2020. The timing of reductions in aviation was important, and this may change how we think about the future effects of contrails.
Yongjie Huang, Wei Wu, Greg M. McFarquhar, Xuguang Wang, Hugh Morrison, Alexander Ryzhkov, Yachao Hu, Mengistu Wolde, Cuong Nguyen, Alfons Schwarzenboeck, Jason Milbrandt, Alexei V. Korolev, and Ivan Heckman
Atmos. Chem. Phys., 21, 6919–6944, https://doi.org/10.5194/acp-21-6919-2021, https://doi.org/10.5194/acp-21-6919-2021, 2021
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Numerous small ice crystals in the tropical convective storms are difficult to detect and could be potentially hazardous for commercial aircraft. This study evaluated the numerical models against the airborne observations and investigated the potential cloud processes that could lead to the production of these large numbers of small ice crystals. It is found that key microphysical processes are still lacking or misrepresented in current numerical models to realistically simulate the phenomenon.
Jim M. Haywood, Steven J. Abel, Paul A. Barrett, Nicolas Bellouin, Alan Blyth, Keith N. Bower, Melissa Brooks, Ken Carslaw, Haochi Che, Hugh Coe, Michael I. Cotterell, Ian Crawford, Zhiqiang Cui, Nicholas Davies, Beth Dingley, Paul Field, Paola Formenti, Hamish Gordon, Martin de Graaf, Ross Herbert, Ben Johnson, Anthony C. Jones, Justin M. Langridge, Florent Malavelle, Daniel G. Partridge, Fanny Peers, Jens Redemann, Philip Stier, Kate Szpek, Jonathan W. Taylor, Duncan Watson-Parris, Robert Wood, Huihui Wu, and Paquita Zuidema
Atmos. Chem. Phys., 21, 1049–1084, https://doi.org/10.5194/acp-21-1049-2021, https://doi.org/10.5194/acp-21-1049-2021, 2021
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Every year, the seasonal cycle of biomass burning from agricultural practices in Africa creates a huge plume of smoke that travels many thousands of kilometres over the Atlantic Ocean. This study provides an overview of a measurement campaign called the cloud–aerosol–radiation interaction and forcing for year 2017 (CLARIFY-2017) and documents the rationale, deployment strategy, observations, and key results from the campaign which utilized the heavily equipped FAAM atmospheric research aircraft.
Georgia Sotiropoulou, Étienne Vignon, Gillian Young, Hugh Morrison, Sebastian J. O'Shea, Thomas Lachlan-Cope, Alexis Berne, and Athanasios Nenes
Atmos. Chem. Phys., 21, 755–771, https://doi.org/10.5194/acp-21-755-2021, https://doi.org/10.5194/acp-21-755-2021, 2021
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Summer clouds have a significant impact on the radiation budget of the Antarctic surface and thus on ice-shelf melting. However, these are poorly represented in climate models due to errors in their microphysical structure, including the number of ice crystals that they contain. We show that breakup from ice particle collisions can substantially magnify the ice crystal number concentration with significant implications for surface radiation. This process is currently missing in climate models.
Haochi Che, Philip Stier, Hamish Gordon, Duncan Watson-Parris, and Lucia Deaconu
Atmos. Chem. Phys., 21, 17–33, https://doi.org/10.5194/acp-21-17-2021, https://doi.org/10.5194/acp-21-17-2021, 2021
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The south-eastern Atlantic is semi-permanently covered by some of the largest stratocumulus clouds and is influenced by one-third of the biomass burning emissions from African fires. A UKEMS1 model simulation shows that the absorption effect of biomass burning aerosols is the most significant on clouds and radiation. The dominate cooling and rapid adjustments induced by the radiative effects of biomass burning aerosols result in an overall cooling in the south-eastern Atlantic.
Daniel T. McCoy, Paul Field, Hamish Gordon, Gregory S. Elsaesser, and Daniel P. Grosvenor
Atmos. Chem. Phys., 20, 4085–4103, https://doi.org/10.5194/acp-20-4085-2020, https://doi.org/10.5194/acp-20-4085-2020, 2020
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Incomplete understanding of how aerosol affects clouds degrades our ability to predict future climate. In particular, it is unclear how aerosol affects the lifetime of clouds. Does it increase or decrease it? This confusion is partially because causality flows from aerosol to clouds and clouds to aerosol, and it is hard to tell what is happening in observations. Here, we use simulations to tell us about how clouds affect aerosol and use this to interpret observations, showing increased lifetime.
