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
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
Related authors
No articles found.
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
Short summary
Short summary
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.
Ewa M. Bednarz, Amy H. Butler, Daniele Visioni, Yan Zhang, Ben Kravitz, and Douglas G. MacMartin
EGUsphere, https://doi.org/10.5194/egusphere-2023-495, https://doi.org/10.5194/egusphere-2023-495, 2023
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We use a state-of-the-art earth system model and a set of stratospheric aerosol injection strategies achieving 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.
Yan Zhang, Douglas G. MacMartin, Daniele Visioni, Ewa Bednarz, and Ben Kravitz
EGUsphere, https://doi.org/10.5194/egusphere-2023-117, https://doi.org/10.5194/egusphere-2023-117, 2023
Short summary
Short summary
Injecting SO2 into the lower stratosphere can temporarily reduce the global mean temperature and mitigate some of the risks associated with climate change, but injecting at different latitudes and seasons would have different impacts. This study introduces a comprehensive set of SAI strategies and systematically 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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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)
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
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
Aerosol-cloud impacts on aerosol detrainment and rainout in shallow maritime tropical clouds
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
Mixed-phase Direct Numerical Simulation: Ice Growth in Cloud-Top Generating Cells
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
Cloud adjustments from large-scale smoke–circulation interactions strongly modulate the southeastern Atlantic stratocumulus-to-cumulus transition
The influence of multiple groups of biological ice nucleating particles on microphysical properties of mixed-phase clouds observed during MC3E
Quantifying vertical wind shear effects in shallow cumulus clouds over Amazonia
Cirrus cloud thinning using a more physically based ice microphysics scheme in the ECHAM-HAM general circulation model
Impacts of combined microphysical and land-surface uncertainties on convective clouds and precipitation in different weather regimes
Weakening of tropical sea breeze convective systems through interactions of aerosol, radiation, and soil moisture
Sensitivity analysis of an aerosol-aware microphysics scheme in Weather Research and Forecasting (WRF) during case studies of fog in Namibia
Do Arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations
Contrail formation within cirrus: ICON-LEM simulations of the impact of cirrus cloud properties on contrail formation
Impact of Holuhraun volcano aerosols on clouds in cloud-system-resolving simulations
Warm and moist air intrusions into the winter Arctic: a Lagrangian view on the near-surface energy budgets
Convective updrafts near sea-breeze fronts
Evaluation of modelled summertime convective storms using polarimetric radar observations
Evaluating seasonal and regional distribution of snowfall in regional climate model simulations in the Arctic
Modeling impacts of ice-nucleating particles from marine aerosols on mixed-phase orographic clouds during 2015 ACAPEX field campaign
Influences of an entrainment–mixing parameterization on numerical simulations of cumulus and stratocumulus clouds
Investigation of ice cloud modeling capabilities for the irregularly shaped Voronoi ice scattering models in climate simulations
Assessing the potential for simplification in global climate model cloud microphysics
Technical note: Parameterising cloud base updraft velocity of marine stratocumuli
Radiative and microphysical responses of clouds to an anomalous increase in fire particles over the Maritime Continent in 2015
Intricate relations among particle collision, relative motion and clustering in turbulent clouds: computational observation and theory
The effect of marine ice-nucleating particles on mixed-phase clouds
A strong statistical link between aerosol indirect effects and the self-similarity of rainfall distributions
Quantifying albedo susceptibility biases in shallow clouds
Primary and secondary ice production: interactions and their relative importance
Microphysical processes producing high ice water contents (HIWCs) in tropical convective clouds during the HAIC-HIWC field campaign: dominant role of secondary ice production
Importance of aerosols and shape of the cloud droplet size distribution for convective clouds and precipitation
Secondary ice production processes in wintertime alpine mixed-phase clouds
Multi-thermals and high concentrations of secondary ice: a modelling study of convective clouds during the Ice in Clouds Experiment – Dust (ICE-D) campaign
Subgrid-scale horizontal and vertical variation of cloud water in stratocumulus clouds: a case study based on LES and comparisons with in situ observations
A vertical transport window of water vapor in the troposphere over the Tibetan Plateau with implications for global climate change
Box model trajectory studies of contrail formation using a particle-based cloud microphysics scheme
Updraft dynamics and microphysics: on the added value of the cumulus thermal reference frame in simulations of aerosol–deep convection interactions
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Gabrielle R. Leung, Stephen M. Saleeby, G. Alexander Sokolowsky, Sean W. Freeman, and Susan C. van den Heever
EGUsphere, https://doi.org/10.5194/egusphere-2022-1406, https://doi.org/10.5194/egusphere-2022-1406, 2022
Short summary
Short summary
This study explores 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.
