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: 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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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)
Above-cloud concentrations of cloud condensation nuclei help to sustain some Arctic low-level clouds
Contrail formation on ambient aerosol particles for aircraft with hydrogen combustion: a box model trajectory study
Effects of intermittent aerosol forcing on the stratocumulus-to-cumulus transition
Cloud properties and their projected changes in CMIP models with low to high climate sensitivity
Water isotopic characterisation of the cloud–circulation coupling in the North Atlantic trades – Part 2: The imprint of the atmospheric circulation at different scales
Impact of urban land use on mean and heavy rainfall during the Indian summer monsoon
Distribution and morphology of non-persistent and persistent contrail formation areas in ERA5
Towards a more reliable forecast of ice supersaturation: concept of a one-moment ice-cloud scheme that avoids saturation adjustment
Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts
Variability of the properties of the distribution of the relative humidity with respect to ice: Implications for contrail formation
Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model
Water isotopic characterisation of the cloud–circulation coupling in the North Atlantic trades – Part 1: A process-oriented evaluation of COSMOiso simulations with EUREC4A observations
Simulating the seeder-feeder impacts on cloud ice and precipitation over the Alps
Assimilation of 3D polarimetric microphysical retrievals in a convective-scale NWP system
Sensitivity of cloud-phase distribution to cloud microphysics and thermodynamics in simulated deep convective clouds and SEVIRI retrievals
Interactions between trade-wind clouds and local forcings over the Great Barrier Reef: A case study using convection-permitting simulations
Assessing the destructiveness of tropical cyclones induced by anthropogenic aerosols in an atmosphere–ocean coupled framework
Opinion: A critical evaluation of the evidence for aerosol invigoration of deep convection
Impact of ice multiplication on the cloud electrification of a cold-season thunderstorm: a numerical case study
Historical (1960–2014) lightning and LNOx trends and their controlling factors in a chemistry–climate model
The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations
Evaluation of hygroscopic cloud seeding in warm-rain processes by a hybrid microphysics scheme using a Weather Research and Forecasting (WRF) model: a real case study
Effects of longwave radiative cooling on advection fog over the Northwest Pacific Ocean: Observations and large eddy simulations
Radiation fog properties in two consecutive events under polluted and clean conditions in the Yangtze River Delta, China: a simulation study
A bin microphysics parcel model investigation of secondary ice formation in an idealised shallow convective cloud
Influence of atmospheric rivers and associated weather systems on precipitation in the Arctic
Insights of warm-cloud biases in Community Atmospheric Model 5 and 6 from the single-column modeling framework and Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) observations
Interaction of microphysics and dynamics in a warm conveyor belt simulated with the ICOsahedral Nonhydrostatic (ICON) model
Does prognostic seeding along flight tracks produce the desired effects of cirrus cloud thinning?
Large-eddy simulation of a two-layer boundary-layer cloud system from the Arctic Ocean 2018 expedition
Opposing trends of cloud coverage over land and ocean under global warming
Aerosol–cloud–radiation interaction during Saharan dust episodes: the dusty cirrus puzzle
Aerosol–cloud impacts on aerosol detrainment and rainout in shallow maritime tropical clouds
Mixed-phase direct numerical simulation: ice growth in cloud-top generating cells
Aerosol impacts on the entrainment efficiency of Arctic mixed-phase convection in a simulated air mass over open water
Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System
Evaluation of aerosol–cloud interactions in E3SM using a Lagrangian framework
Cloud response to co-condensation of water and organic vapors over the boreal forest
Impact of formulations of the homogeneous nucleation rate on ice nucleation events in cirrus
Temperature and cloud condensation nuclei (CCN) sensitivity of orographic precipitation enhanced by a mixed-phase seeder–feeder mechanism: a case study for the 2015 Cumbria flood
Aerosol–precipitation elevation dependence over the central Himalayas using cloud-resolving WRF-Chem numerical modeling
Machine learning of cloud types in satellite observations and climate models
A modeling study of an extreme rainfall event along the northern coast of Taiwan on 2 June 2017
Long-term upper-troposphere climatology of potential contrail occurrence over the Paris area derived from radiosonde observations
Equilibrium climate sensitivity increases with aerosol concentration due to changes in precipitation efficiency
Southern Ocean cloud and shortwave radiation biases in a nudged climate model simulation: does the model ever get it right?