Edward Gryspeerdt, Johannes Mülmenstädt, Andrew Gettelman, Florent F. Malavelle, Hugh Morrison, David Neubauer, Daniel G. Partridge, Philip Stier, Toshihiko Takemura, Hailong Wang, Minghuai Wang, and Kai Zhang
Atmos. Chem. Phys., 20, 613–623, https://doi.org/10.5194/acp-20-613-2020, https://doi.org/10.5194/acp-20-613-2020, 2020
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Aerosol radiative forcing is a key uncertainty in our understanding of the human forcing of the climate, with much of this uncertainty coming from aerosol impacts on clouds. Observation-based estimates of the radiative forcing are typically smaller than those from global models, but it is not clear if they are more reliable. This work shows how the forcing components in global climate models can be identified, highlighting similarities between the two methods and areas for future investigation.
Max Heikenfeld, Peter J. Marinescu, Matthew Christensen, Duncan Watson-Parris, Fabian Senf, Susan C. van den Heever, and Philip Stier
Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, https://doi.org/10.5194/gmd-12-4551-2019, 2019
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We present tobac (Tracking and Object-Based Analysis of Clouds), a newly developed framework for tracking and analysing clouds in different types of datasets. It provides a flexible new way to include the evolution of individual clouds in a wide range of analyses. It is developed as a community project to provide a common basis for the inclusion of existing tracking algorithms and the development of new analyses that involve tracking clouds and other features in geoscientific research.
Øivind Hodnebrog, Gunnar Myhre, Bjørn H. Samset, Kari Alterskjær, Timothy Andrews, Olivier Boucher, Gregory Faluvegi, Dagmar Fläschner, Piers M. Forster, Matthew Kasoar, Alf Kirkevåg, Jean-Francois Lamarque, Dirk Olivié, Thomas B. Richardson, Dilshad Shawki, Drew Shindell, Keith P. Shine, Philip Stier, Toshihiko Takemura, Apostolos Voulgarakis, and Duncan Watson-Parris
Atmos. Chem. Phys., 19, 12887–12899, https://doi.org/10.5194/acp-19-12887-2019, https://doi.org/10.5194/acp-19-12887-2019, 2019
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Different greenhouse gases (e.g. CO2) and aerosols (e.g. black carbon) impact the Earth’s water cycle differently. Here we investigate how various gases and particles impact atmospheric water vapour and its lifetime, i.e., the average number of days that water vapour stays in the atmosphere after evaporation and before precipitation. We find that this lifetime could increase substantially by the end of this century, indicating that important changes in precipitation patterns are excepted.
Duncan Watson-Parris, Nick Schutgens, Carly Reddington, Kirsty J. Pringle, Dantong Liu, James D. Allan, Hugh Coe, Ken S. Carslaw, and Philip Stier
Atmos. Chem. Phys., 19, 11765–11790, https://doi.org/10.5194/acp-19-11765-2019, https://doi.org/10.5194/acp-19-11765-2019, 2019
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The vertical distribution of aerosol in the atmosphere affects its ability to act as cloud condensation nuclei and changes the amount of sunlight it absorbs or reflects. Common global measurements of aerosol provide no information about this vertical distribution. Using a global collection of in situ aircraft measurements to compare with an aerosol–climate model (ECHAM-HAM), we explore the key processes controlling this distribution and find that wet removal plays a key role.
Lei Lin, Andrew Gettelman, Yangyang Xu, Chenglai Wu, Zhili Wang, Nan Rosenbloom, Susan C. Bates, and Wenjie Dong
Geosci. Model Dev., 12, 3773–3793, https://doi.org/10.5194/gmd-12-3773-2019, https://doi.org/10.5194/gmd-12-3773-2019, 2019
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Here we evaluate the performance of the Community Atmosphere Model version 6 (CAM6) released in 2018, with the default 1º horizontal resolution and a higher-resolution simulation (approximately 0.25º), against various precipitation observational datasets over Asia. With the prognostic treatment of precipitation processes (which is missing in CAM5) and the new microphysics module, CAM6 is able to better simulate climatological mean and extreme precipitation over Asia.
