Articles | Volume 20, issue 4
https://doi.org/10.5194/acp-20-2303-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-2303-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
26 Feb 2020
Technical note |
| 26 Feb 2020
| 26 Feb 2020
Technical note: Deep learning for creating surrogate models of precipitation in Earth system models
Theodore Weber
Computer Science Department, Western Washington University,
Bellingham, WA, USA
Austin Corotan
Computer Science Department, Western Washington University,
Bellingham, WA, USA
Brian Hutchinson
CORRESPONDING AUTHOR
Computer Science Department, Western Washington University,
Bellingham, WA, USA
Computing and Analytics Division, Pacific Northwest
National Laboratory, Seattle, WA, USA
Ben Kravitz
Department of Earth and Atmospheric Sciences, Indiana University,
Bloomington, IN, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, WA, USA
Robert Link
Joint Global Change Research
Institute, Pacific Northwest National Laboratory, College Park, MD, USA
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Ezra Brody, Yan Zhang, Douglas G. MacMartin, Daniele Visioni, Ben Kravitz, and Ewa M. Bednarz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3974, https://doi.org/10.5194/egusphere-2024-3974, 2025
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Stratospheric aerosol injection (SAI) is being studied as a possible supplement to emission reduction to temporarily mitigate some of the risks associated with climate change. The latitudes at which SAI is done determine the effect on the climate. We try to find if there are combinations of latitudes that do a better job of counteracting climate change than existing strategies. We found that there are, but just how significant these improvements are depends on the amount of cooling.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
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This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Yan Zhang, Douglas G. MacMartin, Daniele Visioni, Ewa M. Bednarz, and Ben Kravitz
Earth Syst. Dynam., 15, 191–213, https://doi.org/10.5194/esd-15-191-2024, https://doi.org/10.5194/esd-15-191-2024, 2024
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Injecting SO2 into the lower stratosphere can temporarily reduce global mean temperature and mitigate some risks associated with climate change, but injecting it at different latitudes and seasons would have different impacts. This study introduces new stratospheric aerosol injection (SAI) strategies and explores the importance of the choice of SAI strategy, demonstrating that it notably affects the distribution of aerosol cloud, injection efficiency, and various surface climate impacts.
Ewa M. Bednarz, Amy H. Butler, Daniele Visioni, Yan Zhang, Ben Kravitz, and Douglas G. MacMartin
Atmos. Chem. Phys., 23, 13665–13684, https://doi.org/10.5194/acp-23-13665-2023, https://doi.org/10.5194/acp-23-13665-2023, 2023
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We use a state-of-the-art Earth system model and a set of stratospheric aerosol injection (SAI) strategies to achieve the same level of global mean surface cooling through different combinations of location and/or timing of the injection. We demonstrate that the choice of SAI strategy can lead to contrasting impacts on stratospheric and tropospheric temperatures, circulation, and chemistry (including stratospheric ozone), thereby leading to different impacts on regional surface climate.
Daniele Visioni, Ben Kravitz, Alan Robock, Simone Tilmes, Jim Haywood, Olivier Boucher, Mark Lawrence, Peter Irvine, Ulrike Niemeier, Lili Xia, Gabriel Chiodo, Chris Lennard, Shingo Watanabe, John C. Moore, and Helene Muri
Atmos. Chem. Phys., 23, 5149–5176, https://doi.org/10.5194/acp-23-5149-2023, https://doi.org/10.5194/acp-23-5149-2023, 2023
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Geoengineering indicates methods aiming to reduce the temperature of the planet by means of reflecting back a part of the incoming radiation before it reaches the surface or allowing more of the planetary radiation to escape into space. It aims to produce modelling experiments that are easy to reproduce and compare with different climate models, in order to understand the potential impacts of these techniques. Here we assess its past successes and failures and talk about its future.
Daniele Visioni, Ewa M. Bednarz, Walker R. Lee, Ben Kravitz, Andy Jones, Jim M. Haywood, and Douglas G. MacMartin
Atmos. Chem. Phys., 23, 663–685, https://doi.org/10.5194/acp-23-663-2023, https://doi.org/10.5194/acp-23-663-2023, 2023
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The paper constitutes Part 1 of a study performing a first systematic inter-model comparison of the atmospheric responses to stratospheric sulfate aerosol injections (SAIs) at various latitudes as simulated by three state-of-the-art Earth system models. We identify similarities and differences in the modeled aerosol burden, investigate the differences in the aerosol approaches between the models, and ultimately show the differences produced in surface climate, temperature and precipitation.
Ewa M. Bednarz, Daniele Visioni, Ben Kravitz, Andy Jones, James M. Haywood, Jadwiga Richter, Douglas G. MacMartin, and Peter Braesicke
Atmos. Chem. Phys., 23, 687–709, https://doi.org/10.5194/acp-23-687-2023, https://doi.org/10.5194/acp-23-687-2023, 2023
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Building on Part 1 of this two-part study, we demonstrate the role of biases in climatological circulation and specific aspects of model microphysics in driving the differences in simulated sulfate distributions amongst three Earth system models. We then characterize the simulated changes in stratospheric and free-tropospheric temperatures, ozone, water vapor, and large-scale circulation, elucidating the role of the above aspects in the surface responses discussed in Part 1.
Mari R. Tye, Katherine Dagon, Maria J. Molina, Jadwiga H. Richter, Daniele Visioni, Ben Kravitz, and Simone Tilmes
Earth Syst. Dynam., 13, 1233–1257, https://doi.org/10.5194/esd-13-1233-2022, https://doi.org/10.5194/esd-13-1233-2022, 2022
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We examined the potential effect of stratospheric aerosol injection (SAI) on extreme temperature and precipitation. SAI may cause daytime temperatures to cool but nighttime to warm. Daytime cooling may occur in all seasons across the globe, with the largest decreases in summer. In contrast, nighttime warming may be greatest at high latitudes in winter. SAI may reduce the frequency and intensity of extreme rainfall. The combined changes may exacerbate drying over parts of the global south.
Ilaria Quaglia, Daniele Visioni, Giovanni Pitari, and Ben Kravitz
Atmos. Chem. Phys., 22, 5757–5773, https://doi.org/10.5194/acp-22-5757-2022, https://doi.org/10.5194/acp-22-5757-2022, 2022
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Carbonyl sulfide is a gas that mixes very well in the atmosphere and can reach the stratosphere, where it reacts with sunlight and produces aerosol. Here we propose that, by increasing surface fluxes by an order of magnitude, the number of stratospheric aerosols produced may be enough to partially offset the warming produced by greenhouse gases. We explore what effect this would have on the atmospheric composition.
Huiying Ren, Erol Cromwell, Ben Kravitz, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 1727–1743, https://doi.org/10.5194/hess-26-1727-2022, https://doi.org/10.5194/hess-26-1727-2022, 2022
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We used a deep learning method called long short-term memory (LSTM) to fill gaps in data collected by hydrologic monitoring networks. LSTM accounted for correlations in space and time and nonlinear trends in data. Compared to a traditional regression-based time-series method, LSTM performed comparably when filling gaps in data with smooth patterns, while it better captured highly dynamic patterns in data. Capturing such dynamics is critical for understanding dynamic complex system behaviors.
Andy Jones, Jim M. Haywood, Adam A. Scaife, Olivier Boucher, Matthew Henry, Ben Kravitz, Thibaut Lurton, Pierre Nabat, Ulrike Niemeier, Roland Séférian, Simone Tilmes, and Daniele Visioni
Atmos. Chem. Phys., 22, 2999–3016, https://doi.org/10.5194/acp-22-2999-2022, https://doi.org/10.5194/acp-22-2999-2022, 2022
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Simulations by six Earth-system models of geoengineering by introducing sulfuric acid aerosols into the tropical stratosphere are compared. A robust impact on the northern wintertime North Atlantic Oscillation is found, exacerbating precipitation reduction over parts of southern Europe. In contrast, the models show no consistency with regard to impacts on the Quasi-Biennial Oscillation, although results do indicate a risk that the oscillation could become locked into a permanent westerly phase.
Daniele Visioni, Simone Tilmes, Charles Bardeen, Michael Mills, Douglas G. MacMartin, Ben Kravitz, and Jadwiga H. Richter
Atmos. Chem. Phys., 22, 1739–1756, https://doi.org/10.5194/acp-22-1739-2022, https://doi.org/10.5194/acp-22-1739-2022, 2022
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Aerosols are simulated in a simplified way in climate models: in the model analyzed here, they are represented in every grid as described by three simple logarithmic distributions, mixing all different species together. The size can evolve when new particles are formed, particles merge together to create a larger one or particles are deposited to the surface. This approximation normally works fairly well. Here we show however that when large amounts of sulfate are simulated, there are problems.
Yan Zhang, Douglas G. MacMartin, Daniele Visioni, and Ben Kravitz
Earth Syst. Dynam., 13, 201–217, https://doi.org/10.5194/esd-13-201-2022, https://doi.org/10.5194/esd-13-201-2022, 2022
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Adding SO2 to the stratosphere could temporarily cool the planet by reflecting more sunlight back to space. However, adding SO2 at different latitude(s) and season(s) leads to significant differences in regional surface climate. This study shows that, to cool the planet by 1–1.5 °C, there are likely six to eight choices of injection latitude(s) and season(s) that lead to meaningfully different distributions of climate impacts.
Dawn L. Woodard, Alexey N. Shiklomanov, Ben Kravitz, Corinne Hartin, and Ben Bond-Lamberty
Geosci. Model Dev., 14, 4751–4767, https://doi.org/10.5194/gmd-14-4751-2021, https://doi.org/10.5194/gmd-14-4751-2021, 2021
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We have added a representation of the permafrost carbon feedback to the simple, open-source global carbon–climate model Hector and calibrated the results to be consistent with historical data and Earth system model projections. Our results closely match previous work, estimating around 0.2 °C of warming from permafrost this century. This capability will be useful to explore uncertainties in this feedback and for coupling with integrated assessment models for policy and economic analysis.
Daniele Visioni, Douglas G. MacMartin, Ben Kravitz, Olivier Boucher, Andy Jones, Thibaut Lurton, Michou Martine, Michael J. Mills, Pierre Nabat, Ulrike Niemeier, Roland Séférian, and Simone Tilmes
Atmos. Chem. Phys., 21, 10039–10063, https://doi.org/10.5194/acp-21-10039-2021, https://doi.org/10.5194/acp-21-10039-2021, 2021
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A new set of simulations is used to investigate commonalities, differences and sources of uncertainty when simulating the injection of SO2 in the stratosphere in order to mitigate the effects of climate change (solar geoengineering). The models differ in how they simulate the aerosols and how they spread around the stratosphere, resulting in differences in projected regional impacts. Overall, however, the models agree that aerosols have the potential to mitigate the warming produced by GHGs.