Aerosol characteristics and polarimetric signatures for a deep convective storm over the northwestern part of Europe – modeling and observations
Evaluation of tropical water vapour from CMIP6 global climate models using the ESA CCI Water Vapour climate data records
Aerosol–stratocumulus interactions: towards a better process understanding using closures between observations and large eddy simulations
The impacts of secondary ice production on microphysics and dynamics in tropical convection
Lucas J. Sterzinger and Adele L. Igel
Atmos. Chem. Phys., 24, 3529–3540, https://doi.org/10.5194/acp-24-3529-2024, https://doi.org/10.5194/acp-24-3529-2024, 2024
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Using idealized large eddy simulations, we find that clouds forming in the Arctic in environments with low concentrations of aerosol particles may be sustained by mixing in new particles through the cloud top. Observations show that higher concentrations of these particles regularly exist above cloud top in concentrations that are sufficient to promote this sustenance.
Andreas Bier, Simon Unterstrasser, Josef Zink, Dennis Hillenbrand, Tina Jurkat-Witschas, and Annemarie Lottermoser
Atmos. Chem. Phys., 24, 2319–2344, https://doi.org/10.5194/acp-24-2319-2024, https://doi.org/10.5194/acp-24-2319-2024, 2024
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Using hydrogen as aviation fuel affects contrails' climate impact. We study contrail formation behind aircraft with H2 combustion. Due to the absence of soot emissions, contrail ice crystals are assumed to form only on ambient particles mixed into the plume. The ice crystal number, which strongly varies with temperature and aerosol number density, is decreased by more than 80 %–90 % compared to kerosene contrails. However H2 contrails can form at lower altitudes due to higher H2O emissions.
Prasanth Prabhakaran, Fabian Hoffmann, and Graham Feingold
Atmos. Chem. Phys., 24, 1919–1937, https://doi.org/10.5194/acp-24-1919-2024, https://doi.org/10.5194/acp-24-1919-2024, 2024
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In this study, we explore the impact of deliberate aerosol perturbation in the northeast Pacific region using large-eddy simulations. Our results show that cloud reflectivity is sensitive to the aerosol sprayer arrangement in the pristine system, whereas in the polluted system it is largely proportional to the total number of aerosol particles injected. These insights would aid in assessing the efficiency of various aerosol injection strategies for climate intervention applications.
Lisa Bock and Axel Lauer
Atmos. Chem. Phys., 24, 1587–1605, https://doi.org/10.5194/acp-24-1587-2024, https://doi.org/10.5194/acp-24-1587-2024, 2024
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Climate model simulations still show a large range of effective climate sensitivity (ECS) with high uncertainties. An important contribution to ECS is cloud climate feedback. We investigate the representation of cloud physical and radiative properties from Coupled Model Intercomparison Project models grouped by ECS. We compare the simulated cloud properties of today’s climate from three ECS groups and quantify how the projected changes in cloud properties and cloud radiative effects differ.
Leonie Villiger and Franziska Aemisegger
Atmos. Chem. Phys., 24, 957–976, https://doi.org/10.5194/acp-24-957-2024, https://doi.org/10.5194/acp-24-957-2024, 2024
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Three numerical simulations performed with an isotope-enabled weather forecast model are used to investigate the cloud–circulation coupling between shallow trade-wind cumulus clouds and atmospheric circulations on different scales. It is shown that stable water isotopes near cloud base in the tropics reflect (1) the diel cycle of the atmospheric circulation, which drives the formation and dissipation of clouds, and (2) changes in the large-scale circulation over the North Atlantic.
Renaud Falga and Chien Wang
Atmos. Chem. Phys., 24, 631–647, https://doi.org/10.5194/acp-24-631-2024, https://doi.org/10.5194/acp-24-631-2024, 2024
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The impact of urban land use on regional meteorology and rainfall during the Indian summer monsoon has been assessed in this study. Using a cloud-resolving model centered around Kolkata, we have shown that the urban heat island effect led to a rainfall enhancement via the amplification of convective activity, especially during the night. Furthermore, the results demonstrated that the kinetic effect of the city induced the initiation of a nighttime storm.
Kevin Wolf, Nicolas Bellouin, and Olivier Boucher
EGUsphere, https://doi.org/10.5194/egusphere-2023-3086, https://doi.org/10.5194/egusphere-2023-3086, 2024
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The contrail formation potential and its tempo-spatial distribution are estimated for the North Atlantic flight corridor. Meteorological conditions of temperature and relative humidity are taken from the ERA5 re-analysis and IAGOS. Based on IAGOS flight tracks, crossing length, size, orientation, frequency of occurrence, and overlap of persistent contrail formation areas are determined. The presented conclusions might provide a guide for statistical flight track optimization to reduce contrails.
Dario Sperber and Klaus Gierens
Atmos. Chem. Phys., 23, 15609–15627, https://doi.org/10.5194/acp-23-15609-2023, https://doi.org/10.5194/acp-23-15609-2023, 2023
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A significant share of aviation's climate impact is due to persistent contrails. Avoiding their creation is a step toward sustainable air transportation. For this purpose, a reliable forecast of so-called ice-supersaturated regions is needed, which then allows one to plan aircraft routes without persistent contrails. Here, we propose a method that leads to the better prediction of ice-supersaturated regions.