George S. Fanourgakis, Maria Kanakidou, Athanasios Nenes, Susanne E. Bauer, Tommi Bergman, Ken S. Carslaw, Alf Grini, Douglas S. Hamilton, Jill S. Johnson, Vlassis A. Karydis, Alf Kirkevåg, John K. Kodros, Ulrike Lohmann, Gan Luo, Risto Makkonen, Hitoshi Matsui, David Neubauer, Jeffrey R. Pierce, Julia Schmale, Philip Stier, Kostas Tsigaridis, Twan van Noije, Hailong Wang, Duncan Watson-Parris, Daniel M. Westervelt, Yang Yang, Masaru Yoshioka, Nikos Daskalakis, Stefano Decesari, Martin Gysel-Beer, Nikos Kalivitis, Xiaohong Liu, Natalie M. Mahowald, Stelios Myriokefalitakis, Roland Schrödner, Maria Sfakianaki, Alexandra P. Tsimpidi, Mingxuan Wu, and Fangqun Yu
Atmos. Chem. Phys., 19, 8591–8617, https://doi.org/10.5194/acp-19-8591-2019, https://doi.org/10.5194/acp-19-8591-2019, 2019
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Effects of aerosols on clouds are important for climate studies but are among the largest uncertainties in climate projections. This study evaluates the skill of global models to simulate aerosol, cloud condensation nuclei (CCN) and cloud droplet number concentrations (CDNCs). Model results show reduced spread in CDNC compared to CCN due to the negative correlation between the sensitivities of CDNC to aerosol number concentration (air pollution) and updraft velocity (atmospheric dynamics).
Ann M. Fridlind, Marcus van Lier-Walqui, Scott Collis, Scott E. Giangrande, Robert C. Jackson, Xiaowen Li, Toshihisa Matsui, Richard Orville, Mark H. Picel, Daniel Rosenfeld, Alexander Ryzhkov, Richard Weitz, and Pengfei Zhang
Atmos. Meas. Tech., 12, 2979–3000, https://doi.org/10.5194/amt-12-2979-2019, https://doi.org/10.5194/amt-12-2979-2019, 2019
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Weather radars are offering improved capabilities to investigate storm physics, which remain poorly understood. We investigate enhanced use of such data near Houston, Texas, where pollution sources often provide a convenient contrast between polluted and clean air. We conclude that Houston is a favorable location to conduct a future field campaign during June through September because isolated storms are common and tend to last an hour, allowing frequent observations of a full life cycle.
Ina Tegen, David Neubauer, Sylvaine Ferrachat, Colombe Siegenthaler-Le Drian, Isabelle Bey, Nick Schutgens, Philip Stier, Duncan Watson-Parris, Tanja Stanelle, Hauke Schmidt, Sebastian Rast, Harri Kokkola, Martin Schultz, Sabine Schroeder, Nikos Daskalakis, Stefan Barthel, Bernd Heinold, and Ulrike Lohmann
Geosci. Model Dev., 12, 1643–1677, https://doi.org/10.5194/gmd-12-1643-2019, https://doi.org/10.5194/gmd-12-1643-2019, 2019
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We describe a new version of the aerosol–climate model ECHAM–HAM and show tests of the model performance by comparing different aspects of the aerosol distribution with different datasets. The updated version of HAM contains improved descriptions of aerosol processes, including updated emission fields and cloud processes. While there are regional deviations between the model and observations, the model performs well overall.
Edward Gryspeerdt, Tom Goren, Odran Sourdeval, Johannes Quaas, Johannes Mülmenstädt, Sudhakar Dipu, Claudia Unglaub, Andrew Gettelman, and Matthew Christensen
Atmos. Chem. Phys., 19, 5331–5347, https://doi.org/10.5194/acp-19-5331-2019, https://doi.org/10.5194/acp-19-5331-2019, 2019
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The liquid water path (LWP) is the strongest control on cloud albedo, such that a small change in LWP can have a large radiative impact. By changing the droplet number concentration (Nd) aerosols may be able to change the LWP, but the sign and magnitude of the effect is unclear. This work uses satellite data to investigate the relationship between Nd and LWP at a global scale and in response to large aerosol perturbations, suggesting that a strong decrease in LWP at high Nd may be overestimated.