Nikolas O. Aksamit, Ben Kravitz, Douglas G. MacMartin, and George Haller
Atmos. Chem. Phys., 21, 8845–8861, https://doi.org/10.5194/acp-21-8845-2021, https://doi.org/10.5194/acp-21-8845-2021, 2021
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There exist robust and influential material features evolving within turbulent fluids that behave as the skeleton for fluid transport pathways. Recent developments in applied mathematics have made the identification of these time-varying structures more rigorous and insightful than ever. Using short-range wind forecasts, we detail how and why these material features can be exploited in an effort to optimize the spread of aerosols in the stratosphere for climate geoengineering.
Ben Kravitz, Douglas G. MacMartin, Daniele Visioni, Olivier Boucher, Jason N. S. Cole, Jim Haywood, Andy Jones, Thibaut Lurton, Pierre Nabat, Ulrike Niemeier, Alan Robock, Roland Séférian, and Simone Tilmes
Atmos. Chem. Phys., 21, 4231–4247, https://doi.org/10.5194/acp-21-4231-2021, https://doi.org/10.5194/acp-21-4231-2021, 2021
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This study investigates multi-model response to idealized geoengineering (high CO2 with solar reduction) across two different generations of climate models. We find that, with the exception of a few cases, the results are unchanged between the different generations. This gives us confidence that broad conclusions about the response to idealized geoengineering are robust.
Andy Jones, Jim M. Haywood, Anthony C. Jones, Simone Tilmes, Ben Kravitz, and Alan Robock
Atmos. Chem. Phys., 21, 1287–1304, https://doi.org/10.5194/acp-21-1287-2021, https://doi.org/10.5194/acp-21-1287-2021, 2021
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Two different methods of simulating a geoengineering scenario are compared using data from two different Earth system models. One method is very idealised while the other includes details of a plausible mechanism. The results from both models agree that the idealised approach does not capture an impact found when detailed modelling is included, namely that geoengineering induces a positive phase of the North Atlantic Oscillation which leads to warmer, wetter winters in northern Europe.
Walker Lee, Douglas MacMartin, Daniele Visioni, and Ben Kravitz
Earth Syst. Dynam., 11, 1051–1072, https://doi.org/10.5194/esd-11-1051-2020, https://doi.org/10.5194/esd-11-1051-2020, 2020
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The injection of aerosols into the stratosphere to reflect sunlight could reduce global warming, but this type of
geoengineeringwould also impact other variables like precipitation and sea ice. In this study, we model various climate impacts of geoengineering on a 3-D graph to show how trying to meet one climate goal will affect other variables. We also present two computer simulations which validate our model and show that geoengineering could regulate precipitation as well as temperature.
Bethany Sutherland, Ben Kravitz, Philip J. Rasch, and Hailong Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-228, https://doi.org/10.5194/gmd-2020-228, 2020
Preprint withdrawn
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Through a cascade of physical mechanisms, a change in one location can trigger a response in a different location. These responses and the mechanisms that cause them are difficult to detect. Here we propose a method, using global climate models, to detect possible relationships between changes in one region and responses throughout the globe caused by that change. A change in the Pacific ocean is used as a test case to determine the effectiveness of the method.
Simone Tilmes, Douglas G. MacMartin, Jan T. M. Lenaerts, Leo van Kampenhout, Laura Muntjewerf, Lili Xia, Cheryl S. Harrison, Kristen M. Krumhardt, Michael J. Mills, Ben Kravitz, and Alan Robock
Earth Syst. Dynam., 11, 579–601, https://doi.org/10.5194/esd-11-579-2020, https://doi.org/10.5194/esd-11-579-2020, 2020
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This paper introduces new geoengineering model experiments as part of a larger model intercomparison effort, using reflective particles to block some of the incoming solar radiation to reach surface temperature targets. Outcomes of these applications are contrasted based on a high greenhouse gas emission pathway and a pathway with strong mitigation and negative emissions after 2040. We compare quantities that matter for societal and ecosystem impacts between the different scenarios.
Robert Link, Abigail Snyder, Cary Lynch, Corinne Hartin, Ben Kravitz, and Ben Bond-Lamberty
Geosci. Model Dev., 12, 1477–1489, https://doi.org/10.5194/gmd-12-1477-2019, https://doi.org/10.5194/gmd-12-1477-2019, 2019
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Earth system models (ESMs) produce the highest-quality future climate data available, but they are costly to run, so only a few runs from each model are publicly available. What is needed are emulators that tell us what would have happened, if we had been able to perform as many ESM runs as we might have liked. Much of the existing work on emulators has focused on deterministic projections of average values. Here we present a way to imbue emulators with the variability seen in ESM runs.
Katherine Calvin, Pralit Patel, Leon Clarke, Ghassem Asrar, Ben Bond-Lamberty, Ryna Yiyun Cui, Alan Di Vittorio, Kalyn Dorheim, Jae Edmonds, Corinne Hartin, Mohamad Hejazi, Russell Horowitz, Gokul Iyer, Page Kyle, Sonny Kim, Robert Link, Haewon McJeon, Steven J. Smith, Abigail Snyder, Stephanie Waldhoff, and Marshall Wise
Geosci. Model Dev., 12, 677–698, https://doi.org/10.5194/gmd-12-677-2019, https://doi.org/10.5194/gmd-12-677-2019, 2019
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This paper describes GCAM v5.1, an open source model that represents the linkages between energy, water, land, climate, and economic systems. GCAM examines the future evolution of these systems through the end of the 21st century. It can be used to examine, for example, how changes in population, income, or technology cost might alter crop production, energy demand, or water withdrawals, or how changes in one region’s demand for energy affect energy, water, and land in other regions.
Christopher G. Fletcher, Ben Kravitz, and Bakr Badawy
Atmos. Chem. Phys., 18, 17529–17543, https://doi.org/10.5194/acp-18-17529-2018, https://doi.org/10.5194/acp-18-17529-2018, 2018
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The most important number for future climate projections is Earth's climate sensitivity (CS), or how much warming will result from increased carbon dioxide. We cannot know the true CS, and estimates of CS from climate models have a wide range. This study identifies the major factors that control this range, and we show that the choice of methods used in creating a climate model are three times more important than fine-tuning the details of the model after it is created.
Ben Kravitz, Philip J. Rasch, Hailong Wang, Alan Robock, Corey Gabriel, Olivier Boucher, Jason N. S. Cole, Jim Haywood, Duoying Ji, Andy Jones, Andrew Lenton, John C. Moore, Helene Muri, Ulrike Niemeier, Steven Phipps, Hauke Schmidt, Shingo Watanabe, Shuting Yang, and Jin-Ho Yoon
Atmos. Chem. Phys., 18, 13097–13113, https://doi.org/10.5194/acp-18-13097-2018, https://doi.org/10.5194/acp-18-13097-2018, 2018
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Marine cloud brightening has been proposed as a means of geoengineering/climate intervention, or deliberately altering the climate system to offset anthropogenic climate change. In idealized simulations that highlight contrasts between land and ocean, we find that the globe warms, including the ocean due to transport of heat from land. This study reinforces that no net energy input into the Earth system does not mean that temperature will necessarily remain unchanged.
Duoying Ji, Songsong Fang, Charles L. Curry, Hiroki Kashimura, Shingo Watanabe, Jason N. S. Cole, Andrew Lenton, Helene Muri, Ben Kravitz, and John C. Moore
Atmos. Chem. Phys., 18, 10133–10156, https://doi.org/10.5194/acp-18-10133-2018, https://doi.org/10.5194/acp-18-10133-2018, 2018
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We examine extreme temperature and precipitation under climate-model-simulated solar dimming and stratospheric aerosol injection geoengineering schemes. Both types of geoengineering lead to lower minimum temperatures at higher latitudes and greater cooling of minimum temperatures and maximum temperatures over land compared with oceans. Stratospheric aerosol injection is more effective in reducing tropical extreme precipitation, while solar dimming is more effective over extra-tropical regions.
David P. Keller, Andrew Lenton, Vivian Scott, Naomi E. Vaughan, Nico Bauer, Duoying Ji, Chris D. Jones, Ben Kravitz, Helene Muri, and Kirsten Zickfeld
Geosci. Model Dev., 11, 1133–1160, https://doi.org/10.5194/gmd-11-1133-2018, https://doi.org/10.5194/gmd-11-1133-2018, 2018
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There is little consensus on the impacts and efficacy of proposed carbon dioxide removal (CDR) methods as a potential means of mitigating climate change. To address this need, the Carbon Dioxide Removal Model Intercomparison Project (or CDR-MIP) has been initiated. This project brings together models of the Earth system in a common framework to explore the potential, impacts, and challenges of CDR. Here, we describe the first set of CDR-MIP experiments.
Camilla W. Stjern, Helene Muri, Lars Ahlm, Olivier Boucher, Jason N. S. Cole, Duoying Ji, Andy Jones, Jim Haywood, Ben Kravitz, Andrew Lenton, John C. Moore, Ulrike Niemeier, Steven J. Phipps, Hauke Schmidt, Shingo Watanabe, and Jón Egill Kristjánsson
Atmos. Chem. Phys., 18, 621–634, https://doi.org/10.5194/acp-18-621-2018, https://doi.org/10.5194/acp-18-621-2018, 2018
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Marine cloud brightening (MCB) has been proposed to help limit global warming. We present here the first multi-model assessment of idealized MCB simulations from the Geoengineering Model Intercomparison Project. While all models predict a global cooling as intended, there is considerable spread between the models both in terms of radiative forcing and the climate response, largely linked to the substantial differences in the models' representation of clouds.
Abigail C. Snyder, Robert P. Link, and Katherine V. Calvin
Geosci. Model Dev., 10, 4307–4319, https://doi.org/10.5194/gmd-10-4307-2017, https://doi.org/10.5194/gmd-10-4307-2017, 2017
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Experiments conducting a model forecast for a period in which observational data are available are rarely undertaken in the integrated assessment model (IAM) community. When undertaken, results are often evaluated using global aggregates that mask deficiencies. Comparing land allocation simulations in GCAM with FAO observational data from 1990 to 2010, we find quantitative evidence that global aggregates alone are not sufficient for evaluating IAMs with global supply constraints similar to GCAM.