Blaž Gasparini, Sylvia C. Sullivan, Adam B. Sokol, Bernd Kärcher, Eric Jensen, and Dennis L. Hartmann
Atmos. Chem. Phys., 23, 15413–15444, https://doi.org/10.5194/acp-23-15413-2023, https://doi.org/10.5194/acp-23-15413-2023, 2023
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Tropical cirrus clouds are essential for climate, but our understanding of these clouds is limited due to their dependence on a wide range of small- and large-scale climate processes. In this opinion paper, we review recent advances in the study of tropical cirrus clouds, point out remaining open questions, and suggest ways to resolve them.
Sidiki Sanogo, Olivier Boucher, Nicolas Bellouin, Audran Borella, Kevin Wolf, and Susanne Rohs
EGUsphere, https://doi.org/10.5194/egusphere-2023-2601, https://doi.org/10.5194/egusphere-2023-2601, 2023
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Relative humidity relative to ice (RHi) is a key variable in the formation of cirrus clouds and contrails. This study shows that the properties of the probability density function of RHi differ between the tropics and higher latitudes. In link with RHi and temperature variability, aircraft are likely to produce more contrails with bioethanol and hydrogen as fuel. The impact of this fuel change decreases with decreasing pressure levels, but increases from high latitudes to the tropics.
Ulrike Proske, Sylvaine Ferrachat, and Ulrike Lohmann
EGUsphere, https://doi.org/10.5194/egusphere-2023-2783, https://doi.org/10.5194/egusphere-2023-2783, 2023
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Climate models include treatment of aerosol particles because these influence clouds and radiation. Over time their representation has grown increasingly detailed. This complexity may hinder our understanding of model behaviour. Thus here we simplify the aerosol representation of our climate model by prescribing a mean concentration, which saves runtime and helps to discover unexpected model behaviour. We conclude that simplifications provide a new perspective for model study and development.
Leonie Villiger, Marina Dütsch, Sandrine Bony, Marie Lothon, Stephan Pfahl, Heini Wernli, Pierre-Etienne Brilouet, Patrick Chazette, Pierre Coutris, Julien Delanoë, Cyrille Flamant, Alfons Schwarzenboeck, Martin Werner, and Franziska Aemisegger
Atmos. Chem. Phys., 23, 14643–14672, https://doi.org/10.5194/acp-23-14643-2023, https://doi.org/10.5194/acp-23-14643-2023, 2023
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This study evaluates three numerical simulations performed with an isotope-enabled weather forecast model and investigates the coupling between shallow trade-wind cumulus clouds and atmospheric circulations on different scales. We show that the simulations reproduce key characteristics of shallow trade-wind clouds as observed during the field experiment EUREC4A and that the spatial distribution of stable-water-vapour isotopes is shaped by the overturning circulation associated with these clouds.
Zane Dedekind, Ulrike Proske, Sylvaine Ferrachat, Ulrike Lohmann, and David Neubauer
EGUsphere, https://doi.org/10.5194/egusphere-2023-874, https://doi.org/10.5194/egusphere-2023-874, 2023
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Ice particles precipitating into lower clouds from an upper cloud, the seeder-feeder process, can enhance precipitation. A numerical modeling study conducted in the Swiss Alps found that 48 % of observed clouds were overlapping, in which the seeder-feeder process occurred 10 % of these clouds. Inhibiting the seeder-feeder process reduced the surface precipitation and ice particle growth rates, which were further reduced when additional ice multiplication processes were included in the model.
Lucas Reimann, Clemens Simmer, and Silke Trömel
Atmos. Chem. Phys., 23, 14219–14237, https://doi.org/10.5194/acp-23-14219-2023, https://doi.org/10.5194/acp-23-14219-2023, 2023
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Polarimetric radar observations were assimilated for the first time in a convective-scale numerical weather prediction system in Germany and their impact on short-term precipitation forecasts was evaluated. The assimilation was performed using microphysical retrievals of liquid and ice water content and yielded slightly improved deterministic 9 h precipitation forecasts for three intense summer precipitation cases with respect to the assimilation of radar reflectivity alone.
Cunbo Han, Corinna Hoose, Martin Stengel, Quentin Coopman, and Andrew Barrett
Atmos. Chem. Phys., 23, 14077–14095, https://doi.org/10.5194/acp-23-14077-2023, https://doi.org/10.5194/acp-23-14077-2023, 2023
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Cloud phase has been found to significantly impact cloud thermodynamics and Earth’s radiation budget, and various factors influence it. This study investigates the sensitivity of the cloud-phase distribution to the ice-nucleating particle concentration and thermodynamics. Multiple simulation experiments were performed using the ICON model at the convection-permitting resolution of 1.2 km. Simulation results were compared to two different retrieval products based on SEVIRI measurements.