Joseph A. Finlon, Greg M. McFarquhar, Stephen W. Nesbitt, Robert M. Rauber, Hugh Morrison, Wei Wu, and Pengfei Zhang
Atmos. Chem. Phys., 19, 3621–3643, https://doi.org/10.5194/acp-19-3621-2019, https://doi.org/10.5194/acp-19-3621-2019, 2019
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A new approach describing the relationship between ice crystal mass (m) and dimension (D) is derived, characterizing it as a set of
equally realizableparameters based on the natural variability in cloud conditions observed by aircraft over the Great Plains. Results from this approach address shortcomings of microphysical parameterization schemes and remote sensing retrievals that employ a single m–D relation for a given ice species or environment.
Grégory Cesana, Anthony D. Del Genio, Andrew S. Ackerman, Maxwell Kelley, Gregory Elsaesser, Ann M. Fridlind, Ye Cheng, and Mao-Sung Yao
Atmos. Chem. Phys., 19, 2813–2832, https://doi.org/10.5194/acp-19-2813-2019, https://doi.org/10.5194/acp-19-2813-2019, 2019
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The response of low clouds to climate change (i.e., cloud feedbacks) is still pointed out as being the largest source of uncertainty in climate models. Here we use CALIPSO observations to discriminate climate models that reproduce observed interannual change of cloud fraction with SST forcings, referred to as a present-day cloud feedback. Modeling moist processes in the planetary boundary layer is crucial to produce large stratocumulus decks and realistic present-day cloud feedbacks.
Daniel T. McCoy, Paul R. Field, Gregory S. Elsaesser, Alejandro Bodas-Salcedo, Brian H. Kahn, Mark D. Zelinka, Chihiro Kodama, Thorsten Mauritsen, Benoit Vanniere, Malcolm Roberts, Pier L. Vidale, David Saint-Martin, Aurore Voldoire, Rein Haarsma, Adrian Hill, Ben Shipway, and Jonathan Wilkinson
Atmos. Chem. Phys., 19, 1147–1172, https://doi.org/10.5194/acp-19-1147-2019, https://doi.org/10.5194/acp-19-1147-2019, 2019
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The largest single source of uncertainty in the climate sensitivity predicted by global climate models is how much low-altitude clouds change as the climate warms. Models predict that the amount of liquid within and the brightness of low-altitude clouds increase in the extratropics with warming. We show that increased fluxes of moisture into extratropical storms in the midlatitudes explain the majority of the observed trend and the modeled increase in liquid water within these storms.
Daniel T. McCoy, Paul R. Field, Anja Schmidt, Daniel P. Grosvenor, Frida A.-M. Bender, Ben J. Shipway, Adrian A. Hill, Jonathan M. Wilkinson, and Gregory S. Elsaesser
Atmos. Chem. Phys., 18, 5821–5846, https://doi.org/10.5194/acp-18-5821-2018, https://doi.org/10.5194/acp-18-5821-2018, 2018
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Here we use a combination of global convection-permitting models, satellite observations and the Holuhraun volcanic eruption to demonstrate that aerosol enhances the cloud liquid content and brightness of midlatitude cyclones. This is important because the strength of anthropogenic radiative forcing is uncertain, leading to uncertainty in the climate sensitivity consistent with observed temperature record.
Daniel T. McCoy, Frida A.-M. Bender, Daniel P. Grosvenor, Johannes K. Mohrmann, Dennis L. Hartmann, Robert Wood, and Paul R. Field
Atmos. Chem. Phys., 18, 2035–2047, https://doi.org/10.5194/acp-18-2035-2018, https://doi.org/10.5194/acp-18-2035-2018, 2018
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The interaction between clouds and aerosols represents the largest source of uncertainty in the anthropogenic radiative forcing. Cloud droplet number concentration (CDNC) is the state variable that moderates the interaction between aerosol and clouds. Here we show that CDNC decreases off the coasts of East Asia and North America due to controls on emissions. We support this analysis through an examination of volcanism in Hawaii and Vanuatu.
Peter A. Bogenschutz, Andrew Gettelman, Cecile Hannay, Vincent E. Larson, Richard B. Neale, Cheryl Craig, and Chih-Chieh Chen
Geosci. Model Dev., 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018, https://doi.org/10.5194/gmd-11-235-2018, 2018
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This paper compares results of developmental versions of a widely used climate model. The simulations only differ in the choice of how to model the sub-grid-scale physics in the atmospheric model. This work is novel because it is the first time that a particular physics option has been tested in a fully coupled climate model. Here, we demonstrate that this physics option has the ability to produce credible coupled climate simulations, with improved metrics in certain fields.