Lars Ahlm, Andy Jones, Camilla W. Stjern, Helene Muri, Ben Kravitz, and Jón Egill Kristjánsson
Atmos. Chem. Phys., 17, 13071–13087, https://doi.org/10.5194/acp-17-13071-2017, https://doi.org/10.5194/acp-17-13071-2017, 2017
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We present results from coordinated simulations with three Earth system models focusing on the response of Earth’s radiation balance to the injection of sea salt particles. We find that in most regions the effective radiative forcing by the injected particles is equally large in cloudy and clear-sky conditions, suggesting a more important role of the aerosol direct effect in sea spray climate engineering than previously thought.
Cary Lynch, Corinne Hartin, Ben Bond-Lamberty, and Ben Kravitz
Earth Syst. Sci. Data, 9, 281–292, https://doi.org/10.5194/essd-9-281-2017, https://doi.org/10.5194/essd-9-281-2017, 2017
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Pattern scaling climate model output is a computationally efficient way to produce a large amount of data for purposes of uncertainty quantification. Using a multi-model ensemble we explore pattern scaling methodologies across two future forcing scenarios. We find that the simple least squares approach to pattern scaling produces a close approximation of actual model output, and we use this as a justification for the creation of an open-access pattern library at multiple time increments.
Ben Kravitz, Cary Lynch, Corinne Hartin, and Ben Bond-Lamberty
Geosci. Model Dev., 10, 1889–1902, https://doi.org/10.5194/gmd-10-1889-2017, https://doi.org/10.5194/gmd-10-1889-2017, 2017
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Pattern scaling is a way of approximating regional changes without needing to run a full, complex global climate model. We compare two methods of pattern scaling for precipitation and evaluate which methods is
betterin particular circumstances. We also decompose precipitation into a CO2 portion and a non-CO2 portion. The methodologies discussed in this paper can help provide precipitation fields for other models for a wide variety of scenarios of future climate change.
Hiroki Kashimura, Manabu Abe, Shingo Watanabe, Takashi Sekiya, Duoying Ji, John C. Moore, Jason N. S. Cole, and Ben Kravitz
Atmos. Chem. Phys., 17, 3339–3356, https://doi.org/10.5194/acp-17-3339-2017, https://doi.org/10.5194/acp-17-3339-2017, 2017
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This study analyses shortwave radiation (SW) in the G4 experiment of the Geoengineering Model Intercomparison Project. G4 involves stratospheric injection of 5 Tg yr−1 of SO2 against the RCP4.5 scenario. The global mean forcing of the sulphate geoengineering has an inter-model variablity of −3.6 to −1.6 W m−2, implying a high uncertainty in modelled processes of sulfate aerosols. Changes in water vapour and cloud amounts due to the SO2 injection weaken the forcing at the surface by around 50 %.
Ben Kravitz, Douglas G. MacMartin, Philip J. Rasch, and Hailong Wang
Atmos. Chem. Phys., 17, 2525–2541, https://doi.org/10.5194/acp-17-2525-2017, https://doi.org/10.5194/acp-17-2525-2017, 2017
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We introduce system identification techniques to climate science wherein multiple dynamic input–output relationships can be simultaneously characterized in a single simulation. This method, involving multiple small perturbations (in space and time) of an input field while monitoring output fields to quantify responses, allows for identification of different timescales of climate response to forcing without substantially pushing the climate far away from a steady state.
Corey J. Gabriel, Alan Robock, Lili Xia, Brian Zambri, and Ben Kravitz
Atmos. Chem. Phys., 17, 595–613, https://doi.org/10.5194/acp-17-595-2017, https://doi.org/10.5194/acp-17-595-2017, 2017
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The National Center for Atmospheric Research CESM-CAM4-CHEM global climate model was modified to simulate a scheme in which the albedo of the ocean surface is raised over the subtropical ocean gyres in the Southern Hemisphere. Global mean surface temperature in G4Foam is 0.6K lower than RCP6.0, with statistically significant cooling relative to RCP6.0 south of 30° N and an increase in rainfall over land, most pronouncedly during the JJA season, relative to both G4SSA and RCP6.0.
Douglas G. MacMartin and Ben Kravitz
Atmos. Chem. Phys., 16, 15789–15799, https://doi.org/10.5194/acp-16-15789-2016, https://doi.org/10.5194/acp-16-15789-2016, 2016
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Solar geoengineering has been proposed as a possible additional approach for managing risks of climate change, by reflecting some sunlight back to space. To project climate effects resulting from future choices regarding both greenhouse gas emissions and solar geoengineering, it is useful to have a computationally efficient "emulator" that approximates the behavior of more complex climate models. We present such an emulator here, and validate the underlying assumption of linearity.
Yannick Le Page, Tris O. West, Robert Link, and Pralit Patel
Geosci. Model Dev., 9, 3055–3069, https://doi.org/10.5194/gmd-9-3055-2016, https://doi.org/10.5194/gmd-9-3055-2016, 2016
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A computer program was developed to transform maps of regional land use (e.g., crops) and land cover (e.g., forests) areas into gridded maps actually representing their spatial distribution within each region. This is important for studies of future environmental change: economic models project agricultural activities at the regional scale, but Earth system models need gridded information to project the impact of such activities on climate, biodiversity, water availability, and other aspects.
Cary Lynch, Corinne Hartin, Ben Bond-Lamberty, and Ben Kravitz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-170, https://doi.org/10.5194/gmd-2016-170, 2016
Revised manuscript not accepted
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Pattern scaling is used to explore uncertainty in future forcing scenarios and assess local climate sensitivity to global temperature change. This paper examines the two dominant pattern scaling methods using a multi-model ensemble with two future socio-economic storylines. We find that high latitudes show the strongest sensitivity to global temperature change and that the simple least squared regression approach to generation of patterns is a better fit to projected global temperature.
Ben Kravitz, Douglas G. MacMartin, Hailong Wang, and Philip J. Rasch
Earth Syst. Dynam., 7, 469–497, https://doi.org/10.5194/esd-7-469-2016, https://doi.org/10.5194/esd-7-469-2016, 2016
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Most simulations of solar geoengineering prescribe a particular strategy and evaluate its modeled effects. Here we first choose example climate objectives and then design a strategy to meet those objectives in climate models. We show that certain objectives can be met simultaneously even in the presence of uncertainty, and the strategy for meeting those objectives can be ported to other models. This is part of a broader illustration of how uncertainties in solar geoengineering can be managed.
B. Kravitz, A. Robock, S. Tilmes, O. Boucher, J. M. English, P. J. Irvine, A. Jones, M. G. Lawrence, M. MacCracken, H. Muri, J. C. Moore, U. Niemeier, S. J. Phipps, J. Sillmann, T. Storelvmo, H. Wang, and S. Watanabe
Geosci. Model Dev., 8, 3379–3392, https://doi.org/10.5194/gmd-8-3379-2015, https://doi.org/10.5194/gmd-8-3379-2015, 2015
C. A. Hartin, P. Patel, A. Schwarber, R. P. Link, and B. P. Bond-Lamberty
Geosci. Model Dev., 8, 939–955, https://doi.org/10.5194/gmd-8-939-2015, https://doi.org/10.5194/gmd-8-939-2015, 2015
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Simple climate models play an integral role in policy and scientific communities. Hector v1.0 is an open-source, object-oriented, simple global climate carbon-cycle model. Hector reproduces the global historical trends of atmospheric [CO2], radiative forcing, and surface temperatures. Hector simulates all four representative concentration pathways with equivalent rates of change of key variables over time compared to current observations and other models.
S. Tilmes, M. J. Mills, U. Niemeier, H. Schmidt, A. Robock, B. Kravitz, J.-F. Lamarque, G. Pitari, and J. M. English
Geosci. Model Dev., 8, 43–49, https://doi.org/10.5194/gmd-8-43-2015, https://doi.org/10.5194/gmd-8-43-2015, 2015
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A new Geoengineering Model Intercomparison Project (GeoMIP) experiment “G4 specified stratospheric aerosols” (G4SSA) is proposed to investigate the impact of stratospheric aerosol geoengineering on atmosphere, chemistry, dynamics, climate, and the environment. In contrast to the earlier G4 GeoMIP experiment, which requires an emission of sulfur dioxide (SO2) into the model, a prescribed aerosol forcing file is provided to the community, to be consistently applied to future model experiments.
Related subject area
Subject: Clouds and Precipitation | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Impact of wildfire smoke on Arctic cirrus formation – Part 2: Simulation of MOSAiC 2019–2020 cases
Constraining aerosol–cloud adjustments by uniting surface observations with a perturbed parameter ensemble
Investigating ice formation pathways using a novel two-moment multi-class cloud microphysics scheme
Microphysics regimes due to haze–cloud interactions: cloud oscillation and cloud collapse
Impact of secondary ice production on thunderstorm electrification under different aerosol conditions
Accelerated impact of airborne glaciogenic seeding of stratiform clouds by turbulence
Model analysis of biases in the satellite-diagnosed aerosol effect on the cloud liquid water path
Evaluation of biases in mid-to-high-latitude surface snowfall and cloud phase in ERA5 and CMIP6 using satellite observations
Failed cyclogenesis of a mesoscale convective system near Cape Verde: The role of the Saharan trade wind layer among other inhibiting factors observed during the CADDIWA field campaign
Dynamical imprints on precipitation cluster statistics across a hierarchy of high-resolution simulations
Ice formation processes key in determining WCB outflow cirrus properties
Technical note: Phase space depiction of CCN activation and cloud droplet diffusional growth
Role of a key microphysical factor in mixed-phase stratocumulus clouds and their interactions with aerosols
High-resolution modelling of early contrail evolution from hydrogen-powered aircraft
Investigating the impact of subgrid-scale aerosol-cloud interaction on mesoscale meteorology prediction
Correction of ERA5 temperature and relative humidity biases by bivariate quantile mapping for contrail formation analysis
Can pollen affect precipitation?