Wenhui Zhao, Yi Huang, Steven Thomas Siems, Michael James Manton, and Daniel Patrick Harrison
EGUsphere, https://doi.org/10.5194/egusphere-2023-2633, https://doi.org/10.5194/egusphere-2023-2633, 2023
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We studied how shallow clouds and rain behave over the Great Barrier Reef (GBR) using a detailed weather model. We found that the shape of the land, especially mountains, and particles in the air play big roles in influencing these clouds. Surprisingly, the sea's temperature had a smaller effect. Our research helps us understand the GBR's climate and how various factors can influence it, where the importance of the local cloud in thermal coral bleaching has recently been identified.
Yun Lin, Yuan Wang, Jen-Shan Hsieh, Jonathan H. Jiang, Qiong Su, Lijun Zhao, Michael Lavallee, and Renyi Zhang
Atmos. Chem. Phys., 23, 13835–13852, https://doi.org/10.5194/acp-23-13835-2023, https://doi.org/10.5194/acp-23-13835-2023, 2023
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Tropical cyclones (TCs) can cause catastrophic damage to coastal regions. We used a numerical model that explicitly simulates aerosol–cloud interaction and atmosphere–ocean coupling. We show that aerosols and ocean coupling work together to make TC storms bigger but weaker. Moreover, TCs in polluted air have more rainfall and higher sea levels, leading to more severe storm surges and flooding. Our research highlights the roles of aerosols and ocean-coupling feedbacks in TC hazard assessment.
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.
Jing Yang, Shiye Huang, Qilin Zhang, Xiaoqin Jing, Yuting Deng, and Yubao Liu
EGUsphere, https://doi.org/10.5194/egusphere-2023-2188, https://doi.org/10.5194/egusphere-2023-2188, 2023
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This study contributes to fill the dearth of understanding the impacts of different secondary ice production (SIP) processes on the cloud electrification in cold-season thunderstorm. The results suggest the SIP, especially the rime-splintering process and the shattering of freezing drops, have significant impacts on the charge structure of the storm. In addition, the modelled radar composite reflectivity and flash rate are improved after implementing the three SIP processes in the model.
Yanfeng He and Kengo Sudo
Atmos. Chem. Phys., 23, 13061–13085, https://doi.org/10.5194/acp-23-13061-2023, https://doi.org/10.5194/acp-23-13061-2023, 2023
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Lightning has big social impacts. Lightning-produced NOx (LNOx) plays a vital role in atmospheric chemistry and climate. Investigating past lightning and LNOx trends can provide essential indicators of all lightning-related phenomena. Simulations show almost flat global lightning and LNOx trends during 1960–2014. Past global warming enhances the trends positively, but increases in aerosol have the opposite effect. Moreover, global lightning decreased markedly after the Pinatubo eruption.
Hannah C. Frostenberg, André Welti, Mikael Luhr, Julien Savre, Erik S. Thomson, and Luisa Ickes
Atmos. Chem. Phys., 23, 10883–10900, https://doi.org/10.5194/acp-23-10883-2023, https://doi.org/10.5194/acp-23-10883-2023, 2023
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Observations show that ice-nucleating particle concentrations (INPCs) have a large variety and follow lognormal distributions for a given temperature. We introduce a new immersion freezing parameterization that applies this lognormal behavior. INPCs are drawn randomly from a temperature-dependent lognormal distribution. We then show that the ice content of the modeled Arctic stratocumulus cloud is highly sensitive to the probability of drawing large INPCs.
Kai-I Lin, Kao-Shen Chung, Sheng-Hsiang Wang, Li-Hsin Chen, Yu-Chieng Liou, Pay-Liam Lin, Wei-Yu Chang, Hsien-Jung Chiu, and Yi-Hui Chang
Atmos. Chem. Phys., 23, 10423–10438, https://doi.org/10.5194/acp-23-10423-2023, https://doi.org/10.5194/acp-23-10423-2023, 2023
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This study develops a hybrid microphysics scheme to enable the complex model simulation of cloud seeding based on observational cloud condensation nuclei size distribution. Our results show that more precipitation can be developed in the scenarios seeding in the in-cloud region, and seeding over an area of tens km2 is the most efficient strategy due to the strengthening of the accretion process. Moreover, particles bigger than 0.4 μm are the main factor contributing to cloud-seeding effects.