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
Short summary
Short summary
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
Short summary
Short summary
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).
Sisi Chen, Lulin Xue, Sarah Tessendorf, Kyoko Ikeda, Courtney Weeks, Roy Rasmussen, Melvin Kunkel, Derek Blestrud, Shaun Parkinson, Melinda Meadows, and Nick Dawson
EGUsphere, https://doi.org/10.5194/egusphere-2022-1142, https://doi.org/10.5194/egusphere-2022-1142, 2022
Short summary
Short summary
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 are critical to 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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Michael S. Diamond, Pablo E. Saide, Paquita Zuidema, Andrew S. Ackerman, Sarah J. Doherty, Ann M. Fridlind, Hamish Gordon, Calvin Howes, Jan Kazil, Takanobu Yamaguchi, Jianhao Zhang, Graham Feingold, and Robert Wood
Atmos. Chem. Phys., 22, 12113–12151, https://doi.org/10.5194/acp-22-12113-2022, https://doi.org/10.5194/acp-22-12113-2022, 2022
Short summary
Short summary
Smoke from southern Africa blankets the southeast Atlantic from June-October, overlying a major transition region between overcast and scattered clouds. The smoke affects Earth's radiation budget by absorbing sunlight and changing cloud properties. We investigate these effects in regional climate and large eddy simulation models based on international field campaigns. We find that large-scale circulation changes more strongly affect cloud transitions than smoke microphysical effects in our case.
Sachin Patade, Deepak Waman, Akash Deshmukh, Ashok Kumar Gupta, Arti Jadav, Vaughan T. J. Phillips, Aaron Bansemer, Jacob Carlin, and Alexander Ryzhkov
Atmos. Chem. Phys., 22, 12055–12075, https://doi.org/10.5194/acp-22-12055-2022, https://doi.org/10.5194/acp-22-12055-2022, 2022
Short summary
Short summary
This modeling study focuses on the role of multiple groups of primary biological aerosol particles as ice nuclei on cloud properties and precipitation. This was done by implementing a more realistic scheme for biological ice nucleating particles in the aerosol–cloud model. Results show that biological ice nucleating particles have a limited role in altering the ice phase and precipitation in deep convective clouds.
Micael Amore Cecchini, Marco de Bruine, Jordi Vilà-Guerau de Arellano, and Paulo Artaxo
Atmos. Chem. Phys., 22, 11867–11888, https://doi.org/10.5194/acp-22-11867-2022, https://doi.org/10.5194/acp-22-11867-2022, 2022
Short summary
Short summary
Shallow clouds (vertical extent up to 3 km height) are ubiquitous throughout the Amazon and are responsible for redistributing the solar heat and moisture vertically and horizontally. They are a key component of the water cycle because they can grow past the shallow phase to contribute significantly to the precipitation formation. However, they need favourable environmental conditions to grow. In this study, we analyse how changing wind patterns affect the development of such shallow clouds.
Colin Tully, David Neubauer, Nadja Omanovic, and Ulrike Lohmann
Atmos. Chem. Phys., 22, 11455–11484, https://doi.org/10.5194/acp-22-11455-2022, https://doi.org/10.5194/acp-22-11455-2022, 2022
Short summary
Short summary
The proposed geoengineering method, cirrus cloud thinning, was evaluated using a more physically based microphysics scheme coupled to a more realistic approach for calculating ice cloud fractions in the ECHAM-HAM GCM. Sensitivity tests reveal that using the new ice cloud fraction approach and increasing the critical ice saturation ratio for ice nucleation on seeding particles reduces warming from overseeding. However, this geoengineering method is unlikely to be feasible on a global scale.