Potential impacts of marine fuel regulations on an Arctic stratocumulus case and its radiative response
The impact of the mesh size and microphysics scheme on the representation of mid-level clouds in the ICON model in hilly and complex terrain
The role of ascent timescales for warm conveyor belt (WCB) moisture transport into the upper troposphere and lower stratosphere (UTLS)
Magnitude and timescale of liquid water path adjustments to cloud droplet number concentration perturbations for nocturnal non-precipitating marine stratocumulus
On the Processes Determining the Slope of Cloud-Water Adjustments in Non-Precipitating Stratocumulus
Estimating the concentration of silver iodide needed to detect unambiguous signatures of glaciogenic cloud seeding
On the impact of thunder on cloud ice crystals and droplets
Ice-nucleating particle concentration impacts cloud properties over Dronning Maud Land, East Antarctica, in COSMO-CLM2
Numerical simulation of aerosol concentration effects on cloud droplet size spectrum evolutions of warm stratiform clouds in Jiangxi, China
The impact of aerosol on cloud water: a heuristic perspective
Cold pools mediate mesoscale adjustments of trade-cumulus fields to changes in cloud-droplet number concentration
The presence of clouds lowers climate sensitivity in the MPI-ESM1.2 climate model
Diurnal variation in an amplified canopy urban heat island during heat wave periods in the megacity of Beijing: roles of mountain–valley breeze and urban morphology
Diurnal evolution of non-precipitating marine stratocumuli in a large-eddy simulation ensemble
Ambient and Intrinsic Dependencies of Evolving Ice-Phase Particles within a Decaying Winter Storm During IMPACTS
Counteracting Influences of Gravitational Settling Modulate Aerosol Impacts on Cloud Base Lowering Fog Characteristics
Numerical Case Study of the Aerosol-Cloud-Interactions in Warm Boundary Layer Clouds over the Eastern North Atlantic with an Interactive Chemistry Module
High ice water content in tropical mesoscale convective systems (a conceptual model)
Evolution of cloud droplet temperature and lifetime in spatiotemporally varying subsaturated environments with implications for ice nucleation at cloud edges
Effect of secondary ice production processes on the simulation of ice pellets using the Predicted Particle Properties microphysics scheme
Simulated particle evolution within a winter storm: contributions of riming to radar moments and precipitation fallout
Arctic Multilayer Clouds Require Accurate Thermodynamic Profiles and Efficient Primary and Secondary Ice Processes for a Realistic Structure and Composition
A thermal-driven graupel generation process to explain dry-season convective vigor over the Amazon
Modeling homogeneous ice nucleation from drop-freezing experiments: impact of droplet volume dispersion and cooling rates
Cloud water adjustments to aerosol perturbations are buffered by solar heating in non-precipitating marine stratocumuli
Glaciation of mixed-phase clouds: insights from bulk model and bin-microphysics large-eddy simulation informed by laboratory experiment
Influence of Temperature and Humidity on Contrail Formation Regions in EMAC: A Spring Case Study
Microphysical processes involving the vapour phase dominate in simulated low-level Arctic clouds
Understanding aerosol–cloud interactions using a single-column model for a cold-air outbreak case during the ACTIVATE campaign
The Critical Number and Size of Precipitation Embryos to Accelerate Warm Rain Initiation
On the sensitivity of aerosol–cloud interactions to changes in sea surface temperature in radiative–convective equilibrium
Exploring aerosol–cloud interactions in liquid-phase clouds over eastern China and its adjacent ocean using the WRF-Chem–SBM model
Impact on the stratocumulus-to-cumulus transition of the interaction of cloud microphysics and macrophysics with large-scale circulation
Albert Ansmann, Cristofer Jimenez, Daniel A. Knopf, Johanna Roschke, Johannes Bühl, Kevin Ohneiser, and Ronny Engelmann
Atmos. Chem. Phys., 25, 4867–4884, https://doi.org/10.5194/acp-25-4867-2025, https://doi.org/10.5194/acp-25-4867-2025, 2025
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In this study, we focus on the potential impact of wildfire smoke on cirrus formation. Aerosol and cirrus observations with lidar and radar during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, presented in the companion paper (Ansmann et al., 2025), are closely linked to comprehensive modeling of ice nucleation in cirrus evolution processes, presented in this article. A clear impact of wildfire smoke on cirrus formation was found.
August Mikkelsen, Daniel T. McCoy, Trude Eidhammer, Andrew Gettelman, Ci Song, Hamish Gordon, and Isabel L. McCoy
Atmos. Chem. Phys., 25, 4547–4570, https://doi.org/10.5194/acp-25-4547-2025, https://doi.org/10.5194/acp-25-4547-2025, 2025
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Whether increased aerosol increases or decreases liquid cloud mass has been a longstanding question. Observed correlations suggest that aerosols thin liquid cloud, but we are able to show that observations were consistent with an increase in liquid cloud in response to aerosols by leveraging a model where causality could be traced.
Tim Lüttmer, Peter Spichtinger, and Axel Seifert
Atmos. Chem. Phys., 25, 4505–4529, https://doi.org/10.5194/acp-25-4505-2025, https://doi.org/10.5194/acp-25-4505-2025, 2025
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We investigate ice formation pathways in idealized convective clouds using a novel microphysics scheme that distinguishes between five ice classes each with their own unique formation mechanism. Ice crystals from rime splintering form the lowermost layer of ice crystals around the updraft core. The majority of ice crystals in the anvil of the convective cloud stems from frozen droplets. Ice stemming from homogeneous and deposition nucleation was only relevant in the overshoot.
Fan Yang, Hamed Fahandezh Sadi, Raymond A. Shaw, Fabian Hoffmann, Pei Hou, Aaron Wang, and Mikhail Ovchinnikov
Atmos. Chem. Phys., 25, 3785–3806, https://doi.org/10.5194/acp-25-3785-2025, https://doi.org/10.5194/acp-25-3785-2025, 2025
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Large-eddy simulations of a convection cloud chamber show two new microphysics regimes, cloud oscillation and cloud collapse, due to haze–cloud interactions. Our results suggest that haze particles and their interactions with cloud droplets should be considered especially in polluted conditions. To properly simulate haze–cloud interactions, we need to resolve droplet activation and deactivation processes, instead of using Twomey-type activation parameterization.
Shiye Huang, Jing Yang, Jiaojiao Li, Qian Chen, Qilin Zhang, and Fengxia Guo
Atmos. Chem. Phys., 25, 1831–1850, https://doi.org/10.5194/acp-25-1831-2025, https://doi.org/10.5194/acp-25-1831-2025, 2025
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Aerosol and secondary ice production are both vital to charge separation in thunderstorms, but the relative importance of different SIP processes to cloud electrification under different aerosol conditions is not well understood. In this study, we show in a clean environment, the shattering of freezing drops has the greatest effect on the charging rate, while in a polluted environment, both rime splintering and the shattering of freezing drops have a significant effect on cloud electrification.
Meilian Chen, Xiaoqin Jing, Jiaojiao Li, Jing Yang, Xiaobo Dong, Bart Geerts, Yan Yin, Baojun Chen, Lulin Xue, Mengyu Huang, Ping Tian, and Shaofeng Hua
EGUsphere, https://doi.org/10.5194/egusphere-2025-47, https://doi.org/10.5194/egusphere-2025-47, 2025
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Several recent studies have reported complete cloud glaciation induced by airborne-based glaciogenic cloud seeding over plains. Since turbulence is an important factor to maintain clouds in mixed-phase, it is hypothesized that turbulence may have an impact on the seeding effect. This hypothesis is evident in the present study, which shows turbulence can accelerate the impact of airborne glaciogenic seeding of stratiform clouds.
Harri Kokkola, Juha Tonttila, Silvia M. Calderón, Sami Romakkaniemi, Antti Lipponen, Aapo Peräkorpi, Tero Mielonen, Edward Gryspeerdt, Timo Henrik Virtanen, Pekka Kolmonen, and Antti Arola
Atmos. Chem. Phys., 25, 1533–1543, https://doi.org/10.5194/acp-25-1533-2025, https://doi.org/10.5194/acp-25-1533-2025, 2025
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Understanding how atmospheric aerosols affect clouds is a scientific challenge. One question is how aerosols affects the amount of cloud water. We used a cloud-scale model to study these effects on marine clouds. The study showed that variations in cloud properties and instrument noise can cause bias in satellite-derived cloud water content. However, our results suggest that for similar weather conditions with well-defined aerosol concentrations, satellite data can reliably track these effects.
Franziska Hellmuth, Tim Carlsen, Anne Sophie Daloz, Robert Oscar David, Haochi Che, and Trude Storelvmo
Atmos. Chem. Phys., 25, 1353–1383, https://doi.org/10.5194/acp-25-1353-2025, https://doi.org/10.5194/acp-25-1353-2025, 2025
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This article compares the occurrence of supercooled liquid-containing clouds (sLCCs) and their link to surface snowfall in CloudSat–CALIPSO, ERA5, and the CMIP6 models. Significant discrepancies were found, with ERA5 and CMIP6 consistently overestimating sLCC and snowfall frequency. This bias is likely due to cloud microphysics parameterization. This conclusion has implications for accurately representing cloud phase and snowfall in future climate projections.
Guillaume Feger, Jean-Pierre Chaboureau, Thibaut Dauhut, Julien Delanoë, and Pierre Coutris
EGUsphere, https://doi.org/10.5194/egusphere-2025-105, https://doi.org/10.5194/egusphere-2025-105, 2025
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The Saharan air at trade wind layer, cold pools, and upper tropospheric dry air are identified as the three main factors inhibiting the cyclogenesis of the Pierre Henri mesoscale convective system. The findings were obtained trough observations made during two flights of the CADDIWA campaign and a convection-permitting simulation run with the Meso-NH model. They provide new insights into the complex dynamics of cyclogenesis in the Cape Verde region and challenge the existing model of the SAL.
Claudia Christine Stephan and Bjorn Stevens
Atmos. Chem. Phys., 25, 1209–1226, https://doi.org/10.5194/acp-25-1209-2025, https://doi.org/10.5194/acp-25-1209-2025, 2025
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Tropical precipitation cluster area and intensity distributions follow power laws, but the physical processes responsible for this behavior remain unknown. We analyze global simulations that realistically represent precipitation processes. We consider Earth-like planets as well as virtual planets to realize different types of large-scale dynamics. Our finding is that power laws in Earth’s precipitation cluster statistics stem from the robust power laws in Earth’s atmospheric wind field.
Tim Lüttmer, Annette Miltenberger, and Peter Spichtinger
EGUsphere, https://doi.org/10.5194/egusphere-2025-185, https://doi.org/10.5194/egusphere-2025-185, 2025
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We investigate ice formation pathways in a warm conveyor belt case study. We employ a multi-phase microphysics scheme that distinguishes between ice from different nucleation processes. Ice crystals in the cirrus outflow mostly stem from in-situ formation. Hence they were formed directly from the vapor phase. Sedimentational redistribution modulates cirrus properties and leads to a disagreement between cirrus origin classifications based on thermodynamic history and nucleation processes.