Liu Yang, Saisai Ding, Jing-Wu Liu, and Su-Ping Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2023-1494, https://doi.org/10.5194/egusphere-2023-1494, 2023
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Advection fog occurs when warm and moist air moves over a cold sea surface. In this situation, the temperature of the foggy air usually drops below the sea surface temperature (SST), particularly at night. High-resolution simulations show that the cooling effect of longwave radiation from the top of the fog layer permeates through the fog, resulting in a cooling of the surface air below SST. This study emphasizes the significance of monitoring air temperature to enhance sea fog forecasting.
Naifu Shao, Chunsong Lu, Xingcan Jia, Yuan Wang, Yubin Li, Yan Yin, Bin Zhu, Tianliang Zhao, Duanyang Liu, Shengjie Niu, Shuxian Fan, Shuqi Yan, and Jingjing Lv
Atmos. Chem. Phys., 23, 9873–9890, https://doi.org/10.5194/acp-23-9873-2023, https://doi.org/10.5194/acp-23-9873-2023, 2023
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Fog is an important meteorological phenomenon that affects visibility. Aerosols and the planetary boundary layer (PBL) play critical roles in the fog life cycle. In this study, aerosol-induced changes in fog properties become more remarkable in the second fog (Fog2) than in the first fog (Fog1). The reason is that aerosol–cloud interaction (ACI) delays Fog1 dissipation, leading to the PBL meteorological conditions being more conducive to Fog2 formation and to stronger ACI in Fog2.
Rachel L. James, Jonathan Crosier, and Paul J. Connolly
Atmos. Chem. Phys., 23, 9099–9121, https://doi.org/10.5194/acp-23-9099-2023, https://doi.org/10.5194/acp-23-9099-2023, 2023
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Secondary ice production (SIP) may significantly enhance the ice particle concentration in mixed-phase clouds. We present a systematic modelling study of secondary ice formation in idealised shallow convective clouds for various conditions. Our results suggest that the SIP mechanism of collisions of supercooled water drops with more massive ice particles may be a significant ice formation mechanism in shallow convective clouds outside the rime-splintering temperature range (−3 to −8 °C).
Melanie Lauer, Annette Rinke, Irina Gorodetskaya, Michael Sprenger, Mario Mech, and Susanne Crewell
Atmos. Chem. Phys., 23, 8705–8726, https://doi.org/10.5194/acp-23-8705-2023, https://doi.org/10.5194/acp-23-8705-2023, 2023
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We present a new method to analyse the influence of atmospheric rivers (ARs), cyclones, and fronts on the precipitation in the Arctic, based on two campaigns: ACLOUD (early summer 2017) and AFLUX (early spring 2019). There are differences between both campaign periods: in early summer, the precipitation is mostly related to ARs and fronts, especially when they are co-located, while in early spring, cyclones isolated from ARs and fronts contributed most to the precipitation.
Yuan Wang, Xiaojian Zheng, Xiquan Dong, Baike Xi, and Yuk L. Yung
Atmos. Chem. Phys., 23, 8591–8605, https://doi.org/10.5194/acp-23-8591-2023, https://doi.org/10.5194/acp-23-8591-2023, 2023
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Marine boundary layer clouds remain poorly predicted in global climate models due to multiple entangled uncertainty sources. This study uses the in situ observations from a recent field campaign to constrain and evaluate cloud physics in a simplified version of a climate model. Progress and remaining issues in the cloud physics parameterizations are identified. We systematically evaluate the impacts of large-scale forcing, microphysical scheme, and aerosol concentrations on the cloud property.
Annika Oertel, Annette K. Miltenberger, Christian M. Grams, and Corinna Hoose
Atmos. Chem. Phys., 23, 8553–8581, https://doi.org/10.5194/acp-23-8553-2023, https://doi.org/10.5194/acp-23-8553-2023, 2023
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Warm conveyor belts (WCBs) are cloud- and precipitation-producing airstreams in extratropical cyclones that are important for the large-scale flow and cloud radiative forcing. We analyze cloud formation processes during WCB ascent in a two-moment microphysics scheme. Quantification of individual diabatic heating rates shows the importance of condensation, vapor deposition, rain evaporation, melting, and cloud-top radiative cooling for total heating and WCB-related potential vorticity structure.
Colin Tully, David Neubauer, Diego Villanueva, and Ulrike Lohmann
Atmos. Chem. Phys., 23, 7673–7698, https://doi.org/10.5194/acp-23-7673-2023, https://doi.org/10.5194/acp-23-7673-2023, 2023
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This study details the first attempt with a GCM to simulate a fully prognostic aerosol species specifically for cirrus climate intervention. The new approach is in line with the real-world delivery mechanism via aircraft. However, to achieve an appreciable signal from seeding, smaller particles were needed, and their mass emissions needed to be scaled by at least a factor of 100. These biases contributed to either overseeding or small and insignificant effects in response to seeding cirrus.