Bethan White, Edward Gryspeerdt, Philip Stier, Hugh Morrison, Gregory Thompson, and Zak Kipling
Atmos. Chem. Phys., 17, 12145–12175, https://doi.org/10.5194/acp-17-12145-2017, https://doi.org/10.5194/acp-17-12145-2017, 2017
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Aerosols influence cloud and precipitation by modifying cloud droplet number concentrations (CDNCs). We simulate three different types of convective cloud using two different cloud microphysics parameterisations. The simulated cloud and precipitation depends much more strongly on the choice of microphysics scheme than on CDNC. The uncertainty differs between types of convection. Our results highlight a large uncertainty in cloud and precipitation responses to aerosol in current models.
Gavin A. Schmidt, David Bader, Leo J. Donner, Gregory S. Elsaesser, Jean-Christophe Golaz, Cecile Hannay, Andrea Molod, Richard B. Neale, and Suranjana Saha
Geosci. Model Dev., 10, 3207–3223, https://doi.org/10.5194/gmd-10-3207-2017, https://doi.org/10.5194/gmd-10-3207-2017, 2017
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The development of coupled ocean atmosphere climate models is a complex process that inevitably includes multiple calibration steps (sometimes called
tuning). Tuning uses degrees of freedom allowed by uncertainties in model approximations to modify parameters to make the simulation better align with some selected observed target(s). We describe how these tuning targets, parameters, and philosophy vary across six US modeling centers in order to increase the transparency of the practice.
Ann M. Fridlind, Xiaowen Li, Di Wu, Marcus van Lier-Walqui, Andrew S. Ackerman, Wei-Kuo Tao, Greg M. McFarquhar, Wei Wu, Xiquan Dong, Jingyu Wang, Alexander Ryzhkov, Pengfei Zhang, Michael R. Poellot, Andrea Neumann, and Jason M. Tomlinson
Atmos. Chem. Phys., 17, 5947–5972, https://doi.org/10.5194/acp-17-5947-2017, https://doi.org/10.5194/acp-17-5947-2017, 2017
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Understanding observed storm microphysics via computer simulation requires measurements of aerosol on which most hydrometeors form. We prepare aerosol input data for six storms observed over Oklahoma. We demonstrate their use in simulations of a case with widespread ice outflow well sampled by aircraft. Simulations predict too few ice crystals that are too large. We speculate that microphysics found in tropical storms occurred here, likely associated with poorly understood ice multiplication.
Chenglai Wu, Xiaohong Liu, Minghui Diao, Kai Zhang, Andrew Gettelman, Zheng Lu, Joyce E. Penner, and Zhaohui Lin
Atmos. Chem. Phys., 17, 4731–4749, https://doi.org/10.5194/acp-17-4731-2017, https://doi.org/10.5194/acp-17-4731-2017, 2017
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This study utilizes a novel approach to directly compare the CAM5-simulated cloud macro- and microphysics with the collocated HIPPO observations for the period of 2009 to 2011. The model cannot capture the large spatial variabilities of observed RH, which is responsible for much of the model missing low-level warm clouds. A large portion of the RH bias results from the discrepancy in water vapor. The model underestimates the observed number concentration and ice water content.
Andrew Gettelman, Chih-Chieh Chen, Mark Z. Jacobson, Mary A. Cameron, Donald J. Wuebbles, and Arezoo Khodayari
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-218, https://doi.org/10.5194/acp-2017-218, 2017
Revised manuscript not accepted
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Aviation emissions create several impacts on climate. Condensation trails (contrails) are aviation produced cirrus clouds. Aircraft also emit aerosols, including soot (black carbon) and sulfate. Analyses of the climate effects of 2050 aviation emissions have been conducted with two coupled Chemistry Climate Models (CCMs) including experiments with coupled ocean models.
Duncan Watson-Parris, Nick Schutgens, Nicholas Cook, Zak Kipling, Philip Kershaw, Edward Gryspeerdt, Bryan Lawrence, and Philip Stier
Geosci. Model Dev., 9, 3093–3110, https://doi.org/10.5194/gmd-9-3093-2016, https://doi.org/10.5194/gmd-9-3093-2016, 2016
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In this paper we describe CIS, a new command line tool for the easy visualization, analysis and comparison of a wide variety of gridded and ungridded data sets used in Earth sciences. Users can now use a single tool to not only view plots of satellite, aircraft, station or model data, but also bring them onto the same spatio-temporal sampling. This allows robust, quantitative comparisons to be made easily. CIS is an open-source project and welcomes input from the community.