Wojciech W. Grabowski and Hanna Pawlowska
EGUsphere, https://doi.org/10.5194/egusphere-2024-4104, https://doi.org/10.5194/egusphere-2024-4104, 2025
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A simple diagram to depict cloud droplets formation via activation of cloud condensation nuclei (CCN) as well as their subsequent growth and evaporation is presented.
Seoung Soo Lee, Chang Hoon Jung, Jinho Choi, Young Jun Yoon, Junshik Um, Youtong Zheng, Jianping Guo, Manguttathil G. Manoj, Sang-Keun Song, and Kyung-Ja Ha
Atmos. Chem. Phys., 25, 705–726, https://doi.org/10.5194/acp-25-705-2025, https://doi.org/10.5194/acp-25-705-2025, 2025
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This study attempts to test a general factor that explains differences in the properties of different mixed-phase clouds using a modeling tool. Although this attempt is not to identify a factor that can perfectly explain and represent the properties of different mixed-phase clouds, we believe that this attempt acts as a valuable stepping stone towards a more complete, general way of using climate models to better predict climate change.
Annemarie Lottermoser and Simon Unterstraßer
EGUsphere, https://doi.org/10.5194/egusphere-2024-3859, https://doi.org/10.5194/egusphere-2024-3859, 2025
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Contrail-cirrus significantly contributes to aviation's overall climate impact. As hydrogen combustion and fuel cell use are emerging technologies for aircraft propulsion, we simulated individual contrails from hydrogen propulsion during the first six minutes after exhaust emission, termed the vortex phase. The ice crystal loss during that stage is crucial as the number of ice crystals has a large impact on the further evolution of contrails into contrail-cirrus and their radiative forcing.
Wenjie Zhang, Hong Wang, Xiaoye Zhang, Yue Peng, Zhaodong Liu, Deying Wang, Da Zhang, Chen Han, Yang Zhao, Junting Zhong, Wenxing Jia, Huiqiong Ning, and Huizheng Che
EGUsphere, https://doi.org/10.5194/egusphere-2024-3677, https://doi.org/10.5194/egusphere-2024-3677, 2025
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We implement a real-time subgrid-scale aerosol-cloud interaction (ACI) mechanism in a mesoscale atmospheric chemistry system and find that subgrid-scale ACI can improve meteorological factors predictions. This study demonstrates the importance of real-time subgrid-scale ACI to weather forecast and the necessity of multiscale ACI studies.
Kevin Wolf, Nicolas Bellouin, Olivier Boucher, Susanne Rohs, and Yun Li
Atmos. Chem. Phys., 25, 157–181, https://doi.org/10.5194/acp-25-157-2025, https://doi.org/10.5194/acp-25-157-2025, 2025
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ERA5 atmospheric reanalysis and airborne in situ observations from IAGOS are compared in terms of the representation of the contrail formation potential and the presence of supersaturation. Differences are traced back to biases in ERA5 relative humidity fields. Those biases are addressed by applying a quantile mapping technique that significantly improved contrail estimation based on post-processed ERA5 data.
Marje Prank, Juha Tonttila, Xiaoxia Shang, Sami Romakkaniemi, and Tomi Raatikainen
Atmos. Chem. Phys., 25, 183–197, https://doi.org/10.5194/acp-25-183-2025, https://doi.org/10.5194/acp-25-183-2025, 2025
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Large primary bioparticles such as pollen can be abundant in the atmosphere. In humid conditions pollen can rupture and release a large number of fine sub-pollen particles (SPPs). The paper investigates what kind of birch pollen concentrations are needed for the pollen and SPPs to start playing a noticeable role in cloud processes and alter precipitation formation. In the studied cases only the largest observed pollen concentrations were able to noticeably alter the precipitation formation.
Luís Filipe Escusa dos Santos, Hannah C. Frostenberg, Alejandro Baró Pérez, Annica M. L. Ekman, Luisa Ickes, and Erik S. Thomson
Atmos. Chem. Phys., 25, 119–142, https://doi.org/10.5194/acp-25-119-2025, https://doi.org/10.5194/acp-25-119-2025, 2025
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The Arctic is experiencing enhanced surface warming. The observed decline in Arctic sea-ice extent is projected to lead to an increase in Arctic shipping activity, which may lead to further climatic feedbacks. Using an atmospheric model and results from marine engine experiments that focused on fuel sulfur content reduction and exhaust wet scrubbing, we investigate how ship exhaust particles influence the properties of Arctic clouds. Implications for radiative surface processes are discussed.
Nadja Omanovic, Brigitta Goger, and Ulrike Lohmann
Atmos. Chem. Phys., 24, 14145–14175, https://doi.org/10.5194/acp-24-14145-2024, https://doi.org/10.5194/acp-24-14145-2024, 2024
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We evaluated the numerical weather model ICON in two horizontal resolutions with two bulk microphysics schemes over hilly and complex terrain in Switzerland and Austria, respectively. We focused on the model's ability to simulate mid-level clouds in summer and winter. By combining observational data from two different field campaigns, we show that an increase in the horizontal resolution and a more advanced cloud microphysics scheme is strongly beneficial for cloud representation.
Cornelis Schwenk and Annette Miltenberger
Atmos. Chem. Phys., 24, 14073–14099, https://doi.org/10.5194/acp-24-14073-2024, https://doi.org/10.5194/acp-24-14073-2024, 2024
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Warm conveyor belts (WCBs) transport moisture into the upper atmosphere, where it acts as a greenhouse gas. This transport is not well understood, and the role of rapidly rising air is unclear. We simulate a WCB and look at fast- and slow-rising air to see how moisture is (differently) transported. We find that for fast-ascending air more ice particles reach higher into the atmosphere and that frozen cloud particles are removed differently than during slow ascent, which has more water vapour.
Yao-Sheng Chen, Prasanth Prabhakaran, Fabian Hoffmann, Jan Kazil, Takanobu Yamaguchi, and Graham Feingold
EGUsphere, https://doi.org/10.5194/egusphere-2024-3891, https://doi.org/10.5194/egusphere-2024-3891, 2024
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Injecting sea salt aerosols into marine stratiform clouds can distribute the cloud water over more droplets in smaller sizes. This process is expected to make the clouds brighter, allowing them to reflect more sunlight back to space. However, it may also cause the clouds to lose water over time, reducing their ability to reflect sunlight. We use a computer model to show that the loss of cloud water occurs relatively quickly and does not completely offset the initial brightening.
Fabian Hoffmann, Yao-Sheng Chen, and Graham Feingold
EGUsphere, https://doi.org/10.5194/egusphere-2024-3893, https://doi.org/10.5194/egusphere-2024-3893, 2024
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Clouds reflect a substantial portion of the incoming solar radiation back into space. This capacity is determined by the number of cloud droplets, which in turn is influenced by the number of aerosol particles, forming the basis for aerosol-cloud-climate interactions. In this study, we use a simple mixed-layer approach to understand the effect of aerosol on cloud water in non-precipitating stratocumulus.
Jing Yang, Jiaojiao Li, Meilian Chen, Xiaoqin Jing, Yan Yin, Bart Geerts, Zhien Wang, Yubao Liu, Baojun Chen, Shaofeng Hua, Hao Hu, Xiaobo Dong, Ping Tian, Qian Chen, and Yang Gao
Atmos. Chem. Phys., 24, 13833–13848, https://doi.org/10.5194/acp-24-13833-2024, https://doi.org/10.5194/acp-24-13833-2024, 2024
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Detecting unambiguous signatures is vital for examining cloud-seeding impacts, but often, seeding signatures are immersed in natural variability. In this study, reflectivity changes induced by glaciogenic seeding using different AgI concentrations are investigated under various conditions, and a method is developed to estimate the AgI concentration needed to detect unambiguous seeding signatures. The results aid in operational seeding-based decision-making regarding the amount of AgI dispersed.
Konstantinos Kourtidis, Stavros Stathopoulos, and Vassilis Amiridis
EGUsphere, https://doi.org/10.5194/egusphere-2024-3314, https://doi.org/10.5194/egusphere-2024-3314, 2024
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The loud sound of thunder will induce mechanical effects on cloud droplets and ice particles, causing changes in their size distribution.
Florian Sauerland, Niels Souverijns, Anna Possner, Heike Wex, Preben Van Overmeiren, Alexander Mangold, Kwinten Van Weverberg, and Nicole van Lipzig
Atmos. Chem. Phys., 24, 13751–13768, https://doi.org/10.5194/acp-24-13751-2024, https://doi.org/10.5194/acp-24-13751-2024, 2024
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We use a regional climate model, COSMO-CLM², enhanced with a module resolving aerosol processes, to study Antarctic clouds. We prescribe different concentrations of ice-nucleating particles to our model to assess how these clouds respond to concentration changes, validating results with cloud and aerosol observations from the Princess Elisabeth Antarctica station. Our results show that aerosol–cloud interactions vary with temperature, providing valuable insights into Antarctic cloud dynamics.
Yi Li, Xiaoli Liu, and Hengjia Cai
Atmos. Chem. Phys., 24, 13525–13540, https://doi.org/10.5194/acp-24-13525-2024, https://doi.org/10.5194/acp-24-13525-2024, 2024
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The influence of different aerosol modes on cloud processes remains controversial. We modified the aerosol spectra and concentrations to simulate a warm stratiform cloud process in Jiangxi, China, using the WRF-SBM scheme. Research shows that different aerosol spectra have diverse effects on cloud droplet spectra, cloud development, and the correlation between dispersion (ε) and cloud physics quantities. Compared to cloud droplet concentration, ε is more sensitive to the volume radius.
Fabian Hoffmann, Franziska Glassmeier, and Graham Feingold
Atmos. Chem. Phys., 24, 13403–13412, https://doi.org/10.5194/acp-24-13403-2024, https://doi.org/10.5194/acp-24-13403-2024, 2024
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Clouds constitute a major cooling influence on Earth's climate system by reflecting a large fraction of the incident solar radiation back to space. This ability is controlled by the number of cloud droplets, which is governed by the number of aerosol particles in the atmosphere, laying the foundation for so-called aerosol–cloud–climate interactions. In this study, a simple model to understand the effect of aerosol on cloud water is developed and applied.