Christian Barthlott, Amirmahdi Zarboo, Takumi Matsunobu, and Christian Keil
Atmos. Chem. Phys., 22, 10841–10860, https://doi.org/10.5194/acp-22-10841-2022, https://doi.org/10.5194/acp-22-10841-2022, 2022
Short summary
Short summary
The relevance of microphysical and land-surface uncertainties for convective-scale predictability is evaluated with a combined-perturbation strategy in realistic convection-resolving simulations. We find a large ensemble spread which demonstrates that the uncertainties investigated here and, in particular, their collective effect are highly relevant for quantitative precipitation forecasting of summertime convection in central Europe.
J. Minnie Park and Susan C. van den Heever
Atmos. Chem. Phys., 22, 10527–10549, https://doi.org/10.5194/acp-22-10527-2022, https://doi.org/10.5194/acp-22-10527-2022, 2022
Short summary
Short summary
This study explores how increased aerosol particles impact tropical sea breeze cloud systems under different environments and how a range of environments modulate these cloud responses. Overall, sea breeze flows and clouds that develop therein become weaker due to interactions between aerosols, sunlight, and land surface. In addition, surface rainfall also decreases with more aerosol particles. Weakening of cloud and rain with more aerosols is found irrespective of 130 different environments.
Michael John Weston, Stuart John Piketh, Frédéric Burnet, Stephen Broccardo, Cyrielle Denjean, Thierry Bourrianne, and Paola Formenti
Atmos. Chem. Phys., 22, 10221–10245, https://doi.org/10.5194/acp-22-10221-2022, https://doi.org/10.5194/acp-22-10221-2022, 2022
Short summary
Short summary
An aerosol-aware microphysics scheme is evaluated for fog cases in Namibia. AEROCLO-sA campaign observations are used to access and parameterise the model. The model cloud condensation nuclei activation is lower than the observations. The scheme is designed for clouds with updrafts, while fog typically forms in stable conditions. A pseudo updraft speed assigned to the lowest model levels helps achieve more realistic cloud droplet number concentration and size distribution in the model.
Lucas J. Sterzinger, Joseph Sedlar, Heather Guy, Ryan R. Neely III, and Adele L. Igel
Atmos. Chem. Phys., 22, 8973–8988, https://doi.org/10.5194/acp-22-8973-2022, https://doi.org/10.5194/acp-22-8973-2022, 2022
Short summary
Short summary
Aerosol particles are required for cloud droplets to form, and the Arctic atmosphere often has much fewer aerosols than at lower latitudes. In this study, we investigate whether aerosol concentrations can drop so low as to no longer support a cloud. We use observations to initialize idealized model simulations to investigate a worst-case scenario where all aerosol is removed from the environment instantaneously. We find that this mechanism is possible in two cases and is unlikely in the third.
Pooja Verma and Ulrike Burkhardt
Atmos. Chem. Phys., 22, 8819–8842, https://doi.org/10.5194/acp-22-8819-2022, https://doi.org/10.5194/acp-22-8819-2022, 2022
Short summary
Short summary
This paper investigates contrail ice formation within cirrus and the impact of natural cirrus on the contrail ice formation in the high-resolution ICON-LEM simulations over Germany. Contrail formation often leads to increases in cirrus ice crystal number concentration by a few orders of magnitude. Contrail formation is affected by pre-existing cirrus, leading to changes in contrail formation conditions and ice nucleation rates that can be significant in optically thick cirrus.
Mahnoosh Haghighatnasab, Jan Kretzschmar, Karoline Block, and Johannes Quaas
Atmos. Chem. Phys., 22, 8457–8472, https://doi.org/10.5194/acp-22-8457-2022, https://doi.org/10.5194/acp-22-8457-2022, 2022
Short summary
Short summary
The impact of aerosols emitted by the Holuhraun volcanic eruption on liquid clouds was assessed from a pair of cloud-system-resolving simulations along with satellite retrievals. Inside and outside the plume were compared in terms of their statistical distributions. Analyses indicated enhancement for cloud droplet number concentration inside the volcano plume in model simulations and satellite retrievals, while there was on average a small effect on both liquid water path and cloud fraction.