Chih-Chieh Chen and Andrew Gettelman
Atmos. Chem. Phys., 16, 7317–7333, https://doi.org/10.5194/acp-16-7317-2016, https://doi.org/10.5194/acp-16-7317-2016, 2016
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The impact of aviation emissions through 2050 is simulated by a comprehensive global climate model. Four different future emission scenarios of the same flight tracks are considered. The results reveal that the global radiative forcing of contrail cirrus is positive and can increase by a factor of 7 in 2050 from the 2006 level. The aviation aerosols can produce negative forcing, mainly over the oceans, and increase by a factor of 4 in 2050 from the 2006 level.
Shipeng Zhang, Minghuai Wang, Steven J. Ghan, Aijun Ding, Hailong Wang, Kai Zhang, David Neubauer, Ulrike Lohmann, Sylvaine Ferrachat, Toshihiko Takeamura, Andrew Gettelman, Hugh Morrison, Yunha Lee, Drew T. Shindell, Daniel G. Partridge, Philip Stier, Zak Kipling, and Congbin Fu
Atmos. Chem. Phys., 16, 2765–2783, https://doi.org/10.5194/acp-16-2765-2016, https://doi.org/10.5194/acp-16-2765-2016, 2016
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The variation of aerosol indirect effects (AIE) in several climate models is investigated across different dynamical regimes. Regimes with strong large-scale ascent are shown to be as important as stratocumulus regimes in studying AIE. AIE over regions with high monthly large-scale surface precipitation rate contributes the most to the total aerosol indirect forcing. These results point to the need to reduce the uncertainty in AIE in different dynamical regimes.
K. Thayer-Calder, A. Gettelman, C. Craig, S. Goldhaber, P. A. Bogenschutz, C.-C. Chen, H. Morrison, J. Höft, E. Raut, B. M. Griffin, J. K. Weber, V. E. Larson, M. C. Wyant, M. Wang, Z. Guo, and S. J. Ghan
Geosci. Model Dev., 8, 3801–3821, https://doi.org/10.5194/gmd-8-3801-2015, https://doi.org/10.5194/gmd-8-3801-2015, 2015
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This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that is implemented in CAM v5.3. We show mean climate and tropical variability results from global simulations. The model has a degradation in precipitation skill but improvements in shortwave cloud forcing, liquid water path, long-wave cloud forcing, precipitable water, and tropical wave simulation. We also show estimation of computational expense and sensitivity to number of subcolumns.
A. Gettelman
Atmos. Chem. Phys., 15, 12397–12411, https://doi.org/10.5194/acp-15-12397-2015, https://doi.org/10.5194/acp-15-12397-2015, 2015
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Aerosols affect cloud properties, and the radiative effects of clouds. Human emissions of aerosol particles and precursors may alter the radiative effects of clouds. This is generally a cooling effect that offsets other warming effects of human emissions of gases. Simulating these aerosol effects on clouds are highly dependent on the formulation of the microphysical (cloud droplet scale) processes. This work uses model simulations to show these effects are large, and depend on certain processes.