Pouriya Alinaghi, Fredrik Jansson, Daniel A. Blázquez, and Franziska Glassmeier
EGUsphere, https://doi.org/10.5194/egusphere-2024-3501, https://doi.org/10.5194/egusphere-2024-3501, 2024
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Shallow clouds in the trades are a major source of uncertainty in climate projections. These clouds organize into striking mesoscale patterns that are exactly what climate models lack. This study explores the origin of such patterns and investigates how variations in microscale properties control them. The importance of microscale effects is compared to that of large-scale forcing on the mesoscale organization of trade-cumulus fields.
Andrea Mosso, Thomas Hocking, and Thorsten Mauritsen
Atmos. Chem. Phys., 24, 12793–12806, https://doi.org/10.5194/acp-24-12793-2024, https://doi.org/10.5194/acp-24-12793-2024, 2024
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Clouds play a crucial role in the Earth's energy balance, as they can either warm up or cool down the area they cover depending on their height and depth. They are expected to alter their behaviour under climate change, affecting the warming generated by greenhouse gases. This paper proposes a new method to estimate their overall effect on this warming by simulating a climate where clouds are transparent. Results show that with the model used, clouds have a stabilising effect on climate.
Tao Shi, Yuanjian Yang, Ping Qi, and Simone Lolli
Atmos. Chem. Phys., 24, 12807–12822, https://doi.org/10.5194/acp-24-12807-2024, https://doi.org/10.5194/acp-24-12807-2024, 2024
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This paper explored the formation mechanisms of the amplified canopy urban heat island intensity (ΔCUHII) during heat wave (HW) periods in the megacity of Beijing from the perspectives of mountain–valley breeze and urban morphology. During the mountain breeze phase, high-rise buildings with lower sky view factors (SVFs) had a pronounced effect on the ΔCUHII. During the valley breeze phase, high-rise buildings exerted a dual influence on the ΔCUHII.
Yao-Sheng Chen, Jianhao Zhang, Fabian Hoffmann, Takanobu Yamaguchi, Franziska Glassmeier, Xiaoli Zhou, and Graham Feingold
Atmos. Chem. Phys., 24, 12661–12685, https://doi.org/10.5194/acp-24-12661-2024, https://doi.org/10.5194/acp-24-12661-2024, 2024
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Marine stratocumulus cloud is a type of shallow cloud that covers the vast areas of Earth's surface. It plays an important role in Earth's energy balance by reflecting solar radiation back to space. We used numerical models to simulate a large number of marine stratocumuli with different characteristics. We found that how the clouds develop throughout the day is affected by the level of humidity in the air above the clouds and how closely the clouds connect to the ocean surface.
Andrew DeLaFrance, Lynn McMurdie, Angela Rowe, and Andrew Heymsfield
EGUsphere, https://doi.org/10.5194/egusphere-2024-3423, https://doi.org/10.5194/egusphere-2024-3423, 2024
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Numerical modeling simulations are used to investigate ice crystal growth and decay processes within a banded region of enhanced precipitation rates during a prominent winter storm. We identify robust primary ice growth in the upper portion of the cloud but decay exceeding 70 % during fallout through a subsaturated layer. The ice fall characteristics and decay rate are sensitive to the ambient cloud properties which has implications for radar-based measurements and precipitation accumulations.
Nathan H. Pope and Adele L. Igel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3214, https://doi.org/10.5194/egusphere-2024-3214, 2024
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We used PAFOG, an atmospheric model that simulates a single column, to study the sensitivity of marine fog formed through the lowering of the base of a stratus cloud to meteorology and aerosols. We found that higher aerosol concentration reduces the likelihood and duration of fog, but leads to denser fog. This overall trend was caused by multiple physical mechanisms depending on conditions.
Hsiang-He Lee, Xue Zheng, Shaoyue Qiu, and Yuan Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-3199, https://doi.org/10.5194/egusphere-2024-3199, 2024
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The study investigates how aerosol-cloud interactions affect warm boundary layer stratiform clouds over the Eastern North Atlantic. High-resolution WRF-Chem simulations reveal that non-rain clouds at the edges of cloud systems are prone to evaporation, leading to an aerosol drying effect and a transition of aerosols back to accumulation mode for future activation. The study emphasizes that this dynamic behavior is often not adequately represented in most previous prescribed-aerosol simulations.
Alexei Korolev, Zhipeng Qu, Jason Milbrandt, Ivan Heckman, Mélissa Cholette, Mengistu Wolde, Cuong Nguyen, Greg M. McFarquhar, Paul Lawson, and Ann M. Fridlind
Atmos. Chem. Phys., 24, 11849–11881, https://doi.org/10.5194/acp-24-11849-2024, https://doi.org/10.5194/acp-24-11849-2024, 2024
Short summary
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The phenomenon of high ice water content (HIWC) occurs in mesoscale convective systems (MCSs) when a large number of small ice particles with typical sizes of a few hundred micrometers is found at high altitudes. It was found that secondary ice production in the vicinity of the melting layer plays a key role in the formation and maintenance of HIWC. This study presents a conceptual model of the formation of HIWC in tropical MCSs based on in situ observations and numerical simulation.
Puja Roy, Robert M. Rauber, and Larry Di Girolamo
Atmos. Chem. Phys., 24, 11653–11678, https://doi.org/10.5194/acp-24-11653-2024, https://doi.org/10.5194/acp-24-11653-2024, 2024
Short summary
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Cloud droplet temperature and lifetime impact cloud microphysical processes such as the activation of ice-nucleating particles. We investigate the thermal and radial evolution of supercooled cloud droplets and their surrounding environments with an aim to better understand observed enhanced ice formation at supercooled cloud edges. This analysis shows that the magnitude of droplet cooling during evaporation is greater than estimated from past studies, especially for drier environments.
Mathieu Lachapelle, Mélissa Cholette, and Julie M. Thériault
Atmos. Chem. Phys., 24, 11285–11304, https://doi.org/10.5194/acp-24-11285-2024, https://doi.org/10.5194/acp-24-11285-2024, 2024
Short summary
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Hazardous precipitation types such as ice pellets and freezing rain are difficult to predict because they are associated with complex microphysical processes. Using Predicted Particle Properties (P3), this work shows that secondary ice production processes increase the amount of ice pellets simulated while decreasing the amount of freezing rain. Moreover, the properties of the simulated precipitation compare well with those that were measured.
Andrew DeLaFrance, Lynn A. McMurdie, Angela K. Rowe, and Andrew J. Heymsfield
Atmos. Chem. Phys., 24, 11191–11206, https://doi.org/10.5194/acp-24-11191-2024, https://doi.org/10.5194/acp-24-11191-2024, 2024
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Using a numerical model, the process whereby falling ice crystals accumulate supercooled liquid water droplets is investigated to elucidate its effects on radar-based measurements and surface precipitation. We demonstrate that this process accounted for 55% of the precipitation during a wintertime storm and is uniquely discernable from other ice crystal growth processes in Doppler velocity measurements. These results have implications for measurements from airborne and spaceborne platforms.
Gabriella Wallentin, Annika Oertel, Luisa Ickes, Peggy Achtert, Matthias Tesche, and Corinna Hoose
EGUsphere, https://doi.org/10.5194/egusphere-2024-2988, https://doi.org/10.5194/egusphere-2024-2988, 2024
Short summary
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Multilayer clouds are common in the Arctic but remain understudied. We use an atmospheric model to simulate multilayer cloud cases from the Arctic expedition MOSAiC 2019/2020. We find that it is complex to accurately model these cloud layers due to the lack of correct temperature and humidity profiles. The model also struggles to capture the observed cloud phase, the relative concentration of cloud droplets and cloud ice. We constrain our model to measured aerosols to mitigate this issue.
Toshi Matsui, Daniel Hernandez-Deckers, Scott E. Giangrande, Thiago S. Biscaro, Ann Fridlind, and Scott Braun
Atmos. Chem. Phys., 24, 10793–10814, https://doi.org/10.5194/acp-24-10793-2024, https://doi.org/10.5194/acp-24-10793-2024, 2024
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Using computer simulations and real measurements, we discovered that storms over the Amazon were narrower but more intense during the dry periods, producing heavier rain and more ice particles in the clouds. Our research showed that cumulus bubbles played a key role in creating these intense storms. This study can improve the representation of the effect of continental and ocean environments on tropical regions' rainfall patterns in simulations.
Ravi Kumar Reddy Addula, Ingrid de Almeida Ribeiro, Valeria Molinero, and Baron Peters
Atmos. Chem. Phys., 24, 10833–10848, https://doi.org/10.5194/acp-24-10833-2024, https://doi.org/10.5194/acp-24-10833-2024, 2024
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Ice nucleation from supercooled droplets is important in many weather and climate modeling efforts. For experiments where droplets are steadily supercooled from the freezing point, our work combines nucleation theory and survival probability analysis to predict the nucleation spectrum, i.e., droplet freezing probabilities vs. temperature. We use the new framework to extract approximately consistent rate parameters from experiments with different cooling rates and droplet sizes.
Jianhao Zhang, Yao-Sheng Chen, Takanobu Yamaguchi, and Graham Feingold
Atmos. Chem. Phys., 24, 10425–10440, https://doi.org/10.5194/acp-24-10425-2024, https://doi.org/10.5194/acp-24-10425-2024, 2024
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Quantifying cloud response to aerosol perturbations presents a major challenge in understanding the human impact on climate. Using a large number of process-resolving simulations of marine stratocumulus, we show that solar heating drives a negative feedback mechanism that buffers the persistent negative trend in cloud water adjustment after sunrise. This finding has implications for the dependence of the cloud cooling effect on the timing of deliberate aerosol perturbations.
Aaron Wang, Steve Krueger, Sisi Chen, Mikhail Ovchinnikov, Will Cantrell, and Raymond A. Shaw
Atmos. Chem. Phys., 24, 10245–10260, https://doi.org/10.5194/acp-24-10245-2024, https://doi.org/10.5194/acp-24-10245-2024, 2024
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We employ two methods to examine a laboratory experiment on clouds with both ice and liquid phases. The first assumes well-mixed properties; the second resolves the spatial distribution of turbulence and cloud particles. Results show that while the trends in mean properties generally align, when turbulence is resolved, liquid droplets are not fully depleted by ice due to incomplete mixing. This underscores the threshold of ice mass fraction in distinguishing mixed-phase clouds from ice clouds.