Ines Bulatovic, Julien Savre, Michael Tjernström, Caroline Leck, and Annica M. L. Ekman
Atmos. Chem. Phys., 23, 7033–7055, https://doi.org/10.5194/acp-23-7033-2023, https://doi.org/10.5194/acp-23-7033-2023, 2023
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We use numerical modeling with detailed cloud microphysics to investigate a low-altitude cloud system consisting of two cloud layers – a type of cloud situation which was commonly observed during the summer of 2018 in the central Arctic (north of 80° N). The model generally reproduces the observed cloud layers and the thermodynamic structure of the lower atmosphere well. The cloud system is maintained unless there are low aerosol number concentrations or high large-scale wind speeds.
Huan Liu, Ilan Koren, Orit Altaratz, and Mickaël D. Chekroun
Atmos. Chem. Phys., 23, 6559–6569, https://doi.org/10.5194/acp-23-6559-2023, https://doi.org/10.5194/acp-23-6559-2023, 2023
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Clouds' responses to global warming contribute the largest uncertainty in climate prediction. Here, we analyze 42 years of global cloud cover in reanalysis data and show a decreasing trend over most continents and an increasing trend over the tropical and subtropical oceans. A reduction in near-surface relative humidity can explain the decreasing trend in cloud cover over land. Our results suggest potential stress on the terrestrial water cycle, associated with global warming.
Axel Seifert, Vanessa Bachmann, Florian Filipitsch, Jochen Förstner, Christian M. Grams, Gholam Ali Hoshyaripour, Julian Quinting, Anika Rohde, Heike Vogel, Annette Wagner, and Bernhard Vogel
Atmos. Chem. Phys., 23, 6409–6430, https://doi.org/10.5194/acp-23-6409-2023, https://doi.org/10.5194/acp-23-6409-2023, 2023
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We investigate how mineral dust can lead to the formation of cirrus clouds. Dusty cirrus clouds lead to a reduction in solar radiation at the surface and, hence, a reduced photovoltaic power generation. Current weather prediction systems are not able to predict this interaction between mineral dust and cirrus clouds. We have developed a new physical description of the formation of dusty cirrus clouds. Overall we can show a considerable improvement in the forecast quality of clouds and radiation.
Gabrielle R. Leung, Stephen M. Saleeby, G. Alexander Sokolowsky, Sean W. Freeman, and Susan C. van den Heever
Atmos. Chem. Phys., 23, 5263–5278, https://doi.org/10.5194/acp-23-5263-2023, https://doi.org/10.5194/acp-23-5263-2023, 2023
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This study uses a suite of high-resolution simulations to explore how the concentration and type of aerosol particles impact shallow tropical clouds and the overall aerosol budget. Under more-polluted conditions, there are more aerosol particles present, but we also find that clouds are less able to remove those aerosol particles via rainout. Instead, those aerosol particles are more likely to be detrained aloft and remain in the atmosphere for further aerosol–cloud interactions.
Sisi Chen, Lulin Xue, Sarah Tessendorf, Kyoko Ikeda, Courtney Weeks, Roy Rasmussen, Melvin Kunkel, Derek Blestrud, Shaun Parkinson, Melinda Meadows, and Nick Dawson
Atmos. Chem. Phys., 23, 5217–5231, https://doi.org/10.5194/acp-23-5217-2023, https://doi.org/10.5194/acp-23-5217-2023, 2023
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The possible mechanism of effective ice growth in the cloud-top generating cells in winter orographic clouds is explored using a newly developed ultra-high-resolution cloud microphysics model. Simulations demonstrate that a high availability of moisture and liquid water is critical for producing large ice particles. Fluctuations in temperature and moisture down to millimeter scales due to cloud turbulence can substantially affect the growth history of the individual cloud particles.
Jan Chylik, Dmitry Chechin, Regis Dupuy, Birte S. Kulla, Christof Lüpkes, Stephan Mertes, Mario Mech, and Roel A. J. Neggers
Atmos. Chem. Phys., 23, 4903–4929, https://doi.org/10.5194/acp-23-4903-2023, https://doi.org/10.5194/acp-23-4903-2023, 2023
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Arctic low-level clouds play an important role in the ongoing warming of the Arctic. Unfortunately, these clouds are not properly represented in weather forecast and climate models. This study tries to cover this gap by focusing on clouds over open water during the spring, observed by research aircraft near Svalbard. The study combines the high-resolution model with sets of observational data. The results show the importance of processes that involve both ice and the liquid water in the clouds.