T. Eidhammer, H. Morrison, A. Bansemer, A. Gettelman, and A. J. Heymsfield
Atmos. Chem. Phys., 14, 10103–10118, https://doi.org/10.5194/acp-14-10103-2014, https://doi.org/10.5194/acp-14-10103-2014, 2014
M. S. Johnston, S. Eliasson, P. Eriksson, R. M. Forbes, A. Gettelman, P. Räisänen, and M. D. Zelinka
Atmos. Chem. Phys., 14, 8701–8721, https://doi.org/10.5194/acp-14-8701-2014, https://doi.org/10.5194/acp-14-8701-2014, 2014
D. Barahona, A. Molod, J. Bacmeister, A. Nenes, A. Gettelman, H. Morrison, V. Phillips, and A. Eichmann
Geosci. Model Dev., 7, 1733–1766, https://doi.org/10.5194/gmd-7-1733-2014, https://doi.org/10.5194/gmd-7-1733-2014, 2014
C.-C. Chen and A. Gettelman
Atmos. Chem. Phys., 13, 12525–12536, https://doi.org/10.5194/acp-13-12525-2013, https://doi.org/10.5194/acp-13-12525-2013, 2013
A. Gettelman, H. Morrison, C. R. Terai, and R. Wood
Atmos. Chem. Phys., 13, 9855–9867, https://doi.org/10.5194/acp-13-9855-2013, https://doi.org/10.5194/acp-13-9855-2013, 2013
Related subject area
Climate and Earth system modeling
The need for carbon-emissions-driven climate projections in CMIP7
Robust handling of extremes in quantile mapping – “Murder your darlings”
A protocol for model intercomparison of impacts of marine cloud brightening climate intervention
Coupling the regional climate model ICON-CLM v2.6.6 to the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Methane dynamics in the Baltic Sea: investigating concentration, flux, and isotopic composition patterns using the coupled physical–biogeochemical model BALTSEM-CH4 v1.0
Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes
CICE on a C-grid: new momentum, stress, and transport schemes for CICEv6.5
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Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
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An urban module coupled with the Variable Infiltration Capacity model to improve hydrothermal simulations in urban systems
Bayesian hierarchical model for bias-correcting climate models
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Reduced floating-point precision in regional climate simulations: an ensemble-based statistical verification
TorchClim v1.0: a deep-learning plugin for climate model physics
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Validating a microphysical prognostic stratospheric aerosol implementation in E3SMv2 using observations after the Mount Pinatubo eruption
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Hector V3.2.0: functionality and performance of a reduced-complexity climate model
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Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0
A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)
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Software sustainability of global impact models
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CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea
Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel)
GOSI9: UK Global Ocean and Sea Ice configurations
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Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
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Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
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When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
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We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
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The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
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We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
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In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
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We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
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In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
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This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
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Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
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In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
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Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
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This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
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We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
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This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
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The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
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A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
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We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
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A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
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Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a test bed for climate attribution studies.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
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Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Matthias Nützel, Laura Stecher, Patrick Jöckel, Franziska Winterstein, Martin Dameris, Michael Ponater, Phoebe Graf, and Markus Kunze
Geosci. Model Dev., 17, 5821–5849, https://doi.org/10.5194/gmd-17-5821-2024, https://doi.org/10.5194/gmd-17-5821-2024, 2024
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We extended the infrastructure of our modelling system to enable the use of an additional radiation scheme. After calibrating the model setups to the old and the new radiation scheme, we find that the simulation with the new scheme shows considerable improvements, e.g. concerning the cold-point temperature and stratospheric water vapour. Furthermore, perturbations of radiative fluxes associated with greenhouse gas changes, e.g. of methane, tend to be improved when the new scheme is employed.
Yibing Wang, Xianhong Xie, Bowen Zhu, Arken Tursun, Fuxiao Jiang, Yao Liu, Dawei Peng, and Buyun Zheng
Geosci. Model Dev., 17, 5803–5819, https://doi.org/10.5194/gmd-17-5803-2024, https://doi.org/10.5194/gmd-17-5803-2024, 2024
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Urban expansion intensifies challenges like urban heat and urban dry islands. To address this, we developed an urban module, VIC-urban, in the Variable Infiltration Capacity (VIC) model. Tested in Beijing, VIC-urban accurately simulated turbulent heat fluxes, runoff, and land surface temperature. We provide a reliable tool for large-scale simulations considering urban environment and a systematic urban modelling framework within VIC, offering crucial insights for urban planners and designers.
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024, https://doi.org/10.5194/gmd-17-5733-2024, 2024
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Climate models are essential tools in the study of climate change and its wide-ranging impacts on life on Earth. However, the output is often afflicted with some bias. In this paper, a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision-making.
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024, https://doi.org/10.5194/gmd-17-5705-2024, 2024
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The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed using different datasets, the coupled model shows strong correlations of key variables, such as land surface temperature, surface albedo and radiation flux. The versatility of the model increases significantly, while the overall performance does not degrade.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
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We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett
Geosci. Model Dev., 17, 5459–5475, https://doi.org/10.5194/gmd-17-5459-2024, https://doi.org/10.5194/gmd-17-5459-2024, 2024
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Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.