Patrick Peter, Sigrun Matthes, Christine Frömming, Patrick Jöckel, Luca Bugliaro, Andreas Giez, Martina Krämer, and Volker Grewe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2142, https://doi.org/10.5194/egusphere-2024-2142, 2024
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Our study examines how temperature and humidity representations influence contrail (-cirrus) formation criteria. Using various model setups, we identified biases that lead to overestimation of contrail formation areas. By comparing simulations with in-flight and satellite observations, we confirmed that humidity threshold choices greatly affect contrail predictions. These findings can help develop strategies for climate-optimized flight routes, potentially reducing aviation's climate effect.
Theresa Kiszler, Davide Ori, and Vera Schemann
Atmos. Chem. Phys., 24, 10039–10053, https://doi.org/10.5194/acp-24-10039-2024, https://doi.org/10.5194/acp-24-10039-2024, 2024
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Microphysical processes impact the phase-partitioning of clouds. In this study we evaluate these processes while focusing on low-level Arctic clouds. To achieve this we used an extensive simulation set in combination with a new diagnostic tool. This study presents our findings on the relevance of these processes and their behaviour under different thermodynamic regimes.
Shuaiqi Tang, Hailong Wang, Xiang-Yu Li, Jingyi Chen, Armin Sorooshian, Xubin Zeng, Ewan Crosbie, Kenneth L. Thornhill, Luke D. Ziemba, and Christiane Voigt
Atmos. Chem. Phys., 24, 10073–10092, https://doi.org/10.5194/acp-24-10073-2024, https://doi.org/10.5194/acp-24-10073-2024, 2024
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We examined marine boundary layer clouds and their interactions with aerosols in the E3SM single-column model (SCM) for a case study. The SCM shows good agreement when simulating the clouds with high-resolution models. It reproduces the relationship between cloud droplet and aerosol particle number concentrations as produced in global models. However, the relationship between cloud liquid water and droplet number concentration is different, warranting further investigation.
Jung-Sub Lim, Yign Noh, Hyunho Lee, and Fabian Hoffmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-2636, https://doi.org/10.5194/egusphere-2024-2636, 2024
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The onset of rain is not fully understood. In this study, we address the impact of comparably large particles (precipitation embryos), speculated to initiate rain in clouds that do not contain ice. We showed that these particles can accelerate rain initiation only if their size and number exceed a threshold. As this threshold depends on the cloud's micro- and macrostructure, the impact of large particles on rain initiation is situation-dependent.
Suf Lorian and Guy Dagan
Atmos. Chem. Phys., 24, 9323–9338, https://doi.org/10.5194/acp-24-9323-2024, https://doi.org/10.5194/acp-24-9323-2024, 2024
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We examine the combined effect of aerosols and sea surface temperature (SST) on clouds under equilibrium conditions in cloud-resolving radiative–convective equilibrium simulations. We demonstrate that the aerosol–cloud interaction's effect on top-of-atmosphere energy gain strongly depends on the underlying SST, while the shortwave part of the spectrum is significantly more sensitive to SST. Furthermore, increasing aerosols influences upper-troposphere stability and thus anvil cloud fraction.
Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Hailing Jia
Atmos. Chem. Phys., 24, 9101–9118, https://doi.org/10.5194/acp-24-9101-2024, https://doi.org/10.5194/acp-24-9101-2024, 2024
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We explore aerosol–cloud interactions in liquid-phase clouds over eastern China and its adjacent ocean in winter based on the WRF-Chem–SBM model, which couples a spectral-bin microphysics scheme and an online aerosol module. Our study highlights the differences in aerosol–cloud interactions between land and ocean and between precipitation clouds and non-precipitation clouds, and it differentiates and quantifies their underlying mechanisms.
Je-Yun Chun, Robert Wood, Peter N. Blossey, and Sarah J. Doherty
EGUsphere, https://doi.org/10.5194/egusphere-2024-2439, https://doi.org/10.5194/egusphere-2024-2439, 2024
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This study explores how aerosols affect clouds transitioning from stratocumulus to cumulus along trade winds under varying atmospheric conditions. We found that aerosols typically reduce precipitation and raise cloud height, but their impact changes when subsidence changes by aerosol enhancement are considered. Our findings indicate that the cooling effect of aerosols might be overestimated if these atmospheric changes are not accounted for.
Cited articles
Arora, V. K. and Boer, G. J.: Uncertainties in the 20th century carbon budget
associated with land use change, Glob. Change Biol., 16, 3327–3348,
https://doi.org/10.1111/j.1365-2486.2010.02202.x, 2011. a
Arora, V. K., Scinocca, J. F., Boer, G. J., Christian, J. R., Denman, K. L.,
Flato, G. M., Kharin, V. V., Lee, W. G., and Merryfield, W. J.: Carbon
emission limits required to satisfy future representative concentration
pathways of greenhouse gases, Geophys. Res. Lett., 38, L05805,
https://doi.org/10.1029/2010GL046270, 2011. a
Bellucci, A., Haarsma, R., Bellouin, N., Booth, B., Cagnazzo, C., van den Hurk,
B., Keenlyside, N., Koenigk, T., Massonnet, F., Materia, S., and Weiss, M.:
Advancements in decadal climate predictability: The role of nonoceanic
drivers, Rev. Geophys., 53, 165–202, https://doi.org/10.1002/2014RG000473, 2015. a
Bengio, Y.: Practical recommendations for gradient-based training of deep
architectures, in: Neural networks: Tricks of the trade, 437–478,
Springer, 2012. a
Bergstra, J. and Bengio, Y.: Random search for hyper-parameter optimization,
J. Mach. Learn. Res., 13, 281–305, 2012. a
Boer, G. J., Kharin, V. V., and Merryfield, W. J.: Differences in potential and
actual skill in a decadal prediction experiment, Clim. Dynam., 52,
6619–6631, https://doi.org/10.1007/s00382-018-4533-4, 2019. a
Branstator, G., Teng, H., and Meehl, G. A.: Systematic Estimates of
Initial-Value Decadal Predictability for Six AOGCMs, J. Climate, 25, 1827–1846,
https://doi.org/10.1175/JCLI-D-11-00227.1, 2012. a
Castruccio, S., McInerney, D. J., Stein, M. L., Crouch, F. L., Jacob, R. L.,
and Moyer, E. J.: Statistical Emulation of Climate Model Projections Based on
Precomputed GCM Runs, J. Climate, 27, 1829–1844,
https://doi.org/10.1175/JCLI-D-13-00099.1, 2014. a
Chan, W., Jaitly, N., Le, Q., and Vinyals, O.: Listen, attend and spell: A
neural network for large vocabulary conversational speech recognition, in:
Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International
Conference on, 4960–4964, IEEE, 2016. a
Cohen, J., Screen, J. A., Furtado, J. C., Barlow, M., Whittleston, D., Coumou,
D., Francis, J., Dethloff, K., Entekhabi, D., Overland, J., and Jones, J.:
Recent Arctic amplification and extreme mid-latitude weather, Nature
Geosci., 7, 627–637, https://doi.org/10.1038/ngeo2234, 2014. a
Deo, R. V., Chandra, R., and Sharma, A.: Stacked transfer learning for tropical
cyclone intensity prediction, ArXiv e-prints,
http://arxiv.org/abs/1708.06539, 2017. a
Finn, C., Goodfellow, I., and Levine, S.: Unsupervised learning for physical
interaction through video prediction, in: Advances in neural information
processing systems, NIPS Proceedings, 64–72, 2016. a
Friedman, J., Hastie, T., and Tibshirani, R.: The elements of statistical
learning, vol. 1, Springer series in statistics New York, NY, USA, 2001. a
Fyfe, J. C., Meehl, G. A., England, M. H., Mann, M. E., Santer, B. D., Flato,
G. M., Hawkins, E., Gillett, N. P., Xie, S.-P., Kosaka, Y., and Swart, N. C.:
Making sense of the early-2000s warming slowdown, Nat. Clim. Change, 6,
224–228, https://doi.org/10.1038/nclimate2938, 2016. a
Gawehn, E., Hiss, J. A., and Schneider, G.: Deep learning in drug discovery,
Mol. Inform., 35, 3–14, https://doi.org/10.1002/minf.201501008, 2016. a
Glorot, X. and Bengio, Y.: Understanding the difficulty of training deep
feedforward neural networks, in: Proceedings of the thirteenth international
conference on artificial intelligence and statistics, J. Mach. Learn. Res., 9, 249–256, 2010. a
Goddard, L., Kumar, A., Solomon, A., Smith, D., Boer, G., Gonzalez, P., Kharin,
V., Merryfield, W., Deser, C., Mason, S. J., Kirtman, B. P., Msadek, R.,
Sutton, R., Hawkins, E., Fricker, T., Hegerl, G., Ferro, C. A. T.,
Stephenson, D. B., Meehl, G. A., Stockdale, T., Burgman, R., Greene, A. M.,
Kushnir, Y., Newman, M., Carton, J., Fukumori, I., and Delworth, T.: A
verification framework for interannual-to-decadal predictions experiments,
Clim. Dynam., 40, 245–272, https://doi.org/10.1007/s00382-012-1481-2, 2013. a, b
Goodfellow, I.: NIPS 2016 Tutorial: Generative Adversarial Networks, available at: http://arxiv.org/abs/1701.00160 (last access: 24 February 2020), 2016. a
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair,
S., Courville, A., and Bengio, Y.: Generative Adversarial Nets, in: Advances
in Neural Information Processing Systems 27, edited by: Ghahramani, Z.,
Welling, M., Cortes, C., Lawrence, N. D., and Weinberger, K. Q.,
2672–2680, Curran Associates, Inc., available at:
http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf (last access: 24 February 2020),
2014. a
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, MIT Press, available at:
http://www.deeplearningbook.org (last access: 24 February 2020), 2016. a
Guemas, V., Doblas-Reyes, F. J., Andreu-Burillo, I., and Asif, M.:
Retrospective prediction of the global warming slowdown in the past decade,
Nat. Clim. Change, 3, 649–653, https://doi.org/10.1038/nclimate1863, 2013. a