Gillian Young McCusker, Jutta Vüllers, Peggy Achtert, Paul Field, Jonathan J. Day, Richard Forbes, Ruth Price, Ewan O'Connor, Michael Tjernström, John Prytherch, Ryan Neely III, and Ian M. Brooks
Atmos. Chem. Phys., 23, 4819–4847, https://doi.org/10.5194/acp-23-4819-2023, https://doi.org/10.5194/acp-23-4819-2023, 2023
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In this study, we show that recent versions of two atmospheric models – the Unified Model and Integrated Forecasting System – overestimate Arctic cloud fraction within the lower troposphere by comparison with recent remote-sensing measurements made during the Arctic Ocean 2018 expedition. The overabundance of cloud is interlinked with the modelled thermodynamic structure, with strong negative temperature biases coincident with these overestimated cloud layers.
Matthew W. Christensen, Po-Lun Ma, Peng Wu, Adam C. Varble, Johannes Mülmenstädt, and Jerome D. Fast
Atmos. Chem. Phys., 23, 2789–2812, https://doi.org/10.5194/acp-23-2789-2023, https://doi.org/10.5194/acp-23-2789-2023, 2023
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An increase in aerosol concentration (tiny airborne particles) is shown to suppress rainfall and increase the abundance of droplets in clouds passing over Graciosa Island in the Azores. Cloud drops remain affected by aerosol for several days across thousands of kilometers in satellite data. Simulations from an Earth system model show good agreement, but differences in the amount of cloud water and its extent remain despite modifications to model parameters that control the warm-rain process.
Liine Heikkinen, Daniel G. Partridge, Wei Huang, Sara Blichner, Rahul Ranjan, Emanuele Tovazzi, Tuukka Petäjä, Claudia Mohr, and Ilona Riipinen
EGUsphere, https://doi.org/10.5194/egusphere-2023-164, https://doi.org/10.5194/egusphere-2023-164, 2023
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The organic vapor condensation with water vapor (co-condensation) is modeled in this work over the boreal forest environment because the forest air is rich in naturally emitted organic vapors. The simulations show that the number of cloud droplets can enhance by 20 % if the co-condensation process is considered. The enhancements are particularly high if the air contains small, naturally produced particles. Such conditions are most frequently met in Spring in the boreal forest.
Peter Spichtinger, Patrik Marschalik, and Manuel Baumgartner
Atmos. Chem. Phys., 23, 2035–2060, https://doi.org/10.5194/acp-23-2035-2023, https://doi.org/10.5194/acp-23-2035-2023, 2023
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We investigate the impact of the homogeneous nucleation rate on nucleation events in cirrus. As long as the slope of the rate is represented sufficiently well, the resulting ice crystal number concentrations are not crucially affected. Even a change in the prefactor over orders of magnitude does not change the results. However, the maximum supersaturation during nucleation events shows strong changes. This quantity should be used for diagnostics instead of the popular nucleation threshold.
Julia Thomas, Andrew Barrett, and Corinna Hoose
Atmos. Chem. Phys., 23, 1987–2002, https://doi.org/10.5194/acp-23-1987-2023, https://doi.org/10.5194/acp-23-1987-2023, 2023
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We study the sensitivity of rain formation processes during a heavy-rainfall event over mountains to changes in temperature and pollution. Total rainfall increases by 2 % K−1, and a 6 % K−1 increase is found at the highest altitudes, caused by a mixed-phase seeder–feeder mechanism (frozen cloud particles melt and grow further as they fall through a liquid cloud layer). In a cleaner atmosphere this process is enhanced. Thus the risk of severe rainfall in mountains may increase in the future.
Pramod Adhikari and John F. Mejia
Atmos. Chem. Phys., 23, 1019–1042, https://doi.org/10.5194/acp-23-1019-2023, https://doi.org/10.5194/acp-23-1019-2023, 2023
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We used an atmospheric model to assess the impact of aerosols through radiation and cloud interaction on elevation-dependent precipitation and surface temperature over the central Himalayan region. Results showed contrasting altitudinal precipitation responses to the increased aerosol concentration, which can significantly impact the hydroclimate of the central Himalayas, increasing the risk for extreme events and influencing the regional supply of water resources.
Peter Kuma, Frida A.-M. Bender, Alex Schuddeboom, Adrian J. McDonald, and Øyvind Seland
Atmos. Chem. Phys., 23, 523–549, https://doi.org/10.5194/acp-23-523-2023, https://doi.org/10.5194/acp-23-523-2023, 2023
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We present a machine learning method for determining cloud types in climate model output and satellite observations based on ground observations of cloud genera. We analyse cloud type biases and changes with temperature in climate models and show that the bias is anticorrelated with climate sensitivity. Models simulating decreasing stratiform and increasing cumuliform clouds with increased CO2 concentration tend to have higher climate sensitivity than models simulating the opposite tendencies.