Cheng You, Michael Tjernström, and Abhay Devasthale
Atmos. Chem. Phys., 22, 8037–8057, https://doi.org/10.5194/acp-22-8037-2022, https://doi.org/10.5194/acp-22-8037-2022, 2022
Short summary
Short summary
In winter when solar radiation is absent in the Arctic, the poleward transport of heat and moisture into the high Arctic becomes the main contribution of Arctic warming. Over completely frozen ocean sectors, total surface energy budget is dominated by net long-wave heat, while over the Barents Sea, with an open ocean to the south, total net surface energy budget is dominated by the surface turbulent heat.
Shizuo Fu, Richard Rotunno, and Huiwen Xue
Atmos. Chem. Phys., 22, 7727–7738, https://doi.org/10.5194/acp-22-7727-2022, https://doi.org/10.5194/acp-22-7727-2022, 2022
Short summary
Short summary
The convective updrafts near the sea-breeze fronts (SBFs) play important roles in initiating deep convection, but their characteristics are not well understood. By performing large-eddy simulations, we explain why the updrafts near the SBF are larger than but have similar strength to the updrafts ahead of the SBF. The results should also apply to other boundary-layer convergence zones similar to the SBF.
Prabhakar Shrestha, Silke Trömel, Raquel Evaristo, and Clemens Simmer
Atmos. Chem. Phys., 22, 7593–7618, https://doi.org/10.5194/acp-22-7593-2022, https://doi.org/10.5194/acp-22-7593-2022, 2022
Short summary
Short summary
The study makes use of ensemble numerical simulations with forward operator to evaluate the simulated cloud and precipitation processes with radar observations. While comparing model data with radar has its own challenges due to errors in the forward operator and processed radar measurements, the model was generally found to underestimate the high reflectivity, width/magnitude (value) of ZDR columns and high precipitation.
Annakaisa von Lerber, Mario Mech, Annette Rinke, Damao Zhang, Melanie Lauer, Ana Radovan, Irina Gorodetskaya, and Susanne Crewell
Atmos. Chem. Phys., 22, 7287–7317, https://doi.org/10.5194/acp-22-7287-2022, https://doi.org/10.5194/acp-22-7287-2022, 2022
Short summary
Short summary
Snowfall is an important climate indicator. However, microphysical snowfall processes are challenging for atmospheric models. In this study, the performance of a regional climate model is evaluated in modeling the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations. Excellent agreement in averaged annual snowfall rates is found, and the shown methodology offers a promising diagnostic tool to investigate the shown differences further.
Yun Lin, Jiwen Fan, Pengfei Li, Lai-yung Ruby Leung, Paul J. DeMott, Lexie Goldberger, Jennifer Comstock, Ying Liu, Jong-Hoon Jeong, and Jason Tomlinson
Atmos. Chem. Phys., 22, 6749–6771, https://doi.org/10.5194/acp-22-6749-2022, https://doi.org/10.5194/acp-22-6749-2022, 2022
Short summary
Short summary
How sea spray aerosols may affect cloud and precipitation over the region by acting as ice-nucleating particles (INPs) is unknown. We explored the effects of INPs from marine aerosols on orographic cloud and precipitation for an atmospheric river event observed during the 2015 ACAPEX field campaign. The marine INPs enhance the formation of ice and snow, leading to less shallow warm clouds but more mixed-phase and deep clouds. This work suggests models need to consider the impacts of marine INPs.
Xiaoqi Xu, Chunsong Lu, Yangang Liu, Shi Luo, Xin Zhou, Satoshi Endo, Lei Zhu, and Yuan Wang
Atmos. Chem. Phys., 22, 5459–5475, https://doi.org/10.5194/acp-22-5459-2022, https://doi.org/10.5194/acp-22-5459-2022, 2022
Short summary
Short summary
A new entrainment–mixing parameterization which can be directly implemented in microphysics schemes without requiring the relative humidity of the entrained air is proposed based on the explicit mixing parcel model. The parameterization is implemented in the two-moment microphysics scheme and exhibits different effects on different types of clouds and even on different stages of stratocumulus clouds, which are affected by turbulent dissipation rate and aerosol concentration.