Herger, N., Sanderson, B. M., and Knutti, R.: Improved pattern scaling
approaches for the use in climate impact studies, Geophys. Res. Lett., 42,
3486–3494, https://doi.org/10.1002/2015GL063569, 2015. a
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-R., Jaitly, N., Senior,
A., Vanhoucke, V., Nguyen, P., Sainath, T. N., and Kingsbury, B.: Deep neural networks
for acoustic modeling in speech recognition: The shared views of four
research groups, IEEE Signal Processing Magazine, 29, 82–97, 2012. a
Hong, S., Kim, S., Joh, M., and Song, S.-K.: GlobeNet: Convolutional Neural
Networks for Typhoon Eye Tracking from Remote Sensing Imagery, ArXiv
e-prints, http://arxiv.org/abs/1708.03417, 2017. a
Ioffe, S. and Szegedy, C.: Batch normalization: Accelerating deep network
training by reducing internal covariate shift, arXiv preprint
arXiv:1502.03167, 2015. a
Jay, A., Reidmiller, D., Avery, C., Barrie, D., DeAngelo, B., Dave, A.,
Dzaugis, M., Kolian, M., Lewis, K., Reeves, K., and Winner, D.: Overview, in:
Impacts, Risks, and Adaptation in the United States: Fourth National Climate
Assessment, Volume II, edited by: Reidmiller, D., Avery, C., Easterling, D.,
Kunkel, K., Lewis, K., Maycock, T., and Stewart, B., 33–71, U.S. Global
Change Research Program, Washington, DC, USA, https://doi.org/10.7930/NCA4.2018.CH1,
2018. a
Jiang, G.-Q., Xu, J., and Wei, J.: A Deep Learning Algorithm of Neural Network
for the Parameterization of Typhoon-Ocean Feedback in Typhoon Forecast
Models, Geophys. Res. Lett., 45, 3706–3716, https://doi.org/10.1002/2018GL077004,
2018. a
JMA: Verification Indices, available at:
https://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline2013-nwp/pdf/outline2013_Appendix_A.pdf (last access: February 2020),
2019. a
Joliffe, I. and Stephenson, D.: Forecast verification, John Wiley and Sons,
2003. a
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., and Fei-Fei,
L.: Large-scale video classification with convolutional neural networks, in:
Proceedings of the IEEE conference on Computer Vision and Pattern
Recognition, Computer Vision Foundation, 1725–1732, 2014. a
Lean, J. L. and Rind, D. H.: How will Earth's surface temperature change in
future decades?, Geophys. Res. Lett., 36, L15708,
https://doi.org/10.1029/2009GL038923, 2009. a
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P.: Gradient-based learning
applied to document recognition, Proceedings of the IEEE, 86, 2278–2324,
1998. a
LeCun, Y. A., Bottou, L., Orr, G. B., and Müller, K.-R.: Efficient
backprop, in: Neural networks: Tricks of the trade, 9–48, Springer,
2012. a
Liu, Y., Racah, E., Prabhat, Correa, J., Khosrowshahi, A., Lavers, D., Kunkel,
K., Wehner, M., and Collins, W.: Application of Deep Convolutional Neural
Networks for Detecting Extreme Weather in Climate Datasets, ArXiv e-prints,
http://arxiv.org/abs/1605.01156, 2016. a
Lu, D. and Ricciuto, D.: Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques, Geosci. Model Dev., 12, 1791–1807, https://doi.org/10.5194/gmd-12-1791-2019, 2019. a
Lynch, C., Hartin, C., Bond-Lamberty, B., and Kravitz, B.: An open-access CMIP5 pattern library for temperature and precipitation: description and methodology, Earth Syst. Sci. Data, 9, 281–292, https://doi.org/10.5194/essd-9-281-2017, 2017. a
MacMartin, D. G. and Kravitz, B.: Dynamic climate emulators for solar geoengineering, Atmos. Chem. Phys., 16, 15789–15799, https://doi.org/10.5194/acp-16-15789-2016, 2016. a
McDermott, P. L. and Wikle, C. K.: Deep echo state networks with uncertainty
quantification for spatio-temporal forecasting, Environmetrics, 30, e2553,
https://doi.org/10.1002/env.2553, 2018. a
Miller, J., Nair, U., Ramachandran, R., and Maskey, M.: Detection of transverse
cirrus bands in satellite imagery using deep learning, Comput.
Geosci., 118, 79–85, https://doi.org/10.1016/j.cageo.2018.05.012, 2018. a
Mitchell, T. D.: Pattern Scaling: An Examination of the Accuracy of the
Technique for Describing Future Climates, Clim. Change, 60, 217–242,
https://doi.org/10.1023/A:1026035305597, 2003. a
Moss, R. H., Kravitz, B., Delgado, A., Asrar, G., Brandenberger, J., Wigmosta,
M., Preston, K., Buzan, T., Gremillion, M., Shaw, P., Stocker, K., Higuchi,
S., Sarma, A., Kosmal, A., Lawless, S., Marqusee, J., Lipschultz, F.,
O'Connell, R., Olsen, R., Walker, D., Weaver, C., Westley, M., and Wright,
R.: Nonstationary Weather Patterns and Extreme Events: Informing Design and
Planning for Long-Lived Infrastructure, Tech. rep., ESTCP, ESTCP Project
RC-201591, 2017. a
Nair, V. and Hinton, G. E.: Rectified linear units improve restricted boltzmann
machines, in: Proceedings of the 27th international conference on machine
learning (ICML-10), Association for Computing Machinery, 807–814, 2010. a
Ouyang, Q. and Lu, W.: Monthly rainfall forecasting using echo state networks coupled with data preprocessing methods, Water Resour. Mange., 32, 659–674,
https://doi.org/10.1007/s11269-017-1832-1, 2018. a
Pradhan, R., Aygun, R. S., Maskey, M., Ramachandran, R., and Cecil, D. J.:
Tropical Cyclone Intensity Estimation Using a Deep Convolutional Neural
Network, IEEE Transactions on Image Processing, 27, 692–702,
https://doi.org/10.1109/TIP.2017.2766358, 2018. a
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid
processes in climate models, P. Natl. Acad. Sci., 115, 9684–9689,
https://doi.org/10.1073/pnas.1810286115, 2018. a
Robertson, A. W., Kumar, A., Peña, M., and Vitart, F.: Improving and
Promoting Subseasonal to Seasonal Prediction, B. Am. Meteor. Soc., 96,
ES49–ES53, https://doi.org/10.1175/BAMS-D-14-00139.1, 2015. a
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for
Biomedical Image Segmentation, in: Medical Image Computing and
Computer-Assisted Intervention (MICCAI), vol. 9351 of LNCS,
234–241, Springer, available at:
http://lmb.informatik.uni-freiburg.de/Publications/2015/RFB15a (last access: February 2020),
(available on arXiv:1505.04597 [cs.CV]), 2015. a
Santer, B., Wigley, T., Schlesinger, M., and Mitchell, J.: Developing Climate
Scenarios from Equilibrium GCM Results, Tech. rep., Hamburg, Germany,
1990. a
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-k., and WOO, W.-c.:
Convolutional LSTM Network: A Machine Learning Approach for Precipitation
Nowcasting, in: Advances in Neural Information Processing Systems 28, edited
by: Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R.,
802–810, Curran Associates, Inc., available at:
http://papers.nips.cc/paper/5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting.pdf (last access: February 2020),
2015. a
Srivastava, N., Hinton, G., Krizhevkskey, A., Sutskever, I., and Salakhutdinov,
R.: Dropout: A simple way to prevent neural networks for overfitting, J. Mach. Learn. Res., 15, 1929–1958, 2014. a
Stocker, T. F., Qin, D., Plattner, G.-K., Alexander, L. V., Allen, S. K., Bindoff, N. L., Bréon, F.-M., Church, J. A., Cubasch, U., Emori, S., Forster, P., Friedlingstein, P., Gillett, N., Gregory, J. M., Hartmann, D. L., Jansen, E., Kirtman, B., Knutti, R., Krishna Kumar, K., Lemke, P., Marotzke, J., Masson-Delmotte, V., Meehl, G. A., Mokhov, I. I., Piao, S., Ramaswamy, V., Randall, D., Rhein, M., Rojas, M., Sabine, C., Shindell, D., Talley, L. D., Vaughan, D. G., and Xie, S.-P.: Technical Summary, in: Climate Change 2013: The Physical
Science Basis. Contribution of Working Group I to the Fifth Assessment Report
of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York,
NY, USA, 2013. a
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D.,
Vanhoucke, V., and Rabinovich, A.: Going deeper with convolutions, in:
Proceedings of the IEEE conference on computer vision and pattern
recognition, 8–10 June 2015, Boston, Massachusetts, 1–9, 2015. a
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard,
K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T.,
Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The
representative concentration pathways: An overview, Clim. Change, 109,
5–31, https://doi.org/10.1007/s10584-011-0148-z, 2011. a
Weber, T., Corotan, A., Hutchinson, B., Kravitz, B., and Link, R. P.: A Deep Neural Network approach for estimating precipitation fields in Earth System Models, available at: https://github.com/hutchresearch/deep_climate_emulator, last access: 24 February 2020. a
Yao, Y., Rosasco, L., and Caponnetto, A.: On early stopping in gradient descent
learning, Constructive Approximation, 26, 289–315, 2007. a
Yeager, S., Danabasoglu, G., Rosenbloom, N., Strand, W., Bates, S., Meehl, G.,
Karspeck, A., Lindsay, K., Long, M., Teng, H., and Lovenduski, N.: Predicting
Near-Term Changes in the Earth System: A Large Ensemble of Initialized
Decadal Prediction Simulations Using the Community Earth System Model,
B. Am. Meteor. Soc., 99, 1867–1886, https://doi.org/10.1175/BAMS-D-17-0098.1,
2018. a
Yeh, S.-W., Cai, W., Min, S.-K., McPhaden, M. J., Dommenget, D., Dewitte, B.,
Collins, M., Ashok, K., An, S.-I., Yim, B.-Y., and Kug, J.-S.: ENSO
Atmospheric Teleconnections and Their Response to Greenhouse Gas Forcing,
Rev. Geophys., 56, 185–206, https://doi.org/10.1002/2017RG000568, 2018. a
Yuan, N., Huang, Y., Duan, J., Zhu, C., Xoplaki, E., and Luterbacher, J.: On
climate prediction: How much can we expect from climate memory?, Clim.
Dynam., 52, 855–864, https://doi.org/10.1007/s00382-018-4168-5, 2019. a
Zhang, S. and Sutton, R. S.: A Deeper Look at Experience Replay, CoRR,
abs/1712.01275, 2017. a
Short summary
Climate model emulators can save computer time but are less accurate than full climate models. We use neural networks to build emulators of precipitation, trained on existing climate model runs. By doing so, we can capture nonlinearities and how the past state of a model (to some degree) shapes the future state. Our emulator outperforms a persistence forecast of precipitation.
Climate model emulators can save computer time but are less accurate than full climate models....
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