Minjin Lee, Charles A. Stock, John P. Dunne, and Elena Shevliakova
Geosci. Model Dev., 17, 5191–5224, https://doi.org/10.5194/gmd-17-5191-2024, https://doi.org/10.5194/gmd-17-5191-2024, 2024
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Modeling global freshwater solid and nutrient loads, in both magnitude and form, is imperative for understanding emerging eutrophication problems. Such efforts, however, have been challenged by the difficulty of balancing details of freshwater biogeochemical processes with limited knowledge, input, and validation datasets. Here we develop a global freshwater model that resolves intertwined algae, solid, and nutrient dynamics and provide performance assessment against measurement-based estimates.
Hunter York Brown, Benjamin Wagman, Diana Bull, Kara Peterson, Benjamin Hillman, Xiaohong Liu, Ziming Ke, and Lin Lin
Geosci. Model Dev., 17, 5087–5121, https://doi.org/10.5194/gmd-17-5087-2024, https://doi.org/10.5194/gmd-17-5087-2024, 2024
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Explosive volcanic eruptions lead to long-lived, microscopic particles in the upper atmosphere which act to cool the Earth's surface by reflecting the Sun's light back to space. We include and test this process in a global climate model, E3SM. E3SM is tested against satellite and balloon observations of the 1991 eruption of Mt. Pinatubo, showing that with these particles in the model we reasonably recreate Pinatubo and its global effects. We also explore how particle size leads to these effects.
Deifilia Aurora To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
EGUsphere, https://doi.org/10.5194/egusphere-2024-1714, https://doi.org/10.5194/egusphere-2024-1714, 2024
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers three-dimensional atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20–30%. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases accessibility of training and working with the model.
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
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Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
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The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
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Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
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We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
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The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Maria Rosa Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-73, https://doi.org/10.5194/gmd-2024-73, 2024
Revised manuscript accepted for GMD
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Observational data and modelling capabilities are expanding in recent years, but there are still barriers preventing these two data sources to be used in synergy. Proper comparison requires generating, storing and handling a large amount of data. This manuscript describes the first step in the development of a new set of software tools, the ‘VISION toolkit’, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
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This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
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We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
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The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1456, https://doi.org/10.5194/egusphere-2024-1456, 2024
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We evaluate downscaled products by examining locally relevant covariances during convective and frontal precipitation events. Common statistical downscaling techniques preserve expected covariances during convective precipitation. However, they dampen future intensification of frontal precipitation captured in global climate models and dynamical downscaling. This suggests statistical downscaling may not fully resolve non-stationary hydrologic processes as compared to dynamical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-97, https://doi.org/10.5194/gmd-2024-97, 2024
Revised manuscript accepted for GMD
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Research software is crucial for scientific progress but is often developed by scientists with limited training, time, and funding, leading to software that is hard to understand, (re)use, modify, and maintain. Our study across 10 research sectors highlights strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. Recommendations include workshops, code quality metrics, funding, and adherence to FAIR standards.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-70, https://doi.org/10.5194/gmd-2024-70, 2024
Revised manuscript accepted for GMD
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Hurricanes may worsen the water quality in the lower Mississippi River Basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate-nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in LMRB during Hurricane Ida in 2021, but less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni G. Seijo-Ellis, Donata Giglio, Gustavo M. Marques, and Frank O. Bryan
EGUsphere, https://doi.org/10.5194/egusphere-2024-1378, https://doi.org/10.5194/egusphere-2024-1378, 2024
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A CESM/MOM6 regional configuration of the Caribbean Sea was developed as a response to the rising need of high-resolution models for climate impact studies. The configuration is validated for the period of 2000–2020 and improves significant errors in a low resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon river are well captured and the mean flows across the multiple passages in the Caribbean Sea agree with observations.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
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Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Catherine Guiavarc'h, Dave Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene T. Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
EGUsphere, https://doi.org/10.5194/egusphere-2024-805, https://doi.org/10.5194/egusphere-2024-805, 2024
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GOSI9 is the new UK’s hierarchy of global ocean and sea ice models. Developed as part of a collaboration between several UK research institutes it will be used for various applications such as weather forecast and climate prediction. The models, based on NEMO, are available at three resolutions 1°, ¼° and 1/12°. GOSI9 improves upon previous version by reducing global temperature and salinity biases and enhancing the representation of the Arctic sea ice and of the Antarctic Circumpolar Current.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
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To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
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This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
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We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
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Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
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Short summary
We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
We describe a dataset where 45 parameters related to cloud processes in the Community Earth...