Ming Li, Husi Letu, Yiran Peng, Hiroshi Ishimoto, Yanluan Lin, Takashi Y. Nakajima, Anthony J. Baran, Zengyuan Guo, Yonghui Lei, and Jiancheng Shi
Atmos. Chem. Phys., 22, 4809–4825, https://doi.org/10.5194/acp-22-4809-2022, https://doi.org/10.5194/acp-22-4809-2022, 2022
Short summary
Short summary
To build on the previous investigations of the Voronoi model in the remote sensing retrievals of ice cloud products, this paper developed an ice cloud parameterization scheme based on the single-scattering properties of the Voronoi model and evaluate it through simulations with the Community Integrated Earth System Model (CIESM). Compared with four representative ice cloud schemes, results show that the Voronoi model has good capabilities of ice cloud modeling in the climate model.
Ulrike Proske, Sylvaine Ferrachat, David Neubauer, Martin Staab, and Ulrike Lohmann
Atmos. Chem. Phys., 22, 4737–4762, https://doi.org/10.5194/acp-22-4737-2022, https://doi.org/10.5194/acp-22-4737-2022, 2022
Short summary
Short summary
Cloud microphysical processes shape cloud properties and are therefore important to represent in climate models. Their parameterization has grown more complex, making the model results more difficult to interpret. Using sensitivity analysis we test how the global aerosol–climate model ECHAM-HAM reacts to changes to these parameterizations. The model is sensitive to the parameterization of ice crystal autoconversion but not to, e.g., self-collection, suggesting that it may be simplified.
Jaakko Ahola, Tomi Raatikainen, Muzaffer Ege Alper, Jukka-Pekka Keskinen, Harri Kokkola, Antti Kukkurainen, Antti Lipponen, Jia Liu, Kalle Nordling, Antti-Ilari Partanen, Sami Romakkaniemi, Petri Räisänen, Juha Tonttila, and Hannele Korhonen
Atmos. Chem. Phys., 22, 4523–4537, https://doi.org/10.5194/acp-22-4523-2022, https://doi.org/10.5194/acp-22-4523-2022, 2022
Short summary
Short summary
Clouds are important for the climate, and cloud droplets have a significant role in cloud properties. Cloud droplets form when air rises and cools and water vapour condenses on small particles that can be natural or of anthropogenic origin. Currently, the updraft velocity, meaning how fast the air rises, is poorly represented in global climate models. In our study, we show three methods that will improve the depiction of updraft velocity and which properties are vital to updrafts.
Azusa Takeishi and Chien Wang
Atmos. Chem. Phys., 22, 4129–4147, https://doi.org/10.5194/acp-22-4129-2022, https://doi.org/10.5194/acp-22-4129-2022, 2022
Short summary
Short summary
Nanometer- to micrometer-sized particles in the atmosphere, namely aerosols, play a crucial role in cloud formation as cloud droplets form on aerosols. This study uses a weather forecasting model to examine the impacts of a large emission of aerosol particles from biomass burning activities over Southeast Asia. We find that additional cloud droplets brought by fire-emitted particles can lead to taller and more reflective convective clouds with increased rainfall.
Ewe-Wei Saw and Xiaohui Meng
Atmos. Chem. Phys., 22, 3779–3788, https://doi.org/10.5194/acp-22-3779-2022, https://doi.org/10.5194/acp-22-3779-2022, 2022
Short summary
Short summary
Collision–coagulation of small droplets in turbulent clouds leads to the production of rain. Turbulence causes droplet clustering and higher relative droplet velocities, and these should enhance the collision–coagulation rate. We find, surprisingly, that collision–coagulation starkly diminishes clustering and strongly alters relative velocities. We provide a theory that explains this result. Our results call for a new perspective on how we understand particle/droplet collision in clouds.