Chung-Chieh Wang, Ting-Yu Yeh, Chih-Sheng Chang, Ming-Siang Li, Kazuhisa Tsuboki, and Ching-Hwang Liu
Atmos. Chem. Phys., 23, 501–521, https://doi.org/10.5194/acp-23-501-2023, https://doi.org/10.5194/acp-23-501-2023, 2023
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The extreme rainfall event (645 mm in 24 h) at the northern coast of Taiwan on 2 June 2017 is studied using a cloud model. Two 1 km experiments with peak amounts of 541 and 400 mm are compared to isolate the reasons for such a difference. It is found that the frontal rainband remains fixed in location for a longer period in the former run due to a low disturbance that acts to focus the near-surface convergence. Therefore, the rainfall is more concentrated and there is a higher total amount.
Kevin Wolf, Nicolas Bellouin, and Olivier Boucher
Atmos. Chem. Phys., 23, 287–309, https://doi.org/10.5194/acp-23-287-2023, https://doi.org/10.5194/acp-23-287-2023, 2023
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Recent studies estimate the radiative impact of contrails to be similar to or larger than that of emitted CO2; thus, contrail mitigation might be an opportunity to reduce the climate effects of aviation. A radiosonde data set is analyzed in terms of the vertical distribution of potential contrails, contrail mitigation by flight altitude changes, and linkages with the tropopause and jet stream. The effect of prospective jet engine developments and alternative fuels are estimated.
Guy Dagan
Atmos. Chem. Phys., 22, 15767–15775, https://doi.org/10.5194/acp-22-15767-2022, https://doi.org/10.5194/acp-22-15767-2022, 2022
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Using idealized simulations we demonstrate that the equilibrium climate sensitivity (ECS), i.e. the increase in surface temperature under equilibrium conditions due to doubling of the CO2 concentration, increases with the aerosol concentration. The ECS increase is explained by a faster increase in precipitation efficiency with warming under high aerosol concentrations, which more efficiently depletes the water from the cloud and thus is manifested as an increase in the cloud feedback parameter.
Sonya L. Fiddes, Alain Protat, Marc D. Mallet, Simon P. Alexander, and Matthew T. Woodhouse
Atmos. Chem. Phys., 22, 14603–14630, https://doi.org/10.5194/acp-22-14603-2022, https://doi.org/10.5194/acp-22-14603-2022, 2022
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Climate models have difficulty simulating Southern Ocean clouds, impacting how much sunlight reaches the surface. We use machine learning to group different cloud types observed from satellites and simulated in a climate model. We find the model does a poor job of simulating the same cloud type as what the satellite shows and, even when it does, the cloud properties and amount of reflected sunlight are incorrect. We have a lot of work to do to model clouds correctly over the Southern Ocean.
Prabhakar Shrestha, Jana Mendrok, and Dominik Brunner
Atmos. Chem. Phys., 22, 14095–14117, https://doi.org/10.5194/acp-22-14095-2022, https://doi.org/10.5194/acp-22-14095-2022, 2022
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The study extends the Terrestrial Systems Modeling Platform with gas-phase chemistry aerosol dynamics and a radar forward operator to enable detailed studies of aerosol–cloud–precipitation interactions. This is demonstrated using a case study of a deep convective storm, which showed that the strong updraft in the convective core of the storm produced aerosol-tower-like features, which affected the size of the hydrometeors and the simulated polarimetric features (e.g., ZDR and KDP columns).
Jia He, Helene Brogniez, and Laurence Picon
Atmos. Chem. Phys., 22, 12591–12606, https://doi.org/10.5194/acp-22-12591-2022, https://doi.org/10.5194/acp-22-12591-2022, 2022
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A 2003–2017 satellite-based atmospheric water vapour climate data record is used to assess climate models and reanalyses. The focus is on the tropical belt, whose regional variations in the hydrological cycle are related to the tropospheric overturning circulation. While there are similarities in the interannual variability, the major discrepancies can be explained by the presence of clouds, the representation of moisture fluxes at the surface and cloud processes in the models.
Silvia M. Calderón, Juha Tonttila, Angela Buchholz, Jorma Joutsensaari, Mika Komppula, Ari Leskinen, Liqing Hao, Dmitri Moisseev, Iida Pullinen, Petri Tiitta, Jian Xu, Annele Virtanen, Harri Kokkola, and Sami Romakkaniemi
Atmos. Chem. Phys., 22, 12417–12441, https://doi.org/10.5194/acp-22-12417-2022, https://doi.org/10.5194/acp-22-12417-2022, 2022
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The spatial and temporal restrictions of observations and oversimplified aerosol representation in large eddy simulations (LES) limit our understanding of aerosol–stratocumulus interactions. In this closure study of in situ and remote sensing observations and outputs from UCLALES–SALSA, we have assessed the role of convective overturning and aerosol effects in two cloud events observed at the Puijo SMEAR IV station, Finland, a diurnal-high aerosol case and a nocturnal-low aerosol case.
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.
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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