Tomi Raatikainen, Marje Prank, Jaakko Ahola, Harri Kokkola, Juha Tonttila, and Sami Romakkaniemi
Atmos. Chem. Phys., 22, 3763–3778, https://doi.org/10.5194/acp-22-3763-2022, https://doi.org/10.5194/acp-22-3763-2022, 2022
Short summary
Short summary
Mineral dust or similar ice-nucleating particles (INPs) are needed to initiate cloud droplet freezing at temperatures common in shallow clouds. In this work we examine how INPs that are released from the sea surface impact marine clouds. Our high-resolution simulations show that turbulent updraughts carry these particles effectively up to the clouds, where they initiate cloud droplet freezing. Sea surface INP emissions become more important with decreasing background dust INP concentrations.
Kalli Furtado and Paul Field
Atmos. Chem. Phys., 22, 3391–3407, https://doi.org/10.5194/acp-22-3391-2022, https://doi.org/10.5194/acp-22-3391-2022, 2022
Short summary
Short summary
The complex processes involved mean that no simple answer to this
question has so far been discovered: do aerosols increase or decrease precipitation? Using high-resolution weather simulations, we find a self-similar property of rainfall that is not affected by aerosols. Using this invariant, we can collapse all our simulations to a single curve. So, although aerosol effects on rain are many, there may be a universal constraint on the number of degrees of freedom needed to represent them.
Graham Feingold, Tom Goren, and Takanobu Yamaguchi
Atmos. Chem. Phys., 22, 3303–3319, https://doi.org/10.5194/acp-22-3303-2022, https://doi.org/10.5194/acp-22-3303-2022, 2022
Short summary
Short summary
The evaluation of radiative forcing associated with aerosol–cloud interactions remains a significant source of uncertainty in future climate projections. Using high-resolution numerical model output, we mimic typical satellite retrieval methodologies to show that data aggregation can introduce significant error (hundreds of percent) in the cloud albedo susceptibility metric. Spatial aggregation errors tend to be countered by temporal aggregation errors.
Xi Zhao and Xiaohong Liu
Atmos. Chem. Phys., 22, 2585–2600, https://doi.org/10.5194/acp-22-2585-2022, https://doi.org/10.5194/acp-22-2585-2022, 2022
Short summary
Short summary
The goal of this study is to investigate the relative importance and interactions of primary and secondary ice production in the Arctic mixed-phase clouds. Our results show that the SIP is not only a result of ice crystals produced from ice nucleation, but also competes with the ice production; conversely, strong ice nucleation also suppresses SIP.
Yongjie Huang, Wei Wu, Greg M. McFarquhar, Ming Xue, Hugh Morrison, Jason Milbrandt, Alexei V. Korolev, Yachao Hu, Zhipeng Qu, Mengistu Wolde, Cuong Nguyen, Alfons Schwarzenboeck, and Ivan Heckman
Atmos. Chem. Phys., 22, 2365–2384, https://doi.org/10.5194/acp-22-2365-2022, https://doi.org/10.5194/acp-22-2365-2022, 2022
Short summary
Short summary
Numerous small ice crystals in tropical convective storms are difficult to detect and could be potentially hazardous for commercial aircraft. Previous numerical simulations failed to reproduce this phenomenon and hypothesized that key microphysical processes are still lacking in current models to realistically simulate the phenomenon. This study uses numerical experiments to confirm the dominant role of secondary ice production in the formation of these large numbers of small ice crystals.
Christian Barthlott, Amirmahdi Zarboo, Takumi Matsunobu, and Christian Keil
Atmos. Chem. Phys., 22, 2153–2172, https://doi.org/10.5194/acp-22-2153-2022, https://doi.org/10.5194/acp-22-2153-2022, 2022
Short summary
Short summary
The relative impact of cloud condensation nuclei (CCN) concentrations and the shape parameter of the cloud droplet size distribution is evaluated in realistic convection-resolving simulations. We find that an increase in the shape parameter can produce almost as large a variation in precipitation as a CCN increase from maritime to polluted conditions. The choice of the shape parameter may be more important than previously thought for determining cloud radiative characteristics.
Paraskevi Georgakaki, Georgia Sotiropoulou, Étienne Vignon, Anne-Claire Billault-Roux, Alexis Berne, and Athanasios Nenes
Atmos. Chem. Phys., 22, 1965–1988, https://doi.org/10.5194/acp-22-1965-2022, https://doi.org/10.5194/acp-22-1965-2022, 2022
Short summary
Short summary
The modelling study focuses on the importance of ice multiplication processes in orographic mixed-phase clouds, which is one of the least understood cloud types in the climate system. We show that the consideration of ice seeding and secondary ice production through ice–ice collisional breakup is essential for correct predictions of precipitation in mountainous terrain, with important implications for radiation processes.
Zhiqiang Cui, Alan Blyth, Yahui Huang, Gary Lloyd, Thomas Choularton, Keith Bower, Paul Field, Rachel Hawker, and Lindsay Bennett
Atmos. Chem. Phys., 22, 1649–1667, https://doi.org/10.5194/acp-22-1649-2022, https://doi.org/10.5194/acp-22-1649-2022, 2022
Short summary
Short summary
High concentrations of ice particles were observed at temperatures greater than about –8 C. The default scheme of the secondary ice production cannot explain the high concentrations. Relaxing the conditions for secondary ice production or considering dust aerosol alone is insufficient to produce the observed amount of ice particles. It is likely that multi-thermals play an important role in producing very high concentrations of secondary ice particles in some tropical clouds.
Justin A. Covert, David B. Mechem, and Zhibo Zhang
Atmos. Chem. Phys., 22, 1159–1174, https://doi.org/10.5194/acp-22-1159-2022, https://doi.org/10.5194/acp-22-1159-2022, 2022
Short summary
Short summary
Stratocumulus play an important role in Earth's radiative balance. The simulation of these cloud systems in climate models is difficult due to the scale at which cloud microphysical processes occur compared with model grid sizes. In this study, we use large-eddy simulation to analyze subgrid-scale variability of cloud water and its implications on a cloud water to drizzle model enhancement factor E. We find current values of E may be too large and that E should be vertically dependent in models.
Xiangde Xu, Chan Sun, Deliang Chen, Tianliang Zhao, Jianjun Xu, Shengjun Zhang, Juan Li, Bin Chen, Yang Zhao, Hongxiong Xu, Lili Dong, Xiaoyun Sun, and Yan Zhu
Atmos. Chem. Phys., 22, 1149–1157, https://doi.org/10.5194/acp-22-1149-2022, https://doi.org/10.5194/acp-22-1149-2022, 2022
Short summary
Short summary
A vertical transport window of tropospheric vapor exists on the Tibetan Plateau (TP). The TP's thermal forcing drives the vertical transport
windowof vapor in the troposphere. The effects of the TP's vertical transport window of vapor are of importance in global climate change.
Andreas Bier, Simon Unterstrasser, and Xavier Vancassel
Atmos. Chem. Phys., 22, 823–845, https://doi.org/10.5194/acp-22-823-2022, https://doi.org/10.5194/acp-22-823-2022, 2022
Short summary
Short summary
We investigate contrail formation in an aircraft plume with a particle-based multi-trajectory 0D model. Due to the high plume heterogeneity, contrail ice crystals form first near the plume edge and then in the plume centre. The number of ice crystals varies strongly with ambient conditions and soot properties near the contrail formation threshold. Our results imply that the multi-trajectory approach does not necessarily lead to improved scientific results compared to a single mean trajectory.
Daniel Hernandez-Deckers, Toshihisa Matsui, and Ann M. Fridlind
Atmos. Chem. Phys., 22, 711–724, https://doi.org/10.5194/acp-22-711-2022, https://doi.org/10.5194/acp-22-711-2022, 2022
Short summary
Short summary
We investigate how the concentration of aerosols (small particles that serve as seeds for cloud droplets) affect the dynamics of simulated clouds using two different frameworks, i.e., the traditional selection of cloudy rising grid points and tracking small-scale coherent rising features (cumulus thermals). By doing so, we find that these cumulus thermals reveal useful information about the coupling between internal cloud circulations and cloud droplet and raindrop formation.
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....
Altmetrics
Final-revised paper
Preprint